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Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs

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  • Ben Hamida, Imen
  • Salah, Saoussen Brini
  • Msahli, Faouzi
  • Mimouni, Mohamed Faouzi

Abstract

Several studies of distribution network enhancement focused only on the optimization of either the integration of distributed generations (DG) or network reconfiguration. However, very few researches have been done for distribution network reconfiguration simultaneously with the DG location and sizing. This paper presents a multi-objective management operations based on network reconfiguration in parallel with renewable DGs allocation and sizing for minimizing active power loss, annual operation costs (installation, maintenance, and active power loss costs) and pollutant gas emissions. The time sequence variation in wind speed, solar irradiation and load are taken into consideration. An efficient evolutionary technique based on the Pareto optimality is adopted to solve the problem. A fuzzy set theory is used to select the best compromise solution among obtained Pareto set. The obtained results prove the efficiency and the accuracy of the suggested method for the network manager to find the optimal network configuration simultaneously with DG location and sizing considering multiple criteria.

Suggested Citation

  • Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    • Handle: RePEc:eee:renene:v:121:y:2018:i:c:p:66-80
    DOI: 10.1016/j.renene.2017.12.106
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    6. O. D. Montoya & W. Gil-González & J. C. Hernández & D. A. Giral-Ramírez & A. Medina-Quesada, 2020. "A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders," Energies, MDPI, vol. 13(17), pages 1-22, August.
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    8. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    9. Xiao, Hao & Pei, Wei & Deng, Wei & Ma, Tengfei & Zhang, Shizhong & Kong, Li, 2021. "Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection," Applied Energy, Elsevier, vol. 284(C).
    10. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    11. Mahmoud M. Sayed & Mohamed Y. Mahdy & Shady H. E. Abdel Aleem & Hosam K. M. Youssef & Tarek A. Boghdady, 2022. "Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions," Energies, MDPI, vol. 15(6), pages 1-27, March.
    12. Manuel S. Alvarez-Alvarado & Johnny Rengifo & Rommel M. Gallegos-Núñez & José G. Rivera-Mora & Holguer H. Noriega & Washington Velasquez & Daniel L. Donaldson & Carlos D. Rodríguez-Gallegos, 2022. "Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation," Energies, MDPI, vol. 15(22), pages 1-12, November.
    13. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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