Copyright © 2014 The Brattle Group, Inc.

Transmission Topology Control Applications to Outage Scheduling, Market Efficiency and Overload Relief

Presented by : Ju d y C h a n g a n d Pa b l o A. R u i z Acknowledgement: The work in these slides has been conducted by Pablo A. Ruiz, Michael Caramanis, Evgeniy Goldis, David Hislop, Bhavana Keshavamurthy, Xiaoguang Li, Dan Moscovitz, Russ Philbrick, Alex Rudkevich, Richard Tabors, Bruce Tsuchida

W I RE S S umme r M e et i ng 2 0 15 B os t on , MA Ju l y 2 3 , 2 0 1 5

| brattle.com 1

▀ Topology Control: Legacy and Automated Applications

▀ Illustrative Example

▀ ARPA-E Topology Control Algorithm Project

▀ Example from Experience with PJM

− Relieving Overloads Through TCA

− Congestion Management

▀ Applications for Practical Use Today

▀ A Transmission Owner’s Perspective

Content

| brattle.com 2

▀ Topology control was used to control wireless sensor networks that collect and send data from geographically dispersed areas. Topology control algorithms were applied to increase the efficiency of the network. (Applications include: monitoring, tracking and surveillance of borders and the battlefields; traffic and road conditions; changes in environment.)

▀ Applied to electric transmission network, Transmission Topology Control (line switching) can reduce system congestion and reduce the cost of delivered power.

▀ Today, transmission topology control is conducted via manual ad-hoc interventions by system operators. The interventions usually are based on prior experience or on fixed procedures.

What is Topology Control?

| brattle.com 3

▀ Topology Control can increase transfer capabilities from low-cost generation to high cost regions, relieve overloads, reduce congestion, and increase system flexibility.

▀ Today, system operators adjust transmission topology on an ad-hoc basis for the following applications: − Contingency Planning: identify pre- and post-contingency

reconfiguration to mitigate overloads (e.g., Operating Guides) − Outage Coordination/Scheduling: enable planned outages that

otherwise would cause reliability violations/congestion increase − Congestion Management: allow more efficient unit commitment and

economic dispatch (used in limited cases)

▀ Developing switching solutions and efficient topology changes is a time-consuming process.

Topology Control Today

| brattle.com 4

▀ Automated Topology Control is now possible with fast computers, parallel processing and new optimization approaches. − With DOE’s Advanced Research Project (ARPA-E) support, a team

developed software technology that automates and speeds up the identification of switching solutions to address reliability- and congestion-related challenges.

▀ Topology Control Algorithms (TCA) co-optimize electricity generation economic dispatch with the connectivity of transmission network elements (breaker status).

▀ The TCA can automatically identify operational controls that support system operators’ in: − Responding to contingency situations − Planning for outages − Managing congestion

Automated Topology Control Technology

| brattle.com 5

Illustrative Example

Overloaded Bus with High Locational Price $40/MWh

$15/MWh

| brattle.com 6

Illustrative Example

Relieve Overloads and Reduce Prices $40/MWh

$15/MWh

| brattle.com 7

With Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) support, a team of experts developed full-scale algorithms and software for transmission topology control

▀ Teamed up with PJM Operations to test the software and the solutions obtained.

▀ The project team designed and developed a protocol that replicates PJM’s market operations.

▀ The technology can be used with existing operations and market tools (including Energy Management Systems, Market Management Systems, and contingency planning).

▀ The tool meets PJM’s computational time requirements for operations.

Specifications of Protocol: ▀ Handled simulating large systems: PJM’s >20,000 bus (>100,000 EMS nodes). ▀ Applied forward-looking topology control in Economic Dispatch and Unit

Commitment. ▀ Delivered solutions that meet reliability criteria, including connectivity, security

constraints, stability and voltage criteria.

ARPA-E Topology Control Algorithm Project

Protocol Built to Simulate PJM

| brattle.com 8

ARPA-E Topology Control Algorithm Project

Topology Control Software Architecture

Topology Control Contingency and Voltage Stability

Evaluation

Voltage, MVA and Stability Assessment: Feasible/Infeasible, Constraints to Return to Feasibility

Topology, Dispatch, Commitment, Marginal Costs

* The simulation results in this presentation include AC modeling and linear contingency evaluation and enforcement, but do not include transient stability evaluation

Optimization: Reduces System Costs

Feasibility: Maintains Reliability

| brattle.com 9

Example from Experience with PJM: Relieving Overloads Through TCA

South Canton Potential Overload Relief

Source: http://www.pjm.com/~/media/committees-groups/committees/mrc/20130829/20130829-item-13-hot-weather-operations-presentation.ashx

| brattle.com 10

Example from Experience with PJM: Relieving Overloads Through TCA

South Canton Potential Overload Relief

Source: http://www.pjm.com/

PJM Real Time Prices, 18-Jul-2013, 15:30

South Canton

Prices over $800/MWh

| brattle.com 11

Example from Experience with PJM: Relieving Overloads Through TCA

PJM Overload Relief

Before South Canton transformer was overloaded in base case, and 4 post contingency constraints were also overloaded

After 8 branches (three 345 kV and five <200 kV) were opened to divert flows and relieve all overloads in the area, without changing the system dispatch

July 15th, 2013 at 3PM

| brattle.com 12

Example from Experience with PJM: Relieving Overloads Through TCA

South Canton Potential Overload Relief ▀ July 15, 2013, 3 PM Event: The South Canton transformer was

severely congested and with base case overloads. − There were post-contingency overloads in the area as well. − PJM deployed demand response to reduce congestion in the area.

▀ We simulated using solely transmission topology modifications to relieve overloads.

▀ TCA produced solutions to divert flow away from the transformer and fully relieved the base case and post contingency overloads in the area (without causing other overloads).

▀ TCA could have reduced the required DR deployment. ▀ Very little change in base case voltage profile after the topology

change.

| brattle.com 13

▀ Simulated PJM’s system and provided security-constrained topology control solutions − Each solutions is obtained for a snapshot of the system in only a

few minutes. ▀ When applied to constraint overload events, TCA identified switching

solutions that would have alleviated the events. − Solutions deemed innovative relative to experience by PJM staff.

▀ The team estimates PJM congestion-related costs can be reduced by about 50% based on simulations of PJM’s 5-minute real time market with system-wide “re-dispatch” of the system enabled by TCA. − Estimated annual savings >$100 million in 2010.

▀ Currently, continuing to evaluate the potential impact of using Topology Control Algorithm on financial transmission right settlements and FTR revenue adequacy (expected by Q3 2015).

Example from Experience with PJM: Congestion Management

TC Congestion Impacts – PJM Simulation Results

| brattle.com 14

 While the TCA technology is developed with the long-term goal of automating transmission system switching in day-ahead and real-time, several practical applications are available now: ▀ TCA can very efficiently identify switching solutions to

address specific reliability and congestion events. ▀ TCA can define temporary operating guides that help

transmission owners/operators plan for and manage transmission outages. This can significantly reduce the typical reliability and cost impacts of construction-related outages.

▀ TCA can identify operating guides to reduce congestion on a regular basis and reduce utilities’ exposures to unhedged congestion costs.

Valuable Applications for Practical Use Today

| brattle.com 15

▀ TCA helps to reduce the costs and increase feasibility of construction-related outages.

▀ TCA Increases the value of system expansions that provide operational flexibility (e.g., investments that create more switching options).

▀ TCA Increases the effective capability of the existing grid. − Would avoid/defer certain upgrades (usually lower voltage ones). − May increase the reliability and economic benefit of system expansions

and upgrades (making it easier to pass B/C test). ▀ TCA increases the long-term attractiveness of transmission

solutions compared to non-transmission alternatives (such as generation-heavier solutions). − TCA will likely move the optimal spending mix more toward

transmission (i.e., spending more on transmission and less on generation will be the more cost effective overall solution).

Long-Term benefits for Transmission Planning

A Transmission Owner’s Perspective

| brattle.com 16

Judy Chang, Principal The Brattle Group, Judy.Chang@brattle.com, (617) 234-5630 Pablo A. Ruiz, Principal Investigator The Brattle Group and Boston University, Pablo.Ruiz@brattle.com, (617) 234-5748 http://www.topologycontrol.com

Contacts

Funding for the TCA project is provided by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under the Green Energy Network Integration (GENI) program, Award Number DE-AR0000223, by the Massachusetts Clean Energy Center (MassCEC), and by the team member institutions.

Acknowledgments

Privileged and Confidential Prepared at the Request of Counsel | brattle.com 17

Appendix

| brattle.com 18

▀ Models based on one operational power flow snapshot per hour for the summer peak week in 2013 (July 14-20) and for three representative historical weeks in 2010 (summer, shoulder and winter weeks).

− Data used from the power flows: transmission topology, branch parameters, unit commitment and dispatch, loads, shunt devices, interchange, initial voltage state

▀ Generation economic and transmission constraint data from real-time market ▀ Assumptions made include:

− Fixed interface constraint limits at historical value used by RTO for same interval − Fixed dispatch of hydro, wind, landfill, nuclear and reliability must-run thermal

units for the interval − Network service requirements for all non-radial loads and generators

▀ Model dimensions: up to 15,200 nodes and 650 dispatchable thermal units, about 4,700 monitored branches and 6,100 single and multi-element contingencies (fixed contingency list assumed to be sufficient for all relevant topology changes)

Appendix

PJM RT Market Models: Historical Conditions

| brattle.com 19

$0M

$2M

$4M

$6M

$8M

Summer Winter Shoulder

56%

44%

32%

68%

33% 67%

Production Cost Savings

Remaining Cost of Congestion

* Savings and remaining cost of congestion shown as a percentage of the initial cost of congestion

Savings in the Representative Weeks

Production Cost Savings = production cost without TCA (full topology) – production costs with TCA Cost of Congestion = production cost with transmission constraints – production costs without transmission constraints

Appendix: TCA Simulation on PJM RT Markets

RT Market Production Cost Savings

| brattle.com 20

Appendix: TCA Simulation on PJM RT Markets

Transfers Enabled by TC and Breaker Ops.

Percentages are in reference to the weekly transfer without TCA. Flow pattern and transfer vary depending on seasons and system conditions. Overall, TCA significantly increases the transfer capability within the system.

Transfer Between RTO Regions

+7%

+14%

+5%

+28%

50%

21%

19%

5% 5%

< 200kV

230kV

345kV

500kV

765kV

Breaker Operations by Voltage Hours Facilities are Open by Voltage 765 kV equipment is primarily switched out of service in anticipation of low load periods, such as the weekend or very early mornings, when they are not needed for reliability, are lightly loaded, and may cause over-voltage issues.

64%15%

13%

5%3% < 200kV

230kV

345kV

500kV

765kV

< 200kV

230kV

345kV

500kV

765kV

Summer Peak 2013 Week

| brattle.com 21

References [1] E. A. Goldis, M. C. Caramanis, C. R. Philbrick, A. M. Rudkevich and P. A. Ruiz, “Security-constrained MIP

formulation of topology control using loss-adjusted shift factors,” in Proc. 47th Hawaii Int. Conf. System Science, Waikoloa, HI, January 2014

[2] P. A. Ruiz, M. C. Caramanis, E. Goldis, B. Keshavamurthy, X. Li, M. Patel, C. R. Philbrick, A. M. Rudkevich, R. D. Tabors, T. B. Tsuchida, “Transmission Topology Control for System Efficiency: Simulations on PJM Real Time Markets,” presented at 2013 IEEE Power and Energy Society General Meeting, Vancouver, Canada, July 2013.

[3] P. A. Ruiz, J. M. Foster, A. Rudkevich and M. C. Caramanis, “Tractable transmission topology control using sensitivity analysis,” IEEE Transactions on Power Systems, vol. 27, no. 3, Aug 2012, pp. 1550 – 1559.

[4] P. A. Ruiz, A. Rudkevich, M. C. Caramanis, E. Goldis, E. Ntakou and C. R. Philbrick, “Reduced MIP formulation for transmission topology control,” in Proc. 50th Allerton Conference on Communications, Control and Computing, Monticello, IL, October 2012.

[5] J. M. Foster, P. A. Ruiz, A. Rudkevich and M. C. Caramanis, “Economic and corrective applications of tractable transmission topology control,” in Proc. 49th Allerton Conference on Communications, Control and Computing, Monticello, IL, September 2011.

[6] P. A. Ruiz, J. M. Foster, A. Rudkevich and M. C. Caramanis, “On fast transmission topology control heuristics,” in Proc. 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, July 2011.

[7] R. O’Neill, R. Baldick, U. Helman, M. Rothkopf, and W. Stewart, “Dispatchable transmission in RTO markets,” IEEE Trans. Power Syst., vol. 20, no. 1, pp. 171–179, Feb. 2005

[8] E. B. Fisher, R. P. O’Neill, and M. C. Ferris, “Optimal transmission switching,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1346–1355, Aug. 2008.

[9] K. W. Hedman, R. P. O’Neill, E. B. Fisher, and S. S. Oren, “Optimal transmission switching with contingency analysis,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1577–1586, Aug. 2009.