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Impact of uncertain information on the detection of future congestions in distribution networks
In recent years, the penetration of renewable energy resources in distribution grids has been steadily increasing, raising new issues such as voltage violations or line congestion. Due to their large inertia, individual buildings may regulate their heating system to help distribution system operators alleviating these congestion.
Previous works have proposed methods to anticipate future congestions, given forecasts and uncertainties [1,2]. In this project, we aim at assessing the impact of the spatial-temporal uncertainties on the congestion detection. In other words, how the grid placement of an uncertain consumer/producer or the knowledge on its future consumption/production may impact the congestion detection.
The aim of this project is to assess the impact of spatio-temporal uncertainties on the detection of future grid congestions.
The aim of this project is to assess the impact of spatio-temporal uncertainties on the detection of future grid congestions.
The tasks that will be carried out in this project are:
1. Conduct a brief review on challenges imposed by an increase in consumption and a massive integration of renewable energy technologies in distribution grids.
2. Get familiar with power flow algorithms.
3. Explore the literature on congestion detection and forecast algorithms.
4. Based on previous studies, develop an uncertainty framework for the node consumption and renewable production in a distribution grid.
5. Detect future grid congestion under different scenarios of uncertainties.
6. Analyze the results and write a report.
The tasks that will be carried out in this project are: 1. Conduct a brief review on challenges imposed by an increase in consumption and a massive integration of renewable energy technologies in distribution grids. 2. Get familiar with power flow algorithms. 3. Explore the literature on congestion detection and forecast algorithms. 4. Based on previous studies, develop an uncertainty framework for the node consumption and renewable production in a distribution grid. 5. Detect future grid congestion under different scenarios of uncertainties. 6. Analyze the results and write a report.
Julie Rousseau (jrousseau@ehtz.ch)
Dr. Hanmin Cai (hanmin.cai@empa.ch)
Julie Rousseau (jrousseau@ehtz.ch) Dr. Hanmin Cai (hanmin.cai@empa.ch)