A multi-objective framework for distributed energy resources planning and storage management

Ahmadi B., CEYLAN O., Özdemir A., Fotuhi-Firuzabad M.

APPLIED ENERGY, vol.314, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 314
  • Publication Date: 2022
  • Doi Number: 10.1016/j.apenergy.2022.118887
  • Journal Name: APPLIED ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Biotechnology Research Abstracts, CAB Abstracts, Communication Abstracts, Environment Index, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Distributed energy sources, Heuristic algorithms, Multi-objective optimization, Power system planning, Reliability, Network losses, RADIAL-DISTRIBUTION SYSTEM, OPTIMAL PLACEMENT, CAPACITORS, RELIABILITY, REANALYSIS
  • Istanbul Technical University Affiliated: Yes


The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine-cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD.