Exploring market power using deep reinforcement learning for intelligent bidding strategies
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough

TL;DR
This paper uses deep reinforcement learning within a simulation to analyze how market power concentration by generator companies influences electricity prices, providing insights for regulatory policies to maintain competitiveness.
Contribution
It introduces a novel application of deep reinforcement learning to model strategic bidding in electricity markets and assesses the impact of capacity concentration on prices.
Findings
Controlling more than ~11% capacity raises prices by ~25%.
Controlling over ~35% capacity causes exponential price increases.
Market caps around double the average price help maintain competitiveness.
Abstract
Decentralized electricity markets are often dominated by a small set of generator companies who control the majority of the capacity. In this paper, we explore the effect of the total controlled electricity capacity by a single, or group, of generator companies can have on the average electricity price. We demonstrate this through the use of ElecSim, a simulation of a country-wide energy market. We develop a strategic agent, representing a generation company, which uses a deep deterministic policy gradient reinforcement learning algorithm to bid in a uniform pricing electricity market. A uniform pricing market is one where all players are paid the highest accepted price. ElecSim is parameterized to the United Kingdom for the year 2018. This work can help inform policy on how to best regulate a market to ensure that the price of electricity remains competitive. We find that capacity…
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