How Retail Markets Can Optimize Electricity Distribution D. P. Chassin Pacific Northwest National Laboratory

Overview Introduction to real-time capacity markets Purpose, theory, basic examples, issues Examine Olypen market design/results Objectives, implementation, results, insights Preview AEP NE Columbus RTP-DA rate Rate design and valuation process

Purpose of Retail Real-Time Pricing Discover retail price of energy Time-varying value of (constrained) supply Incorporates time-varying value of demand response Addresses 3 major distribution issues: Load growth, distributed resource control, demand response

Markets as optimizers Auctions solve allocation problem Computationally efficient (parallelizable) Equilibrium assignment of buyers and sellers Interative (either explicit or implicit) Linear program discovers price Maximizes total benefit (primal) Minimize local costs (dual) Price solution is Pareto optimal See DP Bertsekas, Linear Network Optimization: Algorithms and Codes, MIT Press, 1991

Buyer surplus Seller surplus Retail Capacity Market Power [MW] Energy price [$/MWh] Cleared price Cleared load

Incorporate Day-Ahead Schedule Day-ahead Price is low Real-time price is high RTP customers actual response Retail price between DA and RT Load (MW) Price ($/MWh) Scheduled Load Maximum Load Unresponsive Load Cleared price

Some potential issues/FAQs Should utility be allowed to own/coordinate distributed resources (analog to generation/transmission conflict)? How to ensure costs are not double-embedded? How is seller surplus from feeder congestion used? How does utility fairly compensate consumers? Are there any subsidies built into the rate scheme? How is misbid/misresponse handled? What kind of security is really needed? How is rebound managed?

Rebound peaks occur with load control Fixed price Time-of-use price

Complex pricing strategies mitigate rebound Time-of-use group 1 Time-of-use group 4 Time-of-use group 2 Time-of-use group 5 Time-of-use group 3 Time-of-use group 6

At some point a capacity market is easier Real-time priceFixed price

11 GridWise Testbed Participants Bonneville Power AdministrationIBM PacificorpWhirlpool/Sears Kenmore Portland General ElectricClallum County Public Utility District City of Port Angeles Municipal Utility Pacific NW GridWise Testbed Projects

Virtual Distribution Utility Operation 12 Invensys Johnson Controls IBM $ MW Market Internet broadband communications

Olympic Peninsula RTP Market

Customer participation $35 ComfortEconomy

15 Economic Cooling Response k T max T min k Temperature Price T current P bid P avg P clear T set T desired User sets: T desired, comfort (based on occupancy calendar) These imply: T max, T min, k (price response parameters) Price is expressed as std. deviation from mean (over a short period, e.g., 24 hrs)

16 Managing Constraints DG required above feeder limit Market failed to cap demand for one 5-min. interval in 12 months of operation Price ($/MWh) Load (kW) Hour

17 Load Shifting RTP Customers Winter peak load shifted by pre-heating Resulting new peak load at 3 AM is non- coincident with system peak at 7 AM Illustrates key finding that a portfolio of contract types may be preferred – i.e., we dont want to just create a new peak

Mixing rates also manages uncertainty 18 It is impossible to choose a portfolio in this white region because no combination of contracts can yield such risk/return

Peak energy uncertainty 19

Gross margin volatility 20

21 Response Manages New Resources normal fluctuations in load Demand management to a capacity cap with real-time prices eliminated load fluctuations for 12 hours! Regulation: one or more fast-responding power plants continually throttle to match normal fluctuations in load Highest cost generation in markets (zero net energy sales, wear & tear, fuel consumption) Intermittency of wind output can exceed regulation capability and reduces cost effectiveness of wind Hour Load (kW)

AEP NE Columbus Project Many tariffs are planned Fixed Rate (standard) Interruptible Tariff (direct load control) 2-Tier Time of Use (2-TOU) 3-Tier Time of Use (3-TOU) Real Time Price Double Auction (RTP DA ) Each tariff enable a difference kind of response

RTP Rate Design Determine RTP-DA pricing method PJM DA Hourly LMP 5-minute RTP LMP Customer bids (Heating, AC, hotwater) Feeder constraints (physical limits) System limits not expressed in LMP Residential (exc. RR1), small commercial May include special terms (e.g., 1 yr harmless) May also include other resources TBD PUCO approval required

System requirements Advanced Metering Infrastructure (AMI) Home Energy Manager (HEM) Advanced equipment controls Heating systems (electric only) Air-conditioning system Hotwater heaters (electric only) Resource control (e.g., CES strategies) Smart Grid Dispatch engine

RTP-DA Valuation Values included Wholesale energy production Generation capacity Ancillary services (regulation and reserves) Transmission congestion Distribution congestion Values excluded Scarcity pricing Subtrans. constraints Environment constraints Wind/bundling/firming Reactive power Emergency/reliability Financial transmission rights Determine costs/benefits of RTP-DA

How Does RTP DA work?

Conclusions Retail capacity markets Energy price of Pareto-optimal allocation Olypen project a simple/full example Demonstrated basic concept Showed important of enabling technology AEP NE Columbus project Significant scaling up of implementation Stronger integration into wholesale operations

Questions/Comments Contact: David P. Chassin Pacific Northwest National Laboratory