Distributed Multi-Agent Energy Management for Microgrids in a Co-Simulation Framework

Janaína Barbosa Almada, Fernando Lessa Tofoli, Raquel Cristina Filiagi Gregory, Raimundo Furtado Sampaio, Lucas Sampaio Melo and Ruth Pastôra Saraiva Leão ()
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Janaína Barbosa Almada: Department of Electrical Engineering, Federal University of Ceará, Fortaleza 60455-760, Brazil
Fernando Lessa Tofoli: Department of Electrical Engineering, Federal University of São João del-Rei, São João del-Rei 36307-352, Brazil
Raquel Cristina Filiagi Gregory: Department of Electrical Engineering, Federal University of Ceará, Fortaleza 60455-760, Brazil
Raimundo Furtado Sampaio: Department of Electrical Engineering, Federal University of Ceará, Fortaleza 60455-760, Brazil
Lucas Sampaio Melo: Department of Electrical Engineering, Federal University of Ceará, Fortaleza 60455-760, Brazil
Ruth Pastôra Saraiva Leão: Department of Electrical Engineering, Federal University of São João del-Rei, São João del-Rei 36307-352, Brazil

Energies, 2025, vol. 18, issue 17, 1-32

Abstract: The diversity of energy resources in distribution networks requires new strategies for planning and operation. In this context, microgrids are solutions that can integrate renewable energy sources, energy storage systems (ESSs), and demand response (DR), thereby decentralizing operations and utilizing digital technologies to create more proactive energy markets. Given the above, this work proposes a distributed optimal dispatch strategy for microgrids with multiple energy resources, with a focus on scalability. Simulations are performed using agent modeling on the Python Agent Development (PADE) platform, leveraging distributed computing resources and agent communication. A co-simulation environment, coordinated by Mosaik, synchronizes data exchange, while a plug-and-play system allows dynamic agent modification. The main contribution of the present study relies on a system integration approach, combining a multi-agent system (MAS) and Mosaik co-simulation framework with plug-and-play agent support for the very short-term (five-minute) dispatch of energy resources. Optimization algorithms, namely particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO), are framed as an incremental improvement tailored to this distributed architecture. Case studies show that distributed MAPSO performs better, with lower objective function values and a smaller relative standard deviation (15.6%), while distributed PSO had a higher deviation (33.9%). Although distributed MAPSO takes up to three times longer to provide a solution, with an average of 9.0 s, this timeframe is compatible with five-minute dispatch intervals.

Keywords: co-simulation; distributed optimization; microgrids; meta-heuristics; multi-agent systems (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: 2025
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