Effects of Risk Aversion on Market Outcomes: A Stochastic Two-Stage Equilibrium Model

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Abstract

This paper evaluates how different risk preferences of electricity producers alter the market-clearing outcomes. Toward this goal, we propose a stochastic equilibrium model for electricity markets with two settlements, i.e., day-ahead and balancing, in which a number of conventional and stochastic renewable (e.g., wind power) producers compete. We assume that all producers are price-taking and can be risk-averse, while loads are inelastic to price. Renewable power production is the only source of uncertainty considered. The risk of profit variability of each producer is incorporated into the model using the conditional value-at-risk (CVaR) metric. The proposed equilibrium model consists of several risk-constrained profit maximization problems (one per producer), several curtailment cost minimization problems (one per load), and power balance constraints. Each optimization problem is then replaced by its optimality conditions, resulting in a mixed complementarity problem. Numerical results from a case study based on the IEEE one-area reliability test system are derived and discussed.
Original languageEnglish
Title of host publicationProceedings of International Conference on Probabilistic Methods Applied to Power Systems 2016
Number of pages6
PublisherIEEE
Publication date2016
ISBN (Print)9781509019700
DOIs
Publication statusPublished - 2016
EventInternational Conference on Probabilistic Methods Applied to Power Systems - Beijing, China
Duration: 16 Oct 201620 Oct 2016

Conference

ConferenceInternational Conference on Probabilistic Methods Applied to Power Systems
CountryChina
CityBeijing
Period16/10/201620/10/2016

Keywords

  • Risk
  • Equilibrium
  • Wind power
  • Uncertainties
  • Day- ahead
  • Balancing

Cite this

Kazempour, J., & Pinson, P. (2016). Effects of Risk Aversion on Market Outcomes: A Stochastic Two-Stage Equilibrium Model. In Proceedings of International Conference on Probabilistic Methods Applied to Power Systems 2016 IEEE. https://doi.org/10.1109/PMAPS.2016.7764200