three-part auctions versus self-commitment in day-ahead electricity markets

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 – 08/03/14

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