1. Interpretable data-driven solar power plant trading strategies
- Author
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Parginos, Konstantinos, Bessa, Ricardo, Camal, Simon, Kariniotakis, Georges, Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques (PERSEE), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa (INESC-ID), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST)-Instituto de Engenharia de Sistemas e Computadores (INESC), European Project: 864337,Smart4RES, and European Project: 945304,Ai4theSciences
- Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Symbolic Regression ,[SPI.NRJ]Engineering Sciences [physics]/Electric power ,Genetic Programming ,Solar ,Smart Grids ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,Trading ,[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ,Artificial Intelligence ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,Interpretability ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,Renewables - Abstract
International audience; Standard practices of decision-making in energy systems are dynamic, non-linear, complex, and chaotic processes in nature. Trading the power produced by solar photovoltaic (PV) plants in electricity markets is an important decisionmaking problem which receives increasing attention in the past few decades. The main objective of this paper is to build an interpretable data-driven decision aid model for the case study of a solar power plant with the objective to minimize imbalance costs and thus maximise the revenue, using Symbolic Regression (SR) through Genetic Programming. The use of SR in the experiments and analysis developed in this paper show numerous advantages. SR evolves linear combinations of nonlinear functions of the input variables. Three penalty metrics are introduced to enhance the interpretability of the final solutions. SR shows robust results, especially in the case study.
- Published
- 2022
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