A Dynamic Market Mechanism for Integration of Renewables and Demand Response

Transcription

1 1 A Dynamic Market Mechanism for Integration of Renewables and Demand Response Anuradha Annaswamy aanna@mit.edu Panel on Market-based Approaches for Demand Response IEEE Power and Energy Society General Meeting 2015 Denver, CO 07/28/2015

2 Outline Demand Response (DR) Importance, potential, and current practice An emerging taxonomy based on the constraints of DR-devices Buckets, Batteries, Bakeries DR real-time market integration role in a Dynamic Market Mechanism Case study (IEEE-118 bus, Polish grid)

3 Outline Demand Response (DR) Importance, potential, and current practice An emerging taxonomy based on the constraints of DR-devices Buckets, Batteries, Bakeries DR real-time market integration role in a Dynamic Market Mechanism Case study (IEEE-118 bus, Polish grid)

4 Importance of Demand Response Usual practice: Generation = Demand GG + DD = 0 Practice with renewables: GG + GG rr + DD + DD dd = 0 Renewables Demand Response Source: Vision for Smart Grid Control: 2030 and Beyond, (Eds. M. Amin, A.M. Annaswamy, C. DeMarco, and T. Samad), IEEE Standards Publication, June 2013.

5 Importance of Demand Response Demand response refers to changes in electric usage by demand-side resources from their normal consumption patterns in response to: Changes in the price of electricity over time, or Incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized Demand response benefits: Peak reduction Lower electricity prices at the wholesale market Higher system reliability Reduced need for reserves Figure: EIA

6 DR Program Types and Potentials Incentive-based Time-based Source: Federal Energy Regulatory Commission Assessment of Demand Response and Advanced Metering. Staff Report, 2012.

7 ISO-NE: DR in Today s Wholesale Markets* On call response if needed settled through Forward Capacity Market (FCM) Not allowed to bid in Day-Ahead Market (DAM) or Real-Time Market (RTM) PJM: Allowed to bid in DAM and Regulation Market (REGM), not allowed in RTM NY-ISO: On call response if needed settled through FCM Allowed to bid in FRM, DAM, REGM, not allowed in RTM CAISO: Allowed to bid in FRM, DAM, REGM * Currently in a state of flux due to halt of FERC order 745 Figure source: Monitoring Analytics, State of the Market Report for PJM, August 2014

8 Wholesale Markets and the Role of DR Cost curves ($/MW) ConCos Market Players GenCos minimize cost ISO ensure grid feasibility compute optimal price price ($) power dispatch (MW) and price ($) ISO-NE CAISO CAISO NY-ISO NY-ISO PJM PJM Forward Capacity Markets Forward Reserve Markets Day Ahead Markets Real Time Markets Regulation Markets

9 Limitations of Today s Implementations In RTM: Consumers are price-takers, not price-setters ISOs are not relying on response of demand to real-time prices Much needed are mechanisms to ensure response to real-time price signals

10 Outline Demand Response (DR) Importance, potential, and current practice A taxonomy of DR-devices Buckets, Batteries, Bakeries DR real-time market integration role in a Dynamic Market Mechanism Case study (IEEE-118 bus, Polish grid)

11 Demand Response: Type of Consumers Demand characterization 26% 3% 34% 37% Residential level Single household Aggregated level Residential Commercial Industrial PHEVs Industrial Food industry refrigeration 16% Chemical sector metal and paper Data centers 1.3% of total energy Commercial Source: EIA

12 Demand Response Models vv(λ, μμ, θθ) Consumer Decision Making rr(vv) Device Constraints Adjustable Demand Low rr(vv) Device characteristics: Curtailable Bucket Risk aversion High Interruptible Battery Value function vv Value function vv(λλ, μμ, θθ): Economic component λλ Comfort component μμ Environment component θθ Deferrable Bakery

13 Dynamic Characteristics of Loads Defer in inherent magnitude, run-time and integral constraints Bucket Bakery Home battery system HVAC Refrigerator Chemical process Battery EV battery Water heater Swimming pool filtering Washer

14 A New Demand Response Taxonomy Buckets P Dc Most flexible type of demand (can consume or supply power) Example: energy storage units, HVAC Batteries P Dt Have a deadline for achieving a fully charged state Example: Plug-in Hybrid Electric Vehicle Bakeries P Dk Energy must be consumed in an uninterrupted stretch Example: industrial production cycles The BBB Configuration Source: M. K. Petersen, K. Edlund, L. H. Hansen, J. Bendtsen, and J. Stoustrup, A Taxonomy for Modeling Flexibility and a Computationally Efficient Algorithm for Dispatch in Smart Grids, 2013 American Control Conference, 2013.

15 Outline Demand Response (DR) Importance, potential, and current practice An emerging taxonomy based on the constraints of DR-devices Buckets, Batteries, Bakeries DR real-time market integration role in a Dynamic Market Mechanism Case study (IEEE-118 bus, Polish grid)

16 Our Approach A Dynamic Market Mechanism (DMM) for RTM consumers are price-setters, not price-takers The DMM is an alternative to the current wholesale electricity market clearing process. Rather than submitting one-time bids, generators and consumers repeatedly exchange information with each other and with the ISO to negotiate generation, consumption, and prices. Allows direction integration of DR (ex. BBB) into the DMM

17 OPF Formulation (including BBB) Note: The index k corresponds with the market clearing instance Nodal Power Balance Line Capacity The index K corresponds with the negotiation iterations Generation/Deman d Power Limits Generation Rates of Change Demand Energy Limits Source: J. Knudsen, J. Hansen, and A.M. Annaswamy A Scaleable Dynamic Market Mechanism for Integration of Renewables and Demand-Side Management, IEEE Trans. Control Systems Technology

18 DMM structure Iterative negotiations over a wide area grid Market Players Market Players GenCos GenCos ConCos ConCos minimize cost price ($) Suggested Cost curves bids ($/MW) (MW) maximize minimize utility cost power dispatch (MW) and price ($) Suggested price ($) Challenges addressed: Computation time ISO ensure grid feasibility compute optimal price Most information must be kept private Stability x + = x + x k 1 k k k 1 k k λ + = λ + λ xx: states of players and ISO λλ: Lagrange multiplier (LMP) xx kk = kk PP GGGG kk PP GGGG kk PP DDrr δδ kk Conventional generation Renewable generation Demand response Voltage angles

19 20 Economic Dispatch Today Collect cost curves Find optimal dispatch Communicate set-points Generation ISO Flexible demand Periodic with a regular interval. Single iteration process. Centralized computation. Inflexible load Automatic generation control Generation set-points Economic dispatch interval Time

20 Our Solution: Dynamic Market Mechanism (DMM) 21 Negotiate and converge to an optimal solution Most recent information is included. Individual constraints remain private. Start negotiations Inflexible load Sufficiently long period for convergence Automatic generation control Implement set-points Generation set-points Benefits when addressing: o Fuel uncertainty Wind Solar Natural gas o Change in operating conditions of components Saturation limits Protection tripping Emergency conditions o Dynamic price response Lower real-time prices before dispatching Close-loop price control Economic dispatch interval Time

21 22 DMM and shorter dispatch interval Negotiate and converge to an optimal solution Implement dispatch on shorter intervals. Start negotiations Sufficiently long period for convergence Implement set-points Opportunities for addressing: o Significant and unpredicted penetration of renewables o Non-zero mean volatility of renewable generation o High regulation requirements in presence of renewables Inflexible load Automatic generation control Generation set-points Economic dispatch interval Time

22 23 Integrated DMM (economic dispatch + AGC) Conventional architecture Proposed approach Energy Market Regulation Market Energy Market Regulation Market Automatic Generation Control Automatic Generation Control Assumption of magnitude and time-scale separation between OPF and AGC. Large penetration of intermittent energy represents a challenge. Aggregated feedback from AGC Simultaneous decisions at both markets.

23 24 Time-scales Introduced by DMM DMM Negotiation s AGC Updates 4 DMM Market Clearing OPF Market Clearing Existing timescales New time-scales

24 25 Feedback from AGC to DMM tt mm 2 tt mm 1 tt mm tt mm+1 Measurements Negotiations Operation Frequency measurements averaged over tt mm 2, tt mm 1 are used in negotiations during tt mm 1, tt mm, which take effect during the operating period tt mm, tt mm+1.

25 DMM Iterates Final Form Approximated Hessian Increases rate of convergence Preserves privacy Distributed gradient updates A single cost/utility bid per iteration Preserves privacy State and price update equations xx kk+1 = xx kk αα HH 1 xx kk λλ kk+1 = λλ kk+1 αα cc h xx kk + NN λλ kk+1 Modified power balance Integrates real-time market and AGC

26 Outline Demand Response (DR) Importance, potential, and current practice An emerging taxonomy based on the constraints of DR-devices Buckets, Batteries, Bakeries DR real-time market integration role in a Dynamic Market Mechanism Case study (IEEE-118 bus, Polish grid)

27 Modified IEEE 118 Bus Test Case Bus consists of: 45 conventional generators 9 renewable generators (30% penetration) 7 consumers (10% penetration) 186 transmission lines

28 Implications of Our Architecture Conven Gen. Demand Response Units Renewables TRANSACTIVE ARCHITECTURE

29 DMM Market Clearings (50 clearings) s Generation [MW] Flexible Demand [MW] Conventional Generation mm PP GGGG Time [s] Renewable Generation mm PP GGGG Flexible Demand mm PP DDDD

30 Negotiations over a single 30 second period Flexible Consumption [MW] Conventional Generation [MW] Time [s] Time [s] xx kk = kk PP GGGG kk PP GGGG kk PP DDrr δδ kk Conventional generation Renewable generation Demand response Voltage angles

31 Actual Generation and Demand (AGC time-scale) 2600 Generation [MW] Flexible Demand [MW] Conventional Generation KK PP GGGG Time [s] Renewable Generation KK PP GGGG Flexible Demand KK PP DDDD

32 Impact on Area Control Error Peaks less severe using DMM than OPF Adding feedback shifts ACE closer to zero

33 Summary of DMM Benefits 1. Allows flexible consumers to act as price-setters at the real-time market (and not only to respond to price) 2. Admits the most recent weather predictions in market clearing (every 30 seconds) 3. Enables feedback from AGC layer into the market layer, reducing regulation requirements 4. Preserves privacy of market players sensitive information e.g. cost curves, generation/consumption bounds Is this scaleable?

34 Polish 3120 Bus Test System The system consists of: 3120 buses 3693 transmission lines with line capacities of 250 MW 505 generators with linear cost curves and capacities in the range 10MW- 150MW Extension to renewable energy resources and demand response is straight forward. Data source: Matpower Figure source: www-pub.iaea.org

35 Single DMM Clearing on Polish 3120 System Line 59 congestion Lines 31,32 congestion Transmission line flows Power generation tt 2 tt 1 =30 s tt 30ms per iteration 1 tt 2 Locational marginal prices Generation and price increase at bus 3010 once three transmission lines reach their limits.

36 Number of iterations to convergence Matpower test cases Number of iterations does not increase with decision variables The convergence time depends on: Step size Congestion Cost curves Demonstrates the scalability of the DMM

37 Key Open Questions Closer to real-time DR integration: Reliability and security Communication infrastructure vs. scalability Direct load control vs. transactive load control Sensitivity of customers to the price