Distributed multi-objective grey wolf optimizer for distributed multi-objective economic dispatch of multi-area interconnected power systems

With the gradual opening and rapid development of power markets, large-scale multi-area interconnected power systems (LMIPSs) have become an inevitable pattern. The traditional centralized economic dispatch optimization method has the disadvantages of slow calculation speed, easy exposure of private...

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Vydáno v:Applied soft computing Ročník 117; s. 108345
Hlavní autoři: Yin, Linfei, Sun, Zhixiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.03.2022
Témata:
Distributed multi-objective economic dispatch
Distributed multi-objective grey wolf optimizer
Distributed optimization
Performance test
Distributed multi-objective grey wolf optimizer
Distributed optimization
Distributed multi-objective economic dispatch
Performance test
ISSN:1568-4946, 1872-9681
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Shrnutí:With the gradual opening and rapid development of power markets, large-scale multi-area interconnected power systems (LMIPSs) have become an inevitable pattern. The traditional centralized economic dispatch optimization method has the disadvantages of slow calculation speed, easy exposure of private equipment information, and considers only one cost objective. This paper introduces the distributed concept into the multi-objective grey wolf optimizer (MOGWO) to mitigate these deficiencies; then proposes the distributed MOGWO (DMOGWO). When the DMOGWO solves the LMIPS problems, the sub-problems of each area are optimized independently, and the overall optimization can be realized by sharing only part of the boundary bus information between areas. Case studies are carried out in two cases of the Institute of Electrical and Electronics Engineers (IEEE) 39-bus and 118-bus systems. The results show that when solving the multi-objective economic dispatch in LMIPS, compared with centralized optimization, the proposed DMOGWO can effectively ensure the privacy of information, the obtained objective values are smaller, and the performance test is better. •Distributed multi-objective optimization problems are considered.•Distributed multi-objective economic dispatch framework is built.•A distributed multi-objective grey wolf optimizer is proposed.•The optimizer fills the gap of distributed intelligent methods for optimization.•Optimal unit cost and carbon emissions are obtained by the optimizer simultaneously.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2021.108345