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Quantifying the impact of load forecasting accuracy on congestion management in distribution grids

Year: 2025
Type: article
Abstract: Digitalization is a mega-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.We find that congestion actions resulting from forecasts based on smart meters result in significant cost savings. Our findings also indicate that the cost of congestion management decreases progressively with the number of smart meters installed. Additionally, we explore different smart meter rollout strategies to assess the benefit of rolling out smart meters in the most crucial nodes first. We show that prioritizing critical locations within the network for smart meter rollout is more cost-efficient than a symmetric rollout.
DOI: 10.1016/j.ijepes.2025.110713
Language: English
Open Access status: gold
Estimated APC paid: $3,920
OpenAlex ID: W4410217783
FWCI: 1.36
Cites: 57
Cited by: 7
Related to: 10