Enhancing transient response in AC microgrids: A multi-objective optimization approach for improved active power management
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| Názov: | Enhancing transient response in AC microgrids: A multi-objective optimization approach for improved active power management |
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| Autori: | Oscar Gonzales-Zurita, Mario González-Rodríguez, Jean-Michel Clairand, Guillermo Escrivá-Escrivá |
| Zdroj: | Energy Conversion and Management: X, Vol 30, Iss , Pp 101602- (2026) |
| Informácie o vydavateľovi: | Elsevier |
| Rok vydania: | 2026 |
| Zbierka: | Directory of Open Access Journals: DOAJ Articles |
| Predmety: | Second-order sliding mode controller (SMC-2), Particle swarm optimization (PSO), Multi-objective evolutionary algorithm (MOGA), Multi-objective differential evolution (MODE), Multi-objective adaptive simulated annealing (MOASA), Engineering (General). Civil engineering (General), TA1-2040 |
| Popis: | Many residential and local consumers have embraced single-phase inverters for self-consumption and energy trading. However, their adoption challenges the efficient management of electrical energy within microgrids (MGs), particularly regarding transient responses like rise time and overshoot, an appropriate active power control, or an optimum performance under different operating conditions. Conventional inverter controllers, while easy to program, often face conflicting objectives, where improving one parameter degrades another. This limitation complicates the control of nonlinear systems, risking high-energy transients that can damage components and reduce the lifespan of power semiconductors, leading to costly maintenance.This study proposes a robust strategy focused on primary control using a higher-order sliding mode controller (SMC) with a PI sliding surface tuned by multi-objective optimization (MOO) methods to address these issues. The control of active power is performed under the DQ frame synchronized to the main grid under a PLL method. Our approach aims to improve both the rise time and the overshoot of active power simultaneously. MOO techniques such as Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Differential Evolution (MODE), and Multi-Objective Adaptive Simulated Annealing (MOASA) have shown significant promise in our PSCAD-based research using data from an industrial inverter in an MG laboratory.The results were compared with particle swarm optimization (PSO) techniques. Performance indices like integral of absolute error (IAE), integral of square error (ISE), integral of time and absolute error (ITAE), and integral of time and square error (ITSE) demonstrated that SMC-2+MOO outperforms traditional methods like PSO, offering a superior solution for managing MG efficiency. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2590174526000851; https://doaj.org/toc/2590-1745; https://doaj.org/article/62ba4b0977794ce99b3613560601e934 |
| DOI: | 10.1016/j.ecmx.2026.101602 |
| Dostupnosť: | https://doi.org/10.1016/j.ecmx.2026.101602 https://doaj.org/article/62ba4b0977794ce99b3613560601e934 |
| Prístupové číslo: | edsbas.541ECD34 |
| Databáza: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Many residential and local consumers have embraced single-phase inverters for self-consumption and energy trading. However, their adoption challenges the efficient management of electrical energy within microgrids (MGs), particularly regarding transient responses like rise time and overshoot, an appropriate active power control, or an optimum performance under different operating conditions. Conventional inverter controllers, while easy to program, often face conflicting objectives, where improving one parameter degrades another. This limitation complicates the control of nonlinear systems, risking high-energy transients that can damage components and reduce the lifespan of power semiconductors, leading to costly maintenance.This study proposes a robust strategy focused on primary control using a higher-order sliding mode controller (SMC) with a PI sliding surface tuned by multi-objective optimization (MOO) methods to address these issues. The control of active power is performed under the DQ frame synchronized to the main grid under a PLL method. Our approach aims to improve both the rise time and the overshoot of active power simultaneously. MOO techniques such as Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Differential Evolution (MODE), and Multi-Objective Adaptive Simulated Annealing (MOASA) have shown significant promise in our PSCAD-based research using data from an industrial inverter in an MG laboratory.The results were compared with particle swarm optimization (PSO) techniques. Performance indices like integral of absolute error (IAE), integral of square error (ISE), integral of time and absolute error (ITAE), and integral of time and square error (ITSE) demonstrated that SMC-2+MOO outperforms traditional methods like PSO, offering a superior solution for managing MG efficiency. |
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| DOI: | 10.1016/j.ecmx.2026.101602 |