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

Liheng Liu

Liheng Liu

School of Energy and Power Engineering, Nanjing Institute of TechnologyNanjing, China
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Liu, Liheng
, 
Miaomiao Niu

Miaomiao Niu

School of Energy and Power Engineering, Nanjing Institute of TechnologyNanjing, China
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Niu, Miaomiao
, 
Dongliang Zhang

Dongliang Zhang

School of Energy and Power Engineering, Nanjing Institute of TechnologyNanjing, China
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Zhang, Dongliang
, 
Li Liu

Li Liu

School of Business, Hebei Agriculture UniversityBaoding, China
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Liu, Li
 und 
Dietmar Frank

Dietmar Frank

BSH ChinaNanjing, China
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Frank, Dietmar
  
12. Apr. 2021
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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
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© 2021 Liheng Liu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

The structure of multi-agent system
The structure of multi-agent system

Fig. 2

Calculation of crowding distance
Calculation of crowding distance

Fig. 3

Calculation procedure of DMAMOEA
Calculation procedure of DMAMOEA

Fig. 4

One year power load demand
One year power load demand

Fig. 5

Annual average wind speed at a certain place
Annual average wind speed at a certain place

Fig. 6

Annual light intensity
Annual light intensity

Fig. 7

24-Hour electricity price
24-Hour electricity price

Fig. 8

The simulation results of the DMAMOEA and NSGA-II
The simulation results of the DMAMOEA and NSGA-II

Fig. 9

Comparison of Gd Distance DEMAMOME and NSGA-II
Comparison of Gd Distance DEMAMOME and NSGA-II

Calculation of the cost of decigrams

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

Environmental benefits of Solution 1

CO2 CO SO2 NOx 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

Some representative results of DMAMOEA algorithm

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
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