Distributed energy resource allocation using multi-objective grasshopper optimization algorithm

Ahmadi B., Ceylan O., Özdemir A.

ELECTRIC POWER SYSTEMS RESEARCH, vol.201, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 201
  • Publication Date: 2021
  • Doi Number: 10.1016/j.epsr.2021.107564
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Environment Index, INSPEC
  • Keywords: Distribution network planning, Optimal planning, Photovoltaic generation, Wind energy generation, Battery energy storage system, Grasshopper optimization algorithm, ACTIVE DISTRIBUTION NETWORKS, DISTRIBUTION-SYSTEMS, OPTIMAL PLACEMENT, STORAGE SYSTEMS, GENERATION, WIND, INTEGRATION, REANALYSIS, DGS
  • Istanbul Technical University Affiliated: Yes


The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.