Abstract
This paper presents a novel DSO support tool with visualisation capability for forecasting network congestion in distribution systems with a high level of renewables. To incorporate the uncertainties in the distribution systems, the probabilistic power flow framework has been utilised. An advanced photovoltaic production forecast based on sky images and a load forecast using an artificial neural network is used as the input to the tool. In addition, advanced load models and operating modes of photovoltaic inverters have been incorporated into the tool. The tool has been applied in case studies to perform congestion forecasts for two real distribution systems to validate its usability and scalability. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and is able to visualise the cumulative probabilities of nodes voltage deviation and network components (branches and transformers) congestion for a variety of forecast horizons as desired by the DSO. The results have also shown that explicit inclusion of load-voltage dependency models would improve the accuracy of the congestion forecast. For demonstrating the applicability of the tool, it has been integrated into an existing distribution management system via the IoT platform of a DMS vendor, Atos Worldgrid.
Highlights
1.1 MotivationGlobal warming concern leads the transition from fossil fuels to renewable energy sources (RES) for the generation of electricity
The hourly load profiles at different nodes for these areas are presented in Figure 7, which are obtained from the real load data of a local distribution system operators (DSOs) in Sweden
This paper presents a tool to assist the DSO to forecast the congestion levels in their networks as per the preferences specified by the DSO
Summary
Global warming concern leads the transition from fossil fuels to renewable energy sources (RES) for the generation of electricity. Due to the anticipated increased penetration of RES in distribution systems and more uncertain loads such as heat pumps, electric vehicles etc., the distribution system operators (DSOs) are expected to face increasing component congestion and voltage variation issues in their networks [8]. In addition to such operational problems, the DSOs are likely to face issues. [15] which utilises the generation rescheduling and load shedding along with the inclusion of voltage-dependent load models Most of these works have either mentioned the need for the congestion forecast or have assumed that the DSOs have the capability to forecast the congestion. The congestion forecasting functionality is not explicitly included or addressed in these ADMS solutions
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