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EuroMod: modelling European power markets with improved price granularity
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Title: | EuroMod: modelling European power markets with improved price granularity |
Authors: | Mendes, C Staffell, I Green, R |
Item Type: | Journal Article |
Abstract: | Electricity system models are widely used to study future designs of power markets. They are commonly used to represent electricity dispatch decisions but struggle to reproduce realistic variation in prices. We show that current assumption of generators bidding their average variable cost (AVC) underestimates the spread and volatility of hourly wholesale prices.While existing models can accurately estimate the revenues of conventional (thermal) generators, they fail with the revenues of next-generation technologies such as storage and merchant transmission. Imperfect competition makes market prices differ from the theoretical optimum. In this paper we present a bottom-up electricity market model for Europe called EuroMod: a deterministic linear optimization in GAMS which models generation, storage and transmission dispatch at hourly resolution for European markets connected using net transfer capacities. Two additions are tested for their ability to improve wholesale price formation: a simple modification to the short-run marginal cost approach that allows generators to make bids which diverge from AVC; and a post-optimizer transformation of prices with respect to demand net of renewables. These corrections improve both the representation of prices and dispatch decisions across European markets, and reduce errors by 40% for prices, 6% for power station revenues, between 24% to 33% for energy storage profits, and 43% for the median arbitrage value of interconnectors when compared to traditional linear models. |
Issue Date: | Mar-2024 |
Date of Acceptance: | 19-Jan-2024 |
URI: | http://hdl.handle.net/10044/1/109602 |
DOI: | 10.1016/j.eneco.2024.107343 |
ISSN: | 0140-9883 |
Publisher: | Elsevier BV |
Journal / Book Title: | Energy Economics |
Volume: | 131 |
Copyright Statement: | © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Publication Status: | Published |
Article Number: | 107343 |
Online Publication Date: | 2024-01-28 |
Appears in Collections: | Imperial College Business School |
This item is licensed under a Creative Commons License