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Three-part auctions versus self-commitment in day-ahead electricity markets. Ramteen Sishansi, Shmuel Oren, Richard O’Neill. Overview. Power Systems in the US today operate within either 1) an organized central market (RTO/ISO), or 2) a bilateral market. - PowerPoint PPT Presentation
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Three-part auctions versus self-commitment in day-ahead electricity markets
Ramteen Sishansi, Shmuel Oren, Richard O’Neill
Overview
• Power Systems in the US today operate within either 1) an organized central market (RTO/ISO), or 2) a bilateral market.
• In the former, the RTO/ISO is the Control Area/Balancing Authority which has responsibility for reliability across a broad region and provides a market for energy, capacity and ancillary services.
• In the latter, each system is its on Control Area and is essentially responsible for its own reliability.
Overview
Overview
• RTOs/ISOs were sold as new paradigm that would bring better reliability, more efficiency, and more fairness to the markets (price transparency, liquidity, ease and equality of access, etc.)
• Based on literature review, the results have been mixed.
• One significant challenge of these new markets is Optimization.
Overview
• Power systems are complex and difficult to model/simulate.• Generators’ cost structures include energy, startup and no-
load cost components. They are constrained in the time it takes them to startup or shutdown and the rate at which they can adjust their output.
• Thermal units typically have non-zero minimum generating levels. Other types of generating units (e.g., Combined Cycle and Hydro) tend to have complex constraints restricting their operation.
• The transmission grid is subject to constraints.
Overview
• Generators must be able to adjust their outputs in real-time to ensure constant load balance.
• Other random contingencies such as transmission equipment failures, forced generator outages or alternative energy output fluctuations also require generators to adjust their outputs within a short period of time.
• Efficient and reliable operation of the system requires having a sufficient number of generators online and available to react to variations in load and other contingencies at least cost. (Must cover the load + reserves)
Overview
• A centralized market can, in theory, find the most efficient dispatch of the generators given the load and transmission topography, but the market designs suffer equity and incentive problems.
• Decentralized designs can overcome some of these issues but will suffer efficiency losses due to the loss of coordination among resources.
• These design issues arise particularly in the context of determining the proper role for the system operator (SO) in making day-ahead unit commitment decisions.
Overview
• This paper compares the economic consequences of: – A bid-based security-constrained centralized unit
commitment paradigm based on three-part offers, which is the prevalent day-ahead market-clearing mechanism in restructured electricity markets in the United States• Lagrangian Relaxation (LR)• Mixed Integer Programming (MIP)
– An energy-only auction with self-commitment (such as in Australia)
The Centralized Unit Commitment Problem
• Traditionally used the LR algorithm– Faster, but less accurate (and not fast enough at times)
• More recently systems are moving to Mixed Integer Programs (MIP) using branch and bound (B&B) algorithms– Slower, but more accurate
Lagrangian Relaxation Algorithm
Lagrangian Relaxation Algorithm
The Centralized Unit Commitment Problem
The Centralized Unit Commitment Problem
• Solution methods employed do not always/generally find the optimal solution– Close, but not exact– “PJM allows its MIP optimizer to run within a certain period of time or
until the optimality gap is below some maximal threshold, and uses whatever intermediate solution the solver has found.”
– “Inherently approximate”– How fair to the market participants?
• Limited to 24-hr look
The Centralized Unit Commitment Problem
• “Near-optimal solutions may result in large deviations in surplus accrued to individual generators and in energy prices.”
• “While such deviations are inconsequential for regulated utilities, they have a significant economic implications in a deregulated market with dispersed ownership of generation units.”
The Centralized Unit Commitment Problem
The Centralized Unit Commitment Problem
Self Commit + Energy-Only Auction
Self Commit + Energy-Only Auction
Self Commit + Energy-Only AuctionLoad = 1,000 MW
Capacity Startup Cost Energy Cost Output(MW) ($) ($/MWh) (MW) ($) ($/MWh) ($) ($/MWh)
Coal 2,000 75,000 10 1,000 85,000 85.00 - - Gas 200 0 75 - - - - -
1,000 85,000 85.00 - -
Cost Profit
Self Commit + Energy-Only AuctionLoad = 1,000 MW
Capacity Startup Cost Energy Cost Output(MW) ($) ($/MWh) (MW) ($) ($/MWh) ($) ($/MWh)
Coal 2,000 75,000 10 800 83,000 103.75 (4,600) (5.75) Gas 200 0 75 200 15,000 75.00 4,600 23.00
1,000 98,000 98.00 - -
Cost Profit
Self Commit + Energy-Only AuctionLoad = 1,000 MW
Capacity Startup Cost Energy Cost Output(MW) ($) ($/MWh) (MW) ($) ($/MWh) ($) ($/MWh)
Coal 2,000 75,000 10 800 83,000 103.75 - - Gas 200 0 75 200 15,000 75.00 5,750 28.75
1,000 103,750 103.75 5,750 -
Cost Profit
Self Commit + Energy-Only Auction
Self Commit + Energy-Only Auction
Observations/Conclusions
• RTOs are using optimization tools that do not (sometimes/frequently/ever) find the true optimal solution.
• While “close enough” may be OK for one larger, integrated system, it can create significant problems for isolated assets.
• Self-Commit Schemes help the individual asset owners, but are less optimal.
SPP vs ERCOT LMPs – 07/28/14
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SPP vs ERCOT LMPs – 07/29/14
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SPP vs ERCOT LMPs – 07/30/14
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SPP vs ERCOT LMPs – 07/31/14
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SPP vs ERCOT LMPs – 08/01/14
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-60.00
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SPP vs ERCOT LMPs – 08/02/14
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SPP vs ERCOT LMPs – 08/03/14
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SPP vs ERCOT LMPs – 08/04/14
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