<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...

Power type	WT	PV	BS
Unit price	8870 $/kW	8790 $/kW	1200 $/kW
Operation and maintenance cost	7.7 $/kW h	5.5 $/kW h	7 $/kW h
Replacement cost	0 $/KW	0 $/KW	1200/set

	CO₂	CO	SO₂	NO_x	Total
Pollution reduction (10³Kg)	88,122	127.65	817.81	532.03	89,600
Cost saving of pollution treatment(10³$)	326.05	20.425	793.27	686.32	1826.1

	Wind turbine	Photovoltaic	Battery	Investment ($)	Load power shortage rate
Solution 1	260	41	241	6.585×10⁶	0.01839
Solution 2	250	80	221	6.372×10⁶	0.01867
Solution 3	237	55	231	6.059×10⁶	0.01912
Solution 4	228	43	63	5.567×10⁶	0.2413
Solution 5	216	31	16	5.197×10⁶	0.2457

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

Data publikacji: 12 kwi 2021

Zakres stron: 111 - 124

Otrzymano: 30 lis 2020

Przyjęty: 31 sty 2021

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

Słowa kluczowe
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.

Fig. 1

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Fig. 8

Fig. 9

Calculation of the cost of decigrams

Environmental benefits of Solution 1

Some representative results of DMAMOEA algorithm

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

Data publikacji: 12 kwi 2021

Zakres stron: 111 - 124

Otrzymano: 30 lis 2020

Przyjęty: 31 sty 2021

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

Słowa kluczoweallocation 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.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Calculation of the cost of decigrams

Environmental benefits of Solution 1

Some representative results of DMAMOEA algorithm

Słowa kluczowe
allocation optimal, microgrid, multi-agent, multi-objective, environment protection