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Communication Dans Un Congrès Année : 2022

Interpretable data-driven solar power plant trading strategies

Résumé

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.
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Dates et versions

hal-03772848 , version 1 (08-09-2022)

Identifiants

Citer

Konstantinos Parginos, Ricardo J. Bessa, Simon Camal, Georges Kariniotakis. Interpretable data-driven solar power plant trading strategies. IEEE PES ISGT EUROPE 2022, Innovative Smart Grid Technologies Conference, Oct 2022, Novi Sad, Serbia. ⟨10.1109/ISGT-Europe54678.2022.9960432⟩. ⟨hal-03772848⟩
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