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 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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

With the opening and development of power markets, large-scale multi-area interconnected power systems have become inevitable. This paper introduces a distributed concept to mitigate the drawbacks of traditional centralized economic dispatch optimization method and proposes a distributed MOGWO. Case studies show that the proposed method can effectively protect information privacy and achieve smaller objective values compared to centralized optimization.