Smart markets for a Smart Electricity Grid

Transcription

1 Smart markets for a Smart Electricity Grid Shmuel Oren The Earl J. Isaac Professor Department of Industrial Engineering and Operations Research University of California, Berkeley Presented to IEEE GREECE PES CHAPTER Athens, Greece, June 6, 2012 All Rights Reserved to Shmuel Oren

2 POWER INDUSTRY RESTRUCTURING Competitive FERC Regulated State Regulated Customers Demand Management Generation Transmission Distribution Micro-Grids Retail Providers Power Generation SC LSE ISO PX TO SC - Scheduling Coordinator PX - Power Exchange ISO - Indep. System Operator TO - Transmission Owner LSE - Load Serving Entity

3 Electricity Grid Interconnects

4 Regional Transmission Organizations in the US (Cover half the states and 70% of load )

6 Multiple Products and Markets Day-ahead energy market Real-time energy market Forward capacity market Fixed transmission rights (FTRs) auction market Regulation market Operating reserves market Spinning Non-spinning Replacement All Rights Reserved to Shmuel Oren

8 Unit Commitment Optimization - MIP (Solved for 24 hours in Day Ahead market) Decisions (financially binding): on/off, output level and compensated reserves for each unit in each of 24 hours + locational marginal energy prices and reserve prices for each node and hour Minimize (fuel cost + no-load cost + startup cost) s.t. Load balance constraint at each node Unit output constrain for each generator Unit ramping limits for each gen Unit min up time and min down time for each gen Transmission constraints (DC approximation with thermal proxy limits) Reserves margin requirements Contingencies (n-1) (Cost and constraints data provided as offers in day ahead auction)

10 Power Flow Optimization (every five minutes) and Locational Marginal Pricing (LMP) For Generators that are Running and Synchronized Decisions: Price of energy (LMP) at node i = Marginal cost of energy at the node Calculated as the dual variable to energy balance constraint for the node in a linearized Optimal Power Flow approximation (DCOPF) Minimize (Generator Fuel Cost) s.t. Energy balance (net supply = load at each node) Generator limits (including dynamic limits such as ramp rates) Transmission Constraints (AC model with voltage and thermal limits) Reserve requirements (Cost curves and generator limits data provided as offers in real time auction every 15 minutes)

11 LMP / Congestion Example West Limit = 26 MW East ~ ~ 80 MW 90 MW P W P E Key: Q Q 1 Prices/Supplies under 26 MW limit Prices/Supplies with no transmission limit Marginal value of transmission = $10/MWh (=$50 $40) Total congestion revenue = $10*26 = $260/hr Total redispatch cost = $130/hr Congestion cost to consumers: (40*106+50*64) (45*170) = = $210/hr All Rights Reserved to Shmuel Oren

13 Measuring Social Welfare Price ($/MWh) Inverse Demand Function (WTP) - P(q) Benefit Marginal cost curve Local Quantity (MWh) Production = q Production cost C(q) Local Consumption = q + import r

14 DC- Approx. Optimal Power Flow (DCOPF) Maximizes Net Benefit (or minimizes as bid cost) max r i, in in subject to: in r i l l, i i l in 0 losses =0 Pi ( i ) di Ci ( qi ) K D r K, l L r i Import/Export quantity Local production q i Inverse demand function Balancing constraint Flows constraints All Rights Reserved to Shmuel Oren

15 KKT conditions for the DCOPF problem LMP Nodal Markup P( q r ) p i N i i i i ( ) D i m m m, i ml jn r j losses =0 0 D r K 0 l L l l, j j l jn 0 K D r 0 l L l l l, j j jn All Rights Reserved to Shmuel Oren

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18 Over-generation, congestion and no storage capability can lead to negative prices 19

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22 Steep Supply Function and Inelastic Demand leads to High Price Volatility $100 $90 $80 $70 $60 Marginal Cost ($/MWh) Resource stack includes: Hydro units; Nuclear plants; Coal units; Natural gas units; Misc. Marginal Cost $50 $40 $30 $20 $10 Supply Curve Demand shocks $ Demand (MW) All Rights Reserved to Shmuel Oren

23 Energy Price ($/MWh) $400 On-peak Balancing Market Prices at ERCOT January 2002 thru July 2007 $350 $300 UBES 2/25/03 $ Balancing Energy (UBES) Price Spot Price Energy- ERCOT SC $250 $200 $150 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan-05 Mar-05 May-05 Jul-05 Sep-05 Nov-05 Jan-06 Mar-06 May-06 Jul-06 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul $100 $50 $0

25 Electricity Supply Chain Generators Customers (end users served at fixed regulated rate) Wholesale electricity market (spot market) LSE (load serving entity Similar exposure is faced by a trader with a fixed price load following obligation (such contracts were auctioned off in New Jersey and Montana to cover default service needs) 26

26 Demand (MW) Electricity Price ($/MWh) Price and Demand Correlation Electricity Demand and Price in California Load Price , July ~ 16, July Covering expected load with forward contracts will result in a contract deficit when prices are high and contract excess when prices are low resulting in a net revenue exposure due to load fluctuations 27

27 Volumetric Static Hedging Model Setup One-period model At time 0: construct a portfolio with payoff x(p) At time 1: hedged profit Y(p,q,x(p)) = (r-p)q+x(p) Spot market p LSE r Load (q) x(p) Objective Portfolio for a delivery at time 1 Find a zero cost portfolio with exotic payoff which maximizes expected utility of hedged profit under no credit restrictions. 28

28 Mathematical Formulation Objective function x( p) max E U[( r max x( p) p) q U[( r Utility function over profit x( p)] p) q Joint distribution of p and q x( p)] f ( p, q) dqdp Constraint: zero-cost constraint 1 Q E [ x( p)] 0 B Q: risk-neutral probability measure B : price of a bond paying $1 at time 1! A contract is priced as an expected discounted payoff under risk-neutral measure 29

29 Mathematical Formulation Objective function x( p) max E U[( r max x( p) p) q U[( r Utility function over profit x( p)] p) q Joint distribution of p and q x( p)] f ( p, q) dqdp Constraint: zero-cost constraint 1 Q E [ x( p)] 0 B Q: risk-neutral probability measure B : price of a bond paying $1 at time 1! A contract is priced as an expected discounted payoff under risk-neutral measure 30

30 Impact of Hedging on VaR 31

31 Financial Transmission Rights (FTR) Market Under LMP Participants in wholesale spot market or bilateral contracts paying congestion charges are exposed to the LMP difference between the injection and withdrawal nodes FTRs were designed to hedge transmission service customers against congestion charge ( nodal price differences) risks FTRs are defined as LMP swaps between each pair of nodes and are settled based on the realized nodal price difference (to offset congestion charges) FTRs are available as options or as two sided contracts which may become a liability when the path is opposite to the direction of the actually congested flow Auction conducted monthly for new FTRs and for trading of outstanding FTRs All Rights Reserved to Shmuel Oren

32 FTR Auction (ERCOT) Initial design had 72 time slices (24 monthly blocks divided into 3 time blocks Bids (and offers) can cover any subset of the 72 products OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE Year One OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE 72 Periods Optimized Simultaneously = 1300 Hours Year Two Clearing mechanism maximizes auction revenue subject to simultaneous feasibility test (SFT) in every time slice SFT ensures that physical grid could support physical exercise of all outstanding FTRs OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE OP WD WE All Rights Reserved to Shmuel Oren

33 Actual ERCOT Implementation (live 12/1/2010) Annual auction comprised of six separate optimizations Calendar Period TOU Blocks Period Approach OP WD WE Decouple TOU and Decouple Years / Monthly 12 Year 1 12 Year 2 12 Year 1 12 Year 2 12 Year 1 1 WD 1 WD 1 WE 1 WE 1 OP C Monthly granularity 12 Year 2 1 OP 12 WD Year One Year Two WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD 12 Periods Optimized Simultaneously Year One 12 Periods Optimized Simultaneously Year Two WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE WE 12 Periods Optimized Simultaneously 12 Periods Optimized Simultaneously OP Year One Year Two OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP OP 12 Periods Optimized Simultaneously 12 Periods Optimized Simultaneously

34 Simultaneous Feasibility Guarantees Revenue Adequacy (congestion revenues cover FTR settlements ) 100 G1 G G3 2/3 3 1/3 1/3 L3 2 G1 G2 300 G1 G ( G1 G2) MW 300 MW FTR A B 100 MW O 20 MW X Changing capacity of line 2 to 3 Z 2 3 E Y MW 1 3 C D 400 MW FTR1 3 Two sided FTRs must stay within the outer nomogram One sided FTRs (options) must stay within the inner nomogram because we cannot rely on counterflows to alleviate congestion. 35

35 Renewables Making Headlines

39 Conventional Solution Source: CAISO

40 The DR Alternative to Expanding Flexible Thermal Generation

42 Emerging Issues Renewables and distributed resources integration Mitigation of supply and demand uncertainty and resource variability Connectivity (planning and financing of transmission) Stochastic optimization for planning and dispatch Smart Grid Integration: Enable customer choice, demand response, load management and energy efficiency with smart Metering Improve grid visibility and control with SCADA and Phasor Measurement Units (PMUs) Dynamic Co-optimization of grid topology Market implications of smart grid integration Business models and dispatch algorithms for demand response aggregation Impact of mass PHVs EV deployment on the grid Utilization and impact of mass storage Impact of carbon and RPS regulation on grid planning, operation and energy markets

43 Stochastic Unit Commitment N

44 Scenario Selection

45 Wind Modeling and Data Sources

46 Day Types

47 WECC Model

48 Model Summary

49 Topology Control Example Original optimal cost: $20,000 (A=180MW,B=30MW,C=40MW) at {2} Original feasible set: {0,1,2,3} Open Line A-B, optimal cost: $15,000 (A=200MW, B=50MW) at {8} Feasible set with Line A-B open {0, 4, 5, 6} Feasible set with optimal transmission switching: {0, 1, 7, 5, 6} (non-convex) 50

50 Optimal Transmission Switching DCOPF

51 Results DCOPF IEEE 118 IEEE 118 opened lines for J=10 Note: this diagram has additional gens than our model 52

52 Results DCOPF IEEE 118 Transmission switching solution saves 25% of total generation cost J 53

53 More Test Results IEEE 118 Bus Model: 54 DCOPF transmission switching solution with no contingencies saves 25% of total generation cost (10 lines switched off) Up to 16% savings with N-1 DCOPF transmission switching (for feasible solutions) IEEE 73 (RTS 96) Bus Model Up to 8% savings with N-1 DCOPF transmission switching (for feasible solutions) Savings of 3.7% in Unit Commitment for 24 hours with N-1 security constraints ISO-NE 5000 Bus Model: DCOPF transmission switching with approximate solution produced 5-13% savings Current ARPA-E ($5M) Project on Topology Control Test of concept on TVA 7000 bus network

54 Questions?