Dynamic hosting capacity analysis for distributed photovoltaic resources—Framework and case study. (15th December 2020) - Jain, Akshay Kumar; Horowitz, Kelsey; Ding, Fei; Sedzro, Kwami Senam; Palmintier, Bryan; Mather, Barry; Jain, Himanshu
Abstract: Distributed photovoltaic systems can cause adverse distribution system impacts, including voltage violations at customer locations and thermal overload of lines, transformers, and other equipment resulting from high current. The installed capacity at which violations first occur and above which would require system upgrades is called the hosting capacity. Current static methods for determining hosting capacity tend to either consider infrequent worst-case snapshots in time and/or capture coarse time and spatial resolution. Because the duration of violations cannot be captured with these traditional methods, the metric thresholds used in these studies conservatively use the strictest constraints given in operating standards, even though both worse voltage performance and higher overloads may be temporarily acceptable. However, assessing the full details requires accurately capturing time-dependence, voltage-regulating equipment operations, and performance of advanced controls-based mitigation techniques. In this paper, we propose a dynamic distributed photovoltaic hosting capacity methodology to address these issues by conducting power flow analysis for a full year. A key contribution is the formulation of time aware metrics to take these annual results and identify the hosting capacity. Through a case study, we show that this approach can more fully capture grid impacts of distributed photovoltaic than traditional methods and the dynamic hosting capacity was 60%–200% higher than the static hosting capacity in this case study. Highlights: Proposed dynamic study can more accurately capture a feeder's hosting capacity. Proposed time aware metrics comply more closely with interconnection standards. Traditional static methods cannot capture grid impacts like annual energy losses. Static studies cannot assess the efficacy of smart inverter-based control schemes. Case study and sensitivity analysis show the scalability of the proposed approach.