Decentralized Optimization in the Scheduling of Three Virtual Power Plants with Non-Convex Constraints

Gholami, Amir Hossein; Suratgar, Amir Aboulfazl; Menhaj, Mohammad Bagher; Hesamzadeh, Mohammad Reza

doi:10.22060/miscj.2024.23304.5364

Decentralized Optimization in the Scheduling of Three Virtual Power Plants with Non-Convex Constraints

Document Type : Research Article

Authors

¹ Distributed Intelligent Optimization Research Laboratory, Department of Electrical Engineering, Amirkabir University, Tehran, Iran

² Department of Electrical Engineering and Computer Science, KTH, Stockholm, Sweden

10.22060/miscj.2024.23304.5364

Abstract

Virtual power plant planning (VPP) has received much attention in recent years. VPP refers to the integration of multiple power units, considered as a single power plant. In this paper, three VPPs are considered, each consisting of different power plant units and expected to supply the desired load. In addition to providing the desired load, they must maximize their profits. A decentralized optimization method was used to optimize these three VPPs. The reason for using a decentralized approach is to increase network security and eliminate the need for a central computer. However, using decentralized optimization increases the speed of problem solving. Finally, the obtained results are compared with the centralized method. Simulations show that almost the same results are achieved using different optimization methods. These results increase the trend of using decentralized methods in VPP. Another feature of decentralized methods compared to the centralized method is the reduction in the speed of problem solving, which in this article has greatly reduced the solution time. If the considered network becomes wider and the number of problem variables and its limitations increases, the use of decentralized methods will become more efficient, and in those problems, the difference in problem solving time by centralized and decentralized methods will increase.

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