### 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 language | English |
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Title of host publication | Proceedings of International Conference on Probabilistic Methods Applied to Power Systems 2016 |

Number of pages | 6 |

Publisher | IEEE |

Publication date | 2016 |

ISBN (Print) | 9781509019700 |

DOIs | |

Publication status | Published - 2016 |

Event | International Conference on Probabilistic Methods Applied to Power Systems - Beijing, China Duration: 16 Oct 2016 → 20 Oct 2016 |

### Conference

Conference | International Conference on Probabilistic Methods Applied to Power Systems |
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Country | China |

City | Beijing |

Period | 16/10/2016 → 20/10/2016 |

### 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