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Optimal probabilistic based storage planning in tap-changer equipped distribution network including PEVs, capacitor banks and WDGs: A case study for Iran

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  • Ahmadian, Ali
  • Sedghi, Mahdi
  • Aliakbar-Golkar, Masoud
  • Elkamel, Ali
  • Fowler, Michael

Abstract

Due to their cost-effective and environmental-friendly natures, renewable energies as well as Plug-in Electric Vehicles (PEVs) are increasingly utilized nowadays. A critical challenge with renewable energies is natural intermittency and as such can be addressed appropriately using Energy Storage Systems (ESS). In this paper, optimal planning of battery based energy storage units is proposed in distribution network. As an important challenge in optimal storage planning, the uncertainty investigation is dealt with in this work. A new approach which is based on Point Estimate Method (PEM) is introduced as a tool to handle the uncertainty of the load, the Wind-based Distributed Generation (WDG) and PEVs demand, simultaneously. The proposed method is intuitively compared with Monte Carlo Simulation (MCS) as well as conventional PEM for a case study and the results are verified through the comparisons. Moreover, in order to challenge voltage control benefit of the storage units, the under study distribution network is equipped to the tap-changer and the capacitor banks. Whereas the optimal storage planning is a very complicated task, a modified hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS) algorithm is used to solve the related optimization problem. The simulation results for a case study in Iran show the effectiveness of the proposed approach in different scenarios.

Suggested Citation

  • Ahmadian, Ali & Sedghi, Mahdi & Aliakbar-Golkar, Masoud & Elkamel, Ali & Fowler, Michael, 2016. "Optimal probabilistic based storage planning in tap-changer equipped distribution network including PEVs, capacitor banks and WDGs: A case study for Iran," Energy, Elsevier, vol. 112(C), pages 984-997.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:984-997
    DOI: 10.1016/j.energy.2016.06.132
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    Cited by:

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    2. Gianni Celli & Fabrizio Pilo & Giuditta Pisano & Simona Ruggeri & Gian Giuseppe Soma, 2021. "Relieving Tensions on Battery Energy Sources Utilization among TSO, DSO, and Service Providers with Multi-Objective Optimization," Energies, MDPI, vol. 14(1), pages 1-22, January.
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    4. Ahmadian, Ali & Sedghi, Mahdi & Elkamel, Ali & Fowler, Michael & Aliakbar Golkar, Masoud, 2018. "Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2609-2624.
    5. Francisco J. Ruiz-Rodríguez & Jesús C. Hernández & Francisco Jurado, 2017. "Probabilistic Load-Flow Analysis of Biomass-Fuelled Gas Engines with Electrical Vehicles in Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-23, October.
    6. Ali Ahmadian & Ali Elkamel & Abdelkader Mazouz, 2019. "An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network," Energies, MDPI, vol. 12(16), pages 1-14, August.
    7. Saboori, Hedayat & Hemmati, Reza & Ghiasi, Seyyed Mohammad Sadegh & Dehghan, Shahab, 2017. "Energy storage planning in electric power distribution networks – A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1108-1121.
    8. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
    9. Saboori, Hedayat & Hemmati, Reza, 2017. "Maximizing DISCO profit in active distribution networks by optimal planning of energy storage systems and distributed generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 365-372.
    10. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    11. Wang, Sinan & Zhao, Fuquan & Liu, Zongwei & Hao, Han, 2017. "Heuristic method for automakers' technological strategy making towards fuel economy regulations based on genetic algorithm: A China's case under corporate average fuel consumption regulation," Applied Energy, Elsevier, vol. 204(C), pages 544-559.
    12. Ahmadian, Ali & Sedghi, Mahdi & Fgaier, Hedia & Mohammadi-ivatloo, Behnam & Golkar, Masoud Aliakbar & Elkamel, Ali, 2019. "PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect," Energy, Elsevier, vol. 175(C), pages 265-277.
    13. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Frede Blaabjerg & Pu Li, 2020. "Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems," Energies, MDPI, vol. 13(7), pages 1-17, April.
    14. Lin, Lihua & Shen, Shujian & Liao, Yunlin & Wang, Chuanliang & Shahabi, Laleh, 2022. "Shunt capacitor allocation by considering electric vehicle charging stations and distributed generators based on optimization algorithm," Energy, Elsevier, vol. 239(PE).
    15. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1205-1230.

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