Stochastic Optimization of PV Non-Tripping Capacity in Distribution Networks Considering Uncertainties and Faults

Abstract

Against the backdrop of the “dual-carbon” goals, the penetration of distributed photovoltaics (PV) in distribution networks continues to increase. Under the combined effects of stochastic PV generation and load variability, assessing PV hosting capacity in distribution networks under fault conditions has become increasingly challenging. To address the limited consideration of uncertainty and fault conditions in existing studies, this paper proposes a stochastic optimization framework for evaluating PV hosting capacity under fault conditions that considers load uncertainty and mobile shared energy storage (MSES). First, historical data are used to generate representative uncertainty scenarios through Latin hypercube sampling. An optimization model is then formulated subject to secure distribution network operation constraints to determine the optimal capacities and locations of distributed PV installations. Meanwhile, MSES is incorporated to further enhance PV hosting capacity through flexible spatial and temporal energy transfer. Finally, simulations on the modified IEEE 33-bus system demonstrate that MSES can effectively increase PV hosting capacity and renewable energy accommodation. However, under fault conditions, changes in voltage profiles and the emergence of reverse power flows further reduce PV hosting capacity. Although incorporating load uncertainty increases the system cost, the resulting PV hosting capacity is more representative of practical operating conditions, thereby providing quantitative decision-making support for the planning and operation of distribution networks with high PV penetration.