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Article type: Research Article
Authors: Nazari, M.H.a | Hosseinian, S.H.a; * | Azad-farsani, E.b
Affiliations: [a] Department of Electrical Engineering, Amirkabir University of Technology, Hafez Ave, Tehran, Iran | [b] Department of Electrical Engineering, Golpayegan University of Technology, Golpayegan, Iran
Correspondence: [*] Corresponding author. S.H. Hosseinian, Department of Electrical Engineering, Amirkabir University of Technology, Hafez Ave, Tehran 158754431, Iran. Tel.: +98 21 6454 3343; Fax: +98 21 3343 3300; E-mail: hosseinian@aut.ac.ir.
Abstract: This paper presents a new electricity pricing methodology in distribution networks by imploying Distributed Generations (DGs). As long as the Locational Marginal Price (LMP) is used in the pricing of short-term operations as an efficient method, it can be performed in distribution network consequently. The proposed pricing method is modeled as an optimization problem with the specific control variables and objectives. The variables are LMPs and DGs power factors, and objectives are total losses and emission. Also, profit earned from reduction of loss and emission was allocated between DGs. Reduction of loss and emission was compensated by DGs production. As a result, more production resulted to high price of DG buses rather than market price. This price should be provided by Distribution Company (DISCO), and DISCO earns this money form the profit. Because of the multi-objective nature of the problem, a Multi-Objective Genetic Algorithm Optimization (MOGA) is implemented to solve. In order to validate the proposed method, a comparison between MOGA and Multi-Objective Particle Swarm Optimization (MOPSO) is performed consequently. The proposed method allows the decision-makers to apply their preferences among losses/emission reduction and DISCO’s benefit. furthermore, the feasibility of the proposed method is investigated using the IEEE-32 bus test system.
Keywords: Electricity price, multi-objective optimization, locational marginal price, loss reduction, emission reduction
DOI: 10.3233/JIFS-181990
Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6143-6154, 2019
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