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Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks

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  • Ahmed A. Alguhi

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K.A.CARE Energy Research and Innovation Center in Riyadh, King Saud University, Riyadh 11421, Saudi Arabia)

  • Majed A. Alotaibi

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K.A.CARE Energy Research and Innovation Center in Riyadh, King Saud University, Riyadh 11421, Saudi Arabia)

  • Essam A. Al-Ammar

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K.A.CARE Energy Research and Innovation Center in Riyadh, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

Today, many countries are focused on smart grids due to their positive effects on all sectors of a power system, including those of operators, utilities, and consumers. Furthermore, the usage of renewable energy sources for power production is quickly expanding due to the depletion of fossil fuels and the emissions caused by their use. Additionally, intermittent power generation from renewable energy sources, such as wind and solar, necessitates the use of energy storage devices with which to ensure a continuous power supply to meet demand. This can be accomplished by employing an appropriate storage device with a sufficient storage capacity, thus enabling a grid-connected solar PV and wind system to have enhanced performance and to reduce adverse effects on the power quality of the grid. In this study, a probabilistic planning model that takes the intermittent natures of solar irradiances, wind speeds, and system demands into account is introduced. A novel criterion is also adopted to map the three-dimensional spaces of intermittency with the proposed model for optimizing BESS charging/discharging decisions. This planning model is intended to minimize the economic costs of investment and operation of a battery energy storage system (BESS) for a planning period. Moreover, the substation and feeder upgrade costs, as well as the overall system loss costs, are included in the proposed model. Particle swarm optimization (PSO) is utilized to find the optimal sizing, location, and operation of energy storage systems. The proposed methodology was validated using a 69-bus distribution system.

Suggested Citation

  • Ahmed A. Alguhi & Majed A. Alotaibi & Essam A. Al-Ammar, 2023. "Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks," Sustainability, MDPI, vol. 16(1), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:290-:d:1309440
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    References listed on IDEAS

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    1. Simin Peng & Liyang Zhu & Zhenlan Dou & Dandan Liu & Ruixin Yang & Michael Pecht, 2023. "Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources," Energies, MDPI, vol. 16(9), pages 1-13, May.
    2. Boroumandfar, Gholamreza & Khajehzadeh, Alimorad & Eslami, Mahdiyeh & Syah, Rahmad B.Y., 2023. "Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty," Energy, Elsevier, vol. 278(PA).
    3. Alotaibi, Majed A. & Salama, M.M.A., 2016. "An efficient probabilistic-chronological matching modeling for DG planning and reliability assessment in power distribution systems," Renewable Energy, Elsevier, vol. 99(C), pages 158-169.
    4. Hui Wang & Jun Wang & Zailin Piao & Xiaofang Meng & Chao Sun & Gang Yuan & Sitong Zhu, 2020. "The Optimal Allocation and Operation of an Energy Storage System with High Penetration Grid-Connected Photovoltaic Systems," Sustainability, MDPI, vol. 12(15), pages 1-22, July.
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