Distribution Network Reconfiguration based on LMP at DG connected busses using game theory and self-adaptive FWA

• Published in: Energy (Oxford) 2021-01, Vol.215, p.119146, Article 119146
• Main Authors: Azad-Farsani, Ehsan, Sardou, Iman Goroohi, Abedini, Saeed
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Algorithms; Configurations; Distributed generation; Distribution company; Distribution network reconfiguration; Evolutionary algorithms; Game theory; Loss reduction; Methodology; Optimization; Reconfiguration; Search process; Self-adaptive firework algorithm
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2020.119146

Distribution Company (DISCO) uses some price-based policies to maximize the profit of Distributed Generation (DG) owners and minimize the power loss of the network. Meanwhile, loss of the network is minimized by Distribution Network Reconfiguration (DNR). In this paper, a hybrid market-based DNR methodology is proposed to concurrently obtain both the optimal configuration of the network, and Locational Marginal Prices (LMPs) at DG connected busses. Firework Algorithm (FWA) is coupled with an Iterative Game-Based algorithm (IGBA) to procure the optimal configuration of the network with the objective of power loss minimization. In the hybrid methodology, through the search process of FWA in finding the optimal configuration, the IGBA is run for each candidate configuration. Using the IGBA, the LMPs of DG units is calculated based on their share of loss reduction which is determined by the game theory. Also, in the FW algorithm, improper selection of coefficients leads to inefficiency of the algorithm. Therefore, a self-adaptive framework is proposed to specify the value of coefficients during the evolution of algorithm. The effectiveness of the proposed methodology is evaluated using a practical system. •Introducing self-adaptive fire-work algorithm (SAFWA).•Application of SAFWA in DNR problem.•Estimation state of system based on new configuration.•Introducing a market-based DNR policy.