Abstract
Distribution systems operators (DSOs) encounter the challenge of managing network losses in large geographical areas with hundreds of secondary substations and thousands of customers and with an ever-increasing presence of renewable energy sources. This situation complicates the estimation process of power loss, which is paramount to improve the network energy efficiency level in the context of the European Union energy policies. Thus, this article presents a methodology to estimate power losses in large-scale low voltage (LV) smart grids. The methodology is based on a deep-learning loss model to infer the network technical losses considering a large rollout of smart meters, a high penetration of distributed generation (DG) and unbalanced operation, among other network characteristics. The methodology has been validated in a large-scale LV distribution area in Madrid (Spain). The proposed methodology has proven to be a potential network loss estimation tool to improve the energy efficiency level in large-scale smart grids with a high penetration of distributed resources. The accuracy of the proposed methodology outperforms that of the state-of-the-art loss estimation methods, exhibiting a rapid convergence which allows for its use in real-time operations.
Highlights
One of the main challenges that distribution system operators (DSOs) encounter is improving the energy efficiency of smart grids; this entails knowing the degree of energy losses produced, especially in low voltage (LV) distribution networks
This article presents a generic deep learning loss model to estimate the technical losses in large-scale LV smart grid with distributed generation (DG) penetration and unbalanced operation
The proposed loss model is based on a Deep Neural Network that has been formulated as a power loss
Summary
One of the main challenges that distribution system operators (DSOs) encounter is improving the energy efficiency of smart grids; this entails knowing the degree of energy losses produced, especially in low voltage (LV) distribution networks. In [21], eXtreme Gradient Boosting (XGBoost) is used to estimate statistical technical losses of distribution feeders The method requires both a high number of representative feeders and a large amount of historical data to apply the clustering feeder stage. This is an important limitation for expanding its applicability to large-scale smart grids characterised by reduced network data availability. The method is built upon an enhanced representative feeder selection process and a novel deeplearning loss model, and it considers the network TLs in an entire geographical area This information will assist the DSO in determining the variability of technical losses in large distribution areas using only the smart meter measurements of demand customers and DG generation measurements. To the best of the authors’ knowledge, the estimation of power losses in unbalanced large-scale LV smart grids using deep learning techniques remains highly unexplored in the literature
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