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

Author Name SHIMOMURA Mizue (Kyushu University) / KEELEY Alexander Ryota (Kyushu University) / MATSUMOTO Ken'ichi (Toyo University) / TANAKA Kenta (Musashi University) / MANAGI Shunsuke (Faculty Fellow, RIETI)
Creation Date/NO. September 2022 22-E-090
Research Project Institutional design for desirable acceptance of AI technology
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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.