Decentralized Optimization in the Scheduling of Three Virtual Power Plants with Non-Convex Constraints
2024 (English)In: AUT Journal of Modeling and Simulation, ISSN 2588-2953, Vol. 56, no 1, p. 3-18Article in journal (Refereed) Published
Abstract [en]
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 their 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.
Place, publisher, year, edition, pages
Amirkabir University of Technology , 2024. Vol. 56, no 1, p. 3-18
Keywords [en]
ADMM Algorithm, Decentralized Optimization, Fast ADMM Algorithm, Fast ADMM with Restart Algorithm, Virtual Power Plant (VPP)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-358409DOI: 10.22060/miscj.2024.23304.5364Scopus ID: 2-s2.0-85214121775OAI: oai:DiVA.org:kth-358409DiVA, id: diva2:1927884
Note
QC 20250116
2025-01-152025-01-152025-01-16Bibliographically approved