[2022-03] Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels
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날짜 2022.09.11
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[2022-03] Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels Jungyeon Park, Hokyun Kim and Jooyoung Jeon Moon Soul Graduate School of Future Strategy, KAIST Estêvão Alvarenga
Netherlands
Ran Li Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China Fotios Petropoulos School of Management, University of Bath, UK Kwangwon Ahn Department of Industrial Engineering, Yonsei University, South Korea Abstract In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of accuracy and computational efficiency. Keywords Portfolio optimization, Short-term load forecasting, Low-aggregation load, Probabilistic forecasts, Aggregated electricity demand
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