Distributed PV Hosting Capacity Enhancement Under Extreme High-Temperature Conditions Using an Improved Multi-Objective Artificial Bee Colony Algorithm

The frequent occurrence of extreme high-temperature events has significantly affected the operating characteristics and distributed photovoltaic (PV) hosting capacity of distribution networks. However, existing hosting capacity assessment methods rarely consider the accumulated heat effect caused by sustained high temperatures. To address this issue, this paper proposes a coordinated planning method for enhancing distributed PV hosting capacity under extreme high-temperature scenarios. First, an accumulated heat load model is developed to characterize the temporal cumulative influence of sustained high temperatures on temperature-sensitive loads. Meanwhile, the uncertainties associated with PV output fluctuations and load demand variations are considered to represent the stochastic characteristics of source-side generation and load-side consumption. Subsequently, a multi-objective source–network–load coordinated planning model is established to maximize distributed PV hosting capacity while minimizing the hosting capacity enhancement cost. A multi-objective artificial bee colony (MO-ABC) algorithm incorporating Sobol sequence-based quasi-Monte Carlo sampling (Sobol-MC) and a constraint domination-based constraint handling strategy are further developed to solve the proposed model efficiently. Simulation results on the modified IEEE 33-bus distribution system show that the proposed method increases distributed PV hosting capacity by 69.52% under extreme high-temperature scenarios through coordinated optimization of PV inverter reactive power control, VAR compensation, and Incentive-based Demand Response (IDR).