Virtual Energy Storage through Distributed Control of Flexible Loads - - PowerPoint PPT Presentation

▶
virtual energy storage through distributed control of
SMART_READER_LITE
LIVE PREVIEW

Virtual Energy Storage through Distributed Control of Flexible Loads - - PowerPoint PPT Presentation

Apr 11, 2023 205 likes •648 views

Virtual Energy Storage through Distributed Control of Flexible Loads CaFFEET 2015 Innovative Solutions to Integrate Renewable Energy Ana Bu si c Inria and ENS Paris, France Thanks to my colleagues, Prabir Barooah and Sean Meyn, and

slide-1
SLIDE 1

Virtual Energy Storage through Distributed Control of Flexible Loads

CaFFEET 2015 Innovative Solutions to Integrate Renewable Energy

Ana Buˇ si´ c

Inria and ENS – Paris, France Thanks to my colleagues, Prabir Barooah and Sean Meyn, and to our sponsors: French National Research Agency, National Science Foundation, and Google

slide-2
SLIDE 2

March 8th 2014: Impact of wind and solar on net-load at CAISO Ramp limitations cause price-spikes

Price spike due to high net-load ramping need when solar production ramped out Negative prices due to high mid-day solar production

1200 15 2 4 19 17 21 23 27 25 800 1000 600 400 200

  • 200

GW GW Toal Load Wind and Solar Load and Net-load Toal Wind Toal Solar Net-load: Toal Load, less Wind and Solar $/MWh 24 hrs 24 hrs Peak ramp Peak Peak ramp Peak

Challenges

slide-3
SLIDE 3

Challenges

Some of the Challenges

1 Ducks

MISO, CAISO, and others: seek markets for ramping products

2 / 15

slide-4
SLIDE 4

Challenges

Some of the Challenges

1 Ducks 2 Ramps

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

GW (t) = Wind generation in BPA, Jan 2015

Ramps 2 / 15

slide-5
SLIDE 5

Challenges

Some of the Challenges

1 Ducks 2 Ramps 3 Regulation

2 4 6 8 2 4 6 8 0.8

  • 0.8

1

  • 1

0.8

  • 0.8

1

  • 1

Sun Mon Tue October 20-25 October 27 - November 1 Hydro Wed Thur Fri Sun Mon Tue Wed Thur Fri

Generation and Laod GW

GW GW

Regulation GW

Thermal Wind Load Generation Regulation

Error Signal in Feedback Loop

2 / 15

slide-6
SLIDE 6

Challenges

Some of the Challenges

1 Ducks 2 Ramps 3 Regulation

One potential solution: Large-scale storage with fast charging/discharging rates

2 / 15

slide-7
SLIDE 7

Challenges

Some of the Challenges

1 Ducks 2 Ramps 3 Regulation

One potential solution: Large-scale storage with fast charging/discharging rates Let’s consider some alternatives

2 / 15

slide-8
SLIDE 8

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

Gr(t) G1 G2 G

Traditional generation DD: Chillers & Pool Pumps DD: HVAC Fans

3

Gr = G1 + G2 + G3

Virtual Energy Storage

slide-9
SLIDE 9

Virtual Energy Storage

Control Architecture

Frequency Decomposition

Power Grid Control

Flywheels Batteries Coal Gas Turbine

BP BP BP

C

BP BP

Voltage Frequency Phase

H C

Σ −

Actuator feedback loop

A

LOAD

Today: PJM decomposes regulation signal based on bandwidth, R = RegA + RegD Proposal: Each class of DR (and other) resources will have its own bandwidth of service, based on QoS constraints and costs.

3 / 15

slide-10
SLIDE 10

Virtual Energy Storage

Frequency Decomposition

Taming the Duck

March 8th 2014: Impact of wind and solar on net-load at CAISO Ramp limitations cause price-spikes

Price spike due to high net-load ramping need when solar production ramped out Negative prices due to high mid-day solar production

1200 15 2 4 19 17 21 23 27 25 800 1000 600 400 200

  • 200

GW GW Toal Load Wind and Solar Load and Net-load Toal Wind Toal Solar Net-load: Toal Load, less Wind and Solar $/MWh 24 hrs 24 hrs Peak ramp Peak Peak ramp Peak

ISOs need help: ... ramp capability shortages could result in a single, five-minute dispatch interval or multiple consecutive dispatch intervals during which the price of energy can increase significantly due to scarcity pricing, even if the event does not present a significant reliability risk

http://tinyurl.com/FERC-ER14-2156-000 4 / 15

slide-11
SLIDE 11

Virtual Energy Storage

Frequency Decomposition

Taming the Duck

One Day at CAISO 2020 ISO/RTOs are seeking ramping products to address engineering challenges, and to avoid scarcity prices Do we need ramping products?

Net Load Curve

In c r ea s ed ramp

GW

  • 5

5 10 15 20 25

12am 12am 3am 6am 9am 12pm 3pm 6pm 9pm

5 / 15

slide-12
SLIDE 12

Virtual Energy Storage

Frequency Decomposition

Taming the Duck

One Day at CAISO 2020

Net Load Curve

T a m i n g t h e D u c k GW

  • 5

5 10 15 20 25

12am 12am 3am 6am 9am 12pm 3pm 6pm 9pm

This doesn’t look at all scary! We need resources, but anyone here knows how to track this tame duck

5 / 15

slide-13
SLIDE 13

Virtual Energy Storage

Frequency Decomposition

Taming the Duck

One Day at CAISO 2020

Net Load Curve Low pass Mid pass High pass

The duck is a sum of a smooth energy signal, and two zero-energy services GW

  • 5

5 10 15 20 25

12am 12am 3am 6am 9am 12pm 3pm 6pm 9pm

5 / 15

slide-14
SLIDE 14

Virtual Energy Storage

Frequency Decomposition

Regulation

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

GW (t) = Wind generation in BPA, Jan 2015

Ramps

6 / 15

slide-15
SLIDE 15

Virtual Energy Storage

Frequency Decomposition

Regulation

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

G Goal:

W (t) = Wind generation in BPA, Jan 2015 Ramps

GW (t) + Gr(t) ≡ 4GW

6 / 15

slide-16
SLIDE 16

Virtual Energy Storage

Frequency Decomposition

Regulation

Ra

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

G Goal:

W (t) = Wind generation in BPA, Jan 2015 Ramps Ramps Ramps Ramps Ramps

GW (t) + Gr(t) ≡ 4GW

6 / 15

slide-17
SLIDE 17

Virtual Energy Storage

Frequency Decomposition

Regulation

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

Goal: GW (t) + Gr(t) ≡ 4GW

  • btained from

generation? Gr(t) Gr(t)

Ramp

6 / 15

slide-18
SLIDE 18

Virtual Energy Storage

Frequency Decomposition

Regulation

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

Gr(t) Gr = G1 + G2 + G3 G1

6 / 15

slide-19
SLIDE 19

Virtual Energy Storage

Frequency Decomposition

Regulation

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

Gr(t) Gr = G1 + G2 + G3 G1 G2

6 / 15

slide-20
SLIDE 20

Virtual Energy Storage

Frequency Decomposition

Regulation

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

Gr(t) Gr = G1 + G2 + G3 G1 G2 G3

6 / 15

slide-21
SLIDE 21

Virtual Energy Storage

Frequency Decomposition

Regulation

Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06

GW

1 2 3 4

Gr(t) Gr = G1 + G2 + G3 G1 G2 G3 Where do we find these resources?

6 / 15

slide-22
SLIDE 22

Local feedback loop Local Control Load i

ζt Y i

t

U i

t

Xi

t

Grid signal Local decision Power deviation

Demand Dispatch Design

slide-23
SLIDE 23

Demand Dispatch

Demand Dispatch

Gr Gr = G1 + G2 + G3 G1 G2 G3 ?

7 / 15

slide-24
SLIDE 24

Demand Dispatch

Demand Dispatch

Gr Gr = G1 + G2 + G3 G1 G2 G Traditional generation

3 7 / 15

slide-25
SLIDE 25

Demand Dispatch

Demand Dispatch

Gr Gr = G1 + G2 + G3 G1 G2 G Traditional generation Water pumping (e.g. pool pumps) Fans in commercial HVAC

3

Demand Dispatch: Power consumption from loads varies automatically and continuously to provide service to the grid, without impacting QoS to the consumer

7 / 15

slide-26
SLIDE 26

Demand Dispatch

Demand Dispatch

Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer

High quality AS? (Ancillary Service) Does the deviation in power consumption accurately track the desired deviation target?

8 / 15

slide-27
SLIDE 27

Demand Dispatch

Demand Dispatch

Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer

High quality AS? (Ancillary Service) Reliable? Will AS be available each day? It may vary with time, but capacity must be predictable.

8 / 15

slide-28
SLIDE 28

Demand Dispatch

Demand Dispatch

Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer

High quality AS? Reliable? Cost effective? This includes installation cost, communication cost, maintenance, and environmental.

8 / 15

slide-29
SLIDE 29

Demand Dispatch

Demand Dispatch

Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer

High quality AS? Reliable? Cost effective? Customer QoS constraints satisfied? The pool must be clean, fresh fish stays cold, building climate is subject to strict bounds, farm irrigation is subject to strict constraints, data centers require sufficient power to perform their tasks.

8 / 15

slide-30
SLIDE 30

Demand Dispatch

Demand Dispatch

Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer

High quality AS? Reliable? Cost effective? Customer QoS constraints satisfied? Virtual energy storage: achieve these goals simultaneously through distributed control

8 / 15

slide-31
SLIDE 31

Demand Dispatch

General Principles for Design

Two components to local control Local feedback loop Local Control Load i

ζt Y i

t

U i

t

Prefilter Decision

ζt U i

t

Xi

t

Xi

t

Each load monitors its state and a regulation signal from the grid. Prefilter and decision rules designed to respect needs of load and grid Randomized policies required for finite-state loads

9 / 15

slide-32
SLIDE 32

Demand Dispatch

MDP model

MDP model The state for a load is modeled as a controlled Markov chain. Controlled transition matrix: Pζ(x, x′) = P{Xt+1 = x′ | Xt = x, ζt = ζ}

Two components to local control Local feedback loop Local Control Load i ζt Y i

t

U i

t

Prefilter Decision ζt U i

t

Xi

t

Xi

t

10 / 15

slide-33
SLIDE 33

Demand Dispatch

MDP model

MDP model The state for a load is modeled as a controlled Markov chain. Controlled transition matrix: Pζ(x, x′) = P{Xt+1 = x′ | Xt = x, ζt = ζ}

Two components to local control Local feedback loop Local Control Load i ζt Y i

t

U i

t

Prefilter Decision ζt U i

t

Xi

t

Xi

t

Questions:

  • How to analyze aggregate of similar loads?
  • How to design Pζ?

10 / 15

slide-34
SLIDE 34

Demand Dispatch

How to analyze aggregate?

Mean field model

Reference Output deviation (MW) −300 −200 −100 100 200 300 20 40 60 80 100 120 140 160 t/hour 20 40 60 80 100 120 140 160

State process: µt(x) ≈ 1 N

N

  • i=1

I{Xi

t = x},

x ∈ X Evolution: µt+1 = µtPζt Output (mean power): yt =

  • x

µt(x)U(x) Nonlinear state space model Linearization useful for control design

11 / 15

slide-35
SLIDE 35

Demand Dispatch

Control Architecture

Frequency Allocation for Demand Dispatch

10-2 10-1 100 101 Frequency (rad/s) 10-5 10-4 10-3 Frequency (rad/s) Magnitude (dB)

  • 15
  • 10
  • 5

5 10 15 20 Phase (deg)

  • 90
  • 45

45

G r i d T r a n s f e r F u n ct i

  • n

A typical macro model of the power grid Motivation for PI control architecture, and fear of droop gain

  • H. Chavez, R. Baldick, and S. Sharma. Regulation adequacy analysis under high wind penetration scenarios in ERCOT nodal. IEEE Trans. on Sustainable Energy, 3(4):743–750, Oct 2012.

−300 −200 −100

12 / 15

slide-36
SLIDE 36

Demand Dispatch

Control Architecture

Frequency Allocation for Demand Dispatch

10-2 10-1 100 101 Frequency (rad/s) 10-5 10-4 10-3 Frequency (rad/s) Magnitude (dB)

  • 15
  • 10
  • 5

5 10 15 20 Phase (deg)

  • 90
  • 45

45 G r i d T r a n s f e r F u nc t i

  • n

Uncertainty Here

There is significant gain and phase uncertainty in this bandwidth

Fear is justified!

−300 −200 −100

12 / 15

slide-37
SLIDE 37

Demand Dispatch

Control Architecture

Frequency Allocation for Demand Dispatch

10-2 10-1 100 101 Frequency (rad/s) 10-5 10-4 10-3 Frequency (rad/s) Magnitude (dB)

  • 15
  • 10
  • 5

5 10 15 20 Phase (deg)

  • 90
  • 45

45 G r i d T r a n s f e r F u nc t i

  • n

Uncertainty Here Fans in Commercial Buildings

Fans in commercial buildings in the state of Florida can supply all of the RegD and RegA regulation needs of PJM

10 20 30 40 7am 1pm 7pm

Hours Minutes

1am Measured Reference 71

  • 1
  • 0.5

0.5 1 73 72 74

KW F

  • −300

−200 −100

12 / 15

slide-38
SLIDE 38

Demand Dispatch

Control Architecture

Frequency Allocation for Demand Dispatch

10-2 10-1 100 101 Frequency (rad/s) 10-5 10-4 10-3 Frequency (rad/s) Magnitude (dB)

  • 15
  • 10
  • 5

5 10 15 20 Phase (deg)

  • 90
  • 45

45 G r i d T r a n s f e r F u nc t i

  • n

Uncertainty Here Fans in Commercial Buildings Residential Water Heaters Refrigerators

The bandwidth of these devices is centered around their natural cycle the capacity is enormous in this bandwidth

−300 −200 −100

12 / 15

slide-39
SLIDE 39

Demand Dispatch

Control Architecture

Frequency Allocation for Demand Dispatch

10-2 10-1 100 101 Frequency (rad/s) 10-5 10-4 10-3 Frequency (rad/s) Magnitude (dB)

  • 15
  • 10
  • 5

5 10 15 20 Phase (deg)

  • 90
  • 45

45 G r i d T r a n s f e r F u nc t i

  • n

Uncertainty Here Fans in Commercial Buildings Residential Water Heaters Refrigerators Water Pumping Pool Pumps Chiller Tanks

Bandwidth centered around its natural cycle

Reference (from Bonneville Power Authority)

10,000 pools

Output deviation

−300 −200 −100 100 200 300

Tracking BPA Regulation Signal (MW)

20 40 60 80 100 120 140 160 t/hour 20 40 60 80 100 120 140 160

12 / 15

slide-40
SLIDE 40

Demand Dispatch

Control Architecture

Frequency Allocation for Demand Dispatch

−300 −200 −100

10-2 10-1 100 101 Frequency (rad/s) 10-5 10-4 10-3 Frequency (rad/s) Magnitude (dB)

  • 15
  • 10
  • 5

5 10 15 20 Phase (deg)

  • 90
  • 45

45 G r i d T r a n s f e r F u nc t i

  • n

Uncertainty Here Fans in Commercial Buildings Residential Water Heaters Refrigerators Water Pumping Pool Pumps Chiller Tanks

19% of the load Imagine the capacity from water pumping in California?

12 / 15

slide-41
SLIDE 41

Power Grid Control

Water Pump Batteries Coal Gas Turbine

BP BP BP

C

BP BP

Voltage Frequency Phase

H C

Σ −

Actuator feedback loop

A

LOAD

Conclusions

slide-42
SLIDE 42

Conclusions

Conclusions

Volatility appears to be manageable! Randomized control architecture designed so that everyone is happy. The virtual storage capacity from demand dispatch is enormous Open questions on many spatial and temporal scales

1 Most loads could provide synthetic inertia and governor response1.

Is this wise?

2 We don’t know why the grid is so reliable today

– we need better macro models2

3 And of course, incentives are needed: contracts and/or standards 1Scweppe et. al. 1980 2Thorpe et. al. 2004 13 / 15

slide-43
SLIDE 43

Conclusions

Conclusions

Thank You!

14 / 15

slide-44
SLIDE 44

References

Selected References

  • H. Hao, Y. Lin, A. Kowli, P. Barooah, and S. Meyn. Ancillary service to the grid through

control of fans in commercial building HVAC systems. IEEE Trans. on Smart Grid, 5(4):2066–2074, July 2014.

  • S. Meyn, P. Barooah, A. Buˇ

si´ c, Y. Chen, and J. Ehren. Ancillary service to the grid using intelligent deferrable loads. ArXiv e-prints: arXiv:1402.4600 and to appear, IEEE Trans. on

  • Auto. Control, 2014.
  • P. Barooah, A. Buˇ

si´ c, and S. Meyn. Spectral decomposition of demand-side flexibility for reliable ancillary services in a smart grid. In Proc. 48th Annual Hawaii International Conference on System Sciences (HICSS), pages 2700–2709, Kauai, Hawaii, 2015.

  • Y. Chen, A. Buˇ

si´ c, and S. Meyn. Individual risk in mean-field control models for decentralized control, with application to automated demand response. In Proc. of the 53rd IEEE Conference on Decision and Control, pages 6425–6432, Dec. 2014.

  • A. Buˇ

si´ c and S. Meyn. Passive dynamics in mean field control. 53rd IEEE Conf. on Decision and Control (Invited). 2014.

  • J. Mathias, R. Kaddah, A. Buˇ

si´ c, and S. Meyn. Smart fridge / dumb grid? demand dispatch for the power grid of 2020. Proc. 49th Annual Hawaii International Conference on System Sciences (HICSS), Kauai, Hawaii, 2016.

15 / 15