Naughton & Detmers September 10-11, 2015Energy Policy Research Conference 1 Quality Metrics for...

Naughton & DetmersSeptember 10-11, 2015 Energy Policy Research Conference 1

Quality Metrics for Evaluating the Integration of New Renewable Energy Projects with Existing

Resource Portfolios

Jonathan Naughton

Wind Energy Research Center

Laramie, WY

James Detmers

Consultant

Folsom, CA


Introduction

• Penetration of renewable energy has increased rapidly Wind contribution to U.S.

electricity supply has grown• 0.3% in 2003• 4.3% in 2013

Some states have proposed Renewable Portfolio Standards (RPS) of 50%

• As increase of renewables occurs, integration becomes more challenging

• increasingly important to consider variability of supply

• Various mitigation strategies exist Rapid-start gas turbines Energy storage Demand-response strategies Geographical diversity

• Mitigation approaches can be expensive Reduce variability first to limit

further mitigation Apply more expensive

mitigation strategies second


Introduction

• Metrics are needed to identify those combination of resources that reduce variability Evaluate the effects of

geographic or resource diversification

Define the technical requirements for the electrical system

Aid in planning and designing grid so reliability maintained throughout system transformation


Introduction

• Objectives Demonstrate a group or Renewable Energy Quality Metrics (REQMs)

that can be used to evaluate different renewable energy portfolios

• Approach Define Metrics Acquire power data (existing and proposed)

• Anemometer data from Wyoming Convert wind data in to power

• Power data from California (CAISO) Apply REQMs to different portfolios Identify combinations or renewable energy sources that have the best

performance


ApproachRenewable Energy Quality Metrics

• Capacity Factor Traditional performance

metric The power produced by a

group of wind installations normalized by the power that would be produced if the installations ran continuously at full capacity.

A good site is characterized by a high capacity factor.

• 50% is an outstanding wind site

P

25.0P

PSPS

P/Pmax

0 0.2 0.4 0.6 0.8 1

hist

ogra

m0

1000

2000

3000

4000

05.0P

maxP

.


Renewable Energy Quality Metrics

• Relative Variability The variability of power, characterized by its standard deviation, normalized by

the mean power. A good site is characterized by a low relative variability.

P

25.0P

PSPS

P/Pmax

0 0.2 0.4 0.6 0.8 1

hist

ogra

m

0

1000

2000

3000

4000

05.0P

maxP

𝑹𝑽=𝑺𝑷 /𝑷



• P>0.05 P>5% represents the relative amount of time a group of installations is

producing more than 5% of capacity A good site is characterized by a high P>5% value.

• The higher the number, the less frequently the installations drop offline.

P

25.0P

PSPS

P/Pmax

0 0.2 0.4 0.6 0.8 1

hist

ogra

m

0

1000

2000

3000

4000

05.0P

maxP

,



• P>0.25 P>25% represents the relative amount of time a group of installations

is producing more than 25% of capacity A good site is characterized by a high P>25% value.

• The higher the number, the more often the installations are producing significant amounts of power.

P

25.0P

PSPS

P/Pmax

0 0.2 0.4 0.6 0.8 1

hist

ogra

m

0

1000

2000

3000

4000

05.0P

maxP

,


Scenarios• Wyoming and California Wind and Solar

6000 MW in different combinations 100 MW existing with 100 MW added

• Wyoming Wind and California Renewables (NREL 33% Scenario)

• NREL 33% Added to Existing CA Renewables

Technology Capacity Ann. Gen. Capacity Capacity Ann. Gen. Capacity(MW) (GWh) Factor (MW) (GWh) Factor

CA Geo/Bio 513 3642 0.810CA Solar 2563 6297 0.280CA Wind 757 2061 0.311WY Wind 3000 12000 0.457

CA 33% CA/WY 33%

Technology MW % MW % MW %CA Geo/Bio 800 9.6% 1313 10.8% 800 7%CA Solar 4000 48.2% 6563 54.1% 4000 35%CA Wind 3500 42.2% 4257 35.1% 3500 31%WY Wind 0 0.0% 0 0.0% 3000 27%

CA + CA +CA ExistCA 33% CA/WY 33%

D. Corbus et al., “California-Wyoming Grid Integration study,” Technical Report NREL DE-AC36-08GO28308, March 2014.


ResultsQuality Metrics – CA/WY Wind Scenario

Capacity RelativeFactor Variability P>5% P>25%

CA 3 CA 5 WY 1 WY 3100% 0.240 1.03 0.67 0.39

100% 0.266 1.08 0.62 0.41100% 0.440 0.81 0.80 0.59

100% 0.482 0.77 0.79 0.6450% 50% 0.242 0.97 0.71 0.4050% 50% 0.333 0.61 0.91 0.6450% 50% 0.359 0.61 0.90 0.67

50% 50% 0.342 0.65 0.89 0.6450% 50% 0.371 0.64 0.89 0.68

50% 50% 0.462 0.65 0.90 0.7125% 25% 25% 25% 0.340 0.54 0.95 0.69

% Installed Capacity

}


ResultsQuality Metrics – CA/WY Wind Scenario

0

0.1

0.2

0.3

0.4

0.5

Cap

acit

y F

acto

r

CA3

CA3/CA5

CA5

CA3/WY1

CA3/CA5/WY1/WY3

CA5/WY1

CA3/WY3

CA5/WY3

WY1

WY1/WY3

WY3

worst best

0

0.2

0.4

0.6

0.8

1

Rel

ativ

e V

aria

bili

ty

CA3/CA5/WY1/WY3

CA3/WY3

CA3/WY1

CA5/WY3

WY1/WY3

CA5/WY1

WY3

WY1

CA3/CA5

CA3

CA5

best worst

0

0.2

0.4

0.6

0.8

1

P>

5

CA5

CA3

CA3/CA5

WY3

WY1

CA5/WY3

CA5/WY1

WY1/WY3

CA3/WY3

CA3/WY1

CA3/CA5/WY1/WY3

worst best

0

0.2

0.4

0.6

0.8

P>

25

CA3

CA3/CA5

CA5

WY1

CA3/WY1

CA5/WY1

WY3

CA3/CA5/WY1/WY3

CA3/WY3

CA5/WY3

WY1/WY3

worst best


ResultsQuality Metrics – CA/WY Wind/Solar

Scenario

• Adding California Wind Capacity factor about the same Relative variability drops P>5% and P>25% increase slightly

Good addition for this case

Capacity Relative P>5% P>25%CA 2 CA 5 CA 4 CA 5 WY 1 WY 3 Factor Variability

50 50 0.266 0.85 0.77 0.4750 100 50 0.269 0.67 0.88 0.4950 50 100 0.250 1.02 0.70 0.3650 50 100 0.350 0.61 0.93 0.6350 50 100 0.374 0.60 0.90 0.67

Installed Capacity (MW)



Scenario

• Adding California Solar Capacity factor about the same Relative variability increase significantly P>5% and P>25% decrease significanty

Poor addition for this case


50 50 0.266 0.85 0.77 0.4750 100 50 0.269 0.67 0.88 0.4950 50 100 0.250 1.02 0.70 0.3650 50 100 0.350 0.61 0.93 0.6350 50 100 0.374 0.60 0.90 0.67




Scenario

• Adding Wyoming Wind Capacity factor increases significantly Relative variability decreases significantly P>5% and P>25% increases significantly

Best addition for this case


50 50 0.266 0.85 0.77 0.4750 100 50 0.269 0.67 0.88 0.4950 50 100 0.250 1.02 0.70 0.3650 50 100 0.350 0.61 0.93 0.6350 50 100 0.374 0.60 0.90 0.67




Scenario

• Adding Wyoming Wind vs. California Solar Capacity factor nearly 50% higher Relative variability 41% lower P>25% higher by 86%


50 50 0.266 0.85 0.77 0.4750 100 50 0.269 0.67 0.88 0.4950 50 100 0.250 1.02 0.70 0.3650 50 100 0.350 0.61 0.93 0.6350 50 100 0.374 0.60 0.90 0.67



ResultsQuality Metrics

CA Exist + NREL 33% Scenario

• Wyoming and California 33% scenarios added on top of California’s current renewable mix Adding more CA resources only makes problem worse

• Variability goes up Adding WY resources significantly improves performance parameters

• Variability drops significantly• Amount of time resources are producing significantly increases

Capacity RelativeFactor Variability P>5% P>25%

Description WY1 WY3 CA W CA S CA GCurrent CA 0% 0% 42.2% 48.2% 9.6% 0.344 0.59 1.00 0.63CA + CA 33% 0% 0% 35.1% 54.1% 10.8% 0.348 0.62 1.00 0.60CA + WY 33% 13.3% 13.3% 31.0% 35.4% 7.1% 0.365 0.43 1.00 0.77

% Installed Capacity

Wyoming solutionCalifornia solution


Conclusions

• Renewable Energy Quality Metrics (REQMs) have been proposed and applied to different scenarios. Comparison of competing alternative renewable energy projects Addition of renewable energy projects to existing resources

• The results indicate that REQMs provide and objective means of objectively assessing or quantifying renewable energy additions

• Use of REQMS Assessment of mitigation strategies

• Geographical diversity effective Evaluation tool for decision makers

• Additional metrics likely to be defined to better address certain issues Ramp rates, over-generation, maximum deviations

• Change of metrics over time as new resources added should be considered


Acknowledgements

• The financial support of this work by the Wyoming Infrastructure Authority is gratefully Acknowledged.

• Wind and Power Data Power Company of Wyoming

• Ryan Jacobsen Pathfinder Wind

• Holly Wold General Electric

• Skip Brennan and Daniel Fesenmeyer CAISO

• David Timson and Clyde Loutan• Feedback and Input on Analysis

Loyd Drain, Wyoming Infrastructure Authority David Smith, Power Company of Wyoming Jan Strack, San Diego Gas and Electric

Naughton & Detmers September 10-11, 2015Energy Policy Research Conference 1 Quality Metrics for...

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