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NSFDirectorate for Mathematical and Physical Sciences

AMPS: Mathematical Foundations of Market Operations with Renewable Bidders

Completed
Institution
Rensselaer Polytechnic Institute, NY
Principal investigator
John E Mitchell
Funding
$300K
Duration
2023–2026
Open portfolio

Moonbase score

Still being scored

Abstract

This NSF AMPS project will develop mathematical foundations behind markets where renewable generators are allowed to bid their risk-adjusted cost curves into the market. While investments into renewable energy assets have been growing in recent years, renewable generators today typically act as "price takers" and are paid at market clearing prices that are determined from the cost curves submitted by the conventional generators. This market structure implies that even as renewable energy penetration increases over the next several decades, the market prices are going to be determined by a few conventional generators which can provide nearly risk-free energy commitments. To address this issue, in this project the research team will analyze the efficiency of a market where renewable energy suppliers can participate in electricity markets (just like conventional generators) by supplying cost curves associated with guaranteeing a certain amount of renewable supply. Development of risk-adjusted cost curves and their impact on the market operations will be investigated by a cross-disciplinary team of mathematical optimization and power systems researchers. Investigation of a market where renewables bid presents a paradigm shift from existing studies, and fundamental questions on market efficiency and risk-vs-cost trade-off remain to be explored in this context. This will be accomplished through three synergistic thrusts: 1) Analyzing the efficiency of markets under renewable bidders; 2) Deriving the risk-adjusted renewable supply cost curves; and 3) Analyzing the incentives for truthful bidding and convergence to equilibrium. Investigating these three research thrusts involves finding solutions to complex stochastic optimization and equilibrium problems that the research team will undertake in this project. The solutions with be evaluated in NY and TX market data using the EGRET power market simulation tool. The project will realize its broader impacts through cross-disciplinary training of graduate students, involvement of undergraduates in research, K-12 outreach, and collaboration with independent system operators and renewable generators to maximize the practical impact of the project. If successful, the project could lead to introduction of new market mechanisms that would improve the flexibility of renewable generators in managing their supply and associated storage, making renewable based power more economically viable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Research team

  • John E MitchellPI
  • Joe H ChowCo-PI
  • Koushik KarCo-PI

LOADING MOONBASE SCORE

Directorate for Mathematical and Physical SciencesClean Energy TechnologyAMPS-Algorithms for Modern Powgeneratorscurvesmarketmathematical foundationsbidampsmarketsrenewable

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