Metaheuristic Optimization Methods in Energy Community Scheduling: A Benchmark Study

Eduardo Gomes, Lucas Pereira (), Augusto Esteves and Hugo Morais
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Eduardo Gomes: Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisbon, Portugal
Lucas Pereira: Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisbon, Portugal
Augusto Esteves: Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisbon, Portugal
Hugo Morais: Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisbon, Portugal

Energies, 2024, vol. 17, issue 12, 1-18

Abstract: The prospect of the energy transition is exciting and sure to benefit multiple aspects of daily life. However, various challenges, such as planning, business models, and energy access are still being tackled. Energy Communities have been gaining traction in the energy transition, as they promote increased integration of Renewable Energy Sources (RESs) and more active participation from the consumers. However, optimization becomes crucial to support decision making and the quality of service for the effective functioning of Energy Communities. Optimization in the context of Energy Communities has been explored in the literature, with increasing attention to metaheuristic approaches. This paper contributes to the ongoing body of work by presenting the results of a benchmark between three classical metaheuristic methods—Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)—and three more recent approaches—the Mountain Gazelle Optimizer (MGO), the Dandelion Optimizer (DO), and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF). Our results show that newer methods, especially the Dandelion Optimizer (DO) and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF), tend to be more competitive in terms of minimizing the objective function. In particular, the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF) demonstrated the capacity to obtain extremely competitive results, being on average 3% better than the second-best method while boasting between around 2× and 10× the speed of other methods. These insights become highly valuable in time-sensitive areas, where obtaining results in a shorter amount of time is crucial for maintaining system operational capabilities.

Keywords: energy communities; grid management; optimization; metaheuristics; population-based; swarm-based; benchmark (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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