@misc{10498/35296,
year = {2025},
month = {1},
url = {http://hdl.handle.net/10498/35296},
abstract = {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},
organization = {This work was partially supported by Ministerio de Ciencia e Innovación, Agencia Estatal de
Investigación, and Unión Europea “NextGenerationEU/PRTR” (Grant TED2021-129631B-C32
supported by MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR)},
publisher = {Elsevier},
keywords = {Multi-energy microgrids},
keywords = {reinforcement learning},
keywords = {energy management system},
keywords = {electricity},
keywords = {hydrogen},
keywords = {thermal},
title = {A Novel Reinforcement Learning-Based Multi-Objective Energy Management System for Multi-Energy Microgrids Integrating Electrical, Hydrogen, and Thermal Elements},
doi = {10.1016/j.epsr.2025.111474},
author = {Hosseini, Ehsan and García Triviño, Pablo and Horrillo Quintero, Pablo and Carrasco González, David and García Vázquez, Carlos Andrés and Sarrias Mena, Raúl and Sánchez Sainz, Higinio and Fernández Ramírez, Luis Miguel},
}