Development of a DSO support tool for congestion forecast
| Autor: | David Steen, Ankur Srivastava, Le Anh Tuan, Ioannis Bouloumpasis, Lucile Lemius, Ola Carlson, Quoc Tuan Tran |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK1001-1841
Other Electrical Engineering Electronic Engineering Information Engineering Artificial neural network Distribution or transmission of electric power Computer science business.industry 020209 energy 020208 electrical & electronic engineering Photovoltaic system Probabilistic logic Energy Engineering and Power Technology Usability Distribution management system 02 engineering and technology TK3001-3521 Reliability engineering Visualization Network congestion Production of electric energy or power. Powerplants. Central stations Control and Systems Engineering Scalability 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering business |
| Zdroj: | IET Generation, Transmission & Distribution, Vol 15, Iss 23, Pp 3345-3359 (2021) IET Generation, Transmission and Distribution (1751-8687) vol.15(2021) IET Generation, Transmission & Distrbution vol 15 issue 23 |
| ISSN: | 1751-8687 1751-8695 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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