TY - JOUR
T1 - A multi-objective framework for distributed energy resources planning and storage management
AU - Ahmadi, Bahman
AU - Ceylan, Oguzhan
AU - Ozdemir, Aydogan
AU - Fotuhi-Firuzabad, Mahmoud
N1 - Funding Information:
This work is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) , through 1001 - The Scientific and Technological Research Projects Funding Program under Grant 117E773.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/5/15
Y1 - 2022/5/15
N2 - 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.
AB - 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.
KW - Distributed energy sources
KW - Heuristic algorithms
KW - Multi-objective optimization
KW - Network losses
KW - Power system planning
KW - Reliability
KW - 22/4 OA procedure
KW - UT-Hybrid-D
UR - http://www.scopus.com/inward/record.url?scp=85126642714&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2022.118887
DO - 10.1016/j.apenergy.2022.118887
M3 - Article
SN - 0306-2619
VL - 314
JO - Applied energy
JF - Applied energy
M1 - 118887
ER -