Dirichlet sampled capacity and loss estimation for LV distribution networks with partial observability

Telford, R., Stephen, B., Browell, J. and Haben, S. (2021) Dirichlet sampled capacity and loss estimation for LV distribution networks with partial observability. IEEE Transactions on Power Delivery, 36(5), pp. 2676-2686. (doi: 10.1109/TPWRD.2020.3025125)

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With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10-50% of customers have Smart Meters installed.

Item Type:Articles
Additional Information:This work is supported by the EPSRC via the Analytical Middleware for Informed Distribution Networks (AMIDiNe) project (EP/S030131/1). Jethro Browell is supported by an EPSRC Innovation Fellowship (EP/R023484/1)
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Authors: Telford, R., Stephen, B., Browell, J., and Haben, S.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:IEEE Transactions on Power Delivery
ISSN (Online):1937-4208
Published Online:18 September 2020

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