Long-term electricity market agent based model validation using genetic algorithm based optimization

TL;DR

This paper validates an agent-based model for long-term electricity market simulation, demonstrating its ability to replicate historical transitions and project future scenarios with increased realism, supporting policy analysis.

Contribution

It introduces a validated agent-based modeling framework for decentralised electricity markets, capable of simulating heterogeneous agents and complex market dynamics.

Findings

Successfully modeled the coal-to-gas transition in the UK (2013-2018).

Projected future electricity mix aligning with government scenarios up to 2035.

Demonstrated the model's ability to incorporate detailed temporal and weather data.

Abstract

Electricity market modelling is often used by governments, industry and agencies to explore the development of scenarios over differing timeframes. For example, how would the reduction in cost of renewable energy impact investments in gas power plants or what would be an optimum strategy for carbon tax or subsidies? Cost optimization based solutions are the dominant approach for understanding different long-term energy scenarios. However, these types of models have certain limitations such as the need to be interpreted in a normative manner, and the assumption that the electricity market remains in equilibrium throughout. Through this work, we show that agent-based models are a viable technique to simulate decentralised electricity markets. The aim of this paper is to validate an agent-based modelling framework to increase confidence in its ability to be used in policy and decision making. Our framework can model heterogeneous agents with imperfect information. The model uses a rules-based approach to approximate the underlying dynamics of a real world, decentralised electricity market. We use the UK as a case study, however, our framework is generalisable to other countries. We increase the temporal granularity of the model by selecting representative days of electricity demand and weather using a $k$-means clustering approach. We show that our framework can model the transition from coal to gas observed in the UK between 2013 and 2018. We are also able to simulate a future scenario to 2035 which is similar to the UK Government, Department for Business and Industrial Strategy (BEIS) projections. We show a more realistic increase in nuclear power over this time period. This is due to the fact that with current nuclear technology, electricity is generated almost instantaneously and has a low short-run marginal cost \cite{Department2016}.