RT Journal Article
T1 Day-ahead TCLs dispatch optimization: An integer genetic algorithm approach based on microgrids composition
A1 Clavijo Camacho, Jesús
A1 Gómez Ruiz, Gabriel
A1 Hernández Torres, José Antonio
A1 Sánchez Herrera, María Reyes
A1 Clavijo Camacho, Jesús
A1 Gómez Ruiz, Gabriel
AB Day-ahead microgrid optimization has been extensively studied in recent technical literature, which predominantly focuses on microgrids comprised of loads, renewable energy systems (RES), and energy storage systems (ESS). However, many microgrids are only composed of loads (such as homes in buildings). This work studies microgrid optimization through a specific focus on thermostatically controllable loads (TCLs), prevalent components in such microgrids. The optimization objectives are tailored to account for the unique characteristics of each microgrid’s composition. Additionally, the study considers the TCLs’ ability to participate in demand response programs within the power system and addresses challenges stemming from discrepancies between dayahead dispatch and real-time operation. Importantly, the optimization process employs a genetic algorithm (GA) to derive optimal on/off sequences and corresponding temperature profiles for each TCL, instead of adjusting variable temperature setpoints. Furthermore, the GA initial population is generated using a novel method called stratified random sampling, proposed in this work. The study presents a procedure for TCL optimization aimed at maximizing each microgrid’s performance relative to its composition. Results demonstrate a reduction in the targeted metric ranging from 2.4% to 18%.
PB Elsevier
SN 0378-7788
SN 1872-6178 (electrónico)
YR 2025
FD 2025
LK https://hdl.handle.net/10272/27358
UL https://hdl.handle.net/10272/27358
LA eng
NO Clavijo-Camacho, J., Gómez-Ruiz, G., Hernández Torres, J. A., & Sánchez-Herrera, R. (2025). Day-ahead TCLs dispatch optimization: An integer genetic algorithm approach based on microgrids composition. Energy and Buildings, 347, 116403. https://doi.org/10.1016/j.enbuild.2025.116403
NO This research was supported by the grant PID2020-117828RB-I00 funded by MICIU/AEI/10.13039/501100011033 and, by the Spanish Ministry of Science, Innovation and Universities. The author Jesus Clavijo-Camacho is enjoying a “INVESTIGO” research fellowship funded by the European Commission - NextGenerationEU. In addition, the author Gabriel Gómez-Ruiz is enjoying an FPU grant, number FPU21/ 00468, funded by the Spanish Ministry of Science, Innovation and Universities for the training of university teaching staff during his PhD period. Funding for open access charge: Universidad de Huelva / CBUA.
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026