Quantifying the impact of load forecasting accuracy on congestion management in distribution grids
Digitalization is a global trend in energy systems and beyond. However, it is often unclear what digitalization means exactly in the context of energy systems and how the benefits of digitalization can be quantified. Providing additional information, e.g., through sensors and metering equipment, is...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002649 |
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| author | Maximilian Bernecker Marc Gebhardt Souhir Ben Amor Martin Wolter Felix Müsgens |
| author_facet | Maximilian Bernecker Marc Gebhardt Souhir Ben Amor Martin Wolter Felix Müsgens |
| author_sort | Maximilian Bernecker |
| collection | DOAJ |
| description | Digitalization is a global trend in energy systems and beyond. However, it is often unclear what digitalization means exactly in the context of energy systems and how the benefits of digitalization can be quantified. Providing additional information, e.g., through sensors and metering equipment, is one concrete angle where digitalization contributes. This paper provides a framework to quantify the value of such additional information in distribution grids. We analyze to what extent smart meters improve the accuracy of day-ahead load forecasts and quantify the savings in congestion management costs resulting from the improved accuracy.To quantify the cost reduction, we conduct a case study employing a simplified IEEE test system. Historical electricity load data from over 6,000 smart meters was used to improve day-ahead load forecasts. We assessed and compared the forecasting performance to estimates based on standard load profiles with multiple load forecast simulations in the network based on uncertainty parameterizations from forecasts with and without smart meter data.Calculating redispatch cost in the distribution grid, we find that the forecast based on smart meter data reduces key redispatch parameters such as the share of expected voltage violations, the amount of rescheduled generation by more than 90%. These improvements translate into a reduction in congestion management costs by around 97%. Furthermore, we shed light on whether the gains increase linearly with the number of smart meters and available data in the grid. When smart meter shares are increased uniformly throughout the grid, savings are concave, i.e., the first 10% of smart meters reduces congestion management costs by around 20% while the last 10% reduces these costs only marginally. Focusing smart meter installation on the most congested nodes reduces congestion management costs by around 60% with just 10% smart meter coverage, significantly outperforming a uniform rollout. However, savings in congestion management alone are not likely to recover the installation and operation costs of the installed smart meter. |
| format | Article |
| id | doaj-art-6d3eb403884e43169d5f54fe47e028dd |
| institution | OA Journals |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-6d3eb403884e43169d5f54fe47e028dd2025-08-20T02:30:43ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-07-0116811071310.1016/j.ijepes.2025.110713Quantifying the impact of load forecasting accuracy on congestion management in distribution gridsMaximilian Bernecker0Marc Gebhardt1Souhir Ben Amor2Martin Wolter3Felix Müsgens4Brandenburgische Technische Universität, Chair of Energy Economics, Cottbus, Germany; Corresponding author.Otto-von-Guericke-Universität, Chair of Electrical Grids and Renewable Energy, Magdeburg, GermanyBrandenburgische Technische Universität, Chair of Energy Economics, Cottbus, GermanyOtto-von-Guericke-Universität, Chair of Electrical Grids and Renewable Energy, Magdeburg, GermanyBrandenburgische Technische Universität, Chair of Energy Economics, Cottbus, GermanyDigitalization is a global trend in energy systems and beyond. However, it is often unclear what digitalization means exactly in the context of energy systems and how the benefits of digitalization can be quantified. Providing additional information, e.g., through sensors and metering equipment, is one concrete angle where digitalization contributes. This paper provides a framework to quantify the value of such additional information in distribution grids. We analyze to what extent smart meters improve the accuracy of day-ahead load forecasts and quantify the savings in congestion management costs resulting from the improved accuracy.To quantify the cost reduction, we conduct a case study employing a simplified IEEE test system. Historical electricity load data from over 6,000 smart meters was used to improve day-ahead load forecasts. We assessed and compared the forecasting performance to estimates based on standard load profiles with multiple load forecast simulations in the network based on uncertainty parameterizations from forecasts with and without smart meter data.Calculating redispatch cost in the distribution grid, we find that the forecast based on smart meter data reduces key redispatch parameters such as the share of expected voltage violations, the amount of rescheduled generation by more than 90%. These improvements translate into a reduction in congestion management costs by around 97%. Furthermore, we shed light on whether the gains increase linearly with the number of smart meters and available data in the grid. When smart meter shares are increased uniformly throughout the grid, savings are concave, i.e., the first 10% of smart meters reduces congestion management costs by around 20% while the last 10% reduces these costs only marginally. Focusing smart meter installation on the most congested nodes reduces congestion management costs by around 60% with just 10% smart meter coverage, significantly outperforming a uniform rollout. However, savings in congestion management alone are not likely to recover the installation and operation costs of the installed smart meter.http://www.sciencedirect.com/science/article/pii/S0142061525002649C5P4Q4 |
| spellingShingle | Maximilian Bernecker Marc Gebhardt Souhir Ben Amor Martin Wolter Felix Müsgens Quantifying the impact of load forecasting accuracy on congestion management in distribution grids International Journal of Electrical Power & Energy Systems C5 P4 Q4 |
| title | Quantifying the impact of load forecasting accuracy on congestion management in distribution grids |
| title_full | Quantifying the impact of load forecasting accuracy on congestion management in distribution grids |
| title_fullStr | Quantifying the impact of load forecasting accuracy on congestion management in distribution grids |
| title_full_unstemmed | Quantifying the impact of load forecasting accuracy on congestion management in distribution grids |
| title_short | Quantifying the impact of load forecasting accuracy on congestion management in distribution grids |
| title_sort | quantifying the impact of load forecasting accuracy on congestion management in distribution grids |
| topic | C5 P4 Q4 |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525002649 |
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