Contracts in electricity markets under EU ETS: A stochastic programming approach

Arega Abate, Rossana Riccardi and Carlos Ruiz

Energy Economics, 2021, vol. 99, issue C

Abstract: The European Union Emission Trading Scheme (EU ETS) is a cornerstone of the EU's strategy to fight climate change and an important device for plummeting greenhouse gas (GHG) emissions in an economically efficient manner. The power industry has switched to an auction-based allocation system at the onset of Phase III of the EU ETS to bring economic efficiency by negating windfall profits that have been resulted from grandfathered allocation of allowances in the previous phases. In this work, we analyze and simulate the interaction of oligopolistic generators in an electricity market with a game-theoretical framework where the electricity and the emissions markets interact in a two-stage electricity market. For analytical simplicity, we assume a single futures market where the electricity is committed at the futures price, and the emissions allowance is contracted in advance, prior to a spot market where the energy and allowances delivery takes place. Moreover, a coherent risk measure is applied (Conditional Value at Risk) to model both risk averse and risk neutral generators and a two-stage stochastic optimization setting is introduced to deal with the uncertainty of renewable capacity, demand, generation, and emission costs. The performance of the proposed equilibrium model and its main properties are examined through realistic numerical simulations. Our results show that renewable generators are surging and substituting conventional generators without compromising social welfare. Hence, both renewable deployment and emission allowance auctioning are effectively reducing GHG emissions and promoting low-carbon economic path.

Keywords: CVaR; Emissions allowance; Emission trading; Risk aversion; Two-stage stochastic optimization (search for similar items in EconPapers)
Date: 2021
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Working Paper: Contracts in Electricity Markets under EU ETS: A Stochastic Programming Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:99:y:2021:i:c:s0140988321002140

DOI: 10.1016/j.eneco.2021.105309

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