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

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  • Ahmadi, Bahman
  • Ceylan, Oguzhan
  • Ozdemir, Aydogan
  • Fotuhi-Firuzabad, Mahmoud

Abstract

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.

Suggested Citation

  • Ahmadi, Bahman & Ceylan, Oguzhan & Ozdemir, Aydogan & Fotuhi-Firuzabad, Mahmoud, 2022. "A multi-objective framework for distributed energy resources planning and storage management," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003142
    DOI: 10.1016/j.apenergy.2022.118887
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    References listed on IDEAS

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    2. Ibrahim Mohamed Diaaeldin & Mahmoud A. Attia & Amr K. Khamees & Othman A. M. Omar & Ahmed O. Badr, 2023. "A Novel Multiobjective Formulation for Optimal Wind Speed Modeling via a Mixture Probability Density Function," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    3. Abid, Md. Shadman & Ahshan, Razzaqul & Al Abri, Rashid & Al-Badi, Abdullah & Albadi, Mohammed, 2024. "Techno-economic and environmental assessment of renewable energy sources, virtual synchronous generators, and electric vehicle charging stations in microgrids," Applied Energy, Elsevier, vol. 353(PA).
    4. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    5. Patel, Ismail & Shah, Adil & Shen, Boyang & Wei, Haigening & Hao, Luning & Hu, Jintao & Wang, Qi & Coombs, Tim, 2023. "Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications," Applied Energy, Elsevier, vol. 341(C).
    6. Soheil Younesi & Bahman Ahmadi & Oguzhan Ceylan & Aydogan Ozdemir, 2022. "Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems," Energies, MDPI, vol. 15(24), pages 1-18, December.
    7. Chen, Lei & Jiang, Yuqi & Zheng, Shencong & Deng, Xinyi & Chen, Hongkun & Islam, Md. Rabiul, 2023. "A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks," Applied Energy, Elsevier, vol. 350(C).

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