Optimization of Photovoltaic (PV) Hosting Capacity in 20 kV Distribution System Using Grey Wolf Optimizer (GWO) Algorithm

2026-07-22T03:56:58+00:00

The transition toward sustainable energy systems to mitigate global warming caused by greenhouse gas emissions from fossil fuel-based power generation has accelerated the integration of photovoltaic (PV) systems into distribution networks. However, massive and uncontrolled PV integration may lead to operational issues in power systems. Therefore, hosting capacity studies are required to determine the maximum PV capacity that can be integrated without violating technical operating constraints. Due to the complex, non-linear, and non-convex nature of the hosting capacity problem, effective optimization techniques are necessary. This study proposes the Grey Wolf Optimizer (GWO) algorithm to determine the optimal location and capacity of PV with the objectives of maximizing the PV penetration while minimizing system power losses. The Site Planning Model (SPM) method is employed to identify candidate buses for PV installation, thereby reducing space and computational time. By coupling GWO's global search with SPM-based candidate-bus pre-selection, this study reduces the optimization search space while preserving solution quality. The IEEE 33-bus 20 kV test system is used to evaluate the performance of the proposed method in single and multiple PV installations with inverter power factors of unity and 0.95 lagging. The results show that the GWO algorithm achieves stable and consistent convergence, with a maximum PV penetration rate of 87.99% and a system power loss reduction of 85.57% in the scenario involving three PV units on three busbars at a 0.95 lagging power factor. Furthermore, an inverter power factor closer to unity tends to reduce the maximum achievable PV penetration. The proposed approach also improves voltage profiles, reduces line loading, and enhances overall distribution system performance.

Adaptive Sliding Mode Control with a Nonlinear Sliding Surface for DC-Bus Voltage Regulation in a Renewable-Energy-Based DC Microgrid

2026-06-01T13:01:33+00:00

This study proposes an adaptive sliding mode control (ASMC) scheme incorporating a nonlinear sliding surface (NSS), denoted ASMC-NSS, for direct-current (DC)-bus voltage regulation in a renewable-energy-based DC microgrid. ASMC augments conventional sliding mode control (CSMC) through channel-wise switching-gain scheduling based on the integral absolute error (IAE), while the NSS introduces bounded, state-dependent scaling of the current-tracking surface. The gain schedule adjusts the switching authority as the accumulated tracking error crosses prescribed thresholds, whereas the NSS shapes the reaching dynamics to improve transient tracking and suppress overshoot. The controller is applied to a system integrating a wind turbine, a photovoltaic (PV) array, and battery energy storage. MATLAB/Simulink comparisons with CSMC and ASMC without the NSS show that ASMC-NSS reduces the current-tracking IAE by 90.5% and 87.3%, respectively, and achieves a current settling time of 0.054 s. It maintains the 500 V DC bus with a maximum overshoot of 0.28 V and a 0.02 s recovery time to the ±0.5 V band. Lyapunov analysis establishes asymptotic stability of the ideal inner current loops and uniform ultimate boundedness under bounded matched uncertainties.

Journal of Fuzzy Systems and Control

Optimization of Photovoltaic (PV) Hosting Capacity in 20 kV Distribution System Using Grey Wolf Optimizer (GWO) Algorithm

Adaptive Sliding Mode Control with a Nonlinear Sliding Surface for DC-Bus Voltage Regulation in a Renewable-Energy-Based DC Microgrid