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

• Published in: Applied energy Vol. 314; p. 118887
• Main Authors: Ahmadi, Bahman, Ceylan, Oguzhan, Ozdemir, Aydogan, Fotuhi-Firuzabad, Mahmoud
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2022
• Subjects: Distributed energy sources; Heuristic algorithms; Multi-objective optimization; Network losses; Power system planning; Reliability; Distributed energy sources; Multi-objective optimization; Heuristic algorithms; Network losses; Reliability; Power system planning
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2022.118887

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. •Optimal DER planning and ESS management in EDNs using a three-dimensional multiobjective optimization framework•DN Reliability improvement together with power losses and annual cost minimization.•72 h optimization period to account the seasonal characteristics of the load and the DGs.•Real load curve and wind and solar radiation data of Turkish territory.•Multi objective sine–cosine algorithm providing better Pareto fronts according to two performance indices.