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Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units

Author

Listed:
  • Ashraf Ramadan

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Mohamed Ebeed

    (Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag 82524, Egypt)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Almoataz Y. Abdelaziz

    (Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Hassan Haes Alhelou

    (School of Electrical and Electronic Engineering, University College Dublin (UCD), Dublin 4, Ireland)

Abstract

Renewable energy-based distributed generators are widely embedded into distribution systems for several economical, technical, and environmental tasks. The main concern related to the renewable-based distributed generators, especially photovoltaic and wind turbine generators, is the continuous variations in their output powers due to variations in solar irradiance and wind speed, which leads to uncertainties in the power system. Therefore, the uncertainties of these resources should be considered for feasible planning. The main innovation of this paper is that it proposes an efficient stochastic framework for the optimal planning of distribution systems with optimal inclusion of renewable-based distributed generators, considering the uncertainties of load demands and the output powers of the distributed generators. The proposed stochastic framework depends upon the scenario-based method for modeling the uncertainties in distribution systems. In this framework, a multi-objective function is considered for optimal planning, including minimization of the expected total power loss, the total system voltage deviation, the total cost, and the total emissions, in addition to enhancing the expected total voltage stability. A novel efficient technique known as the Equilibrium Optimizer (EO) is actualized to appoint the ratings and locations of renewable-based distributed generators. The effectiveness of the proposed strategy is applied on an IEEE 69-bus network and a 94-bus practical distribution system situated in Portugal. The simulations verify the feasibility of the framework for optimal power planning. Additionally, the results show that the optimal integration of the photovoltaic and wind turbine generators using the proposed method leads to a reduction in the expected power losses, voltage deviations, cost, and emission rate and enhances the voltage stability by 60.95%, 37.09%, 2.91%, 70.66%, and 48.73%, respectively, in the 69-bus system, while in the 94-bus system these values are enhanced to be 48.38%, 39.73%, 57.06%, 76.42%, and 11.99%, respectively.

Suggested Citation

  • Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Almoataz Y. Abdelaziz & Hassan Haes Alhelou, 2021. "Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3566-:d:522530
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    References listed on IDEAS

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    1. Baghaee, H.R. & Mirsalim, M. & Gharehpetian, G.B. & Talebi, H.A., 2016. "Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system," Energy, Elsevier, vol. 115(P1), pages 1022-1041.
    2. Mohamed Ebeed & Ayman Alhejji & Salah Kamel & Francisco Jurado, 2020. "Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems," Energies, MDPI, vol. 13(17), pages 1-19, August.
    3. Yajing Gao & Jianpeng Liu & Jin Yang & Haifeng Liang & Jiancheng Zhang, 2014. "Multi-Objective Planning of Multi-Type Distributed Generation Considering Timing Characteristics and Environmental Benefits," Energies, MDPI, vol. 7(10), pages 1-16, September.
    4. Sam Weckx & Reinhilde D'hulst & Johan Driesen, 2015. "Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration," Energies, MDPI, vol. 8(5), pages 1-22, May.
    5. Zelan Li & Yijia Cao & Le Van Dai & Xiaoliang Yang & Thang Trung Nguyen, 2019. "Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm," Energies, MDPI, vol. 12(15), pages 1-31, August.
    6. Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei & Xu, Zhao & McCulloch, Malcolm D. & Wong, Kit Po, 2017. "A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 439-451.
    7. Cervantes, Jairo & Choobineh, Fred, 2018. "Optimal sizing of a nonutility-scale solar power system and its battery storage," Applied Energy, Elsevier, vol. 216(C), pages 105-115.
    8. Esmaeili, Mobin & Sedighizadeh, Mostafa & Esmaili, Masoud, 2016. "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, Elsevier, vol. 103(C), pages 86-99.
    9. Gustavo Azevedo Xavier & Delly Oliveira Filho & José Helvecio Martins & Paulo Marcos de Barros Monteiro & Antônia Sônia Alves Cardozo Diniz, 2015. "Simulation of Distributed Generation with Photovoltaic Microgrids—Case Study in Brazil," Energies, MDPI, vol. 8(5), pages 1-21, May.
    10. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Jaime A. Valencia-Velásquez, 2018. "A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem," Energies, MDPI, vol. 11(9), pages 1-23, September.
    11. Xiangang Peng & Lixiang Lin & Weiqin Zheng & Yi Liu, 2015. "Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem," Energies, MDPI, vol. 8(12), pages 1-19, December.
    12. Sims, Ralph E. H. & Rogner, Hans-Holger & Gregory, Ken, 2003. "Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation," Energy Policy, Elsevier, vol. 31(13), pages 1315-1326, October.
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    1. Hamza Mubarak & Nurulafiqah Nadzirah Mansor & Hazlie Mokhlis & Mahazani Mohamad & Hasmaini Mohamad & Munir Azam Muhammad & Mohammad Al Samman & Suhail Afzal, 2021. "Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    2. Nasreddine Belbachir & Mohamed Zellagui & Samir Settoul & Claude Ziad El-Bayeh & Ragab A. El-Sehiemy, 2023. "Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Al," Energies, MDPI, vol. 16(4), pages 1-24, February.
    3. Ahmed Amin & Mohamed Ebeed & Loai Nasrat & Mokhtar Aly & Emad M. Ahmed & Emad A. Mohamed & Hammad H. Alnuman & Amal M. Abd El Hamed, 2022. "Techno-Economic Evaluation of Optimal Integration of PV Based DG with DSTATCOM Functionality with Solar Irradiance and Loading Variations," Mathematics, MDPI, vol. 10(14), pages 1-16, July.

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