An incentive-based policy on minimization of GHG emissions and loss using adaptive group search multi-objective optimization algorithm

Document Type : Article

Authors

1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Electrical Engineering, Golpaygan University, Golpaygan, Iran

Abstract

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.

Keywords

References

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Scientia Iranica
Volume 29, Issue 1
Transactions on Computer Science & Engineering and Electrical Engineering (D)
January and February 2022
Pages 230-246

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History

  • Receive Date: 09 June 2019
  • Revise Date: 01 August 2019
  • Accept Date: 02 September 2019

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