%0 Journal Article 
%A Hosseini, Ehsan
%A García Triviño, Pablo
%A Horrillo Quintero, Pablo
%A Carrasco González, David
%A García Vázquez, Carlos Andrés
%A Sarrias Mena, Raúl
%A Sánchez Sainz, Higinio
%A Fernández Ramírez, Luis Miguel
%T A Novel Reinforcement Learning-Based Multi-Objective Energy Management System for Multi-Energy Microgrids Integrating Electrical, Hydrogen, and Thermal Elements
%D 2025
%U http://hdl.handle.net/10498/35296
%X This paper presents an advanced multi-objective function based-energy management system
(MOF-EMS) designed to optimize the economic dispatch and lifespan of electrical, thermal, and
hydrogen systems within a multi-energy microgrid (MEMG). The EMS employs a reinforcement
learning (RL) algorithm (RL-MOF-EMS) to achieve the energy balance, ensuring appropriate
distribution of thermal and electrical power. Three distinct single-objective functions are
considered: 1) minimizing operational cost, 2) reducing the life degradation of the storage devices,
and 3) minimizing heating cost. Each objective is evaluated under three different EMSs based on:
1) priority-based regulator (PBR), 2) proportional regulator (PR), and 3) particle swarm
optimization (PSO). A multi-objective problem combining these three objectives is formulated and
the RL-MOF-EMS is implemented to determine the optimal operation of the MEMG. The
performance is rigorously tested in Simulink under diverse weather conditions and fluctuating2
thermal and electrical demand profiles. The results are compared against an EMS based on a
nonlinear MATLAB optimizer, demonstrating the effectiveness of the RL-MOF-EMS in
coordinating power flows from energy sources and storages. This coordination minimizes costs
and component degradation, while meeting energy demands in all scenarios. The findings indicate
that the MEMG operates with a high degree of self-sufficiency, reducing reliance on the grid
%K Multi-energy microgrids
%K reinforcement learning
%K energy management system
%K electricity
%K hydrogen
%K thermal
%~ Universidad de Cádiz