Market Performance Metric Catalog

Market Performance Report, Meta Document of 125

Market Performance Metric CatalogVersion No.: 1.25

Effective Date 4/29/13

Market Performance Metric Catalog

Version 1.25

March, 2013




ISO Market Services

VERSION HISTORY

Date Version Description Author

5/28/2009 1.0 Creation of document Market Performance Group

6/25/2009 1.1 Document for May Market Performance Group

7/25/2009 1.2 Document for June Market Performance Group

8/24/2009 1.3 Document for July Market Performance Group

9/25/2009 1.4 Document for August Market Performance Group

10/26/2009 1.5 Document for October Market Performance Group

12/2/2009 1.6 Added a section on energy price convergence.Revised the exceptional dispatch section.

Market Performance Group

12/22/2009 1.7 Added the Market Intervention section, under which the market disruption.Added a section on intertie blocking.Added IFM hourly average regulation requirements and hourly average ancillary service price charts in the ancillary service section.


01/25/2010 1.8 Added a subsection on Blocking of Commitment Instructions.


02/25/2010 1.9 Added a subsection of transmission constraint adjustments to the section of Market Intervention.Modified language of perfect hedge to be characterized now as transmission right exemption.Added weekly average price convergence and changed the calculation formula of daily and hourly average prices for default LAPs and interties.


03/24/2010 1.10 Added a subsection of congestion cost per megawatt of load served.Added the subsection of market implied heat rate under the Market Characteristics section.Added the subsection of imbalance offset costs.Added the subsection of bid cost recovery.






Reorganize the structure of the whole document.

04/26/2010 1.11 Added a subsection of blocking of real-time dispatch.Reorganized the metrics in the section of CRR Revenue Adequacy


5/26/2010 1.12 Modified the charts for real-time upward ancillary services procurement, proportion of real-time procurement as percentage of day-ahead requirement, real-time ancillary service average price and system average cost to load, to include the metrics for the spinning and non-spinning ancillary services in the HASP.Remove the appendix of imbalance offset cost from the market performance report to the metric catalog.Added a section of Regulatory Requirement.Added a section of day-ahead scheduled hydro volume.


6/28/2010 1.13 Added a daily profile of transmission bias volume.Modified the plot of uplift costs.Added the section of Other Metrics, which includes the subsection of bilateral transfers of Existing Contract Import Capability.Added sections of losses for both day-ahead and real-time markets.


7/26/2010 1.14 Added the subsection of Make Whole Payment.Added the section of Net Interchange.Added the subsection of comparison of IFM congestion and RTD congestion.


9/24/2010 1.15 Added the subsection of New Market Functionalities.


10/26/2010 1.16 Added the subsection of Generation by Fuel.


12/21/2010 1.17 Added the subsection of System Parameter Excursion.Added the subsection of Analysis of Minimum Online Capacity.


1/28/2011 1.18 Added the subsections of Multi-Stage Generation and Contingent/Non Contingent Ancillary Service


2/23/2011 1.19 Added the subsection of Hourly Inter-Tie Ramping






3/30/2011 1.20 Added the subsection of Convergence Bidding


5/27/2011 1.21 Added the plot of AS hydro volume awarded in the day-ahead market.Removed the subsection of Compensating Injection.Removed the subsection of Multi-Stage Generation.Removed the subsection of Hourly Inter-Tie Ramping.Modified the plot of uplift costs.


6/28/2011 1.22 Added the plot of abandoned runs in real-time market.


2/29/2012 1.23 Removed the plot of abandoned runs in real-time market.Removed the plots of convergence bidding for interties.


4/28/2012 1.24 Replaced the Chart “Market Short Falls and Bid Cost Recovery” with the Chart “Bid Cost Recovery Allocation”.


7/30/2012 1.25 Added 3 charts showing BCR allocation in RUC, IFM and RT.





TABLE OF CONTENTS

Regulatory Requirement .......................................................................................7Market Characteristics ..........................................................................................8

Loads ................................................................................................................8Natural Gas Prices and Inventories...................................................................9Day-Ahead Scheduled Hydro Volume.............................................................10Bilateral Electricity Prices ................................................................................14Market Implied Heat Rate................................................................................15Net Interchange...............................................................................................17

Market Performance Metrics...............................................................................18Energy.............................................................................................................18

Day-Ahead Prices........................................................................................18IFM Bid Stack ..............................................................................................21Real-Time Prices .........................................................................................24Real-Time Price Volatility.............................................................................26

Price Convergence..........................................................................................28Congestion ......................................................................................................42

Congestion Rents on Interties......................................................................42Congestion Rents on Branch Groups and Market Scheduling Limit ............43Congestion Rents on Transmission Lines and Transformers ......................44Congestion Rents on Nomograms...............................................................45Congestion Rents on Nodal Group Constraints ...........................................45Comparison of IFM (Day-ahead) Congestion and Real-Time Dispatch Congestion...................................................................................................46Congestion Rents by Type of Resources ....................................................50Average Congestion Cost per Load Served ................................................51

Congestion Revenue Rights............................................................................52Auction Bids.................................................................................................52Auction Revenues........................................................................................52Monthly Volumes .........................................................................................53Auction Prices..............................................................................................55Price Convergence ......................................................................................56Monthly CRR Revenue Adequacy ...............................................................57Existing Right Exemptions ...........................................................................60

Losses.............................................................................................................62Day-ahead Prices ........................................................................................62Real-Time Prices .........................................................................................64Marginal Losses Surplus .............................................................................65

Ancillary Services ............................................................................................66Requirements ..............................................................................................66Procurements ..............................................................................................67IFM (Day-Ahead) Average Prices ................................................................70Average Regional Ancillary Service Shadow Prices ....................................72




Average Cost to Load ..................................................................................74Contingent/Non-Contingent Ancillary Services ............................................76

Residual Unit Commitment..............................................................................79Deviations of RUC schedule from IFM schedule .........................................79RA/RMR RUC Capacity vs. RUC Award .....................................................81Total RUC Cost............................................................................................86

Indirect Market Performance Metrics ..................................................................87Cost Allocation Metrics....................................................................................87

Imbalance Offset Costs ...............................................................................87Bid Cost Recovery .......................................................................................89Make Whole Payment..................................................................................93

Market Software Metrics..................................................................................94Market Disruption.........................................................................................94System Parameter Excursion ......................................................................95Analysis of Minimum Online Capacity..........................................................97Compensating Injection ...............................................................................99Exceptional Dispatch .................................................................................101Blocking of Intertie Schedules....................................................................104Blocking of Commitment Instructions.........................................................107Blocking of Real-Time Dispatch.................................................................108

Other Metrics ....................................................................................................112Bilateral Transfers of Existing Contract Import Capability .............................112New Market Functionalities ...........................................................................113

Convergence Bidding ................................................................................113Appendix: Imbalance Offset Costs....................................................................120

Example 1 .....................................................................................................120Root Cause for Revenue Deficiency in Example 1........................................122Example 2 .....................................................................................................123Root Cause for Revenue Deficiency in Example 2........................................125




Regulatory Requirement

This section states three sets of metrics which the ISO promised to make publicly to the market participants.

The first set of metrics is about the cost of the existing rights exemptions.1 Figure 62 shows the net cost of the existing right exemptions for schedule changes of ETCs/TORs for both day-ahead and real-time markets. Table 10 lists the monthly summary of exemptions for existing transmission rights in the day-ahead and real-time markets.

The second set of regulatory metrics is about the adjustment of the transmission constraints.2 Figure 118 shows the frequency and average of adjustment of transmission constraints by market for the current month.

The third set of metrics is a report on bilateral import capability transfers received under tariff section 40.4.6.2.2.2 of the tariff. On a quarterly basis, the ISO shall report to FERC the transfer information received under this section and step 8 oftariff section 40.4.6.2.1. Table 17 lists each of the transfers received

1 As required by FERC’s Order Accepting Compliance Filing issued on October 22, 2006 (California Indep. Sys. Operator, Corp., 116 FERC ¶ 61,281, (2006)), the ISO maintains a record of the redispatch costs associated with honoring existing rights and charged to non-existing-rightsloads and makes this information publicly available to market participants on the ISO website in the monthly market performance metric catalog: http://www.caiso.com/2424/2424d14d4a200.html2 As required by FERC’s Order Conditionally Accepting Tariff Revisions issued on October 2, 2009 (California Indep. Sys. Operator, Corp., 129 FERC ¶ 61,009 (2009)), the ISO convenes a stakeholder process with an aim to address concerns raised by parties in that proceeding regarding what additional transparency and visibility can be provided with respect to the ISO’s transmission constraint enforcement practices to account for system conditions in managing the limits of the transmission system. As an outcome of this process, the ISO provides metrics of transmission adjustments applied in the various markets to add transparency of the ISO practices for transmission constraints.




Market Characteristics

Loads

Figure 1 compares total, hourly demand for the California Independent System Operator Corporation control area for the current calendar month with hourly demand for the same month in the prior year. An insert is included in the figure with summary statistics on peak load, average energy, average daily peak and average daily trough.

Figure 1: System Load Comparison

10,000

15,000

20,000

25,000

30,000

35,000

1-M

ar2-

Mar

3-M

ar4-

Mar

5-M

ar6-

Mar

7-M

ar8-

Mar

9-M

ar10

-Mar

11-M

ar12

-Mar

13-M

ar14

-Mar

15-M

ar16

-Mar

17-M

ar18

-Mar

19-M

ar20

-Mar

21-M

ar22

-Mar

23-M

ar24

-Mar

25-M

ar26

-Mar

27-M

ar28

-Mar

29-M

ar30

-Mar

31-M

ar

March 2013 Actual Loads March 2012 Actual Loads

MW




Natural Gas Prices and Inventories

Figure 2 displays the weekly average of daily, natural gas spot prices for three selected trading hubs: PG&E Citygate as a proxy for Northern California, So Cal Border as a proxy for Southern California, and Henry Hub as a proxy for the rest of the U.S. Natural gas prices are important to the market as much of the capacity in the West – especially the newer units – is gas-fired. These units are also often marginal, meaning that they set the price levels in bilateral markets.

Figure 2: Weekly Average Natural Gas Spot Prices

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

2-S

ep-

12

16-S

ep-

12

30-S

ep-

12

14-O

ct-1

2

28-O

ct-1

2

11-N

ov-1

2

25-N

ov-1

2

9-D

ec-1

2

23-D

ec-1

2

6-J

an-

13

20-J

an-

13

3-F

eb-

13

17-F

eb-

13

3-M

ar-1

3

17-M

ar-1

3

31-M

ar-1

3

PG&E Citygate (Northern CA)

SoCal Border (Southern CA)

Henry Hub (National)

Week Beginning

Pri

ce (

$/M

MB

tu)




Day-Ahead Scheduled Hydro Volume

Figure 3 shows the daily average of the scheduled hydro volume in day-ahead market for the ISO control area.

Figure 3: Day-Ahead Scheduled Hydro Volume

0

500

1,000

1,500

2,000

2,500

3,000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb13

-Fe

b15

-Fe

b17

-Fe

b19

-Fe

b21

-Fe

b23

-Fe

b25

-Fe

b27

-Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r11

-Mar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

Vo

lum

e (

MW

)

Figure 4 shows the daily average of the AS hydro volume awarded in day-ahead market.

Figure 4: AS Hydro Volume Awarded in Day-Ahead Market

0

200

400

600

800

1000

1200

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13

-Fe

b1

5-F

eb

17

-Fe

b1

9-F

eb

21

-Fe

b2

3-F

eb

25

-Fe

b2

7-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13

-Ma

r1

5-M

ar

17

-Ma

r1

9-M

ar

21

-Ma

r2

3-M

ar

25

-Ma

r2

7-M

ar

29

-Ma

r3

1-M

ar

Vo

lum

e (

MW

)

AS_Award




Generation by FuelFigure 5 shows the daily generation by import and different types of fuel from the CAISO metered generators which are directly connected to the ISO controlled grid or participating generators. The metered generation data is summed up by types of fuel and by day, then divided by 24 to get the average capacity for each hour.

Figure 5: Generation Capacity by Fuel

-5000

0

5000

10000

15000

20000

25000

30000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW

Coal Oil Hydro Nuclear Renewable Import Gas Other

Figure 6 illustrates the daily average capacity from renewable, which includes solar, wind, small hydro, biogas, biomass and geothermal.

Figure 6: Daily Capacity of Renewable Resources

0

1000

2000

3000

4000

5000

6000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW

Biogas Biomass Geothermal Small Hydro Solar Wind




Figure 7 presents the hourly profile of renewable resource capacity variation for the reporting month. The most variant one is the wind resources which are shown in Figure 8 .

Figure 7: Hourly Profile of Renewable Resources

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

Biogas Biomass Geothermal Small Hydro Solar Wind

Figure 8: Hourly Profile of Wind Resources

0

200

400

600

800

1000

1200

1400

1600

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

February 2013 March 2013




Figure 9 shows the generation percentage by fuel and import from the beginning of the year to the last day of the reporting month.

Figure 9: Year to Date Generation by Fuel in Percent

Oil0.00%

Coal0.24%

Other3.96%

Nuclear6.55%

Hydro7.21%

Renewable12.47%

Gas41.21%

Import28.36%




Bilateral Electricity Prices

Figure 10 displays weekly average on-peak bilateral spot electricity price for Northern and Southern California. Bilateral electricity prices indicate the general level of prices at which electricity is being traded in California outside of the ISOmarkets. In addition, the figure provides for reference the nominal gas costs for two assumed combined cycle gas turbines with average heat rates of 8,000 and 12,000 Btu per kWh, respectively. When loads are light or there is a surplus of generating capacity, spot prices should trend towards the 8,000 Btu cost curve reflecting that more efficient, modern gas turbines are setting the market price. Alternatively, when loads are high or generating capacity is otherwise scarce due to outages, then less efficient, higher cost turbines will become marginal and prices in Figure 10 should trend towards the 12,000 Btu cost curve.

Figure 10: Daily Peak-Hour Bilateral Contract Prices – Weekly Averages

10

20

30

40

50

60

02-S

ep-

12

16-S

ep-

12

30-S

ep-

12

14-O

ct-1

2

28-O

ct-1

2

11-N

ov-1

2

25-N

ov-1

2

09-D

ec-1

2

23-D

ec-1

2

06-J

an-

13

20-J

an-

13

03-F

eb-1

3

17-F

eb-1

3

03-M

ar-1

3

17-M

ar-1

3

31-M

ar-1

3

Pri

ce (

$/M

Wh

)

Week Beginning

N. California On-Peak S. California On-Peak

8000 Heat Rate Unit Cost 12000 Heat Rate Unit Cost




Market Implied Heat Rate

The term “heat rate” refers to the power plant efficiency in converting fuel to electricity. Heat rate is expressed as the number of thousand British thermal units (MBtu) required to convert a megawatt hour (MWh) of electricity. Lower heat rates are associated with more efficient power generating plants. The market implied heat rate is calculated as shown below. The daily average market implied heat rate is an indicator of the heat rate of the marginal unit in the integrated forward market (IFM).

WhereL is set of default load aggregation points (DLAPs); which include PG&E, SCE and SDG&E.H is set of hours for trading day. Energy schedule for the hour h for DLAP lLMP for the hour h for DLAP l Daily energy weighted default LMP for LAP l P is set of all natural gas pricing points. There are two pricing points for California: PG&E city gate and Southern California Border. Note that for the PG&E DLAP, PG&E city gate gas price is used. For the SCE and SDG&E DLAPs, the Southern California Border gas price is used.Daily average natural gas price index published by the “Intelligencepress Inc.” for a pricing point p




Table 1 and Figure 11 shows the monthly and daily IFM average default LAP market implied heat rate.

Table 1: Monthly IFM Average Default LAP Market Implied Heat Rate

Month PGAE (Btu)/kWh SCE (Btu)/kWh SDGE (Btu)/kWhMar 10409.91 12772.19 11354.42Feb 10896.99 13114.79 12366.67Percent Change

-4.47% -2.61% -8.19%

Figure 11: Daily IFM Default LAP Market Implied Heat Rate

02,0004,0006,0008,000

10,00012,00014,00016,00018,00020,000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Btu

/kW

h

PGAE SCE SDGE




Net Interchange

Figure 12 shows the net interchange in the integrated forward market, broken out by intertie. The daily values are simple averages, and the net interchange is obtained as imports less exports. Figure 13 shows the daily profile of netinterchange across the various markets, namely, the integrated forward market, the hour-ahead scheduling process and the real-time dispatch.

Figure 12: Daily Average Volume of Net Interchange in IFM

0

2,000

4,000

6,000

8,000

10,000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb13

-Fe

b15

-Fe

b17

-Fe

b19

-Fe

b21

-Fe

b23

-Fe

b25

-Fe

b27

-Fe

b1

-Mar

3-M

ar5

-Mar

7-M

ar9

-Mar

11-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

arNet

Ave

rag

e

Inte

rch

ang

e (

MW

)

ADLANTO-SP_ITC BLYTHE_ITC CASCADE_ITC CFE_ITC COTPISO_ITC

CTW230_ITC ELDORADO_ITC IID-SCE_ITC IID-SDGE_ITC Other

Figure 13: Daily Average Volume of Net Interchange per Market

01,0002,0003,0004,0005,0006,0007,0008,0009,000

10,000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Net

In

terc

han

ge

(M

W)

DA_PHYSICAL DA_VIRTUAL HASP RTD




Market Performance Metrics

Energy

Day-Ahead Prices

Figure 14, Figure 15 and Figure 16 show the daily energy-weighted average load-aggregation points (LAP) prices for each of the three default LAPs (PG&E, SCE, and SDG&E) for peak hours, off-peak hours, and all hours respectively in the day-ahead market. The formula for daily average price is:

j

ij

ij

jiji SCHE_MW

SCHE_MWLMPP i= PG&E, SCE, and SDG&E

iP is the daily average price for LAP i, while j represents the hour (peak, off-

peak, or all). ijLMP is the LMP for LAP i in hour j. jSCHE_MW is the scheduled

energy in hour j for LAP i.

Figure 14: Day-Ahead Weighted Average LAP Prices (On-Peak Hours)

0102030405060708090

1-F

eb

4-F

eb

6-F

eb

8-F

eb

11-F

eb

13-

Fe

b1

5-F

eb

18-

Fe

b2

0-F

eb

22-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r4-

Ma

r6-

Ma

r8-

Ma

r11

-Ma

r1

3-M

ar

15-

Ma

r1

8-M

ar

20-

Ma

r2

2-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r

$/M

Wh

PGE SCE SDGE VEA




Figure 15: Day-Ahead Weighted Average LAP Prices (Off-Peak Hours)

0

10

20

30

40

50

601

-Fe

b3

-Fe

b5

-Fe

b7

-Fe

b9

-Fe

b11

-Fe

b13

-Fe

b15

-Fe

b17

-Fe

b19

-Fe

b21

-Fe

b23

-Fe

b25

-Fe

b27

-Fe

b1

-Ma

r3

-Ma

r5

-Ma

r7

-Ma

r9

-Ma

r11

-Mar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

$/M

Wh

PGE SCE SDGE VEA

Figure 16: Day-Ahead Weighted Average LAP Prices (All Hours)

0

10

20

30

40

50

60

70

80

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r11

-Ma

r1

3-M

ar

15-

Ma

r1

7-M

ar

19-

Ma

r2

1-M

ar

23-

Ma

r2

5-M

ar

27-

Ma

r2

9-M

ar

31-

Ma

r

$/M

Wh

PGE SCE SDGE VEA




Figure 17 shows the frequency of prices of the default LAP prices in the integrated forward market (IFM). Prices are grouped in several bins. This frequency includes both time of uses, on- and off-peak. For each price bin, the price range is defined by an interval of lower and upper price; in these ranges, the parenthesis means the price range does not include the upper limit value, while the square bracket means the price range does include the lower limit.

Figure 17: Frequency of Day-Ahead LAP Prices (All hours)

0%

19%

38%

57%

76%

95%

Feb-13 Mar-13 Feb-13 Mar-13 Feb-13 Mar-13

PGAE SCE SDGE

Cu

mu

lati

ve F

req

ue

nc

y

<=$0 $(0, 20) $[20, 40) $[40, 60) $[60, 100) >=$100




IFM Bid Stack

Figure 18 shows the daily average IFM bid volume classified into various types of self-schedules and economical bids. It also depicts the daily average cleared generation and imports. In the IFM, sum of cleared generation and imports is equal to the sum of cleared demand, pump schedule, loss and exports. There are three types of self-schedules bidding into the IFM which are classified based on priority. Transmission ownership right (TOR), existing transmission contract (ETC) and converted rights (CVR) self schedules have the highest priority. Reliability must take has a lower priority and the price taker has the lowest priority among all self-schedules.

Figure 18: IFM Bid Stack and Cleared Generation and Imports

0

10,000

20,000

30,000

40,000

50,000

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW

AVG_OF_ETC_CVR AVG_OF_Economical AVG_OF_CLEARED_VALUE




Day-Ahead Volumes

Figure 19 below shows the daily average scheduling deviation percentage in the IFM and the daily average cleared load in the IFM. The average scheduling deviation percentage is calculated as

Percent Over/Under Schedule =

stage_ForecaDaily_Aver

d_load)age_CleareDaily_Averastrage_Forec(Daily_Ave

Figure 19: IFM Scheduling Deviation and Cleared Load

-20,000

-10,000

0

10,000

20,000

30,000

40,000

-20%

-10%

0%

10%

20%

30%

40%

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb

13-

Fe

b1

5-F

eb

17-

Fe

b1

9-F

eb

21-

Fe

b2

3-F

eb

25-

Fe

b2

7-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-

Ma

r1

3-M

ar

15-

Ma

r1

7-M

ar

19-

Ma

r2

1-M

ar

23-

Ma

r2

5-M

ar

27-

Ma

r2

9-M

ar

31-

Ma

r

MW

Pe

rce

net

Per Over/under schedule DA Cleared Load




Figure 20 below shows the daily average day-ahead cleared imports, day-aheadgeneration (within California) and day-ahead cleared demand in the IFM. The day-ahead cleared demand includes day-ahead losses, but excludes exports.

Figure 20: Day-Ahead Cleared Quantity

0

10,000

20,000

30,000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

MW

Imports Generation DA Demand




Real-Time Prices

Figure 21, Figure 22 and Figure 23 show daily energy-weighted average LAP prices for each of the three default LAPs for peak hours, off-peak hours, and all hours respectively in the real-time market. The formula for the daily average price is:

j

j hijh

hijhijh

i SCHE_MW

SCHE_MW*LMPP i= PG&E, SCE, and SDG&E

iP is the daily average price for LAP i, while j represents the hour (peak, off-

peak, or all) and h represents 5-minute interval. ijhLMP is the LMP for LAP i in

hour j, interval h. ijhSCHE_MW is the scheduled energy in hour j, interval h for

LAP i.

Figure 21: RTD Weighted Average LAP Prices (On-Peak Hours)

0

50

100

150

200

250

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

ar

$/M

Wh

PGE SCE SDGE VEA




Figure 22: RTD Weighted Average LAP Prices (Off-Peak Hours)

0

50

100

150

200

250

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

r

$/M

Wh

PGE SCE SDGE VEA

Figure 23: RTD Weighted Average LAP Prices (All Hours)

0

50

100

150

200

250

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r1

1-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

ar

$/M

Wh

PGE SCE SDGE VEA




Real-Time Price Volatility

Figure 24 shows the frequency of prices of the default LAP prices in the real-time market. Prices are grouped in several bins. This plot provides a reference of the frequency of prices that fall below -30/MWh and above $250/MWh. This frequency includes both time of uses, on-peak and off-peak, and is aggregated by default LAP on a monthly basis.

Figure 24: Frequency of RTD LAP Prices (All Hours)

0%

19%

38%

57%

76%

95%


PGAE SCE SDGE

Cu

mu

lati

ve F

req

uen

cy

<$0 $[0, 20) $[20, 40) $[40, 60) $[60,250) >$250

Figure 25 shows the monthly frequency of spikes for RTD default LAP prices that are above $250/MWh, and also negative prices. The prices are aggregated in a monthly basis for each default LAP.

Figure 25: Frequency of RTD LAP Price Spikes and Negative Prices

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%


PGAE SCE SDGE

Cu

mu

lati

ve F

req

uen

cy

<=-$250 $[-250,-100) $[-100,-40) $[-40,-20) $[-20,0)

$[250,500) $[500,750) $[750,1000) $[1000,3000]




Figure 26 show the daily price frequency for prices above $250/MWh and below $0/MWh. Prices are for all three default LAPs. The graph may provide a trend of price spikes over time.

Figure 26: Daily Frequency of RTD LAP Positive Price Spikes and Negative Prices

-12.0%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Cu

mu

lati

ve F

req

uen

cy

<=-$250 $(-100, -250] $(-40,-100] $(-20,-40] $(0,-20] $[250,500) $[500,750) $[750,1000) $[1000,3000]

Table 2 summarizes the monthly average prices and volumes in day-ahead and real-time markets.

Table 2: Summary of Prices and Volumes

`

Day-Ahead Real-Time

Peak Offpeak All Peak Offpeak All

Average Price ($) 49.04 40.07 45.59 48.09 37.17 43.87

Volume (MWh) 9,737,887 6,093,423 15,831,310 9,829,877 6,204,384 16,034,261

Percentage 61.51% 38.49% 100.00% 61.31% 38.69% 100.00%




Price Convergence

Price convergence is measured by the difference between day-ahead (DA), hour-ahead scheduling process (HASP), and real-time dispatch (RTD) prices. Generally speaking, the smaller the difference between the prices, the more convergent the prices are. Figure 27, Figure 28, and Figure 29 show the difference between DA daily average price and RTD daily average price for three default LAPs in all hours, peak hours, and off-peak hours respectively. Figure 30,Figure 31, and Figure 32 show the difference between DA weekly average price and RTD weekly average price for three default LAPs in all hours, peak hours, and off-peak hours respectively.

DA daily simple average price for each of the three default LAPs is calculated as the following:

iP =j

ijLMP /K i= PG&E, SCE, and SDG&E

iP is the daily average price for LAP i while j represents the hour (peak, off-peak,

or all). K is the count of the hours in one day.

The formula for RTD DLAP daily average price is:

iP = j h

ijhLMP /N i= PG&E, SCE, and SDG&E

iP is the daily average price for LAP i while j represents the hour (peak, off-peak, or all) and h represents 5-minute interval. N is the count of the intervals in one day. The similar methods are applied to calculate the DA and RTD weekly average prices for default LAPs.

Figure 33, Figure 34, and Figure 35 show the difference between DA daily average price and RTD daily average price for three trading hubs (NP15, SP15, and ZP26) in all hours, peak hours, and off-peak hours respectively. Figure 36, Figure 37, and Figure 38 show the difference between DA weekly average price and RTD weekly average price for three trading hubs (NP15, SP15, and ZP26) in all hours, peak hours, and off-peak hours respectively.

DA daily average price for each of the three trading hubs is calculated as below:

iP =j

ijLMP /K i= NP15, SP15, and ZP26

iP is the daily average price for hub i while j represents the hour (peak, off-peak,

or all). K is the count of the hours in one day.

The formula for RTD hub daily average price is:




iP = j h

ijhLMP /N i= NP15, SP15, and ZP26

iP is the daily average price for hub i while j represents the hour (peak, off-peak, or all) and h represents 5-minute interval. N is the count of the intervals in one day. The similar methods are applied to calculate the DA and RTD weekly average prices for trading hubs.

Figure 39, Figure 40, and Figure 41 show the difference between DA daily average price and HASP daily average price for three selected interties (Malin, Palo Verde, and Sylmar) in all hours, peak hours, and off-peak hours respectively. Figure 42, Figure 43, and Figure 44 show the difference between DA weekly average price and HASP weekly average price for three selected interties in all hours, peak hours, and off-peak hours respectively.

DA daily simple average price for each of the three selected is calculated as the following:

iP =j

ijLMP /K i= Malin, Palo Verde, and Sylmar

iP is the daily average price for intertie i while j represents the hour (peak, off-

peak, or all). K is the count of the hours in one day.

The formula for HASP intertie daily average price is:

iP = j h

ijhLMP /N i= Malin, Palo Verde, and Sylmar

iP is the daily average price for intertie i while j represents the hour (peak, off-peak, or all) and h represents 15-minute interval. N is the count of the intervals in one day. The similar methods are applied to calculate the DA and HASP weekly average prices for interties.

Table 3, Table 4, and Table 5 show the statistics of the difference between hourly average prices for default LAPs, trading hubs, and three selected interties respectively. RTD hourly simple average price for each of the three default LAPs is calculated as the following:

iP = h

ijhLMP /N i= PG&E, SCE, and SDG&E




ijP is the hourly average price for LAP i in hour j while j represents the hour

(peak, off-peak, or all) and h represents 5-minute interval. N is the count of the intervals in one hour.

RTD hourly average price for each of the three trading hubs (NP15, SP15, and ZP26) is calculated as below:

ijP = h

ijhLMP /N i= NP15, SP15, and ZP26

ijP is the hourly average price for hub i in hour j while j represents the hour


HASP hourly average price for each of the three selected is calculated as the following:

ijP = h

ijhLMP /N i=Malin, Palo Verde, and Sylmar

ijP is the hourly average price for intertie i in hour j while j represents the hour


In the following figures and tables, the notation “PGAE_RT_DA” means the PG&E RTD average price minus DA average price. “PGAE_RT_DA_abs” represents the absolute value of the difference between RTD and DA average prices for PG&E. The same rule applies to other default LAPs, hubs, and interties. In the summary tables, “N” is the count of the observations for current month. “MIN” and “MAX” are the minimum and maximum of the difference. “MEAN” and “STD” are mean and standard deviation of the price difference.




Figure 27: Daily LAP Price Difference (All Hours)

-50

0

50

100

150

2001-

Feb

3-F

eb5-

Feb

7-F

eb9-

Feb

11-

Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-

Ma

r1

3-M

ar

15-

Ma

r1

7-M

ar

19-

Ma

r2

1-M

ar

23-

Ma

r2

5-M

ar

27-

Ma

r2

9-M

ar

31-

Ma

r

$/M

Wh

PGAE_RT_DA SCE_RT_DA SDGE_RT_DA

Figure 28: Daily LAP Price Difference (On-Peak Hours)

-50

0

50

100

150

200

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar

$/M

Wh





Figure 29: Daily LAP Price Difference (Off-Peak Hours)

-50

0

50

100

150

2001-

Feb

3-F

eb5-

Feb

7-F

eb9-

Feb

11-F

eb13

-Feb

15-F

eb17

-Feb

19-F

eb21

-Feb

23-F

eb25

-Feb

27-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

ar

$/M

Wh


Table 3: Summary for DLAP Hourly Average Price Difference (All Hours)

Month Price Difference N MIN MAX MEAN STD

3 PGAE_RT_DA 742 -54.87 569.60 -1.99 29.16

3 SCE_RT_DA 742 -64.08 833.09 -1.18 55.16

3 SDGE_RT_DA 742 -79.84 491.27 -2.86 27.79

3 PGAE_RT_DA_abs 742 0.00 569.60 9.82 27.53

3 SCE_RT_DA_abs 742 0.01 833.09 20.57 51.19

3 SDGE_RT_DA_abs 742 0.01 491.27 11.32 25.54




Figure 30: Weekly LAP Price Difference (All Hours)

-15-10

-505

10152025

30-

Sep

7-O

ct14

-Oct

21-O

ct28

-Oct

4-N

ov1

1-N

ov1

8-N

ov2

5-N

ov2-

Dec

9-D

ec1

6-D

ec2

3-D

ec3

0-D

ec6-

Jan

13-

Jan

20-

Jan

27-

Jan

3-F

eb1

0-F

eb1

7-F

eb2

4-F

eb3-

Ma

r1

0-M

ar

17-

Ma

r2

4-M

ar

31-

Ma

r

$/M

Wh


Figure 31: Weekly LAP Price Difference (On-Peak Hours)

-30

-20

-10

0

10

20

30

30-S

ep7-

Oct

14-

Oct

21-

Oct

28-

Oct

4-N

ov11

-Nov

18-N

ov25

-Nov

2-D

ec9-

Dec

16-D

ec23

-Dec

30-D

ec6

-Jan

13-J

an20

-Jan

27-J

an3

-Feb

10-F

eb17

-Feb

24-F

eb3-

Mar

10-M

ar17

-Mar

24-M

ar

$/M

Wh





Figure 32: Weekly LAP Price Difference (Off-Peak Hours)

-15-10-505

1015202530

30-S

ep7-

Oct

14-O

ct21

-Oct

28-O

ct4-

Nov

11-N

ov18

-Nov

25-N

ov2-

Dec

9-D

ec16

-Dec

23-D

ec30

-Dec

6-Ja

n13

-Ja

n20

-Ja

n27

-Ja

n3-

Feb

10-F

eb17

-Feb

24-F

eb3-

Mar

10-M

ar17

-Mar

24-M

ar31

-Mar

$/M

Wh


Figure 33: Daily Trading Hub Price Difference (All Hours)

-40-20

020406080

100120140160

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb13

-Feb

15-F

eb17

-Feb

19-F

eb21

-Feb

23-F

eb25

-Feb

27-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

ar

$/M

Wh

NP15_RT_DA SP15_RT_DA ZP26_RT_DA




Figure 34: Daily Trading Hub Difference (On-Peak Hours)

-40-20

020406080

100120140

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

$/M

Wh


Figure 35: Daily Trading Hub Difference (Off-Peak Hours)

-40-20

020406080

100120140160180

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb13

-Feb

15-F

eb17

-Feb

19-F

eb21

-Feb

23-F

eb25

-Feb

27-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

ar

$/M

Wh





Table 4: Summary for Trading Hub Hourly Average Price Difference (All Hours)


3 NP15_RT_DA 742 -53.40 635.63 -1.93 31.45

3 SP15_RT_DA 742 -63.54 678.77 -1.55 45.95

3 ZP26_RT_DA 742 -53.92 189.41 -2.78 15.77

3 NP15_RT_DA_abs 742 0.02 635.63 9.73 29.97

3 SP15_RT_DA_abs 742 0.02 678.77 17.98 42.31

3 ZP26_RT_DA_abs 742 0.01 189.41 8.31 13.69

Figure 36: Weekly Trading Hub Price Difference (All Hours)

-10

-5

0

5

10

15

20

30-

Sep

7-O

ct14

-Oct

21-O

ct28

-Oct

4-N

ov1

1-N

ov1

8-N

ov2

5-N

ov2-

Dec

9-D

ec1

6-D

ec2

3-D

ec3

0-D

ec6-

Jan

13-

Jan

20-

Jan

27-

Jan

3-F

eb1

0-F

eb1

7-F

eb2

4-F

eb3-

Ma

r1

0-M

ar

17-

Ma

r2

4-M

ar

31-

Ma

r

$/M

Wh





Figure 37: Weekly Trading Hub Price Difference (On-Peak Hours)

-20-15-10

-505

10152025

30-

Se

p7-

Oct

14-

Oct

21-

Oct

28-

Oct

4-N

ov

11-

No

v1

8-N

ov

25-

No

v2-

De

c9-

De

c1

6-D

ec

23-

De

c3

0-D

ec

6-Ja

n1

3-Ja

n2

0-Ja

n2

7-Ja

n3-

Feb

10-

Feb

17-

Feb

24-

Feb

3-M

ar1

0-M

ar

17-

Ma

r2

4-M

ar

$/M

Wh


Figure 38: Weekly Trading Hub Price Difference (Off-Peak Hours)

-15

-10

-5

0

5

10

15

20

25

30-S

ep7-

Oct

14-O

ct21

-Oct

28-O

ct4-

Nov

11-N

ov18

-Nov

25-N

ov2-

Dec

9-D

ec16

-Dec

23-D

ec30

-Dec

6-Ja

n13

-Ja

n20

-Ja

n27

-Ja

n3-

Feb

10-F

eb

17-F

eb

24-F

eb

3-M

ar

10-M

ar17

-Mar

24-M

ar31

-Mar

$/M

Wh





Figure 39: Daily Intertie Price Difference (All Hours)

-40

-20

0

20

40

60

80

100

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r1

1-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

ar

$/M

Wh

MALIN_HASP_DA PALOVRDE_HASP_DA SYLMARDC_HASP_DA

Figure 40: Daily Intertie Price Difference (On-Peak Hours)

-40

-20

0

20

40

60

80

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Ma

r1

3-M

ar

15-

Ma

r1

7-M

ar

19-

Ma

r2

1-M

ar

23-

Ma

r2

5-M

ar

27-

Ma

r2

9-M

ar

$/M

Wh





Figure 41: Daily Intertie Price Difference (Off-Peak Hours)

-40

-20

0

20

40

60

80

100

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Fe

b13

-Fe

b15

-Fe

b17

-Fe

b19

-Fe

b21

-Fe

b23

-Fe

b25

-Fe

b27

-Fe

b1

-Ma

r3

-Ma

r5

-Ma

r7

-Ma

r9

-Ma

r11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

$/M

Wh


Table 5: Summary for Intertie Hourly Average Price Difference (All Hours)


3 MALIN_HASP_DA 742 -35.88 30.29 -8.79 8.97

3 PALOVRDE_HASP_DA 742 -62.44 43.58 0.33 11.92

3 SYLMARDC_HASP_DA 742 -43.02 27.38 -5.36 9.16

3 MALIN_HASP_DA_abs 742 0.00 35.88 9.94 7.67

3 PALOVRDE_HASP_DA_abs 742 0.00 62.44 8.25 8.61

3 SYLMARDC_HASP_DA_abs 742 0.00 43.02 7.57 7.43




Figure 42: Weekly Intertie Price Difference (All Hours)

-20-15-10

-505

101520

30-

Sep

7-O

ct14

-Oct

21-O

ct28

-Oct

4-N

ov1

1-N

ov1

8-N

ov2

5-N

ov2-

Dec

9-D

ec1

6-D

ec2

3-D

ec3

0-D

ec6-

Jan

13-

Jan

20-

Jan

27-

Jan

3-F

eb1

0-F

eb1

7-F

eb2

4-F

eb3-

Ma

r1

0-M

ar

17-

Ma

r2

4-M

ar

31-

Ma

r

$/M

Wh


Figure 43: Weekly Intertie Price Difference (On-Peak Hours)

-25-20-15-10

-505

101520

30-

Se

p7-

Oct

14-

Oct

21-

Oct

28-

Oct

4-N

ov

11-

No

v1

8-N

ov

25-

No

v2-

De

c9-

De

c1

6-D

ec

23-

De

c3

0-D

ec

6-Ja

n1

3-Ja

n2

0-Ja

n2

7-Ja

n3-

Feb

10-

Feb

17-

Feb

24-

Feb

3-M

ar1

0-M

ar

17-

Ma

r2

4-M

ar

$/M

Wh





Figure 44: Weekly Intertie Price Difference (Off-Peak Hours)

-20-15-10

-505

1015202530

30-S

ep7-

Oct

14-O

ct21

-Oct

28-O

ct4-

Nov

11-N

ov18

-Nov

25-N

ov2-

Dec

9-D

ec16

-Dec

23-D

ec30

-Dec

6-Ja

n13

-Ja

n20

-Ja

n27

-Ja

n3

-Feb

10-F

eb

17-F

eb

24-F

eb

3-M

ar

10-M

ar17

-Mar

24-M

ar31

-Mar

$/M

Wh





Congestion

Congestion occurs when available, least-cost energy cannot be delivered to some loads because transmission facilities do not have sufficient capacity to deliver the energy. When the least-cost, available energy cannot be delivered to load in a transmission-constrained area, higher cost units in the constrained area must be dispatched to meet that load. The result is the price of energy in the constrained area will be higher than in the unconstrained area because of the combination of transmission limitations and the costs of local generation.

Congestion Rents on Interties

Figure 45 below illustrates the IFM congestion costs on interties. The congestion cost is calculated as shadow price ($/MWh) of the intertie constraint multiplied by the flow (MW) on the intertie.

Figure 45: IFM (Day-Ahead) Congestion Rents by Intertie (Import)

$0

$200

$400

$600

$800

$1,000

$1,200

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

Th

ou

san

ds

NOB_ITC PACI_ITC ELDORADO_ITC PALOVRDE_ITC

PARKER_ITC IID-SCE_ITC CASCADE_ITC COTPISO_ITC




Table 6 provides a breakout of the IFM cleared value (MW), the average shadow price ($/MWh) and the number of congested hours by intertie.

Table 6: IFM (Day-Ahead) Congestion Statistics by Intertie (Import)

IntertieAverage Cleared

Value (MW)Shadow Price

($/MWh)Number of

Congested Hours

BLYTHE_ITC 218.00 11.80 22CASCADE_ITC 46.67 3.19 12COTPISO_ITC 18.00 35.93 12ELDORADO_ITC 965.18 7.92 130IID-SCE_ITC 392.00 62.90 19MEAD_ITC 991.82 6.92 173NOB_ITC 1564.00 5.88 122PACI_ITC 1646.82 8.01 378PALOVRDE_ITC 1578.53 25.18 45SUMMIT_ITC 29.00 2.88 14

Congestion Rents on Branch Groups and Market Scheduling Limit

Figure 46 illustrates IFM congestion rents by branch group and market scheduling limit. The congestion rent is calculated as the shadow price ($/MWh) of the branch group or market scheduling limit constraint multiplied by the flow limit (MW) on the branch group or market scheduling limit.

Figure 46: IFM (Day-Ahead) Daily Congestion Rents by Branch Group and Market Scheduling Limit

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r1

1-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

ar

Th

ou

san

ds

IPPUTAH_MSL SCE_PCT_IMP_BG PATH26_BG IPPDCADLN_BGSUTTEROBANION_BG PATH15_BG SCIT_BG WSTWGMEAD_MSLSOUTHLUGO_RV_BG IVALLYBANK_XFBG




Table 7 provides a breakout of the IFM cleared value (MW), the average shadow price ($/MWh) and the number of congested hours by branch groups and market scheduling limits.

Table 7: IFM (Day-Ahead) Congestion Statistics by Branch Group and Market Scheduling Limit

Branch Group/Market Scheduling Limit

Average Cleared Value (MW)

Shadow Price

($/MWh)

Number of Congested Hours

IPPDCADLN_BG 421.00 2.38 2IPPUTAH_MSL 187.00 3.64 23IVALLYBANK_XFBG 1000.00 17.05 17PATH15_BG 2851.75 2.91 127PATH26_BG 990.00 8.61 4SCE_PCT_IMP_BG 6266.07 6.98 408SDGE_CFEIMP_BG 1800.00 5.14 2SDGE_PCT_UF_IMP_BG 1464.33 5.89 27SOUTHLUGO_RV_BG 4150.00 13.10 9

Congestion Rents on Transmission Lines and Transformers

Figure 47 illustrates IFM congestion rents by transmission lines andtransformers. The congestion cost is calculated as the shadow price ($/MWh) of the constraint multiplied by the flow limit (MW).

Figure 47: IFM (Day-Ahead) Congestion Rents by Transmission Lines and Transformers

$0

$500

$1,000

$1,500

$2,000

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Th

ou

san

ds

BLYTHESC-EAGLEMTN-161kV LINE IMPRLVLY-IMPRLVLY-230 XFMR Other

DOUBLTTP-FRIARS -138kV LINE SANMATEO-RAVENSWD-115kV LINE VACA-DIX-LAMBIE -230kV LINE

MORAGA -MORAGA -115 XFMR SERRANO -SERRANO -500 XFMR HPLND JT-_HPLND J-60. XFMR




Congestion Rents on Nomograms

Figure 48 illustrates IFM congestion rents by nomogram. The congestion rent is calculated as the shadow price ($/MWh) of the constraint multiplied by the flow limit (MW).

Figure 48: IFM (Day-Ahead) Daily Congestion Rents by Nomogram

-$500

$0

$500

$1,000

$1,500

$2,000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Th

ou

san

ds

BARRE-LEWIS_NG SLIC 2051445 TL23050_NG T-133 METCALF_NGSLIC 2090447_HIG_BEL_SOL1 SLIC 2090466 and 2090467 SOL MIGUEL_BKs_MXFLW_NGSLIC 2111963 BELL-PLCR SOL1 SLIC 2112931 EL CENTRO BK1_NG SLIC 2113417 ValleySerranoImportOther

Congestion Rents on Nodal Group Constraints

Figure 49 illustrates IFM congestion rents by nodal group constraints. The congestion rent is calculated as the shadow price ($/MWh) of the constraint multiplied by the flow limit (MW).




Figure 49: IFM (Day-Ahead) Daily Congestion Rents by Nodal Group Constraints

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,0001

-Fe

b3

-Fe

b5

-Fe

b7

-Fe

b9

-Fe

b11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

NdGrp: 36411_DIABLO 1_25.0_B1 NdGrp: 34722_KERN_RV _115_B2 NdGrp: 34500_DINUBA _70.0_B2NdGrp: 35926_DOLAN RD_115_B1 NdGrp: 24201_BARRE _66.0_B3 NdGrp: 22120_CARLTNHS_138_B1NdGrp: 24017_BLYTHESC_161_B1 NdGrp: 24201_BARRE _66.0_B1 NdGrp: 25203_ANAHEIMG_13.8_B1OTHER

Comparison of IFM (Day-ahead) Congestion and Real-Time Dispatch Congestion

Table 8 provides a breakout of the average cleared value (MW), the average shadow price ($/MWh) and the number of congested hours in day-ahead and RTD by transmission elements. The frequency of overlapping congestion is the overlapping congested hours in both IFM and RTD as a percentage of congested hours in IFM. Please note this table contains the cleared values for only those instances when the constraint was binding.




Table 8: IFM and RTD Congestion Statistics by Elements




IFM and RTD

Transmission ElementElement Type



Average Shadow Price ($/MWh)

Maximum Shadow Price ($/MWh)

Minimum Shadow Price ($/MWh)


Average Cleared Value(MW)

Average Shadow Price($/MWh)

Maximum Shadow Price($/MWh)

Minimum Shadow Price($/MWh)

Frequency of Overlapping Congestion

CASCADE_ITC ITC 1 50 $0.27 $0.27 $0.27

COTPISO_ITC ITC 10 20 $29.07 $30.12 $27.01 1.7 16 $69.98 $81.28 $60.39 0.00%

ELDORADO_ITC ITC 0.3 1130 $65.87 $67.81 $61.99

IID-SCE_ITC ITC 24 392 $71.26 $77.91 $62.67

NOB_ITC ITC 85 1564 $3.86 $14.04 $0.02 12.4 1441 $75.48 $361.36 $54.54 0.00%

PACI_ITC ITC 210 1585 $7.02 $33.03 $0.02 14.3 755 $27.12 $67.19 $0.08 0.00%

PALOVRDE_ITC ITC 208 1526 $17.05 $39.44 $0.05 9.8 1421 $66.18 $84.14 $43.16 0.00%

IID-SCE_BG TCOR 0.4 392 $477.36 $622.90 $316.06

IPPDCADLN_BG TCOR 42 404 $2.00 $9.50 $0.06 6.7 404 $66.09 $85.06 $59.56 0.00%

IPPUTAH_MSL TCOR 63 187 $2.60 $7.95 $0.03 61.9 187 $69.53 $1,000.00 $0.24 0.00%

IVALLYBANK_XFBG TCOR 57 1000 $16.15 $80.44 $1.44 57.3 844 $57.02 $2,507.08 $0.13 0.00%

PATH15_BG TCOR 31 2375 $3.92 $11.05 $0.06 0.2 2500 $9.68 $11.22 $8.14 0.00%

SCE_PCT_IMP_BG TCOR 471 6036 $10.55 $38.46 $0.02 107.7 6094 $56.71 $2,120.16 $0.01 0.00%

SCIT_BG TCOR 2 8235 $2.24 $4.35 $0.13 0.9 8481 $497.35 $1,754.72 $2.77 0.00%

SOUTHLUGO_RV_BG TCOR 8 4070 $11.73 $17.72 $3.18 0.2 4415 $9.28 $9.90 $8.65 0.00%

TRNBAY_MSL TCOR 0.5 400 $947.68 $965.55 $903.16

WSTWGMEAD_MSL TCOR 8 160 $8.95 $19.91 $3.94 4.2 162 $103.63 $1,000.00 $57.25 0.00%

7820_TL 230S_OVERLOAD_NG NG 7.6 344 $291.61 $1,544.86 $0.12

BARRE-LEWIS_NG NG 88 1367 $33.33 $186.88 -$4.55 6.9 1447 $457.81 $4,999.70 $2.47 0.00%

HUMBOLDT_IMP_NG NG 7 80 $7.76 $15.52 $0.39

MIGUEL_BKs_MXFLW_NG NG 9 1400 $46.54 $65.49 $9.61 0.8 1332 $125.58 $446.29 $2.06 0.00%

PATH15_S-N NG 15.2 2041 $91.74 $985.32 $0.13

SLIC 2090466 and 2090467 SOL NG 1 382 $62.34 $62.34 $62.34

SLIC 2111963 BELL-PLCR SOL1 NG 28 101 $12.25 $23.42 $0.43

SLIC 2112931 EL CENTRO BK1_NG NG 26 329 $56.36 $179.51 $1.17 4.1 354 $507.05 $1,950.87 $1.17 0.00%

SLIC 2113417 DeversValley NG 1 317 $271.37 $1,574.09 $10.45

SLIC 2113417 ValleySerranoExport NG 0.1 55 $2.27 $2.27 $2.27

SLIC 2113417 ValleySerranoImport NG 1 -50 $424.02 $424.02 $424.02 14.3 15 $30.67 $1,356.21 $0.26 0.00%

SLIC 2114805 PLACER-GLDHL SOL1 NG 6 117 $7.03 $7.99 $5.82

SLIC 2116113 ValleySerranoImport NG 0.3 62 $334.96 $1,000.00 $2.41

SLIC 2119136 SOL2 NG 2 96 $15.59 $16.26 $14.92

SLIC 2119455 PITTSB SOL-2 NG 6.1 158 $16.39 $106.07 $0.41

T-133 METCALF_NG NG 90 145 $13.36 $120.37 $0.05 3.3 136 $388.46 $1,000.00 $3.10 0.00%

T-135 VICTVLUGO_EDLG_NG NG 4 2700 $7.81 $16.30 $0.78 0.3 2491 $13.12 $44.24 $1.23 0.00%

22192_DOUBLTTP_138_22300_FRIARS _138_BR_1 _1LINE 42 172 $30.41 $77.13 $0.70

22604_OTAY _69.0_22616_OTAYLKTP_69.0_BR_1 _1LINE 4 50 $16.73 $22.78 $9.78

22828_SYCAMORE_69.0_22756_SCRIPPS _69.0_BR_1 _1LINE 35 161 $21.24 $40.33 $0.37

22831_SYCAMORE_138_22124_CHCARITA_138_BR_1 _1LINE 1 200 $7.44 $7.44 $7.44

24017_BLYTHESC_161_24035_EAGLEMTN_161_BR_1 _1LINE 46 175 $22.18 $69.56 $0.60 3.8 196 $4.10 $10.70 $0.33 0.00%

24087_MAGUNDEN_230_24141_SPRINGVL_230_BR_2 _1LINE 6 376 $9.68 $14.08 $6.37 6.7 369 $8.87 $31.14 $0.41 0.00%

24235_RECTOR _230_24153_VESTAL _230_BR_2 _1LINE 1 271 $2.63 $2.63 $2.63

24807_MIRAGE _115_24829_TAP821 _115_BR_1 _1LINE 0.4 183 $727.65 $1,436.75 $475.30

30900_GATES _230_30970_MIDWAY _230_BR_1 _1LINE 2 295 $78.16 $136.89 $7.27

31080_HUMBOLDT_60.0_31088_HMBLT JT_60.0_BR_1 _1LINE 3 46 15.33 26.97 5.65

31576_WNTU PMS_60.0_31570_BENTON _60.0_BR_1 _1LINE 0.1 47 $59.52 $59.52 $59.52

31580_CASCADE _60.0_31581_OREGNTRL_60.0_BR_1 _1LINE 1 46 $0.63 $0.63 $0.63 0.2 45 $57.78 $57.92 $57.64 0.00%

IFM (Day-Ahead) Real-Time Dispatch (RTD)




IFM and RTD

Transmission ElementElement Type



Average Shadow Price ($/MWh)

Maximum Shadow Price ($/MWh)

Minimum Shadow Price ($/MWh)


Average Cleared Value(MW)

Average Shadow Price($/MWh)

Maximum Shadow Price($/MWh)

Minimum Shadow Price($/MWh)

Frequency of Overlapping Congestion

31996_HALEJ1 _115_32020_JMSN JCT_115_BR_1 _1 LINE 0.1 107 $1,000.00 $1,000.00 $1,000.00

32214_RIO OSO _115_32244_BRNSWKT2_115_BR_2 _1 LINE 1 78 $25.07 $25.07 $25.07 0.5 70 $817.38 $868.42 $778.89 0.00%

32218_DRUM _115_32219_DR360370_115_BR_1 _1 LINE 9 96 $2.00 $4.18 $0.29

32218_DRUM _115_32244_BRNSWKT2_115_BR_2 _1 LINE 2 101 $21.29 $21.32 $21.26 1.1 21 $493.08 $823.72 $266.01 0.00%

32618_NTWRJCT1_115_32020_JMSN JCT_115_BR_1 _1 LINE 0.2 88 $979.96 $1,000.00 $959.91

33200_LARKIN _115_33204_POTRERO _115_BR_2 _1 LINE 1 150 $16.66 $16.66 $16.66

33310_SANMATEO_115_33315_RAVENSWD_115_BR_1 _1 LINE 41 110 $246.01 $803.58 $19.93 9.3 126 $1,000.00 $1,000.00 $1,000.00 0.00%

35202_USWP-WKR_60.0_33777_SBTAP _60.0_BR_1 _1 LINE 215 57 $4.28 $26.24 $0.04

18085_PAHRUMP _138_18023_PAHRUMP _230_XF_3 XFMR 8 97 $21.61 $33.01 $10.18

22768_SOUTHBAY_69.0_22772_SOUTHBAY_138_XF_1 XFMR 4 208 $35.74 $80.08 $11.56

24138_SERRANO _500_24137_SERRANO _230_XF_1 _P XFMR 0.1 466 $1,000.00 $1,000.00 $1,000.00

24138_SERRANO _500_24137_SERRANO _230_XF_2 _P XFMR 1.6 455 $1,111.30 $3,114.67 $1,000.00

24138_SERRANO _500_24137_SERRANO _230_XF_3 XFMR 8 448 $509.79 $607.96 $255.15 2 453 $966.11 $1,216.29 $527.98 0.00%

31336_HPLND JT_60.0_31206_HPLND JT_115_XF_2 XFMR 37 48 $104.36 $301.04 $26.65 30.8 41 $414.06 $645.84 $130.42 0.00%

IFM (Day-Ahead) Real-Time Dispatch (RTD)




Congestion Rents by Type of Resources

Figure 50 shows the DA congestion rents grouped by type of market resource. If congestion arises, power is priced accordingly through a marginal congestion component (MCC). For any given hour of the day-ahead market, demand is charged the scheduled MW times the MCC, and supply is paid the scheduled MW times the MCC. The MCC is at the applicable PNodes, APNodes and scheduling points. The net money surplus collected by the ISO is the congestion rents. The hourly congestion rents are then summed up across all hours of the day. A positive value of congestion rents indicates a payment to the ISO (surplus). Congestion rents may also arise from provision of ancillary services over the interties. Due to the dual nature of pump storage units, they can be treated as supply or demand within the computation of congestion rents.

Figure 50: DA Congestion Rents by Type of Market Resource

-$0.3

$0.3

$0.9

$1.5

$2.1

$2.7

$3.3

$3.9

$4.5

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Ma

r13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

rCo

ng

esti

on

Ren

ts(M

illio

ns)

EXPORT LOAD GENERATION IMPORT PUMP STORAGE ANCILLARY SERVICES




Average Congestion Cost per Load Served

This metric quantifies on average the congestion cost for serving a megawatt of load in the ISO system. The congestion rents of both the integrated forward market and the real-time market are calculated for every hour. These rents are determined by multiplying the energy schedules by the corresponding marginal congestion components. Day-ahead rents are for the full schedules, while real-time rents are just for the difference of the schedules between the day-ahead and real-time markets. Congestion rents associated with existing transmission rights (ETC, TOR and CVR) are discounted from the congestion rents because the schedules related to existing rights are exempt from congestion charges.

The total measured demand applicable to each hour is then used as a reference of load. Furthermore, the served load associated with existing rights is deducted because such load is exempted from congestion charges. Based on the hourly values for served load, and hourly day-ahead and real-time congestion rents, the weighted average congestion cost to served load is computed as the ratio of daily congestion rents (day-ahead and real-time) to total load served in each day. Similarly, a monthly weighted average cost is estimated. The daily and monthly averages for the day-ahead and real-time markets are depicted with bars and lines, respectively, in Figure 51.

Figure 51: Average Congestion Cost per Megawatt of Served Load

-3.0-2.0-1.00.01.02.03.04.05.06.07.08.0

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-

Ma

r1

3-M

ar

15-

Ma

r1

7-M

ar

19-

Ma

r2

1-M

ar

23-

Ma

r2

5-M

ar

27-

Ma

r2

9-M

ar

31-

Ma

rCo

ng

esti

on

Co

st

($/M

Wh

)

Day Ahead Real Time Day-Ahead Average Real-Time Average




Congestion Revenue Rights

Auction Bids

Figure 52 shows the count of bids submitted in the last six monthly congestion revenue right (CRR) auctions. The count is grouped by time of use. The count is for each individual bid submitted to the auction even if they are not awarded, and regardless of the number of bid segments.

Figure 52: Bid Count for Monthly CRR Auctions

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

No

v-12

De

c-12

Jan

-13

Fe

b-13

Mar

-13

Apr

-13

Bid

Co

un

t

ON Peak OFF Peak

Auction Revenues

Figure 53 shows the monthly revenues with the corresponding net volume awards from CRR auctions. Revenues are from seasonal and monthly auctions and are grouped by time of use. Revenues from annual auctions are spread pro-rata to each month of a season, based on the number of – on-peak or off-peak –hours of each month. The net MW volume is based only on the allocations and awards of the last six monthly processes. This graph provides trends of auctions over time.




Figure 53: Revenues and Award Volumes in Monthly CRR Auctions

0

18

36

54

72

90

$0.0$1.0$2.0$3.0$4.0$5.0$6.0$7.0$8.0$9.0

$10.0

Nov

-12

Dec

-12

Jan-

13

Feb

-13

Mar

-13

Apr

-13 M

W A

war

ds

(T

ho

usa

nd

s)

Au

ctio

n R

even

ues

(M

illio

ns

)

ON Peak Annual OFF Peak Annual ON Peak Monthly OFF Peak Monthly Net Monthly Volume

Monthly Volumes

Figure 54 through Figure 56 show the CRR volumes released in the last six monthly CRR processes. Both allocation and auctions for both times of use are depicted. Figure 54 illustrates the trends of CRR volumes awarded over time and offers an easy reference for comparison of volumes released in allocations versus auctions. This graph can also help visualize the evolution of the monthly processes over time.

Figure 54: Monthly Volumes of CRR Awards –Allocation and Auction

0

15

30

45

60

Nov

-12

Dec

-12

Jan-

13

Fe

b-13

Mar

-13

Apr

-13

MW

(T

ho

us

and

s)

Auction OFF Auction ON Allocation OFF Allocation ON

Figure 55 and Figure 56 compare the volume nominated and bid against the volumes allocated and awarded in the allocation and auction processes over the




last six months, respectively. It also includes the percentage of the volumes that were actually released in the monthly processes. These figures give a compact reference over time and also between allocation and auction.

Figure 55: Volumes of Monthly CRR Allocations

0%

10%

20%

30%

40%

50%

0

6

12

18

24

30N

ov-1

2

Dec

-12

Jan

-13

Feb

-13

Mar

-13

Apr

-13

Aw

ard

Ra

tio

Vo

lum

e (

Th

ou

san

ds

MW

)

NOMINATION AWARD AWARD RATIO

Figure 56: Volumes of Monthly CRR Auctions

0%

6%

12%

18%

24%

30%

36%

0

50

100

150

200

250

300

Nov

-12

Dec

-12

Jan

-13

Fe

b-1

3

Mar

-13

Apr

-13

Aw

ard

Rat

io

Vo

lum

e (

Th

ou

san

ds

MW

)

BID AWARD AWARD RATIO




Auction Prices

Figure 57 and Figure 58 show price distribution trends of the last six monthly CRR auctions. The distributions are given for each time of use. The vertical axis shows the count of prices only for CRRs that have an award greater than zero.The prices are computed as the auction prices divided by the number of hours for the corresponding time of use and month. Therefore, prices are on an hourly basis ($/MWh).

Figure 57: Price Distribution of Monthly CRR Auctions – On-Peak

0

1,000

2,000

3,000

Nov

-12

Dec

-12

Jan-

13

Fe

b-1

3

Mar

-13

Apr

-13

Pri

ce C

ou

nt

<-1 [-1, -0.5) [-0.5, -.25) [-0.25 to 0) 0 (0, 0.25) [0.25, 0.5) [0.5, 1) >= 1

Figure 58: Price Distribution of CRR Monthly Auctions – OFF-Peak

0

1,000

2,000

3,000

Nov

-12

Dec

-12

Jan-

13

Fe

b-13

Mar

-13

Apr

-13

Pri

ce C

ou

nt

<-1 [-1, 0.5) [-0.5, 0.25) [-0.25, 0) 0 (0, 0.25) [0.25, 0.5) [0.5, 1) >=1




Price Convergence

Figure 59 shows the price comparison between CRR auction prices and day-ahead (IFM) prices. For the CRR auction prices, both seasonal and monthlyawarded CRRs are included, and grouped by time of use. This price comparison is useful to estimate the price convergence. Over time, in a healthy market, price convergence should be observed, accounting for the fact that there may be a risk premium associated with acquiring CRRs. Each CRR may have associated a different price; in order to have all CRRs on the same basis, the metrics shown in these plots are computed as weighted average prices. The main steps to obtain such weighted prices are as follows:

1. Obtain all CRRs with awards greater than zero and their associated auction prices and quantities.

2. Divide the CRR prices by their corresponding number of hours for each time of use and season/month to have prices on an hourly basis.

3. Associate the corresponding IFM congestion component prices (sink minus source) to each CRR for each hour by time of use.

4. Obtain the total MW awarded by time of use and month and use it to obtain the weighting factors for each CRR. Multiply both the CRR auction prices and the IFM CRR (congestion) prices by their corresponding factors.

5. Sum all CRR prices by time of use and month to obtain both weighted CRR auction prices and weighted DA (IFM) congestion prices.

Figure 59: Convergence of CRR Prices towards DA Congestion Prices

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

FEB- 2013 MARCH- 2013 FEB- 2013 MARCH- 2013

OFF Peak ON Peak

$/M

Wh

CRR AUCTION PRICE DA CONGESTION PRICE

The value of the auction CRR price indicates how much CRR holders pay to acquire CRRs, while the DA congestion price indicates how much the CRR holders were paid due to the CRR entitlements. A case where the DA congestion price is higher than the auction CRR price means that CRR holders profited from holding CRRs.




Monthly CRR Revenue Adequacy

Figure 60 illustrates the revenue adequacy (congestion rents less exemptions of existing transmission rights less CRR entitlements) for CRRs in the corresponding month for the various transmission elements that experienced congestion during the month. A positive value indicates that there is a surplus and a negative value indicates there is a shortfall. Revenue adequacy for CRRs reflects the extent to which the hourly net congestion revenues (once the exemptions of existing transmission right holders are included) collected from the IFM are sufficient to cover the hourly net payments to CRR holders.3 For illustration purposes, the CRR revenue adequacy amounts are computed hourly and then aggregated across all hours of each day.

Figure 60: Daily CRR Revenue Adequacy by Transmission Element

-$3.00

-$2.50

-$2.00

-$1.50

-$1.00

-$0.50

$0.00

$0.50

$1.00

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11

-Mar

13

-Mar

15

-Mar

17

-Mar

19

-Mar

21

-Mar

23

-Mar

25

-Mar

27

-Mar

29

-Mar

31

-Mar

Rev

enu

e A

deq

ua

cy

(Mill

ion

s)

24017_BLYTHESC_161_24035_EAGLEMT MIGUEL_BKs_MXFLW_NG PACI_ITCSCE_PCT_IMP_BG PALOVRDE_ITC 33310_SANMATEO_115_33315_RAVENSWT-133 METCALF_NG 24138_SERRANO _500_24137_SERRANO 31336_HPLND JT_60.0_31206_HPLNDOther

Furthermore, Figure 61 shows the cumulative CRR revenue adequacy in the month broken out by surpluses and shortfalls through the various transmission elements. The net CRR revenue adequacy in a month is supplemented by the net CRR auction revenues collected by the ISO for the month through the mechanism of the CRR balancing account. The net surplus or deficit in the CRR balancing account at the end of each month is then allocated to all measured demand in accordance with the ISO tariff. Thus, following the principle of full

3

The congestion rents available from the integrated forward market to fund the CRR payments is lessened by the requirement that holders of existing rights (transmission ownership rights (TOR), existing transmission contracts (ETC) and Converted Rights (CVR)) are completely exempt from congestion charges. This requirement is contractual and is written into the ISO tariff. The ISO respects this requirement and enforces it by immediately reversing any and all congestion charges that are levied on these rights holders.




funding of CRRs, any deficit in the CRR balancing account at the end of a month does not adversely affect the payments to CRR holders

Figure 61: CRR Revenue Adequacy by Transmission Element

PALOVRDE_ITC33%

24138_SERRANO _500_24137_SERRANO _230_XF_3

21%

33310_SANMATEO_115_33315_RAVENSWD_115_BR

_1 _112%

PACI_ITC12%

31336_HPLND JT_60.0_31206_H

PLND JT_115_XF_2

6%

MIGUEL_BKs_MXFLW_NG

3%

T-133 METCALF_NG

2% Other11%

Revenue Shortfall, $9.16 Million

SCE_PCT_IMP_BG80%

24017_BLYTHESC_161_24035_EAGLEM

TN_161_BR_1 _14%

SLIC 2112931 EL CENTRO BK1_NG

3%

NdGrp: 36411_DIABLO

1_25.0_B13%

NdGrp: 32214_RIO OSO _115_B1

2%

NOB_ITC1%Other

7%

Revenue Surplus, $4.35 Million

Table 9 provides a summary of the main statistics for CRRs for the current month. Definitions for the concepts listed in Table 9 are as follows:




IFM Congestion Rents are the net monthly rents from IFM congestion, Existing Right Exemptions quantifies the cost of the reversal payment

to holders of existing transmission contracts, Available Congestion revenues is the result of subtracting the existing

right exemptions from the IFM congestion rents, CRR Payments is the money paid to holders of CRRs due to the CRR

entitlements, CRR Revenue Adequacy is the difference between available

congestion revenues and CRR payments, Revenue Adequacy Ratio is the proportion of the available congestion

revenues to the money paid to both the CRR entitlements, Annual Auction Revenues is the pro-rata portion of the annual auction

that applies to the corresponding month, Monthly Auction Revenues is the money obtained from the

corresponding monthly auction. These auction revenues are then added to the net revenue adequacy, to obtain the net monthly balance.

CRR Settlement Rule is put in place to recapture - where warranted –the increase in CRR revenues to CRR holders that are attributable to convergence bidding.

Allocation to Measured Demand is the sum CRR revenue adequacy and monthly auction revenues and CRR settlement rule and represents the money available in the CRR balancing account which is distributed to measured demand.

Table 9: CRR Adequacy Statistics

Concept Amount

IFM Congestion Rents $50,390,301.33

Existing Right Exemptions -$1,615,288.81

Available Congestion Revenues $48,775,012.53

CRR Payments $53,726,848.07

CRR Revenue Adequacy -$4,951,835.54

Revenue Adequacy Ratio 90.78%

Annual Auction Revenues $3,491,701.78

Monthly Auction Revenues $3,702,183.95

CRR Settlement Rule $581,923.20Allocation to Measured Demand $2,823,973.39

Although auction revenues can be used to offset any CRR revenue deficiency that results from the IFM, the intention of the ISO’s CRR release process is that proceeds from the IFM will be sufficient to cover net CRR payments over the course of each month. The annual and monthly processes to release CRRs through allocations and auctions are built upon this concept. In addition,




transmission capacity is set aside in the release processes in order to account forthe perfect hedge congestion payment reversal for existing transmission rights.

Existing Right Exemptions

The ISO collects congestion rents in both the day-ahead and real-time markets as determined by the charges to demand and payments to supply for schedulesin the day-ahead and real-time markets. Depending on contract provisions, some holders of existing rights may utilize their rights to submit day-ahead schedules and real-time adjustments with respect to their accepted day-ahead self-schedules.4 As required by the ISO tariff, these schedules are not subject to congestion charges. This provision applies in both the day-ahead and the real-time markets, and the real-time is independent of any settlement of the day-ahead market. The remaining real-time market congestion rents –surplus or deficit– are allocated to measured demand excluding measured demand associated with valid and balanced portions of existing rights. The real-time congestion rents and the existing rights exemption costs do not impact the settlements of congestion revenue rights, and the ISO accounts for these in real-time funds through a separate real-time mechanism (i.e., the real-time congestion off-set) instead of the CRR balancing account.

Figure 62 shows the net cost of the existing right exemptions for schedule changes of ETCs/TORs for both day-ahead and real-time markets. A negative value of the existing rights exemption indicates a net payment from the ISO to existing right holders to reverse the corresponding congestion charge, i.e., a credit. A positive value of the existing rights exemption indicates a net charge to existing right holders to reverse the corresponding congestion payment.

4 Converted rights are only eligible for the existing rights exemption in association with accepted self-schedules in the integrated forward market.




Figure 62: Cost of Existing Right Exemptions

-$600

-$500

-$400

-$300

-$200

-$100

$0

$100

$200

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1

-Mar

3-M

ar5

-Mar

7-M

ar9

-Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

Th

ou

san

ds

Day Ahead Real Time

Table 10 lists the monthly summary of exemptions for existing transmission rights in the day-ahead and real-time markets. A positive value of the congestion rents is a surplus; a negative value is a shortfall. Any surplus or shortfall is allocated to measured demand, excluding demand associated with ETCs/TORs. The percentage is the ratio of the exemptions to the congestion rents. This provides a reference of the extent of the cost charged to non-ETC demand to honor the exemptions in comparison to the overall congestion rents.

Table 10: Summary of the Existing Right Exemptions

.

Congestion Rents

Existing Right Exemptions Percentage

Congestion Rents

Existing Right Exemptions Percentage

FEBRUARY $49,637,862.93 ($1,381,892.87) -3% ($5,004,143.03) ($1,137,452.30) 23%MARCH $50,390,301.34 ($1,615,288.81) -3% ($4,644,543.07) ($270,315.80) 6%

Day Ahead Market Real Time Market




Losses

The energy markets at the ISO are settled with locational marginal prices (LMP), which consist of three components: energy, congestion, and losses. The marginal cost of losses may be positive or negative depending on whether a power injection at that node marginally increases or decreases losses. Incorporating the marginal cost of losses in the LMP is important both for assuring least-cost dispatch and for establishing nodal prices that accurately reflect the cost of supplying the load at each node. This section provides daily trends of marginal losses prices for DLAPs and trading hubs for both the day-ahead and real-time markets.

Day-ahead Prices

Figure 63 shows the daily schedule-weighted average LAP losses prices for each of the three default LAPs (PG&E, SCE, and SDG&E) in the day-ahead market. The formula for weighted average price is:

j

ij

ij

jiji SCHE_MW

SCHE_MWMLCP i= PG&E, SCE, and SDG&E

iP is the daily average losses price for LAP i, while j represents the hour. ijMLC

is the marginal losses component of the LMP for LAP i in hour j. jSCHE_MW is

the scheduled energy in hour j for LAP i. The daily simple-average losses (in MW) are also shown in the plot.

Similarly, daily prices for trading hubs (NP15, SP15 and ZP26) are presented in Figure 64. These daily values, however, are obtained as the simple average of hourly prices.




Figure 63: Day-Ahead Weighted Average LAP Prices for Losses

0

218

436

654

-8

-6

-4

-2

0

21

-Feb

3-F

eb5

-Feb

7-F

eb9

-Feb

11-F

eb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

arMa

rgin

al L

os

ses

Pri

ce (

$/M

Wh

)

AVG_LOSSES DLAP_PGAE-APND DLAP_SCE-APND DLAP_SDGE-APND

Lo

sses

(M

W)

Figure 64: Day-Ahead Simple Average TH Prices for Losses

0

218

436

654

-8

-6

-4

-2

0

2

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

Mar

gin

al L

oss

es P

rice

($

/MW

h)

AVG_LOSSES TH_NP15_GEN-APND TH_SP15_GEN-APND TH_ZP26_GEN-APND

Lo

sse

s (M

W)




Real-Time Prices

Similar to the day-ahead section, Figure 65 and Figure 66 show the daily average prices for losses in the real-time market for both DLAPs and THs. These prices are computed with the same logic as those of the day-ahead market. Average losses are based on real-time losses in this case.

Figure 65: Real-Time Weighted Average LAP Prices for Losses

0

218

436

655

873

1,091

-8

-6

-4

-2

0

2

4

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

Lo

sses

(M

W)

Mar

gin

al L

os

ses

Pri

ce

($/M

Wh

)

AVG_OF_EN_LOSSES DLAP_PGAE-APND DLAP_SCE-APND DLAP_SDGE-APND

Figure 66: Real-Time Simple Average TH Prices for Losses

0

218

436

654

-8

-6

-4

-2

0

2

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Mar

gin

al L

oss

es P

rice

($/

MW

h)

AVG_OF_EN_LOSSES TH_NP15_GEN-APND TH_SP15_GEN-APND TH_ZP26_GEN-APND




Marginal Losses Surplus

The integrated forward market is settled at LMPs, which consist of three components: energy, congestion, and losses. The marginal cost of losses may be positive or negative depending on whether a power injection at that node marginally increases or decreases losses. Incorporating the marginal cost of losses in the LMP is important both for assuring least-cost dispatch and for establishing nodal prices that accurately reflect the cost of supplying the load at each node. Because marginal losses rise quadratically with the transmission power flows, marginal losses will exceed average losses roughly by a factor of two, resulting in surplus collection for losses.

For every trading hour of the IFM, the ISO marginal losses surplus (MLS) is computed as the ISO total net hourly energy charge minus the ISO total IFM congestion charge exclusive of congestion credits for ETC/TOR/CVR and contract Loss credits to TOR holders. The MLS amount, if any, is then allocated pro-rata to the different SCs based on their measured demand in the ISO control area, excluding TOR demand quantity for which IFM and RTM loss credits were provided. The total daily MLS is shown in Figure 67.

Figure 67: Daily Marginal Losses Surplus Credit Allocation for the IFM

0

100

200

300

400

500

600

-$0.15

$0.05

$0.25

$0.45

$0.65

$0.85

$1.05

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

ar

Ma

rgin

al

Lo

sse

s S

urp

lus

(M

illo

ns

)

AVG_LOSSES MLS

Lo

sse

s (

MW

)




Ancillary Services

Requirements

Figure 68 illustrates the IFM daily average ancillary service requirement for regulation up, regulation down, spinning and non-spinning. Figure 69 shows the IFM hourly average ancillary service requirement for regulation up and regulation down.

Figure 68: IFM (Day-Ahead) Ancillary Services Average Requirement

0

100

200

300

400

500

600

700

800

900

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Fe

b13

-Fe

b15

-Fe

b17

-Fe

b19

-Fe

b21

-Fe

b23

-Fe

b25

-Fe

b27

-Fe

b1

-Ma

r3

-Ma

r5

-Ma

r7

-Ma

r9

-Ma

r11

-Ma

r13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

r

MW

Non-Spinning Regulation Down Regulation Up Spinning

Figure 69: IFM (Day-Ahead) Hourly Average Regulation Requirement

250

300

350

400

450

500

550

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

Regulation Down Regulation Up




Procurements

Figure 70 illustrates the IFM daily average procurement of regulation up, spinningand non-spinning ancillary services.

Figure 70: IFM (Day-Ahead) Upward Ancillary Services Procurement

0

500

1000

1500

2000

2500

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r11

-Ma

r13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

r

MW

Non-Spinning Regulation Up Spinning

Figure 71: IFM (Day-Ahead) Regulation Down Procurement

0

50

100

150

200

250

300

350

400

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Ma

r13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

r

MW

Regulation Down




Figure 72 illustrates the real-time daily average procurement of upward ancillary services. It includes regulation up and regulation down procured in real-time unit commitment (RTUC), and spinning and non-spinning procured in both RTUC and HASP.

Figure 72: Real-Time Upward Ancillary Services Procurement

0

10

20

30

40

50

60

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW

RTUC_Non-Spinning RTUC_Regulation_Up RTUC_SpinningHASP_Non-Spinning HASP_Spinning

Figure 73 illustrates the RTUC daily average procurement of regulation down.

Figure 73: RTUC Regulation Down Procurement

0

10

20

30

40

50

60

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

MW

RTUC_Regulation_Down




The ISO procures 100 percent of its ancillary services requirements in the IFM (day-ahead) based on the IFM load forecast. Incremental procurements in the real-time market occurs under two scenarios. First, ancillary services requirements have changed in real-time market motivated by a change in the real-time load forecast. Second, if a unit which was awarded an ancillary service in IFM (day-ahead) is unable to provide that service in real-time. The market will automatically procure additional services to replace that service. Figure 74displays the percentage of real-time procurement with respect to the IFM (day-ahead) procurement for all four types of ancillary services. The real-time procurement of regulation down and regulation up is actually the procurement in RTUC, while the real-time procurement of spinning and non-spinning is the sum of procurement in both RTUC and HASP. The percentage for each type of ancillary service is calculated as: (hourly average of real-time (RTUC and HASP) procurement in 15 minute intervals) / (hourly IFM (day-ahead) procurement). .

Figure 74: Proportion of Real-Time Procurement as Percentage of Day-Ahead Requirement

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Spinning Non-Spinning Regulation Down Regulation Up Monthly Average




IFM (Day-Ahead) Average Prices

Table 11 shows the monthly IFM average procurements and prices for regulation up, regulation down, spinning and non-spinning ancillary services. Figure 75 and Figure 76 illustrate the IFM daily and hourly average price for regulation up, regulation down, spinning and non-spinning ancillary services. The average price for each type of ancillary services is calculated as: sum (non-self scheduled AS MW * ancillary services marginal price $/MW (ASMP)) / sum (non-self scheduled AS MW).

Table 11: IFM (Day-Ahead) Monthly Ancillary Service Average Procurement and Price

Reg Up Reg Dn Spinning Non-Spinning Reg Up Reg Dn Spinning Non-Spinning

Mar-13 328 318 810 773 $5.80 $3.55 $2.36 $0.11

Feb-13 330 320 807 796 $6.07 $3.97 $3.37 $0.11

Percent Change -0.64% -0.79% 0.43% -2.92% -4.36% -10.64% -30.00% -1.98%

Average Procurred Average Price

Figure 75: IFM (Day-Ahead) Ancillary Service Average Price

0123456789

10

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Fe

b13

-Fe

b15

-Fe

b17

-Fe

b19

-Fe

b21

-Fe

b23

-Fe

b25

-Fe

b27

-Fe

b1

-Ma

r3

-Ma

r5

-Ma

r7

-Ma

r9

-Ma

r11

-Ma

r13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

r

$/M

W





Figure 76: IFM (Day-Ahead) Hourly Average Ancillary Service Price

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

$/M

W


Figure 77 illustrates the real-time daily average price for ancillary services,including the average price for regulation up and regulation down procured in RTUC, and the average price for spinning and non-spinning procured in both RTUC and HASP. The average price for each type of ancillary services is calculated as: hourly average of [sum (non-self scheduled AS MW * ancillary services marginal price $/MW (ASMP)) / sum (non-self scheduled AS MW)] for each of the 15 minute intervals.

Figure 77: RTPD (Real-Time) Ancillary Service Average Price

02468

101214161820

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1

-Mar

3-M

ar5

-Mar

7-M

ar9

-Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

$/M

W

RTUC_Non-Spinning RTUC_Regulation_Down RTUC_Regulation_Up

RTUC_Spinning HASP_Non-Spinning HASP_Spinning




Average Regional Ancillary Service Shadow PricesFigure 78 through Figure 81 displays the IFM daily average regional ancillary service shadow prices (RASSPs) for regulation up, spinning, non-spinning and regulation down.

Figure 78: IFM (Day-Ahead) Regulation Up (RASSP)

0

2

4

6

8

10

12

14

1-Feb

3-Feb

5-Feb

7-Feb

9-Feb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

$/M

W

CAISO-ANDE CAISO EXP-ANDE SP26-ANDE SP26 EXP-ANDE

Figure 79: IFM (Day-Ahead) Spinning (RASSP)

0

1

2

3

4

5

6

7

1-Feb

3-Feb

5-Feb

7-Feb

9-Feb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

$/M

W





Figure 80: IFM (Day-Ahead) Non-Spinning (RASSP)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.71

-Feb

3-F

eb5

-Feb

7-F

eb9

-Feb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

$/M

W

SP26 EXP-ANDE SP26-ANDE CAISO EXP-ANDE CAISO-ANDE

Figure 81: IFM (Day-Ahead) Regulation Down (RASSP)

0

1

2

3

4

5

6

7

8

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb13

-Feb

15-F

eb17

-Feb

19-F

eb21

-Feb

23-F

eb25

-Feb

27-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

$/M

W





Average Cost to Load

Figure 82 below shows IFM average cost to load for ancillary services procurement in the IFM market. The average cost to load is calculated as: average ((total hourly cost of procurement for all four ancillary services) / (total hourly ISO load)).

Figure 82: IFM (Day-Ahead) Average Cost to Load

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r11

-Ma

r13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

r

$/M

Wh


Figure 83 below shows the total system (IFM, HASP and RTUC) average cost to load for ancillary services procurement in the market. The average cost to load is calculated as: average ((total hourly cost of procurement for all four ancillary services) / (total hourly ISO load)).




Figure 83: System (Day-Ahead and Real-Time) Average Cost to Load

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

$0.40

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb

13-

Fe

b1

5-F

eb

17-

Fe

b1

9-F

eb

21-

Fe

b2

3-F

eb

25-

Fe

b2

7-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

$/M

Wh





Contingent/Non-Contingent Ancillary Services

In the day-ahead market, in which 100 percent of the market ancillary service requirement is met, scheduling coordinators can bid in capacity as non-contingency or contingency-only. Any incremental capacity procured in the RTPD market is automatically flagged as contingency-only capacity. Furthermore, any incremental capacity procured in real-time from a unit that also sold non-contingent ancillary services in the day-ahead will trigger conversion of all of that unit’s capacity within that service to contingency-only.

During real-time, non-contingent spin and non-spin capacity is included in the energy bid stack and can be dispatched economically as long as the total operating reserve remains above the requirement. If the percentage starts to drop, ISO grid operators may instruct the market software to skip the spin and non-spin bids to maintain operating reserves. Contingency-only capacity cannot be dispatched for energy in real-time except for a contingency (i.e., major transmission or generation outage) or due to an imminent or actual system emergency. Therefore, the more contingency-only capacity that the ISO procures, the less dispatchable capacity is available for real-time energy (again, provided the NERC standard for operating reserve is met). Under tight supply conditions, this may have an impact on real-time energy prices.

Figure 84 shows the daily average non spin procured in IFM, the non-contingent non-spin converted in real time due to incremental non spin awards and additional procurement made in real time with and without cost.

Figure 84: Procurement of Non-Spin Reserve in IFM and RTPD

0

100

200

300

400

500

600

700

800

900

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

DA_CONTINGENT_AS DA_nonContingent_AS DA_CONVERT_AS RTPD_PROCURE_NONZERO RTPD_PROCURE_ZERO

MW




Figure 85 shows the daily average capacity of non-spin in the categories of contingent and non-contingent in and the daily frequency of skipping non spin bids during the real time dispatch. If skipping bids are instructed, non-contingent non-spin bids will not be placed in the energy bid stack for dispatch determination.

Figure 85: Types of Non Spin Reserve in RTPD and Frequency of Skipping Non Spin Bids

99%

99%

99%

99%

100%

100%

100%

100%

0

100

200

300

400

500

600

700

800

900

1000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

RTPD_CONTINGENT_AS RTPD_NONCONTINGENT_AS Frequency of Skip Spin

MW

Fre

q

Figure 86 shows the daily average spin procured in IFM, the non-contingent spin converted in real time due to incremental spin awards and additional procurement made in real time with and without cost.

Figure 86: Procurement of Spin Reserve in IFM and RTPD

0

100

200

300

400

500

600

700

800

900

1000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b

13-

Fe

b

15-

Fe

b

17-

Fe

b

19-

Fe

b

21-

Fe

b

23-

Fe

b

25-

Fe

b

27-

Fe

b

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

DA_CONTINGENT_AS DA_nonContingent_AS AS_CONVERTED RTPD_PROCURE_nonZero RTPD_PROCURE_Zero

MW




Figure 87 shows the daily average capacity of spin in the categories of contingent and non-contingent in and the daily frequency of skipping spin bids during the real time dispatch. If skipping bids are instructed, non-contingent spin bids will not be placed in the energy bid stack for dispatch determination.

Figure 87: Types of Spin Reserve in RTPD and Frequency of Skipping Spin Bids

99%

99%

99%

99%

100%

100%

100%

100%

0

100

200

300

400

500

600

700

800

900

1000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

RTPD_CONTINGENT_AS RTPD_NONCONTINGENT_AS Frequency of Skip Spin

MW

Fre

q




Residual Unit Commitment

Residual unit commitment (RUC) is a reliability function for committing resources and procuring RUC capacity not scheduled in the IFM as energy or ancillary service capacity. RUC capacity is procured in order to meet the difference between the ISO forecast of ISO demand – including locational differences and adjustments – and the demand scheduled in the IFM for each trading hour of the trading day.

Deviations of RUC schedule from IFM schedule

The RUC schedule is the total hourly capacity committed by RUC, including the capacity committed in the day-ahead schedule. The daily deviation of the RUC schedule from the IFM schedule is presented in Figure 88. The hourly deviation of the RUC schedule from the IFM schedule is presented in Figure 89. Positive deviations indicate that RUC capacity was procured, while negative deviations indicate there was over-scheduling in the IFM compared with the ISO forecast of ISO demand. If there is a positive deviation in any trade hour then RUC capacity was procured in that hour. However, if there are any negative deviations in other trade hours, the daily average deviation might be negative.

leRUC_Schedu

leIFM_Schedu-leRUC_ScheduAvgDeviationDaily

ij

ijijj )(

Here i indicates trading hour and j indicates trading day. The average is taken across 24 hours for each trading day.

leRUC_Schedu

leIFM_Schedu-leRUC_ScheduAvgDeviationHourly

ij

ijiji )(

Here i indicates trading hour and j indicates trading day. The average is taken across all the trading days in this month for each trading hour.




Figure 88: Daily Deviation of RUC Schedule from IFM Schedule

-20%

0%

20%

40%

60%

80%

100%

120%

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar1

1-M

ar1

3-M

ar1

5-M

ar1

7-M

ar1

9-M

ar2

1-M

ar2

3-M

ar2

5-M

ar2

7-M

ar2

9-M

ar3

1-M

ar

Per

cen

tag

e

Figure 89: Hourly Deviation of RUC Schedule from IFM Schedule

0%

1%

2%

3%

4%

5%

6%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Per

cen

tag

e




RA/RMR RUC Capacity vs. RUC Award

RUC capacity is the positive difference between the RUC schedule and the greater of the IFM schedule and the minimum load level of a resource. The RUC award is the portion of RUC capacity in excess of reliability must-run (RMR) capacity or the resource adequacy (RA) RUC obligation. All RUC awards are paid the RUC LMP. RA and RMR units do not receive additional payments for their RUC capacity because they are already compensated through their RMR or RA contracts. Figure 90, Figure 91 and Figure 92 show the daily average RA/RMR RUC capacity and RUC award.

Figure 90: RA/RMR RUC Capacity vs. RUC Award (On-Peak Hours)

0

500

1,000

1,500

2,000

2,500

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW

RUC Award RA/RMR RUC Capacity




Figure 91: RA/RMR RUC Capacity vs. RUC Award (Off-Peak Hours)

0200400600800

100012001400160018002000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

MW


Figure 92: RA/RMR RUC Capacity vs. RUC Award (All Hours)

0

500

1,000

1,500

2,000

2,500

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

ar

MW





RUC Award

The daily RUC award and the weighted average RUC LMP are represented in Figure 93, Figure 94 and Figure 95 for on-peak, off-peak and all hours. The weighted RUC LMP will not be specified if there was no RUC award in a particular day.

RUC_Award

)RUC_Award(RUC_LMP

UC_LMPWeighted_R jijij

j iij

i

Here i indicates individual resource and j indicates trading hour (from 1 to 24).

Figure 93: Daily RUC Award and LMP (On-Peak Hours)

$0$5$10$15$20$25$30$35$40$45$50

0

50

100

150

200

250

300

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar1

1-M

ar1

3-M

ar1

5-M

ar1

7-M

ar1

9-M

ar2

1-M

ar2

3-M

ar2

5-M

ar2

7-M

ar2

9-M

ar3

1-M

ar

$/M

W

MW

RUC MW Procured Weighted Average RUC LMP




Figure 94: Daily RUC Award and LMP (Off-Peak Hours)

$0$5$10$15$20$25$30$35$40$45$50

020406080

100120140160180200

2-F

eb

4-F

eb

6-F

eb

8-F

eb

10-

Fe

b1

2-F

eb

14-

Fe

b1

6-F

eb

18-

Fe

b2

0-F

eb

22-

Fe

b2

4-F

eb

26-

Fe

b2

8-F

eb

2-M

ar

4-M

ar

6-M

ar

8-M

ar

10

-Ma

r1

2-M

ar

14

-Ma

r1

6-M

ar

18

-Ma

r2

0-M

ar

22

-Ma

r2

4-M

ar

26

-Ma

r2

8-M

ar

30

-Ma

r

$/M

W

MW


Figure 95: Daily RUC Award and LMP (All Hours)

$0$2$4$6$8$10$12$14$16$18$20

0

50

100

150

200

250

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar1

1-M

ar1

3-M

ar1

5-M

ar1

7-M

ar1

9-M

ar2

1-M

ar2

3-M

ar2

5-M

ar2

7-M

ar2

9-M

ar3

1-M

ar

$/M

W

MW





Average RUC Price

Figure 96 shows the daily average RUC price and Figure 97 shows the total RUC cost.

)tyRUC_Capaci

)RUC_Award(RUC_LMPAvgRUC_Price

ij

ijij

i

i(

Here i indicates individual resource and j indicates trading hour (from 1 to 24). The average is taken across all trading hours for each trading day.

The average RUC price will be positive only when there was a RUC award and the weighted average RUC LMP was greater than $0. If there was no RUC award or there was some RUC award but the weighted average RUC LMP was $0, average RUC price is $0 for that trading day.

Figure 96: Average RUC Price

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

$3.50

$4.00

$4.50

$5.00

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

$/M

Wh




Total RUC Cost

Figure 97 shows the daily cumulative total RUC cost.

Figure 97: Total RUC Cost

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar




Indirect Market Performance Metrics

Cost Allocation Metrics

The two cost allocation metrics in this section are allocations whereby the preferred outcome would be for the allocation to be exactly zero. This is not always possible given the nature of the market systems; however, to the extent that they converge to a mean of zero the performance of the market is improved.

Imbalance Offset Costs5

The imbalance offset consists of three elements, namely the real-time congestion offset, real-time loss offset and the real-time energy offset.

1. The real-time congestion offset is defined as the real-time congestion fund net of the real-time congestion credit calculated as provided in tariff section 11.5.7. 6 In other words, the real-time congestion offset amount is the difference between the total congestion revenue collected from the real-time market and the total congestion revenue paid out in the real-time market for both energy and ancillary services. The real-time market includes both the hour-ahead scheduling process (HASP) and RTD market. The real-time congestion offset (CC 6774) is allocated to all scheduling coordinators based on measured demand, excluding demand associated with existing transmission rights (ETC), transmission ownership rights (TOR) or converted rights (CVR) self-schedules for which IFM and RTM congestion credits were provided.

2. The real-time loss offset is the difference between loss revenue collected in the real-time market and the loss revenue paid out in the real-time market. This real-time loss offset is allocated to all scheduling coordinators based on measured demand, excluding demand associated with TOR self-schedules.

3. The real-time energy offset is a residual calculation. The settlement amounts for the instructed imbalance energy (IIE), uninstructed imbalance energy (UIE), and unaccounted for energy (UFE) are summed up; this value represents the real-time imbalance revenue. The real-time congestion offset and the real-time loss offset are both subtracted from the real-time imbalance revenue; and the resultant residual value is known as the real-time imbalance energy offset. The real-time imbalance energy offset is allocated to all scheduling coordinators based on a pro rata share of their measured demand excluding demand quantity for the valid and balanced portion of TOR contract and self-schedules in real-time. The

5 An appendix is provided with detailed explanations about this section.6 For further information regarding real-time congestion offset, please refer to the following BPM published on the California Independent System Operator Corporation website: CC 6774 Real Time Congestion Offset.doc




real-time imbalance energy offset allocation is the same as the real-time loss offset allocation.

The imbalance offset amount can either be a net charge or a net payment to demand. Since the implementation of the new market, the imbalance offset amount has been a charge to measured demand. This settlement amount is mainly driven by the price divergence between the HASP and the RTD market and the use of average hourly price for the RT demand imbalance energy settlement.7

Figure 98 shows the daily real-time loss offset, real-time congestion offset, real-time imbalance energy offset costs and the net real-time imbalance offset cost. A positive value indicates a charge to measured demand and a negative value indicates a payment to measured demand. Table 12 below shows the monthly total real-time congestion offset, real-time loss offset and the real-time imbalance energy offset.

Table 12: Monthly Imbalance Offset Costs

Month RT ENGY OFFSET RT LOSS OFFSET RT CONG OFFSET

February-13 $7,299,156 $663,896 $7,240,473

March-13 $7,543,698 $532,765 $5,227,105

Figure 98: Daily Real-Time Offset Allocation

-1.5

-1

-0.5

0

0.5

1

1.5

2

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-

Feb

15-

Feb

17-

Feb

19-

Feb

21-

Feb

23-

Feb

25-

Feb

27-

Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

ar

Mill

ion

s

RT_ENGY_OFFSET RT_LOSS_OFFSET RT_CONG_OFFSET Total

7 The root cause of real-time imbalance energy offset costs are explained in the issue paper “Analysis of Real-Time Imbalance Energy Offset” published on the ISO website at “http://www.caiso.com/2416/2416e7a84a9b0.pdf”




Bid Cost Recovery

Bid cost recovery (BCR) is the process by which the ISO ensures scheduling coordinators (SCs) are able to recover start-up costs (SUC), minimum load costs (MLC), and incremental energy bid costs. 8 The payment resulting from the ISO BCR process is known as a BCR payment which is paid through the settlements charge code 6620. The BCR payment is also known as the uplift payment or the make-whole payment in other LMP based energy markets. In order to recover SUC and MLC, a generator, pumped-storage unit, or resource-specific system resource must be committed by the ISO.

Bid cost recovery is paid to a generator when its market revenue is not sufficient to recover its bid-in costs; this condition is mainly driven by two reasons. First, when there are integer choices in the unit commitment and dispatch optimization software for the day-ahead and real-time market, there is an inherent potential for the optimization to select a solution which is optimal given the choice of these zero-one variables, but is not the globally optimal solution. In the day-ahead and real-time software there are a number of such integer choices, involving unit commitment state, the ability of the resource to provide particular ancillary services, and the unit's ramp range. As a result, there are a number of instances in which the solution is not globally optimal, and instances in which the dispatch is not optimal given the unit commitment. Second, there may be instances when certain constraints are not modeled in the day-ahead and real-time markets and operators have to manually dispatch a unit for reliability purposes. This manual dispatch, at times, distorts the clearing prices. Thus the mixed integer issue and the use of exceptional dispatch are the primary reasons for bid cost recovery payments

For each resource, the total SUC, MLC, bid costs, and market revenues from IFM, RUC, and RTM are netted together for each settlement Interval. If the difference between total costs and market revenues is positive in the relevant market, then the net amount represents a shortfall. If the difference is negative in the relevant market, the net amount represents a surplus. For each resource, the IFM, RUC, and RTM shortfalls and surpluses are then netted over all hours of a trading day. As a result, surpluses from any of the ISO markets offset any shortfalls from the other markets over the entire trading day. If the net trading day amount is positive (a shortfall), then the resource receives a BCR payment equal to the net trading day amount.

8 For more information regarding settlements calculation of bid cost recovery please refer to the following BPMs posted on the ISO website: CC 6620 Bid Cost Recovery Settlement.doc, IFM Net Amount.doc, RTM Net Amount.doc and RUC Net Amount.doc




A conceptual formula for BCR is shown below.

Where H Total number of hours in dayI Total number interval in an hour

Bid cost recovery for each resourceIFM net revenue for each resource RUC net revenue for each resourceRTM net revenue for each resource

And

A generator also receives an uplift payment if it was dispatched manually, in other words, a resource also receives an uplift payment if it was dispatched through the exceptional dispatch process. A resource is compensated for its start up and minimum load cost through the BCR process, however, if the resource is dispatched above its Pmin in the real-time market by the exceptional dispatch process, then it receives a payment through the settlements exceptional dispatch charge codes (CC6482, CC6488, and CC6470). 9

9 For more information regarding settlements calculation of exceptional dispatch please refer to the following BPMs posted on the ISO website: CC 6488 Exceptional Dispatch Uplift Settlement.doc and CC 6482 Real Time Excess Cost for Instructed En.doc




Figure 99 shows the daily BCR exceptional dispatch payments which include thesum of the charge codes 6482, 6488, and 6470.

Figure 99: Daily Exceptional Dispatch Uplift Costs

-$0.02

$0.00

$0.02

$0.04

$0.06

$0.08

$0.101-F

eb3-F

eb5-F

eb7-F

eb9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Millio

ns

Figure 100 shows BCR allocations in the IFM, RUC and RTM markets.

Figure 100: Bid Cost Recovery Allocation

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

$0.60

$0.70

$0.80

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

ar

Mill

ion

s

IFM RUC RTM




Figure 101 shows the bid cost recovery allocation in RUC by Minimum Load Cost (MLC), Capacity Cost (CAP), and Startup Cost (SUC).

Figure 101: Bid Cost Recovery Allocation in RUC

$0

$50,000

$100,000

$150,000

$200,000

$250,000

$300,000

$350,000

$400,000

$450,000

$500,000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

RUC_MLC RUC_CAP_COST RUC_SUC

Figure 102 shows the bid cost recovery allocation in RTD by Ancillary Service (AS), Energy, Minimum Load Cost (MLC), Startup cost (SUC), and Transition Cost.

Figure 102: Bid Cost Recovery Allocation in RTD

-$0.2

$0.0

$0.2

$0.4

$0.6

$0.8

$1.0

$1.2

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Mil

lion

s

RT_AS_COST RT_ENERGY RT_MLC RT_SUC RT_TRANSITION_COST




Figure 103 shows the bid cost recovery allocation in IFM by Ancillary Service Bid Cost, Energy, Minimum Load Cost (MLC), Startup cost, and Transition Cost.

Figure 103: Bid Cost Recovery Allocation in IFM

$0.0

$0.2

$0.4

$0.6

$0.8

$1.0

$1.2

$1.4

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Mil

lion

s

IFM_AS_BID_COST IFM_ENERGY IFM_MLC IFM_SUC IFM_TRANSITION_COST

Make Whole Payment

The “make whole” payment mechanism applies to day-ahead demand and exports and HASP exports. It is designed to compensate market participants (demand and export) for adverse financial impacts if ex post price corrections have led to instances in which demand bids that were cleared in the market are no longer economic when evaluated against the corrected price. If the LMP correction is made in the upward direction that impacts demand in the day-ahead market or HASP such that a market participant’s demand or export bid curve becomes uneconomic, then the ISO will calculate the make whole payment on an hourly basis. The make whole payment amount is calculated as the sum of MWhs in each of the cleared bid segments in the day-ahead schedule or HASP intertie schedule for the affected resource, multiplied by the maximum of zero or the corrected LMP minus the bid segment price. Table 13 shows the summary of make whole payments in both day-ahead and real-time markets.

Table 13: Summary of Make Whole Payment

Month Market Type Make Whole Payment

March DA $0

March HASP $0




Market Software Metrics

Market performance is obviously confounded by software issues, which vary in importance levels, however amongst these various measures the failure of a market run is the most obvious failure.

Market Disruption

A market disruption is an action or event that causes a failure of an ISO market, related to system operation issues or system emergencies .10 Pursuant to section 7.7.15 of the ISO tariff, the ISO can take one or more of a number of specified actions in the event of a market disruption, to prevent a market disruption, or to minimize the extent of a market disruption.

For each of the ISO markets, Table 14 lists the number of market disruptions and the number of times that the ISO removed bids (including self-schedules) during the time period covered by this report. Figure 104 shows the frequency of HASP, RTUC, and RTD failures in the current month.

Table 14: Summary of Market Disruption

Type of CAISO Market Market Disruption or Reportable

Events

Removal of Bids (including Self-

Schedules)Day-Ahead

IFM 0 0

RUC 0 0

Real-Time Real-Time Unit Commitment Interval 1

1 0

Real-Time Unit Commitment Interval 2

0 0


1 0


0 0

Real-Time Dispatch 17 0

10 These system operation issues or system emergencies are referred to in sections 7.6 and 7.7, respectively, of the CAISO tariff. CAISO tariff, appendix A, definition of market disruption. Capitalized terms not otherwise defined herein have the meanings set forth in the CAISO tariff.




Figure 104: Frequency of Market Disruption

02468

101214161820

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

HASP RTUC RTD IFM

System Parameter Excursion

The power balance constraint is one of the requirements that is enforced in the market all the time to ensure the balance between electricity generation and demand. The tendency for 5-minute real-time prices to exceed hour-ahead prices is driven in large part by extreme price spikes in the real-time market when the market software meets the system-wide power balance constraint with small and temporary amounts of regulation resources rather than with energy resources. When this relaxation occurs, the system imposes a penalty price, which then affects the energy prices in the pricing run. This constraint can occur in two different ways:

• When the market software dispatches regulation up as supply to meet projected demand, this represents a “shortage” constraint that causes prices to spike upwards to the $750/MWh bid cap.

• When the market software dispatches regulation down to balance supply with projected demand, this represents an “excess” constraint that causes prices to spike downwards to the -$30/MWh bid floor.

Figure 105 presents how often the power balance constraint is relaxed by day and by class of shortage during the real-time dispatch.

Figure 106 how often the power balance constraint is relaxed by day and by class of excess during the real-time dispatch.




Figure 105: Frequency and Average MW of Shortage

0

5

10

15

20

25

0

5

10

15

20

25

301-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Shortage_Freq Shortage_MW

MW

Fre

qu

ency

Figure 106: Frequency and Average MW of Excess

-8

-7

-6

-5

-4

-3

-2

-1

0

0

5

10

15

20

25

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Excess_Freq Excess_MW

Fre

qu

ency

MW




Analysis of Minimum Online Capacity

The ISO utilizes the minimum online capacity (MOC) constraint to address the operational needs of certain operating procedures that require a minimum quantity of committed online resources in order to maintain reliability11. MOC was deployed into the software systems with the expectation that it would commit an appropriate set of resources that were previously satisfied by either exceptional dispatch or nomogram enforcement in RUC, thus reducing the potential for over-commitment in RUC. This section studies the unit commitments driven by MOC constraint in IFM for the reporting period.

Figure 107 below shows the Pmin and cleared value of the MOC units in the market for November. Table 15 presents the daily count of MOC units in November. Figure 108 summarizes the number of MOC units, the sum of Pmin and the sum of cleared value in November.

Figure 107: MOC Unit Commitment and Dispatch

0100200300400500600700800900

1000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW

Cleared Value Pmin

11 For more details, please refer to the technical bulletin at http://www.caiso.com/271d/271dedc860760.pdf.




Table 15: Summary of MOC Units

Month Count of MOC Units Pmin Cleared Value

March 14 2,351 3,125

Figure 108: Daily Count of MOC Units

0

1

2

3

4

5

6

7

8

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Co

un

t




Compensating Injection

The process of modeling compensating injections (CI) is an automatedmechanism used in the real-time ISO market processes (i.e., Hour-AheadScheduling Process and the Real-Time Market) to align the modeled flows toaccount for differences between scheduled flows and actual flows on theinterfaces between the ISO Balancing Authority Area (BAA) and the neighboringBAAs. It utilizes pseudo MW injections and withdrawals at a variety of locationsexternal to the ISO to account for unscheduled flows. In the absence of thisautomated feature, the ISO has the ability to account for unscheduled flows on amanual basis. Prior to CI implementation, the ISO had used DC circulation to manually counter unscheduled flows and used transmission limit conforming to true up market flows with real-time EMS flows.

The CI algorithm in the ISO real-time market software optimizes the power flow solution by injecting MW, positive or negative, at each CI generator location. By varying the output of each CI resource, the simultaneous set of CI is optimized by the market software with the objective to minimize the difference in market scheduled flow and the actual flow, as determined by the State-Estimator (SE) power flow, on the set of defined CI corridors. In short, CI accounts for the unscheduled flow effect that is actually occurring in real-time which the ISO real-time market cannot otherwise reflect due to the lack of full network modeling of the closed WECC system and the lack of information about external load, generation and interchange patterns.

After a period of performance testing and parameter tuning, CI was enabled on a continuous basis in the ISO real-time market processes on July 27th at 0900 hour.




Figure 109 shows the daily profile of net compensating injections in MW.

Figure 109: Daily Profile of Net Compensating Injections

0

5

10

15

20

25

30

35

40

451-

Feb

3-F

eb5-

Feb

7-F

eb9-

Feb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

MW

Net CI Injection

Figure 110 shows the hourly profile of net compensating injections.

Figure 110: Hourly Profile of Net Compensating Injections

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

Hourly Net CI Injection




Manual Market Adjustment

Exceptional Dispatch

Broadly speaking there are two types of exceptional dispatch, namely commitments and energy dispatches. Although commitments can commit a unit on or off, generally most of the exceptional dispatch commitments are “on” commitments and “off” commitments are rare. Likewise regarding energy dispatches the dispatch can either be incremental or decremental. Incremental dispatches are the most common, and decremental dispatches are infrequent but not rare. Regarding the timing of these dispatches, the ISO can issue exceptional dispatch instructions for a resource as a pre day-ahead market unit commitment, a post day-ahead market unit commitment or a real-time energy dispatch. A pre day-ahead market unit commitment is an exceptional dispatch instruction committing a resource at or above its physical minimum (Pmin) operating level prior to any of the day-ahead market runs. A post-day-ahead market unit commitment is an exceptional dispatch instruction committing a resource at or above its Pmin operating level in the real-time market.

A real-time exceptional dispatch instructs a resource to operate at or above its physical minimum operating point. For the purposes of this report, a real-time exceptional energy dispatch above the resource’s day-ahead award is considered an incremental exceptional dispatch instruction and a real-time exceptional energy dispatch below the day-ahead award is considered a decremental dispatch instruction. The ISO issues exceptional dispatch instructions primarily to manage constraints that are not modeled in the market software. In addition to constraints, the ISO also issues exceptional dispatch instructions relating to reliability requirements and, on occasion for software limitations and software failures. Reliability requirements are calculated for both local area and the system wide needs, and are classified into various requirements including local generation, transmission management, non-modeled transmission outages, transmission management due to fires, ramping requirements and intertie emergency assistance. Whenever the ISO issues an exceptional dispatch instruction, these instructions are logged by the operators into the scheduling and logging system (SLIC), including an associated reason for each exceptional dispatch instruction.

Figure 111 below shows exceptional dispatch volume by market type: day-ahead, and real-time. All post-day-ahead exceptional dispatches are classified as real-time because those exceptional dispatches are settled in the real-timemarket. The total volume is calculated as sum of physical minimum of the resource, uneconomical incremental exceptional dispatch or uneconomical decremental exceptional dispatch. For instance, if resource A is exceptionally dispatched in the day-ahead market at its Pmin of 20 MW from 1:00AM till 5:00 AM, then its exceptional dispatch volume is 80 MWh. In another example consider that resource B is exceptionally dispatched in real-time market at its




Pmin of 20MW from 1:00 AM till 5:00AM, and again exceptionally dispatched to 60 MW from 3:00 till 5:00 AM. However, it is economical at 60MW from 4:00 AM till 5:00 AM, then the total exceptional dispatch volume of this resource is sum of 80 MWh ( 20 MW* 4hrs at minimum load) and 40 MWh ( (60-20)MW * 1 hr ofincremental dispatch), which is equal to 120 MWh. Note that even though this resource was exceptionally dispatched to 60 MW for 2 hours, it is uneconomical for only one hour, so only those MWh are shown in the graph.

Figure 111: Total Exceptional Dispatch Volume (MWh) by Market Type

-4

-2

0

2

4

6

8

10

12

14

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Th

ou

san

ds

MW

h P

er

Day

Day-Ahead Real-Time INC Real-Time DEC

Figure 112 below shows the exceptional dispatch volume by reason. All exceptional dispatches issued for generation procedure are shown as ‘G procedure’ and all exceptional dispatches issued for transmission procedure are shown as ‘T-procedure’.




Figure 112: Total Exceptional Dispatch Volume (MWh) by Reason

0

2

4

6

8

10

12

141

-Fe

b3

-Fe

b5

-Fe

b7

-Fe

b9

-Fe

b11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3

-Mar

5-M

ar7

-Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Th

ou

sa

nd

s M

Wh

Pe

r D

ay

Unit Testing COI Mitigation Thermal Margin

Voltage Support Generation Outage Path 15

Transmission Outage Communication Outage Other

Figure 113 shows the total MWh quantity of exceptional dispatch as a percentage of the total load, where the total load is equal to internal generation plus imports minus exports. The horizontal lines in the figure identify the monthly averages for each month.

Figure 113: Exceptional Dispatch Percent of Total Load

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11

-Fe

b1

3-F

eb

15

-Fe

b1

7-F

eb

19

-Fe

b2

1-F

eb

23

-Fe

b2

5-F

eb

27

-Fe

b1

-Ma

r3

-Ma

r5

-Ma

r7

-Ma

r9

-Ma

r1

1-M

ar

13

-Ma

r1

5-M

ar

17

-Ma

r1

9-M

ar

21

-Ma

r2

3-M

ar

25

-Ma

r2

7-M

ar

29

-Ma

r3

1-M

ar

Percent Monthly Average




Blocking of Intertie Schedules

Intertie blocking is the process of blocking the HASP schedule for interties and reverting their schedules to day-ahead awards. Usually intertie blocking is required for reliability reasons. Intertie blocking can occur at three different levels: i) full block of interties, in which all intertie schedules from HASP are blocked, ii) partial blocking, in which operators can selectively block certain interties, and iii) resource blocking, in which specific resources that belong to an intertie can be blocked. When an intertie is blocked, all resources belonging to it are consequently blocked. In HASP, intertie schedules may vary with respect to day-ahead awards, and there may be resources that have no changes between their DA awards and their HASP schedules; in such instances, intertie blocking has no impact on these resources. The metrics developed for intertie blocking of this section are based only on resources that have a change in their HASP schedules with respect to their DA awards.

The volume of blocking interties is estimated separately for imports (I) and exports (E) to avoid netting. First, the total incremental and decremental volumes of blocking interties is computed separately for each hour, intertie and direction, by summing up all the differences between DA awards and HASP schedules of all resources that have their schedules blocked; i.e.,

IEdTtIippifppand

IEdTtIippifpp

dDAti

dRTti

Rr

dDAti

dRTti

dti

dDAti

dRTti

Rr

dDAti

dRTti

dti

i

i

,,,,

,,,,

,,

,,

,,

,,,

,,

,,

,,

,,,

where indices i, t, r and d stand for intertie, hour, resource and direction of flow, respectively, and they belong to the sets I, T, {E,I}. The set Ri. contains only blocked elements. The summation of differences is only with resources having blocked schedules that belong to a given intertie; i.e., resources in the set Ri.. The super-indices RT and DA stand for the HASP and DA references. The symbol dRT

tip ,, is the HASP schedule. A difference d

ti , can be either positive or

negative. A positive difference dti , means that the HASP schedule is greater

than the corresponding DA award; i.e., an increase volume was blocked. It is worthwhile to mention that these metrics are based only on resources which are blocked and have a delta between DA awards and HASP schedules. In instances where the whole intertie was blocked, some resources may not have any difference between DA awards and HASP schedules and, therefore, the blocking has no impact on such resources. If that is the case, such resources are no included in the metrics and statistics for tie blocking. The incremental and decremental volumes for a given day are then computed separately as the sum of the corresponding hourly volumes to avoid netting.




IEdIiand

IEdIi

t

dti

di

t

dti

di

,,,

,,,

,

,

By doing so, in any given day there may be one incremental volume and one decremental volume per intertie, which are separately applied to exports and imports. These volumes are then organized in two charts, one for exports and one for imports, as shown in the following figures. Only interties with the top nine volumes are individually depicted, while the rest is gathered in the group of other.

Figure 114: Daily Volume of Blocking Imports on Interties

-1,000

0

1,000

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW

ADLANTO-SP_ITC PACI_ITC ELDORADO_ITC MEAD_ITC PALOVRDE_ITC CTW230_ITC 0 0 0 Other

Figure 115: Daily Volume of Blocking Exports on Interties

0

200

400

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-

Fe

b1

3-F

eb

15-

Fe

b1

7-F

eb

19-

Fe

b2

1-F

eb

23-

Fe

b2

5-F

eb

27-

Fe

b1-

Ma

r3-

Ma

r5-

Ma

r7-

Ma

r9-

Ma

r1

1-M

ar

13-

Ma

r1

5-M

ar

17-

Ma

r1

9-M

ar

21-

Ma

r2

3-M

ar

25-

Ma

r2

7-M

ar

29-

Ma

r3

1-M

ar

MW

SYLMAR-AC_ITC CFE_ITC VICTVL_ITC 0 0 0 0 0 Other




The statistics of the extent and frequency of intertie blocking is also presented in the following table. The trade day and hour in which intertie blocking occurred are listed by export and import direction. The number of interties and individual schedules that are affected by blocking are shown as well. Finally, the total volume (incremental plus absolute decremental) from intertie blocking is provided.

Table 16: Statistics for Hourly Intertie Blocking

Trade Date Trade Hour DirectionNumber of

TiesNumber of Schedules

Net Volume (MWh)

2/20/2013 13 E 3 3 3382/20/2013 13 I 6 17 837




Blocking of Commitment Instructions

Figure 116 shows the count of commitment instructions that were blocked in any of the four RTUC runs for the corresponding month; the instructions are grouped by startups and shutdowns. This also containts a daily average per month, which is computed as the toal number of instructions blocked in a month divided by the number of days in the month.

Figure 116: Daily Count of Commitment Instructions Blocked in RTUC

0

20

40

60

80

100

120

140

1-F

eb3-F

eb5-F

eb7-F

eb9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

Co

un

t o

f In

str

uct

ion

s

Startup Shutdown Transition Daily_UC_avg




Blocking of Real-Time DispatchFigure 117 shows the daily volume in MWh of dispatches blocked in the real-time five-minute dispatch. First, the difference between the previous dispatch and the current dispatch that is being blocked is estimated. These differences are then converted into MWh by mutiplying the differences by a factor of 1/12, and summing across all intervals of each day. These values are represented in blue bars. The daiy average is also represented with a red line; this is obtained as the total volume in the month divided by the number of days of the month.

Figure 117: Daily Volume of Dispatches Blocked in RTD

0

50

100

150

200

250

300

350

400

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Fe

b13

-Fe

b15

-Fe

b17

-Fe

b19

-Fe

b21

-Fe

b23

-Fe

b25

-Fe

b27

-Fe

b1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

ar

Dai

ly V

olu

me

(MW

h)

Daily Volume Daily Average per Month




Adjustments of Transmission Constraints12

In operating the markets the ISO, under certain circumstances, will adjustoperating limits for selected flowgates (also known as transmission interface) constraints that become binding consistently in the day-ahead and real-time markets. This is done to ensure that measurable or predictable differences between actual and market-calculated flows are accounted for and adequate operating margins are maintained such that reliability of the grid is not adversely impacted.

Adjusting transmission constraints to maintain adequate operating margins is a prudent operating practice that was also used by the ISO prior to the launch of the new markets. With the implementation of the new markets based on locational marginal pricing (LMP), the market optimization tools used in conjunction with the full network model (FNM) in the day-ahead and real-time markets now perform congestion management through automated processes that calculate locational energy prices that reflect the costs of congestion at such locations. The new markets have not, however, eliminated the occurrence of measurable and often predictable differences between actual and market-calculated flows. The process of adjustments is, therefore, a necessary operational tool for ensuring that the markets result in schedules and real-time dispatches that more accurately reflect expected real-time flows, respect actual flow limits and fully support reliable grid operation. Note that adjustments are notapplied to scheduling limits; they are applied only to market operating limits for certain branch groups (flow gates/transmission interfaces), as necessary. The key reasons for adjusting operating limits in the day-ahead and real-time marketsare: A. To align calculated market flows with measurable or predictable actual flows; B. To accommodate mismatch due to inherent design differences of day-ahead market, real-time unit commitment and the real-time dispatch runs; C. To allow reliability margins for certain flowgates; and D. To adjust margins for flowgates impacted by telemetry issues.

Figure 118 shows the transmission adjustment for the given month. This plot has three different metrics.

1. The bars in blue show the frequency of limit adjustments that were applied to the various flow-gates in either the integrated forward market, real-time unit conmmitment or real-time dispatch. This frequency only counts the time in which limits were adjusted while they were being enforced in the markets. The reference time for this frequency is based on all time intervals in the month even if the the transmission constraint was not enforced all the time.

12 A detailed description of transmission adjustments is available at http://www.caiso.com/23ea/23eae8aef980.pdf




Figure 118: Frequency and Average of Adjustments to TransmissionConstraints by Market

0% 25% 50% 75% 100% 125% 150% 175% 200% 225%

HDWSH - N.GILA 500 kV LINE 1

SCE_PCT_IMP_BG

SLIC 2054754 LLAGAS SOL1

SLIC 2111963 BELL-PLCR SOL1

SLIC 2115170 PLACER-GLDHL SOL1

LBS_WITH_PUMPS_NG

SCE_PCT_IMP_BG

PATH15_BG

DEVERS - MIRAGE 230 kV LINE 1

BARRE-LEWIS_NG

IVALLYBANK_XFBG

SLIC 2087802 SOL1

MORAGA - MORAGA 115 - 230 kV XFMR 1

RECTOR - VESTAL 230 kV LINE 2

T-135 VICTVLUGO_EDLG_NG

BLYTHESC - EAGLEMTN 161 kV LINE 1

7830_TL 230S_IV-SX-OUT_NG

SLIC 2113417 DeversValley

SLIC 2113417 ValleySerranoExport


SANMATEO - RAVENSWD 115 kV LINE 1

SLIC 2090466 and 2090467 SOL

SDGE_CFEIMP_BG

RIO OSO - BRNSWKT2 115 kV LINE 2

HALEJ1 - VCVLLE1J 115 kV LINE 1

SCIT_BG

METCALF - MORGN J1 115 kV LINE 1


SOUTHLUGO_RV_BG

SERRANO - SERRANO 500 - 230 kV XFMR 1


LBS_WITH_PUMPS_NG

SCE_PCT_IMP_BG

PATH15_BG

DEVERS - MIRAGE 230 kV LINE 1

BARRE-LEWIS_NG

IVALLYBANK_XFBG

SLIC 2087802 SOL1

MORAGA - MORAGA 115 - 230 kV XFMR 1

RECTOR - VESTAL 230 kV LINE 2

T-135 VICTVLUGO_EDLG_NG

BLYTHESC - EAGLEMTN 161 kV LINE 1

MAGUNDEN - SPRINGVL 230 kV LINE 2



WNTU PMS - BENTON 60.0 kV LINE 1

CASCADE - OREGNTRL 60.0 kV LINE 1

SLIC 2119455 PITTSB SOL-2

NTWRJCT1 - JMSN JCT 115 kV LINE 1

HALEJ1 - VCVLLE1J 115 kV LINE 1

HALEJ1 - JMSN JCT 115 kV LINE 1

DRUM - BRNSWKT2 115 kV LINE 2




..

.

Flow-Gate

IFM

RTD

RTUC

Market




2. The second metric is represented by markers in green and it shows the average adjusted percent of limits applied to the various constraints during the corresponding month; the average adjusted percent is based only on the ajusted values different to 100 percent that were applied to the constraints while there were enforced in the markets.

3. The third metric is given with lines in red; the ends of the lines in red indicates the minimum (left end) and maximum (right end) percent of the adjusted limits. The length of the line indicates, therefore, the range of adjusted percent of limits. In some cases, there is no red line, which indicates that the adjusted percent applied to transmission limits was always at one single value, such as the case for the adjustments applied to transmission limits in the integrated forward market.

Figure 119 shows the daily profile of transmission adjustments for the last two months. Adjustments are grouped by direction: upward and downward. This metric shows the total daily volume (in terms of transmission capacity) of adjustment that acrrued on all transmission elements in all three markets (IFM, RTUC and RTD). Identifying the transmission adjusmtents in all markets, the volume of transmission adjustment (in MVA) is estimated for each transmission element as the difference between the normal limit and the adjusted limit; this is done on a five-minute basis. Then, all adjustments are summed for all transmission elements for all intervals for all markets for each day. The total volume is then divided by a factor of 12 to obtain volume in MVAh. This is further divided by a factor of 1000 to represent the metric in Gigas. The monthly average is obtained by dividing the total volume in a month by the number of days in the month.

Figure 119: Volume of Transmission Adjustments

0

200

400

600

800

1000

1200

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb1

1-F

eb

13

-Fe

b1

5-F

eb

17

-Fe

b1

9-F

eb

21

-Fe

b2

3-F

eb

25

-Fe

b2

7-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11

-Ma

r1

3-M

ar

15

-Ma

r1

7-M

ar

19

-Ma

r2

1-M

ar

23

-Ma

r2

5-M

ar

27

-Ma

r2

9-M

ar

31

-Ma

r

Vo

lum

e

Upward Adjustment Downward Adjustment Monthly Average




Other Metrics

Bilateral Transfers of Existing Contract Import Capability1

Table 17 below describes the bilateral transfers of RA import capability. All reported activities are for pre-RA import commitment capability capacity. This information is also publicly posted at: http://www.caiso.com/2396/239679bc505e0.pdf . The referenced tariff section also requires the ISO to notify FERC of any transfer information received pursuant to step 8 of the ISO tariff section 40.4.6.2.1.

Table 17: Transfer Notifications of Existing Contract Import Capability

Start End Transferor / Transferee Branch Group MWPrice per MW/Mth

1/1/2009 1/31/2009 Shell Energy / Constellation CFE Branch Group 8 $850

1/1/2009 2/28/2009 Shell Energy / Pilot CFE Branch Group 66 $1,500

1/1/2009 2/28/2009 Shell Energy / 3 Phases CFE Branch Group 6 $1,750

2/1/2009 2/28/2009 Shell Energy / Constellation CFE Branch Group 6 $850

2/1/2009 2/28/2009 Shell Energy / NRG CFE Branch Group 2 $1,100




7/1/2009 7/31/2009 Shell Energy / Navy/Wapa CFE Branch Group 12 $1,850


8/1/2009 8/31/2009 Shell Energy / Calpine CFE Branch Group 11 $1,500


9/1/2009 9/30/2009 Shell Energy / Calpine CFE Branch Group 11 $1,500


7/1/2011 9/30/2011Shell Energy / Golden State Water Company CFE Branch Group 18 $2,500

1 In accordance with section 40.4.6.2.2.2, the CAISO must report to the Commission, on quarterly basis, all bilateral transfers of RA import capability. This section provides the relevant information.




New Market Functionalities

Convergence Bidding

On February 1, 2011 the CAISO implemented convergence bidding in its software systems. Convergence bidding is an important market enhancement that enables market prices between the DA and RT markets to converge. This ultimately leads to better price discovery and more efficient dispatch of physical resources. Convergence bidding involves placing purely financial bids, at particular pricing nodes in the day-ahead market. If these bids are cleared in the day-ahead market, they are then liquidated in the opposite direction in the real-time market. The market participant thus earns or is charged the difference between the day-ahead price and the real-time price at the location of the bid.Convergence bids are cleared in the IFM. They are not part of the RUC process that commits additional capacity, if necessary, to meet the next day’s demand forecast. Convergence bids are not part of any dispatch in real-time market processes.

In the CAISO’s implementation there are effectively two markets for convergence bidding, namely the intertie market and the internal market. Both of these markets use the DAM as the original pricing mechanism, but the intertie market settles against the HASP run, whereas the internal market settles against the five-minute RTD run. As of November 28, 2011, The ISO removed the ability to submit convergence bids at intertie scheduling points.

Figure 120 shows the daily average volume of submitted virtual bids in the ISO market for virtual supply and virtual demand. Figure 121 shows the daily average volume of cleared virtual bids in IFM for virtual supply and virtual demand. Figure 122 shows the ratio of cleared virtual bids to submitted bids for virtual supply and virtual demand.




Figure 120: Submitted Virtual Bids

0500

100015002000250030003500400045005000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

MW

Virtual Demand Virtual Supply

Figure 121: Cleared Virtual Bids

0

500

1000

1500

2000

2500

3000

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

MW





Figure 122: Ratio of Cleared Virtual Bids to Submitted Virtual Bids

0%

10%

20%

30%

40%

50%

60%

70%

80%

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

Per

cen

tag

e


Figure 123 shows the daily average volume of submitted virtual bids for internal nodes (or internal market). Figure 124 shows the daily average volume of cleared virtual bids for internal nodes. Figure 125 shows the ratio of cleared virtual bids to submitted bids for internal nodes.

Figure 123: Submitted Virtual Bids for Internal Node

0500

100015002000250030003500400045005000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar1

1-M

ar1

3-M

ar1

5-M

ar1

7-M

ar1

9-M

ar2

1-M

ar2

3-M

ar2

5-M

ar2

7-M

ar2

9-M

ar3

1-M

ar

MW





Figure 124: Cleared Virtual Bids for Internal Node

0

500

1000

1500

2000

2500

3000

1-F

eb3

-Feb

5-F

eb7

-Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Ma

r13

-Ma

r15

-Ma

r17

-Ma

r19

-Ma

r21

-Ma

r23

-Ma

r25

-Ma

r27

-Ma

r29

-Ma

r31

-Ma

r

MW


Figure 125: Ratio of Cleared Virtual Bids to Submitted Virtual Bids for Internal Node

0%

10%

20%

30%

40%

50%

60%

70%

80%

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

Per

cen

tag

e





Nodal MW injection/withdrawal limits were enforced to ensure a feasible Alternate-Current (AC) power flow solution with convergence bidding in IFM. Figure 126 shows the daily count of binding nodal constraints in IFM.

Figure 126: Binding Nodal Constraints

0

10

20

30

40

50

60

70

80

90

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Co

un

t

Figure 127 shows the daily count of the binding transmission constraints in IFM.

Figure 127: Binding Transmission Constraints

0

2

4

6

8

10

12

14

16

1-F

eb3-F

eb5-F

eb7-F

eb9-F

eb11

-Feb

13-F

eb15

-Feb

17-F

eb19

-Feb

21-F

eb23

-Feb

25-F

eb27

-Feb

1-M

ar3-

Mar

5-M

ar7-

Mar

9-M

ar11

-Mar

13-M

ar15

-Mar

17-M

ar19

-Mar

21-M

ar23

-Mar

25-M

ar27

-Mar

29-M

ar31

-Mar

Co

un

t

Flowgate Intertie Nomogram Line




Convergence bidding tends to cause the day-ahead market and real-time market prices to move closer together, or “converge”. Figure 128 shows the energy prices (namely the energy component of the LMP) in IFM, HASP, and RTD. Figure 129 shows the difference between the IFM, HASP, and RTD energy prices.

Figure 128: IFM, HASP, and RTD Prices

0

20

40

60

80

100

120

140

1-F

eb

3-F

eb

5-F

eb

7-F

eb

9-F

eb

11-F

eb

13-F

eb

15-F

eb

17-F

eb

19-F

eb

21-F

eb

23-F

eb

25-F

eb

27-F

eb

1-M

ar

3-M

ar

5-M

ar

7-M

ar

9-M

ar

11-M

ar

13-M

ar

15-M

ar

17-M

ar

19-M

ar

21-M

ar

23-M

ar

25-M

ar

27-M

ar

29-M

ar

31-M

ar

$/M

Wh

IFM HASP RTD

Figure 129: Difference between IFM, HASP, and RTD Prices

-20-10

01020304050607080

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

$/M

Wh

RTD_IFM HASP_IFM RTD_HASP




Figure 130 shows the profits which convergence bidders receive from convergence bidding. It is the sum of three settlement charge codes (CC6013, CC6053, and CC6473).

Figure 130: Convergence Bidding Profits

-$1,000

-$500

$0

$500

$1,000

$1,5001-

Fe

b3-

Fe

b5-

Fe

b7-

Fe

b9-

Fe

b1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-M

ar13

-Mar

15-M

ar17

-Mar

19-M

ar21

-Mar

23-M

ar25

-Mar

27-M

ar29

-Mar

31-M

ar

Pro

fit

(Th

ou

san

ds

)

The congestion revenue rights (CRR) settlement rule provides a targeted way of limiting CRR payments in cases when the CRR holders’ convergence bids may increase their CRR payments. This rule addresses concerns that market participants might attempt to use convergence bids to manipulate the market prices at locations where they hold CRRs and thereby increase the profitability of their CRR holdings. Figure 131 shows the CRR settlement rule payment amount when CRR settlement rule applies. It is a reversal of the increase in CRR revenues attributable to convergence bidding.

Figure 131: CRR Settlement Rule Payment

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

1-F

eb3-

Feb

5-F

eb7-

Feb

9-F

eb1

1-F

eb1

3-F

eb1

5-F

eb1

7-F

eb1

9-F

eb2

1-F

eb2

3-F

eb2

5-F

eb2

7-F

eb1-

Mar

3-M

ar5-

Mar

7-M

ar9-

Mar

11-

Mar

13-

Mar

15-

Mar

17-

Mar

19-

Mar

21-

Mar

23-

Mar

25-

Mar

27-

Mar

29-

Mar

31-

Mar

Am

ou

nt




Appendix: Imbalance Offset Costs

The daily imbalance offset cost in the real-time market is the difference between the daily revenue paid in the real-time market and the revenue collected from the real-time market. In a nodal market, the revenue collected from the load is higher than the revenue paid out to the generator whenever there is congestion in the system. However, since the launch of the new market, the total revenue collected in the real-time market on a daily basis is insufficient to cover the payment made in the real-time market. This is contrary to the general expectation. This phenomenon is explained with the example of a three bus system in this document; as mentioned previously, the ISO has already published a paper which explains the root cause of this issue.

Example 1

Consider a three bus system as shown in Figure 132 below. The system under consideration is a lossless system which implies that no losses occur in the transmission system. This example consists of three buses: A, B and C. Generator G1 is connected to bus A; generator G2 and load D1 is connected to bus B; and load D2 is connected to bus C. Busses B and C are within the ISO system and bus A is a tie point. The bid for each resource at each of the location is represented as a MW-price pair. Figure 132 also shows the outcome of the day-ahead market. The schedule for each resource and the LMP at each bus location are shown below.

Figure 132: Day-Ahead Market Example 1

The day-ahead results are summarized in Table 18. The settlements sign convention is used to characterize the information in Table 18. A generation quantity is represented by a negative sign, whereas, load by positive. Also, a payment is shown with a negative sign and a charge with a positive sign. From Table 18, it is observed that the total payment made to generators G1 and G2

CAISO

Tie Point




based on the LMPs at their nodes is $3,800, and the total charge collected from load D1 and D2 is $5,000. Due to the congestion on the intertie conecting bus A to bus B, the difference between the total charge and the total payment is $1,200. This surplus collected by the ISO would be a day-ahead offset, which is used to pay the CRR holders.

Table 18: Summary of Day-Ahead Market for Example 1

Resource Name Schedule LMP ($/MWh)

Revenue ($)

Energy Compnent ($/MWh)

Congestion Component ($/MWh)

Congestion Revenue

G1 -60.00 30.00 -1800.00 50.00 -20.00 1200.00

G2 -40.00 50.00 -2000.00 50.00 0.00 0.00

D1 70.00 50.00 3500.00 50.00 0.00 0.00

D2 30.00 50.00 1500.00 50.00 0.00 0.00

Figure 133 below shows the real-time market; this is the continuation of the day-ahead market discussed above. This figure shows the dispatch quantites in the real-time market. Note that, only generators bid into the real-time market, clearing against the real-time forecast. Table 19 shows the summary of dispatches and prices from the real-time market. In a multi-settlement system, like the ISO’s new market which was implemented on April 1, 2010, the real-time price (LMP) is settled at the difference between the metered quantity and the day-ahead schedule. In this example the generators dispatch quantities are assumed to be the same as their metered quantities; similarly, the load forecast quantities are assumed to be the same as their metered quantities.

Figure 133: Real-Time Market Example 1.

As seen from Table 19, the generator G1 has a net decrement of 10 MW, that is, its output in the real-time is reduced from 60 MW to 50 MW. A net decrement for a generator results in a positive quantity which is equivalent to a demand.




Similarly, imbalance energy quantities are calculated for generator G2, load D1 and D2.

Based on the real-time dispatch results, as shown in Table 19, the total revenue collected is $800 ($200 from G1 and $600 form D2); but, the revenue paid is $1200 (to generator G2). Clearly, there is a revenue shortfall of $400 in the real-time market. This revenue shortfall represents the real-time offset quantity.

Table 19: Summary of Real-Time Market Dispatch for Example 1.

Resource Name

DA Sched (MWh)

RT Dispatch (MWh)

Imbalance (MWh)

LMP ($/MWh)

Market Revenue ($)

Energy Component ($/MWh)


Energy Component Revenue ($)

Congestion Revenue ($)

G1 -60 -50 10 20 200 20 0 200 0

G2 -40 -60 -20 60 -1200 60 0 -1200 0

D1 70 70 0 60 0 60 0 0 0

D2 30 40 10 60 600 60 0 600 0

Root Cause for Revenue Deficiency in Example 1

There is a revenue deficiency in the real-time market in example 1; this condition occurs because the dispatch in this example is inconsistent with the bid-in quantities. Resource G1, an hourly system resource connected to tie point A, is the cheapest unit. This unit has a net decrement in the real-time market even though it is the least priced unit, whereas, the resource G2 has a net increment in real-time even though it’s more expensive. Such situations occur in the ISO real-time markets mainly because the HASP market, which is settled at an hourly price, is run 75 minute before the actual trade interval. However, the real-time interval dispatch market, which is settled at the five-minute interval price, is run seven minutes before the trading interval. Sometimes, it happens that the HASP market observes a load forecast which is much lower than the real-time five minute market; so, the market application creates a net decrement on interties. However, as the system approaches the actual five minute interval, the load forecast is significantly higher compared to what is seen by the HASP market. Thus, the market application creates a dispatch which results in a net increment. If there is a significant difference between the price from the HASP market and the price from the real-time five minute interval market, the system will experience an imbalance offset, as seen in example 1.




Example 2

The decomposition of imbalance offset costs into imbalance energy offset cost and imbalance congestion offset cost is presented in this example. The imbalance loss offset cost is excluded from this example for simplicity. Figure 134 below shows a similar three bus system to what was used in example 1. Except that in this example, a generator G3 is added to bus C; in addition, the bid-in quantities for both generators and load are modified. Figure 134 also shows the outcome of the day-ahead market run for example 2.

Figure 134: Day-Ahead Market for Example 2

Table 20 below shows the summary of the day-ahead market for example 2. From the data shown in the table, there is no congestion in the day-ahead market in example 2. Also, the generator G3 has no award in the day-ahead market as it is the most expensive unit.

Table 20: Summary of Day-Ahead Market for Example 2

Resource Name Schedule

LMP ($/MWh)

Revenue ($)

Energy Compnent ($/MWh)


Congestion Revenue

Revenue Energy Component

Revenue Congestion Component

G1 -60 50 -3000 50 0 0 -3000 0

G2 -40 50 -2000 50 0 0 -2000 0

G3 0 50 0 50 0 0 0 0

D1 70 50 3500 50 0 0 3500 0

D2 30 50 1500 50 0 0 1500 0




Figure 135 below shows the real-time market; this is the continuation of the day-ahead market discussed in example 2. This figure also shows the dispatch quantites in the real-time market. Table 21 shows the summary of the real-time market dispatch for example 2. As mentioned previously, geneator G1 is connected to bus A which is a tie point. The dispatch and price at this node is settled by the hourly HASP process. In this real-time example the line connecting the bus A to bus B is congested in HASP. In the real-time five minute market, the line connecting bus B to bus C is derated to 30 MW and it is also congested. Because of congestion on these two lines, the LMPs in both HASP and five-minute interval market have a congestion compoent as shown in table 4.

Figure 135: Summary of Real-Time Market Dispatch for Example 2

Table 21: Summary of Real-Time Market Dispatch for Example 1.

Resource Name

DA Sched (MW)

RT Dispatch (MW)

Imbalance (MW)

LMP ($/MWh)

Market Revenue ($)

Energy Component ($/MWh)


Energy Component Revenue($)

Congestion Revenue ($)

G1 -50 -60 -10 60 -600 65 -5 -650 50

G2 -40 -50 -10 30 -300 50 -20 -500 200

G3 0 -10 -10 50 -500 50 0 -500 0

D1 60 80 20 30 600 50 -20 1000 -400

D2 30 40 10 50 500 50 0 500 0

In this real-time example, payments are made to generators G1, G2 and G3; the total payment made to these generators is $1,400. However, the total revenue collected from load D1 and D2 is only $ 1,100. Thus, there is a revenue shortfall of $300 which is the imbalance offset. This imbalance offset is decomposed into the energy imbalance offset and the congestion imbalance offset. From Table 21the imbalance energy offset is a shortfall of $150 and the imbalance congestion offset is a shortfall of $150.




Root Cause for Revenue Deficiency in Example 2

As explained in example 2, the imbalance offset is mainly driven by the difference in the HASP and the real-time five minute market results. In this example, the HASP market expects a higher load forecast and hence the generator connected at the tie point A is dispatched at a $60 bid. However, the real-time market observes a lower forecast and hence the real-time LMPs are lower than the HASP results.

The divergence between the HASP and the RTD energy prices occurs due to various other reasons: over-scheduling in the day-ahead market, under-scheduling in the IFM and under forecasting in RUC and matching supply to demand variation in real-time. These reasons are explained in the document published by the ISO on its website at http://www.caiso.com/2416/2416e7a84a9b0.pdf

Market Performance Metric Catalog

Documents

Transcript of Market Performance Metric Catalog