Minimum PV curtailment for distribution networks based on moment difference analysis theory

Abstract

The high penetration of distributed photovoltaic (DPV) systems in distribution networks (DNs) can lead to a series of issues such as reverse power flows and voltage violations, posing a significant threat to the safe operation of DNs. Achieving the minimum curtailment of DPV in DNs is one of the most direct and cost-effective means to ensure their safe operation and effectively utilize renewable energy sources under existing conditions. Introducing the concepts of PV moments and load moments, the moment difference analysis theory (MDAT) for DNs with DPV is proposed. This theory transforms the integration challenge of DPV into a problem of balancing the moment difference (MD) equations for power restoration and maintenance. For a given DN, when the highest node voltage reaches the specified voltage limit, the MD, defined as the difference between PV moments and load moments, approximates a constant known as the critical moment difference (CMD). This CMD is determined by the topological structure and line parameters of the DN, independent of load distribution and PV deployment. The theoretical derivations and case studies confirm this concept. The CMD represents the limit of a DN’s capacity to integrate DPV. The DPV moment is the quantity that the DN needs to accommodate, while the load moment serves as the resource for accommodating the PV moment. Based on MDAT, a method for the minimum curtailment of DPV in DNs is proposed and applied to the analysis and calculations of a 10 kV feeder line at Sichakou of the State Grid Shandong Electric Power Company and the 12.66 kV IEEE 33 bus and 69 systems. The case studies demonstrate that, compared to traditional particle swarm optimization (PSO) methods, the minimum PV curtailment strategy presented in this paper increases optimization speed by 4736.82 times under an error margin of 0.6 %. This validates the correctness and rapidity of the method, making it suitable for real-time optimization and scheduling for minimum PV curtailment in DNs.