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Optimal probabilistic scenario-based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources
被引:47
作者:
Faraji, Jamal
[1
]
Hashemi-Dezaki, Hamed
[2
]
Ketabi, Abbas
[1
,2
]
机构:
[1] Univ Kashan, Energy Res Inst, Kashan, Iran
[2] Univ Kashan, Dept Elect & Comp Engn, 6 Km Ghotbravandi Blvd, Kashan 8731753153, Iran
关键词:
differential evolution algorithm (DEA);
k-means algorithm;
k-medoids algorithm;
Monte Carlo simulation (MCS);
optimal scenario-based operation and scheduling;
prosumer microgrids (PMGs);
scenario reduction method;
uncertainty;
CYBER-POWER INTERDEPENDENCIES;
DISTRIBUTION NETWORKS;
ELECTRICITY-GENERATION;
STORAGE SYSTEMS;
COST REDUCTION;
LOAD DISPATCH;
MONTE-CARLO;
WIND;
MANAGEMENT;
OPTIMIZATION;
D O I:
10.1002/ese3.788
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day-ahead operation. In this study, a new probabilistic scenario-based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k-means, k-medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k-medoids algorithm has the best performance in comparison with the k-means and the DEA-based clustering under various conditions.
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页码:3942 / 3960
页数:19
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