Leveraging Prosumer Flexibility to Mitigate Grid Congestion in Future Power Distribution Grids

The growing adoption of behind-the-meter (BTM) photovoltaic (PV) systems, electric vehicle (EV) home chargers, and heat pumps (HPs) is causing increased grid congestion issues, particularly in power distribution grids. Leveraging BTM prosumer flexibility offers a cost-effective and readily available solution to address these issues without resorting to expensive and time-consuming infrastructure upgrades. This work evaluated the effectiveness of this solution by introducing a novel modeling framework that combines a rolling horizon (RH) optimal power flow (OPF) algorithm with a customized piecewise linear cost function. This framework allows for the individual control of flexible BTM assets through various control measures, while modeling the power flow (PF) and accounting for grid constraints. We demonstrated the practical utility of the proposed framework in an exemplary residential region in Schutterwald, Germany. To this end, we constructed a PF-ready grid model for the region, geographically allocated a future BTM asset mix, and generated tailored load and generation profiles for each household. We found that BTM storage systems optimized for self-consumption can fully resolve feed-in violations at HV/MV stations but only mitigate 35% of the future load violations. Implementing additional control measures is key for addressing the remaining load violations. While curative measures, e.g., temporarily limiting EV charging or HP usage, have minimal impacts, proactive measures that control both the charging and discharging of BTM storage systems can effectively address the remaining load violations, even for grids that are already operating at or near full capacity.

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Publication Details

Journal
Energies
Published
2024-08-23
DOI
https://doi.org/10.3390/en17174217
Citations
1
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.21
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article

Leveraging Prosumer Flexibility to Mitigate Grid Congestion in Future Power Distribution Grids

Hans Joerg Heger, Stefan Niessen, Domenico Tomaselli, Dieter Most et al.
1 citations
Energies
Smart Grid Energy Management
0.21
article

Leveraging Prosumer Flexibility to Mitigate Grid Congestion in Future Power Distribution Grids

Hans Joerg Heger, Stefan Niessen, Domenico Tomaselli, Dieter Most, Paul Stursberg, Enkel Sinani
article en
1 citations

Abstract

The growing adoption of behind-the-meter (BTM) photovoltaic (PV) systems, electric vehicle (EV) home chargers, and heat pumps (HPs) is causing increased grid congestion issues, particularly in power distribution grids. Leveraging BTM prosumer flexibility offers a cost-effective and readily available solution to address these issues without resorting to expensive and time-consuming infrastructure upgrades. This work evaluated the effectiveness of this solution by introducing a novel modeling framework that combines a rolling horizon (RH) optimal power flow (OPF) algorithm with a customized piecewise linear cost function. This framework allows for the individual control of flexible BTM assets through various control measures, while modeling the power flow (PF) and accounting for grid constraints. We demonstrated the practical utility of the proposed framework in an exemplary residential region in Schutterwald, Germany. To this end, we constructed a PF-ready grid model for the region, geographically allocated a future BTM asset mix, and generated tailored load and generation profiles for each household. We found that BTM storage systems optimized for self-consumption can fully resolve feed-in violations at HV/MV stations but only mitigate 35% of the future load violations. Implementing additional control measures is key for addressing the remaining load violations. While curative measures, e.g., temporarily limiting EV charging or HP usage, have minimal impacts, proactive measures that control both the charging and discharging of BTM storage systems can effectively address the remaining load violations, even for grids that are already operating at or near full capacity.

EnergiesVol. 17(17)
Siemens (Germany) (DE), Technische Universität Darmstadt (DE), Technical University of Munich (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 49%
Smart Grid Energy Management
0.21
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