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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.
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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 • 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: • Abid-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
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.”
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.
