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3536. Risk Measures in Stochastic Complementarity Models of Energy Markets

Invited abstract in session TB-9: Game Theoretic Market Equilibrium Modelling, stream Energy Markets.

Tuesday, 10:30-12:00
Room: 10 (building: 116)

Authors (first author is the speaker)

1. Dáire Byrne
College of Business, University College Dublin
2. Mel Devine
University College Dublin

Abstract

With the transition towards renewable energy sources, there is a growing need to capture uncertainty within energy market models, with firms facing stochasticity in terms of costs, demands, and generation capacity. With this heightened variability comes additional exposure to risk, leading firms to exhibit risk-averse decision-making. In order to account for this, risk measures can be appended to models. A number of such measures, particularly conditional value-at-risk, stochastic dominance constraints, and concave utility functions, have gained varying degrees of traction in energy market modelling literature. This study aims to investigate the consequences of these risk measures on the behaviour of price-making players acting competitively and seeking to maximise profits. It comprises a discussion of these risk measures and their relative merits. It further includes an analysis of the impact of incorporating risk measures into stochastic equilibrium models, both from an analytic and numerical perspective. Moreover, it discusses the circumstances under which these complementarity problems can be converted to an equivalent problems in convex optimisation, and the use of Arrow-Debreu securities to conceptually complete the market. This work aims to elucidate and illustrate the behaviour of these risk measures and their appropriate application to energy markets.

Keywords

Status: accepted

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