Renewable energy resources are essential for addressing global economic and environmental challenges. Renewable energy communities (RECs), which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of RECs resources, formulating the problem as mixed-integer linear programming (MILP) model. The operation tool computes the setpoints, for each period of the next day, of the energy and cross-sector flexible assets of the community members, such as loads, battery energy storage systems (BESS), electric vehicles (EVs), and electric water heaters (EWHs). The sizing tool computes the optimal capacities of the photovoltaic (PV) panels and BESS to minimize the community energy costs considering its optimal operation and the local energy trades among the community members. Given the high computational complexity of MILP-based sizing, an alternative approach based on the metaheuristic Evolutionary Particle Swarm Optimization (EPSO) algorithm is proposed. EPSO integrates evolutionary operators into the Particle Swarm Optimization (PSO) process, enhancing the exploration of the search space and making it more adaptable to dynamic and complex scenarios. These metaheuristic techniques offer a trade-off between solution accuracy and computational effort, providing effective decision-support tools for energy community planning. The proposed algorithms are evaluated using an illustrative case study of a four-members energy community, analyzed over a daily horizon across four representative days. The results demonstrate that, although the metaheuristic approach provides greater flexibility and reduced computational cost, it currently lacks the precision and stability required for optimal energy management in RECs. MILP remains the preferred method for accuracy, but EPSO presents a promising alternative for large-scale applications where computational efficiency is critical.
Le fonti di energia rinnovabile sono fondamentali per affrontare le sfide economiche e ambientali globali. Le comunità energetiche rinnovabili (REC), che riuniscono i consumatori per perseguire obiettivi energetici condivisi, rappresentano una soluzione promettente per ridurre i costi energetici e migliorare la sostenibilità. Questo studio analizza il dimensionamento e la gestione ottimale delle risorse di una REC, formulando il problema mediante un modello di programmazione lineare a numeri interi misti (MILP). Lo strumento operativo calcola i setpoint, per ciascun periodo del giorno successivo, delle risorse energetiche flessibili e intersettoriali dei membri della comunità, come carichi, sistemi di accumulo di energia a batteria (BESS), veicoli elettrici (EVs) e scaldacqua elettrici (EWHs). Lo strumento di dimensionamento determina le capacità ottimali dei pannelli fotovoltaici (PV) e dei BESS al fine di minimizzare i costi energetici della comunità, considerando il loro funzionamento ottimale e gli scambi energetici locali tra i membri della REC. Data l’elevata complessità computazionale del dimensionamento basato su MILP, si propone un approccio alternativo basato sull’algoritmo metaeuristico Evolutionary Particle Swarm Optimization (EPSO). EPSO integra operatori evolutivi nel processo Particle Swarm Optimization (PSO), migliorando l’esplorazione dello spazio di ricerca e rendendolo più adattabile a scenari dinamici e complessi. Queste tecniche metaeuristiche offrono un compromesso tra accuratezza della soluzione e sforzo computazionale, fornendo strumenti decisionali efficaci per la pianificazione delle comunità energetiche. Gli algoritmi proposti vengono valutati attraverso un caso di studio illustrativo di una comunità energetica composta da quattro membri, analizzata su un orizzonte giornaliero considerando quattro giorni rappresentativi. I risultati dimostrano che, sebbene l’approccio metaeuristico offra maggiore flessibilità e un costo computazionale ridotto, attualmente manca della precisione e della stabilità necessarie per una gestione energetica ottimale nelle REC. MILP rimane il metodo preferito in termini di accuratezza, ma EPSO si presenta come un’alternativa promettente per applicazioni su larga scala, dove l’efficienza computazionale è un fattore critico.
Evolutionary particle swarm optimization for the sizing and operation of distributed energy resources in energy communities
Petruzzi, Giulia Ester
2023/2024
Abstract
Renewable energy resources are essential for addressing global economic and environmental challenges. Renewable energy communities (RECs), which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of RECs resources, formulating the problem as mixed-integer linear programming (MILP) model. The operation tool computes the setpoints, for each period of the next day, of the energy and cross-sector flexible assets of the community members, such as loads, battery energy storage systems (BESS), electric vehicles (EVs), and electric water heaters (EWHs). The sizing tool computes the optimal capacities of the photovoltaic (PV) panels and BESS to minimize the community energy costs considering its optimal operation and the local energy trades among the community members. Given the high computational complexity of MILP-based sizing, an alternative approach based on the metaheuristic Evolutionary Particle Swarm Optimization (EPSO) algorithm is proposed. EPSO integrates evolutionary operators into the Particle Swarm Optimization (PSO) process, enhancing the exploration of the search space and making it more adaptable to dynamic and complex scenarios. These metaheuristic techniques offer a trade-off between solution accuracy and computational effort, providing effective decision-support tools for energy community planning. The proposed algorithms are evaluated using an illustrative case study of a four-members energy community, analyzed over a daily horizon across four representative days. The results demonstrate that, although the metaheuristic approach provides greater flexibility and reduced computational cost, it currently lacks the precision and stability required for optimal energy management in RECs. MILP remains the preferred method for accuracy, but EPSO presents a promising alternative for large-scale applications where computational efficiency is critical.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/234957