Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

Dharmesh Dabhi, Kartik Pandya, Joao Soares, Fernando Lezama and Zita Vale
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Dharmesh Dabhi: M & V Patel Department of Electrical Engineering, CSPIT, Charusat University, Changa 388421, India
Kartik Pandya: M & V Patel Department of Electrical Engineering, CSPIT, Charusat University, Changa 388421, India
Joao Soares: GECAD, School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Fernando Lezama: GECAD, School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Zita Vale: GECAD, School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal

Energies, 2022, vol. 15, issue 13, 1-20

Abstract: The increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in which players (e.g., consumers, prosumers, or producers) at the upper level try to maximize their profits, whereas a market mechanism at the lower level maximizes the energy transacted. However, the strategic bidding in local energy markets is a complex NP-hard problem, due to its inherently nonlinear and discontinued characteristics. Thus, this article proposes the application of a hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem. The proposed CE-CMAES uses cross entropy for global exploration of search space and covariance matrix adaptation evolution strategy for local exploitation. The CE-CMAES prevents premature convergence while efficiently exploring the search space, thanks to its adaptive step-size mechanism. The performance of the algorithm is tested through simulation in a practical distribution system with renewable energy penetration. The comparative analysis shows that CE-CMAES achieves superior results concerning overall cost, mean fitness, and Ranking Index (i.e., a metric used in the competition for evaluation) compared with state-of-the-art algorithms. Wilcoxon Signed-Rank Statistical test is also applied, demonstrating that CE-CMAES results are statistically different and superior from the other tested algorithms.

Keywords: bi-level problem; covariance matrix; Cross-Entropy Method; local energy market; optimal bidding (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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