<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The optimal configuration and allocation of a microgrid are one of the key issues to guarantee the economic and reliable working of a microgrid. This is a multi-objective optimisation problem within which the economic index and the load power shortage rate index should be considered when optimising the configuration. In this article, a differential multi-agent multi-objective evolutionary algorithm (DMAMOEA) was designed to optimise the capacity configuration of a microgrid system, which includes three kinds of equipment: wind turbine, photovoltaic equipment and battery. The final optimisation results were compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. Simulation results showed the effectiveness of the algorithm. At the end of this article, the representative solutions in the calculation results are compared and explained and the environmental benefits are analysed, which show the effectiveness of the implementation of the microgrid system.</p>
</abstract>

The optimal configuration and allocation of a microgrid are one of the key issues to guarantee the economic and reliable working of a microgrid. This is a multi-objective optimisation problem within which the economic index and the load power shortage rate index should be considered when optimising the configuration. In this article, a differential multi-agent multi-objective evolutionary algorithm (DMAMOEA) was designed to optimise the capacity configuration of a microgrid system, which includes three kinds of equipment: wind turbine, photovoltaic equipment and battery. The final optimisation results were compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. Simulation results showed the effectiveness of the algorithm. At the end of this article, the representative solutions in the calculation results are compared and explained and the environmental benefits are analysed, which show the effectiveness of the implementation of the microgrid system.

Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The optimal configuration and allocation of a microgrid are one of the key issues to guarantee the economic and reliable working of a microgrid. This is a...

Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm

Online veröffentlicht: 12. Apr. 2021

Seitenbereich: 111 - 124

Eingereicht: 30. Nov. 2020

Akzeptiert: 31. Jan. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00034

Schlüsselwörter
allocation optimal, microgrid, multi-agent, multi-objective, environment protection

© 2021 Liheng Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm

Online veröffentlicht: 12. Apr. 2021

Seitenbereich: 111 - 124

Eingereicht: 30. Nov. 2020

Akzeptiert: 31. Jan. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00034

Schlüsselwörterallocation optimal, microgrid, multi-agent, multi-objective, environment protection

© 2021 Liheng Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
allocation optimal, microgrid, multi-agent, multi-objective, environment protection