Sharif University of Technology Scientia Iranica 1026-3098 29 1 2022 02 01 An incentive-based policy on minimization of GHG emissions and loss using adaptive group search multi-objective optimization algorithm 230 246 21546 10.24200/sci.2019.53554.3326 EN M. H. Nazari Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran S. H. Hosseinian Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran E. Azad Farsani Department of Electrical Engineering, Golpaygan University, Golpaygan, Iran D. Faramarzi Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran Journal Article 2019 06 09 A transactive strategy to purposeful pricing distributed energy resources (DERs) in distribution networks is proposed in this paper. This strategy is presented as a novel heuristic optimization approach. The total network loss and released greenhouse gases (GHGs) emissions are considered as objective functions. In addition, the locational marginal prices (LMPs) and power factors of DERs are considered as decision variables. Each DER, which is more participated in the mitigation of afore-mentioned objectives, will contribute a larger excitement form benefits consequently. Therefore, more contribution consequent to more generation leads to a higher price for DERs bus in comparison to substation market price. Also, the earned benefits from loss/emission mitigations are allocated to DERs directly. The fairness of this pricing process is supervised by the Independent Distribution System Operator (IDSO). Because the problem has two contradictory objective functions, a reliable Multi-Objective method called Chaotic search and Covariance matrix (MGSOACC) is proposed to solve the problem. To evaluate the proposed method, the pricing procedure is applied on modified IEEE-33 and IEEE-69 bus test networks. Furthermore, in order to the validation of the proposed optimization method, the result-oriented comparisons between four conventional Multi-Objective optimization methods and proposed optimization method are presented. https://scientiairanica.sharif.edu/article_21546_cd9edffd2c8bdbbcf9d0c7b8a49d2a40.pdf