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ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning

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  • Alexander J. M. Kell
  • Matthew Forshaw
  • A. Stephen McGough

Abstract

Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing down all fossil fuel energy generation and replacing them with renewable sources -- careful decisions need to be taken to ensure rapid but stable progress. To aid decision makers, we present a new tool, ElecSim, which is an open-sourced agent-based modelling framework used to examine the effect of policy on long-term investment decisions in electricity generation. ElecSim allows non-experts to rapidly prototype new ideas. Different techniques to model long-term electricity decisions are reviewed and used to motivate why agent-based models will become an important strategic tool for policy. We motivate why an open-source toolkit is required for long-term electricity planning. Actual electricity prices are compared with our model and we demonstrate that the use of a Monte-Carlo simulation in the system improves performance by $52.5\%$. Further, using ElecSim we demonstrate the effect of a carbon tax to encourage a low-carbon electricity supply. We show how a {\pounds}40 ($\$50$) per tonne of CO2 emitted would lead to 70% renewable electricity by 2050.

Suggested Citation

  • Alexander J. M. Kell & Matthew Forshaw & A. Stephen McGough, 2019. "ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning," Papers 1911.01203, arXiv.org.
  • Handle: RePEc:arx:papers:1911.01203
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    References listed on IDEAS

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    Cited by:

    1. Jimenez, I. Sanchez & Ribó-Pérez, D. & Cvetkovic, M. & Kochems, J. & Schimeczek, C. & de Vries, L.J., 2024. "Can an energy only market enable resource adequacy in a decarbonized power system? A co-simulation with two agent-based-models," Applied Energy, Elsevier, vol. 360(C).
    2. Alexander J. M. Kell & A. Stephen McGough & Matthew Forshaw, 2021. "The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets," Papers 2103.04327, arXiv.org.
    3. Anwar, Muhammad Bashar & Stephen, Gord & Dalvi, Sourabh & Frew, Bethany & Ericson, Sean & Brown, Maxwell & O’Malley, Mark, 2022. "Modeling investment decisions from heterogeneous firms under imperfect information and risk in wholesale electricity markets," Applied Energy, Elsevier, vol. 306(PA).
    4. Ramiz Qussous & Nick Harder & Anke Weidlich, 2022. "Understanding Power Market Dynamics by Reflecting Market Interrelations and Flexibility-Oriented Bidding Strategies," Energies, MDPI, vol. 15(2), pages 1-24, January.
    5. Frew, Bethany & Bashar Anwar, Muhammad & Dalvi, Sourabh & Brooks, Adria, 2023. "The interaction of wholesale electricity market structures under futures with decarbonization policy goals: A complexity conundrum," Applied Energy, Elsevier, vol. 339(C).

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