Ancillary Service to the Grid Using Intelligent Deferrable Loads PGMO Days 2015 Ana Buˇ si´ c Inria, DI ENS In collaboration with S. Meyn and P. Barooah Thanks to PGMO, NSF, and Google
Outline 1 Challenges of Renewable Energy Integration 2 Virtual Energy Storage 3 Control of Deferrable Loads: Goals and Architecture 4 Mean Field Model 5 Local Control Design 6 Conclusions and Future Directions 0 / 19
27 March 8th 2014: Impact of wind Load and Net-load 25 and solar on net-load at CAISO 23 GW 21 19 $/MWh 17 1200 Price spike due to high net-load ramping 15 Toal Load Net-load: Toal Load, less Wind and Solar 1000 need when solar production ramped out Wind and Solar 4 GW 800 2 Negative prices due to high 0 600 Peak ramp Peak 24 hrs mid-day solar production Toal Wind Toal Solar 400 200 0 Ramp limitations cause price-spikes -200 Peak ramp Peak 24 hrs Challenges
Challenges of Renewable Energy Integration Some of the Challenges 1 Ducks MISO, CAISO, and others: seek markets for ramping products 1 / 19
Challenges of Renewable Energy Integration Some of the Challenges 1 Ducks 2 Ramps 4 G W ( t ) = Wind generation in BPA, Jan 2015 3 GW 2 Ramps 1 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 1 / 19
Challenges of Renewable Energy Integration Some of the Challenges 1 Ducks 2 Ramps 3 Regulation 1 / 19
Challenges of Renewable Energy Integration Some of the Challenges 1 Ducks 2 Ramps 3 Regulation One potential solution: Large-scale storage with fast charging/discharging rates 1 / 19
Challenges of Renewable Energy Integration 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 1 / 19
4 G r ( t ) 3 G r = G 1 + G 2 + G 3 GW 2 G 1 Traditional generation G 2 DD: Chillers & Pool Pumps 1 G DD: HVAC Fans 3 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 Virtual Energy Storage
Virtual Energy Storage Control Architecture Frequency Decomposition 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. 2 / 19
Virtual Energy Storage Frequency Decomposition Taming the Duck 27 March 8th 2014: Impact of wind Load and Net-load 25 and solar on net-load at CAISO 23 GW 21 19 $/MWh 17 1200 Price spike due to high net-load ramping 15 Toal Load Net-load: Toal Load, less Wind and Solar 1000 need when solar production ramped out Wind and Solar 4 GW 800 2 Negative prices due to high 0 600 Peak ramp Peak 24 hrs mid-day solar production Toal Wind Toal Solar 400 200 0 Ramp limitations cause price-spikes -200 Peak ramp Peak 24 hrs 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 3 / 19
Virtual Energy Storage Frequency Decomposition Taming the Duck One Day at CAISO 2020 r a m p 25 Net Load Curve e d 20 s e a r 15 c I n GW 10 ISO/RTOs are seeking ramping products 5 to address engineering challenges, and to avoid scarcity prices 0 Do we need ramping products? -5 12am 3am 6am 9am 12pm 3pm 6pm 9pm 12am 4 / 19
Virtual Energy Storage Frequency Decomposition Taming the Duck One Day at CAISO 2020 25 Net Load Curve 20 k c u D e h 15 t g n GW i m a T 10 5 This doesn’t look at all scary! We need resources, but anyone here 0 knows how to track this tame duck -5 12am 3am 6am 9am 12pm 3pm 6pm 9pm 12am 4 / 19
Virtual Energy Storage Frequency Decomposition Taming the Duck One Day at CAISO 2020 25 Net Load Curve Low pass 20 15 GW 10 The duck is a sum of a smooth energy signal, and two zero-energy services 5 Mid pass 0 High pass -5 12am 3am 6am 9am 12pm 3pm 6pm 9pm 12am 4 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 G W ( t ) = Wind generation in BPA, Jan 2015 3 GW 2 Ramps 1 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 W ( t ) = Wind generation in BPA, Jan 2015 G Goal: G W ( t ) + G r ( t ) ≡ 4 GW 3 GW 2 Ramps 1 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 W ( t ) = Wind generation in BPA, Jan 2015 G Ramps Goal: G W ( t ) + G r ( t ) ≡ 4 GW 3 Ramps Ra GW 2 Ramps Ramps Ramps 1 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 G r ( t ) 3 Ramp GW 2 1 Goal: G W ( t ) + G r ( t ) ≡ 4 GW G r ( t ) obtained from generation? 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 G r ( t ) 3 GW 2 G 1 G r = G 1 + G 2 + G 3 1 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 G r ( t ) 3 GW 2 G 1 G 2 G r = G 1 + G 2 + G 3 1 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 G r ( t ) 3 GW 2 G 1 G 2 G r = G 1 + G 2 + G 3 G 3 1 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Frequency Decomposition Regulation 4 G r ( t ) 3 GW 2 G 1 G 2 G r = G 1 + G 2 + G 3 Where do we find G 3 1 these resources? 0 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 5 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS G r = G 1 + G 2 + G 3 G 1 G 3 ? G 2 G r 6 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS G r = G 1 + G 2 + G 3 G 1 Traditional generation G 2 G 3 G r 6 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS G r = G 1 + G 2 + G 3 G 1 Traditional generation G 2 Water pumping (e.g. pool pumps) G Fans in commercial HVAC 3 G r Demand Dispatch: Power consumption from loads varies automatically and continuously to provide service to the grid, without impacting QoS to the consumer 6 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer High quality Ancillary Service? Does the deviation in power consumption accurately track the desired deviation target? 7 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer High quality Ancillary Service? Reliable? Will AS be available each day? It may vary with time, but capacity must be predictable. 7 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer High quality Ancillary Service? Reliable? Cost effective? This includes installation cost, communication cost, maintenance, and environmental. 7 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer High quality Ancillary Service? 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. 7 / 19
Virtual Energy Storage Demand Dispatch Responsive Regulation and desired QoS – A partial list of the needs of the grid operator, and the consumer High quality Ancillary Service? Reliable? Cost effective? Customer QoS constraints satisfied? Virtual energy storage: achieve these goals simultaneously through distributed control 7 / 19
Control of Deferrable Loads
Control of Deferrable Loads: Goals and Architecture Control Goals and Architecture Prefilter and decision rules designed to respect needs of load and grid Local feedback loop Two components to local control U i Y i U i ζ t ζ t Local t t t Load i Prefilter Decision Control X i t X i t Requirements Minimal communication: Each load monitors its state and a regulation signal from the grid Aggregate must be controllable: Randomized policies required for finite-state loads 8 / 19
Control of Deferrable Loads: Goals and Architecture Control Goals and Architecture Prefilter and decision rules designed to respect needs of load and grid Local feedback loop Two components to local control U i Y i U i ζ t ζ t Local t t t Load i Prefilter Decision Control X i t X i t Requirements Minimal communication: Each load monitors its state and a regulation signal from the grid Aggregate must be controllable: Randomized policies required for finite-state loads Questions • How to analyze aggregate of similar loads? • Local control design? 8 / 19
One Million Pools Proportion of pools on µ t +1 = µ t P ζ t y t = � µ t , U� Disturbance to be rejected ζ t Gain y t desired Control @ Utility Aggregate of similar deferrable loads
Recommend
More recommend
