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Predicting hosting capacity of photovoltaic power production in low-voltage grids using regressive techniques
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this study we predict the hosting capacity (HC) of photovoltaic (PV) power of low-voltage (LV) grids utilizing explanatory variables that are straightforward for stakeholders to determine. The motivation of this study is to avoid the necessity of simulating electricity grids using power flow analysis, which are generally time consuming in terms of both coding and solving. In order to achieve this, we utilize extensive power flow simulations performed on two medium-voltage (MV) grids in Herrljunga, Sweden, and extract explanatory variables that show high correlation with HC. Furthermore, we employ multiple linear regression (MLR), gradient boosting (GB) and Gaussian process (GP) to predict HC. The results reveal that HC can be predicted with reasonable accuracy, achieving MAE between 12.2 kW and 14.0 kW, and RMSE between 15.7 kW and 17.4 kW, and can therefore guide stakeholders by providing an accurate first estimate. 

Place, publisher, year, edition, pages
2017.
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:uu:diva-332893OAI: oai:DiVA.org:uu-332893DiVA: diva2:1154512
Conference
Solar Integration Workshop
Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2017-11-06

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