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Impact of the Rapid Expansion of Renewable Energy on Electricity Market Price: Using machine learning and shapley additive explanation

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  • Mizue SHIMOMURA
  • Alexander Ryota KEELEY
  • Ken'ichi MATSUMOTO
  • Kenta TANAKA
  • Shunsuke MANAGI

Abstract

The increase in variable renewable energy (VRE) has brought significant changes in the power system, including a decrease in the average electricity market price owing to the merit order effect (MOE). In this study, we use machine learning and Shapley additive explanation (SHAP) to comprehensively examine the drivers of market price volatility, including the interaction between VRE and demand, fuel prices, and operation capacity in the Japanese electricity market which solar power installation is expanding rapidly. The results of SHAP reveal that there is a large decline effect for market price in solar power during daytime; however, the effect varies depending on the time of day, season, and demand. In addition, the results suggest that the market price increases when demand is high and solar generation is low, such as during summer evenings, which may be because of natural gas generation with higher marginal costs. The study reveals that impact of expanded VRE will not only have the MOE which decreasing average market prices, but may also prompt structural changes in electricity supply, causing market instability and price spikes in the transition process.

Suggested Citation

  • Mizue SHIMOMURA & Alexander Ryota KEELEY & Ken'ichi MATSUMOTO & Kenta TANAKA & Shunsuke MANAGI, 2022. "Impact of the Rapid Expansion of Renewable Energy on Electricity Market Price: Using machine learning and shapley additive explanation," Discussion papers 22090, Research Institute of Economy, Trade and Industry (RIETI).
    • Handle: RePEc:eti:dpaper:22090
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    References listed on IDEAS

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    1. Paul L Joskow, 2019. "Challenges for wholesale electricity markets with intermittent renewable generation at scale: the US experience," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 35(2), pages 291-331.
    2. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    3. James Bushnell & Kevin Novan, 2018. "Setting with the Sun: The Impacts of Renewable Energy on Wholesale Power Markets," NBER Working Papers 24980, National Bureau of Economic Research, Inc.
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