Anderson, J.; Agalgaonkar, A.P. Low-Voltage Network Modeling and Analysis with Rooftop PV Forecasts: A Realistic Perspective from Queensland, Australia. Energies2023, 16, 5834.
Anderson, J.; Agalgaonkar, A.P. Low-Voltage Network Modeling and Analysis with Rooftop PV Forecasts: A Realistic Perspective from Queensland, Australia. Energies 2023, 16, 5834.
Anderson, J.; Agalgaonkar, A.P. Low-Voltage Network Modeling and Analysis with Rooftop PV Forecasts: A Realistic Perspective from Queensland, Australia. Energies2023, 16, 5834.
Anderson, J.; Agalgaonkar, A.P. Low-Voltage Network Modeling and Analysis with Rooftop PV Forecasts: A Realistic Perspective from Queensland, Australia. Energies 2023, 16, 5834.
Abstract
Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which can ultimately limit the amount of rooftop solar photovoltaics (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer owned rooftop solar PV on Energy Queensland’s distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland’s distribution feeders were modelled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process and quasi dynamic simulation (QDSL) models were used to represent the dynamic volt-watt and volt-var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear regression was used to form trend equations that represent various network characteristics against the number of PV connections. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer owned rooftop solar PV without the need for detail modeling.
Keywords
distributed energy resource; solar PV penetration; voltage rise; network constraints; network modeling automation; reverse power flow, inverter energy systems
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
