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<p>The wide use of renewable energy resources (RERs) and energy storage systems (ESSs) in modern distribution networks increases the complexity of studying the performance of these systems. Estimating the maximum hosting capacity (HC) is essential for the utilities to calculate the maximum penetration of RERs and ESSs that the power system can host without violating pre-specified operational constraints. Therefore, enhancing the performance of the distribution systems is an essential goal for power system operators. Several ways can improve HC, such as network reconfiguration, system reinforcement, and adding external compensators, such as capacitor banks (CBs) and automatic voltage regulators (AVRs). This paper introduces a modeling strategy for modeling the photovoltaic and wind-based distributed generators (DGs) used for planning purposes in the distribution network in the presence of ESSs and AVRs to maximize the HC level. Algorithmically, for optimization purposes, this article applies a new optimization technique called the Snake optimization algorithm, in which the optimizer decides the optimal allocation (i.e., location and size) of DGs, CBs, AVRs, and ESSs to increase the HC at each hour at each season of a rural Egyptian radial feeder system called the Egyptian Talla system. An AC power flow is performed using a forward–backward sweep technique to simulate the system’s operating conditions. The proposed modeling strategy is based on Monte Carlo simulation (MCS). The objective function was formulated to improve the voltage stability index and the loading capacity of the system, maximize the benefits obtained from reducing the system’s active power loss and the apparent power purchased from the utility, and improve the network’s HC while meeting the operator requirements. A techno-economic analysis that includes the fixed and operating costs of DGs, CBs, AVRs, and ESSs, as well as the benefits obtained from the reduction in active power loss and the complex power purchased from the grid, is also performed. The results show that the employed algorithm provides good results in which the probabilistic HC reaches 100% for all seasons.</p>

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Hosting capacity maximization by optimal planning of active and reactive power compensators, voltage regulators, and energy storage units in a rural Egyptian power system

  • 期刊论文

  • 发表日期：

    2025年2月26日

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  • Ahmed M. Mahmoud,
  • Mohamed Ezzat,
  • Shady H. E. Abdel Aleem,
  • Almoataz Y. Abdelaziz

摘要

The wide use of renewable energy resources (RERs) and energy storage systems (ESSs) in modern distribution networks increases the complexity of studying the performance of these systems. Estimating the maximum hosting capacity (HC) is essential for the utilities to calculate the maximum penetration of RERs and ESSs that the power system can host without violating pre-specified operational constraints. Therefore, enhancing the performance of the distribution systems is an essential goal for power system operators. Several ways can improve HC, such as network reconfiguration, system reinforcement, and adding external compensators, such as capacitor banks (CBs) and automatic voltage regulators (AVRs). This paper introduces a modeling strategy for modeling the photovoltaic and wind-based distributed generators (DGs) used for planning purposes in the distribution network in the presence of ESSs and AVRs to maximize the HC level. Algorithmically, for optimization purposes, this article applies a new optimization technique called the Snake optimization algorithm, in which the optimizer decides the optimal allocation (i.e., location and size) of DGs, CBs, AVRs, and ESSs to increase the HC at each hour at each season of a rural Egyptian radial feeder system called the Egyptian Talla system. An AC power flow is performed using a forward–backward sweep technique to simulate the system’s operating conditions. The proposed modeling strategy is based on Monte Carlo simulation (MCS). The objective function was formulated to improve the voltage stability index and the loading capacity of the system, maximize the benefits obtained from reducing the system’s active power loss and the apparent power purchased from the utility, and improve the network’s HC while meeting the operator requirements. A techno-economic analysis that includes the fixed and operating costs of DGs, CBs, AVRs, and ESSs, as well as the benefits obtained from the reduction in active power loss and the complex power purchased from the grid, is also performed. The results show that the employed algorithm provides good results in which the probabilistic HC reaches 100% for all seasons.