Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm. (10th June 2021)

• Record Type: Journal Article
• Authors: Taheri, Seyed Iman; Salles, Mauricio B.C.; Khan, Irfan Ahmad
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-10
• Subjects: distributed energy resources -- distributed generation -- distribution network -- energy management -- heuristic algorithm -- optimization algorithm; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.12974
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem. Abstract : This paper presents a new methodology for the electric utilities to improve the power quality of a distribution system that had decreased because of DERs' integration. In this regard, this paper presents a multiobjective optimization algorithm for AVR placement and sizing on a distribution system. The proposed method may enhance the flexibility in the power system for future development.