New Opportunities for Control and Optimization in the Future Power Industry Environment Chen-Ching Liu University of Washington

Outline New Electricity Market Environment Ancillary Service Selection Optimal Bidding Decisions Flexible Contract Pricing Risk Management in a Competitive Market Defense Plans Control of Available Transfer Capabilities (ATC)

Electricity Market Environment Generation Companies Marketers Other Consumers Distribution Companies ISO / RTO (managing the use of the grid, coordinating the market) ISO / RTO (managing the use of the grid, coordinating the market) Large Consumers Spot Market BilateralContracts

Ancillary Service (AS) Selection Objective: Min Cost =  bid-price  quantity) Controls: Amount of AS cleared per bus, SC Constraints: –System reserve and regulation requirements –Max ramp rates –Max and min bid block amounts –Unit capacity limits ProblemHow to make least-cost decisions for AS selection given a set of AS bids? Problem: How to make least-cost decisions for AS selection given a set of AS bids?

Optimization Model Subject to: Unit capacity Bid Block Limits Power Flow Security System Reliability Ramp Rates reserve regulation reactive power congestion

Bidding into a Bilateral Market Objective: Identify suppliers’ Nash Equilibrium (NE) bidding strategy in a bilateral market. Study the characteristics of NE bidding strategies. Assumptions: –m-generator-n-load –Each generator can supply at most one load –Generators submit bids to each load –Each load accepts the cheapest bid generator at its bid price ProblemHow should a generator set the bid price for each load? Problem: How should a generator set the bid price for each load?

Optimization to Find NE Prices Calculation of NE bid prices for G1: G1’s profit margin: G1’s bid to L1: Lowest cost of a combination without g:

Bidding into a Spot Market Objective: Formulate electricity spot market from supplier’s viewpoint. Identify supplier’s optimal bidding decisions as market conditions change. Problem:Which bidding option is optimal for the market status? Problem: Which bidding option is optimal for the market status? Proprietary databaseMarket statistics Supplier’s information set Bidding decision-making Spot price Load demand Load forecast Cost curves Resource constraints Information about competitors Bid options $20/MWh for peak hours in the next day $18/MWh for an entire day...

Optimal Bidding Decisions Markov Decision Process (MDP) to identify optimal bidding strategy over a planning horizon At state i: Competitors’ model Decision option a: Competitor k’s possible bids: prob.= 0.25 prob.= P ij a = probability that market moves to state j from state i r ij a = profit when market moves to state j from state i  Value iteration:

Flexible Contract Pricing Objective: Determine the price of a flexible contract based on stochastic market model. Contract parameters: –Contract volume, V (MWh) –Starting-time, T 1 and ending-time T 2 –Maximum power that can be drawn in the t’th time-period: C t –There is a minimum time between time of scheduling decision and time of actual delivery of energy. ProblemHow much is this contract worth? Problem: How much is this contract worth? When and how much to deliver? When and how much to deliver?

Optimization to Find Flexible Contract Pricing s.t. Buyer 1 i N 1 i N States at stage t States at stage t+1 P t =$30/MWh V’=800MWh No-arbitrage pricing: Since a buyer can resell into the spot market, if the buyer follows the optimal schedule, (s)he expects to make $800 from the spot market. Flexible contract price = $800 Optimization : Max. exp. resale revenue Schedule decision in period t

Risk Management in a Competitive Market The portfolio together with the operations in the spot-markets will give a profit at the end of a time-horizon. Ahead-of-time, the profit is uncertain, due to fluctuating prices and demand. Decision-makers are risk- averse. Probability density of the profit E STD ProblemWhat portfolio should a decision-maker choose? Problem: What portfolio should a decision-maker choose?

Expected value Standard deviation Physical production Hedging by financial instruments Efficient Frontier Hedging through Optimization

Hedging Using Production Consider profile of fixed sales at spot market prices. Can the profile of sales be chosen to minimize the variance? Mapping out efficient frontier:

Defense Plans in the SPID System Failure Analysis Self-healing Strategies Vulnerability Assessment Information And Sensing Real-Time Security Robustness Dependability Power Infrastructure Satellite, Internet Communication system monitoring and control Hidden Failure Monitoring Adaptive load shedding, generation rejection, islanding, protection Fast and on-line power & comm. System assessment Problem: Design self-healing strategies and adaptive reconfiguration schemes to minimize the impact of power system vulnerabilityProblem: Design self-healing strategies and adaptive reconfiguration schemes to minimize the impact of power system vulnerability

Monitoring and Control with a Multi- Agent System REACTIVE LAYER COORDINATION LAYER DELIBERATIVE LAYER Knowledge/Decision exchange Protection Agents Generation Agents Fault Isolation Agents Frequency Stability Agents Model Update Agents Command Interpretation Agents Planning Agent Restoration Agents Hidden Failure Monitoring Agents Reconfiguration Agents Vulnerability Assessment Agents Power System Controls Inhibition Signal Controls Plans/Decisions Event Identification Agents Triggering Events Event/Alarm Filtering Agents Events/Alarms Inputs Update Model Check Consistency Comm. Agent

Control of Available Transfer Capabilities (ATC) ATC definition: Total Transfer Capability (TTC) - Transmission Reliability Margin (TRM) - Scheduled Capability FACTS expands TTC and ATC. TRM Scheduled ATC W/O FACTSW/ FACTS Problem: Increase power transfer capability of transmission systems using FACTS controlProblem: Increase power transfer capability of transmission systems using FACTS control

Dynamic Security Based FACTS Control ATC calculation procedure incorporates thermal, generator and voltage security constraints. EPRI ETMSP simulates system dynamics. Multiple and simultaneous transfers need to be included. P V

Multi-Agent Coordinated Control System System Voltage Control Agent including OPF Algorithm System Voltage Control Agent including OPF Algorithm Bus ‘I’ Voltage Control Agent Bus ‘I’ Voltage Control Agent Bus ‘J’ Voltage Control Agent Bus ‘J’ Voltage Control Agent