TY  - EJOU
AU  - Soltani, Iman 
AU  - Fardinfar, Farzin 
AU  - Shahnia, Farhad 

TI  - Probabilistic Enhancement of Solar Photovoltaic System Hosting Capacity Using Smart Photovoltaic-STATCOM Inverters and the Fire Hawk Optimizer
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - This paper presents a probabilistic optimization framework for enhancing the photovoltaic hosting capacity of distribution networks through the coordinated operation of smart photovoltaic-static synchronous compensator (PV-STATCOM) inverters. High penetration of photovoltaic systems introduces significant operational challenges, including voltage rise, increased power losses, and reduced system reliability, particularly under uncertain load demand and intermittent solar generation. To address these challenges, a multi-objective optimization problem is formulated to simultaneously maximize the photovoltaic hosting capacity while minimizing voltage deviation and power losses, subject to network operational constraints. The proposed approach employs the Fire Hawk optimizer, a recently developed metaheuristic algorithm, to efficiently solve this nonlinear and non-convex optimization problem. The decision variables include the optimal sizing of photovoltaic systems and the reactive power support provided by PV-STATCOM inverters. Unlike conventional deterministic approaches, uncertainty in load demand is explicitly modeled using a probabilistic framework based on Latin Hypercube sampling and Monte Carlo simulation. The Latin Hypercube sampling is utilized for efficient scenario generation while Monte Carlo studies enable statistical evaluation of system performance under diverse operating conditions. The effectiveness of the proposed method is validated on the IEEE 15-bus distribution system. Simulation results demonstrate that the coordinated control of PV-STATCOM inverters significantly improves voltage profiles and reduces power losses, leading to an increase in photovoltaic hosting capacity from 6.89 to 8.311 MW, corresponding to a 20.62% enhancement in the network under study. Furthermore, studies demonstrate that the Fire Hawk optimizer algorithm exhibits fast convergence and superior performance compared to conventional optimization techniques, including genetic algorithm, particle swarm optimization, and Aquila optimizer. The probabilistic analysis also confirms improved system robustness by reducing voltage violation risks under uncertainty.
KW  - Hosting capacity; PV-STATCOM; Latin Hypercube sampling; Monte Carlo; fire hawk optimizer

DO  - 10.32604/ee.2026.081627