The increasing penetration of distributed generation (DG) in distribution networks and continuous advancements in automation technologies have driven the maturation of active control strategies for distribution systems. To further increase DG integrati on capacity, traditional distribution networks are transitioning towards new-type distribution networks. This paper establishes a multi-objective chance-constrained programming model that incorporates two control approaches: the deployment of reactive power compensation devices and inverter-based regulation. The model aims to maximize distributed generation hosting capacity while minimizing operational costs, taking into account the uncertainty of DG power output. The NSGA-II algorithm is employed to address the problem of improving distribution network hosting capacity through coordinated control devices. Simulations performed on a modified IEEE 33-node distribution system analyze the impact of varying confidence levels of chance constraints on the system's DG hosting capacity. Results demonstrate that reactive power control via source-side DG inverters combined with grid-side reactive compensation equipment can effectively enhance the distribution network's hosting capacity. Furthermore, the confidence level of chance constraints significantly influences the achievable hosting capacity. In planning studies, appropriate confidence levels for chance constraints can be determined according to practical grid conditions, thereby providing technical support for the coordinated planning of renewable energy integration and distribution network development.
Li et al. (Mon,) studied this question.