New Opportunities for Control and New Opportunities for Control and Optimization in the Future Power Optimization in the Future Power

Industry EnvironmentIndustry Environment

Chen-Ching Liu

University of Washington

OutlineOutline

• 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 Electricity Market EnvironmentEnvironment

GenerationCompanies

MarketersOther Consumers

DistributionCompanies

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 MarketSpot Market

BilateralBilateralContractsContracts

Ancillary Service (AS) Selection Ancillary Service (AS) Selection

• Objective: Min Cost = bid-pricequantity)• 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

ProblemProblem: How to make least-cost decisions for How to make least-cost decisions for AS selection gAS selection given a set of AS bidsiven a set of AS bids??

Optimization ModelOptimization Model

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System Reliability

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Ramp Rates

reserve regulation

reactive power

congestion

Bidding into a Bilateral MarketBidding 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

ProblemProblem: How should a generator set the bid How should a generator set the bid price for each load?price for each load?

Optimization to Find NE PricesOptimization to Find NE Prices

• Calculation of NE bid prices for G1:

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Bidding into a Spot MarketBidding into a Spot Market• Objective: Formulate electricity spot market from supplier’s viewpoint.

Identify supplier’s optimal bidding decisions as market conditions change.

Problem:Problem: Which bidding option is optimal for the market status?Which bidding option is optimal for the market status?

Proprietary databaseMarket statisticsSupplier’s information setSupplier’s information set

Bidding decision-makingBidding decision-making

• Spot price • Load demand • Load forecast

• Cost curves• Resource constraints• Information about competitors

Bid options• 50MWh @ $20/MWh for peak hours in the next day• 100MWh @ $18/MWh for an entire day• ...

Optimal Bidding DecisionsOptimal Bidding Decisions

• Markov Decision Process (MDP) to identify optimal bidding strategy over a planning horizon

At state i:Competitors’

model

Decision option a: 50MW@$26/MWh

Competitor k’s possible bids: 50MW@$23/MWh, prob.= 0.25 70MW@30$/MWh, prob.= 0.3 ...

Pija = probability that market moves to state j from state i

rija = profit when market moves to state j from state i

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Value iteration:Value iteration:

Flexible Contract PricingFlexible Contract Pricing

• Objective: Determine the price of a flexible contract based on stochastic market model.

• Contract parameters:– Contract volume, V (MWh)– Starting-time, T1 and ending-time T2

– Maximum power that can be drawn in the t’th time-period: Ct

– There is a minimum time between time of scheduling decision and time of actual delivery of energy.

ProblemProblem: How much is this contract worth?How much is this contract worth? When and how much to deliver?When and how much to deliver?

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Optimization to Find Flexible Optimization to Find Flexible Contract PricingContract Pricing

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States at stage t

States at stage t+1

Pt=$30/MWhV’=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

:tx Schedule decision in period t

Risk Management in a Risk Management in a Competitive MarketCompetitive 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

ProblemProblem: What What portfolio should a decision-maker portfolio should a decision-maker choose? choose?

Expected value

Standarddeviation

Physicalproduction

Hedging byfinancial instruments

Efficient Frontier

Hedging through OptimizationHedging through Optimization

Hedging Using ProductionHedging 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:

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Defense Plans in the SPID SystemDefense Plans in the SPID System

FailureAnalysis

Self-healingStrategies

VulnerabilityAssessment

InformationAnd

SensingReal-Time

Secu

rity Robustn

es

s

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

• ProblemProblem: Design self-healing strategies and adaptive : Design self-healing strategies and adaptive reconfiguration schemes to minimize the impact of reconfiguration schemes to minimize the impact of power system vulnerabilitypower system vulnerability

Monitoring and Control with a Multi-Monitoring and Control with a Multi-Agent SystemAgent System

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Knowledge/Decision exchange

Protection Agents

GenerationAgents

Fault Isolation Agents

FrequencyStabilityAgents

ModelUpdate Agents

CommandInterpretation

Agents

Planning Agent

Restoration Agents

HiddenFailure

Monitoring Agents Reconfiguration Agents

VulnerabilityAssessment

Agents

Power System

Controls

Inhibition Signal

Controls

Plans/Decisions

EventIdentification

Agents

Triggering Events

Event/AlarmFiltering Agents

Events/Alarms

Inputs

Update Model CheckConsistency

Comm.Agent

Control of Available Transfer Control of Available Transfer Capabilities (ATC) Capabilities (ATC)

• ATC definition:

Total Transfer Capability (TTC) - Transmission Reliability Margin (TRM) - Scheduled Capability

• FACTS expands TTC and ATC.

TRM TRM

Scheduled Scheduled

ATC ATC

W/O FACTS W/ FACTS

• ProblemProblem: Increase power transfer capability of : Increase power transfer capability of transmission systemstransmission systems using FACTS control using FACTS control

Dynamic Security Based Dynamic Security Based FACTS ControlFACTS 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 Multi-Agent Coordinated Control SystemControl System

System Voltage ControlAgent including OPF

Algorithm

System Voltage ControlAgent including OPF

Algorithm

Bus ‘I’ VoltageControl Agent

Bus ‘I’ VoltageControl Agent

Bus ‘J’ VoltageControl Agent

Bus ‘J’ VoltageControl Agent