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

Mizue Shimomura, Alexander Ryota Keeley, Ken'ichi Matsumoto, Kenta Tanaka and Shunsuke Managi

Discussion papers from Research Institute of Economy, Trade and Industry (RIETI)

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.

Pages: 29 pages
Date: 2022-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-reg
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Working Paper: Impact of the Rapid Expansion of Renewable Energy on Electricity Market Price: Using machine learning and shapley additive explanation (2022) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eti:dpaper:22090

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