risk-limiting dispatch for power networks

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Risk-Limiting Dispatch for Power Networks David Tse, Berkeley Ram Rajagopal (Stanford) Baosen Zhang (Berkeley)

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Risk-Limiting Dispatch for Power Networks. David Tse , Berkeley. Ram Rajagopal ( Stanford). Baosen Zhang (Berkeley). Motivation. Traditional power generators slow to ramp up and down. Have to be dispatched in advance based on predicted demand. - PowerPoint PPT Presentation

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Page 1: Risk-Limiting  Dispatch  for  Power Networks

Risk-Limiting Dispatch for Power Networks

David Tse, Berkeley

Ram Rajagopal (Stanford)

Baosen Zhang(Berkeley)

Page 2: Risk-Limiting  Dispatch  for  Power Networks

Motivation

• Traditional power generators slow to ramp up and down.

• Have to be dispatched in advance based on predicted demand.

• Increased penetration of renewables comes increased uncertainty.

Questions:• How to do dispatch in face of uncertainty? • How to quantify the impact of uncertainty?

• How to hedge against risks from randomness?

2/15

Page 3: Risk-Limiting  Dispatch  for  Power Networks

• Add 25% wind, 20% error• Total Error~2+5=7%

Motivation

• Currently: 3 rule• Error~2%

3/15

Forecasted load

ErrorReserve𝜎

Forecasted net demand

Error

Reserve

1% is about $50 Million/yr (for CAISO)

$1 Billion$300 Million

Page 4: Risk-Limiting  Dispatch  for  Power Networks

Notation

• Three types of devices in the power system:

4/15

=net demand=Load-Renewable

Generators:Controllable

Renewables:Random,

High Uncertainty

Loads:Random,

Low Uncertainty

Prediction Error Gaussian in this talk

Page 5: Risk-Limiting  Dispatch  for  Power Networks

Two-Stage Formulation

• Two-stage problem

• Dynamic programming problem: numerical solution possible but offers little qualitative insight.

• Make small ¾ assumption.5/15

Stage 1 (day ahead)

Predicted net-demand:

Set slow generators:

Stage 2 (real-time)

Actual net-demand:

Set fast generators

Price ($/MW) Price ($/MW)¿

Page 6: Risk-Limiting  Dispatch  for  Power Networks

Nominal Problem

6/15

Stage 1 Stage 2

�̂� 𝑑𝑔𝛼 𝛽

Nominal Problem

�̂�𝛼

Stage 1 Stage 2

optimal undersmall ¾ assumption

Page 7: Risk-Limiting  Dispatch  for  Power Networks

Impact of uncertainty

• We want to find (as a function of )– Optimal cost– Optimal control

• Also want

• Intrinsic impact of uncertainty– Depend on

7/15

Cost of uncertainty= Optimal Cost Clairvoyant Cost

Page 8: Risk-Limiting  Dispatch  for  Power Networks

Nominally Uncongested Network

• Networks are lightly congested

Result:

8/15

New England ISO

Nominally Uncongested

Single Bus Network

Price of uncertainty

Page 9: Risk-Limiting  Dispatch  for  Power Networks

Single-bus network

• No congestion => single bus network• Easy to get the optimal control

9/150 5 10 15 20 25 30

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

/

Q-1

(/

) ~$100 Million/yr

3

optimal

Res

erve

/

Page 10: Risk-Limiting  Dispatch  for  Power Networks

Price of Uncertainty

• Price of uncertainty is a function of • Small Error

10/15

0

renewable>load renewable<load

Page 11: Risk-Limiting  Dispatch  for  Power Networks

Nominally Congested Network

• One nominally congested line

11/15

Midwest ISO

?

Page 12: Risk-Limiting  Dispatch  for  Power Networks

Dimensionality Reduction

• One congested line• Single bus?

Result:

Reduction to an equivalent two-bus network always possible.

12/15IEEE 13 Bus Network

KVL

x

x

Page 13: Risk-Limiting  Dispatch  for  Power Networks

Two-bus network: Further reduction?

• Nominally congested line from 1 to 2

• Congestion is nominal

• Errors still average

13/15

?

1

2

x1

2Two isolated buses?

x1

2 Supply > expected

Supply < expected

Real-timeNominal x Back-flow

Page 14: Risk-Limiting  Dispatch  for  Power Networks

Nominal solution regions

14/15

𝑐

𝐶

−𝐶

−𝐶

𝐶

x

Page 15: Risk-Limiting  Dispatch  for  Power Networks

Prices of uncertainty

15/15

𝑐𝐶

−𝐶

−𝐶

𝐶

x

Page 16: Risk-Limiting  Dispatch  for  Power Networks

Conclusion

• Management of risk in the presence of renewables• Price of uncertainty

– Intrinsic impact of uncertainties• Dimension reduction for congested networks

16/15