Projects per year
Phase imbalance widely exists in the UK’s low voltage (415V, LV) distribution networks. The imbalances not only lead to insufficient use of LV network assets but also cause energy losses. They lead to hundreds of millions of British pounds each year in the UK. The cost-benefit analyses of phase balancing solutions remained an unresolved question for the majority of the LV networks. The main challenge is data-scarcity – these networks only have peak current and total energy consumption that are collected once a year. To perform a cost-benefit analysis of phase balancing for data-scarce LV networks, this paper develops a customized cluster-wise Gaussian process regression (CGPR) approach. The approach estimates the total cost of phase imbalance for any data-scarce LV network by extracting knowledge from a set of representative data-rich LV networks and extrapolating the knowledge to any data-scarce network. The imbalance-induced cost is then translated into the benefit from phase balancing and this is compared against the costs of phase balancing solutions, e.g. deploying phase balancers. The developed CGPR approach assists distribution network operators (DNOs) to evaluate the cost-benefit of phase balancing solutions for data-scarce networks without the need to invest in additional monitoring devices.
- Cost-benefit analysis
- Gaussian process regression
- low voltage
- phase balancing
- phase imbalance
- power distribution
- three-phase system
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
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- 1 Finished
Li, F., Jeon, J. & Li, R.
1/09/16 → 29/02/20
Project: Research council
- Department of Electronic & Electrical Engineering - Professor
- Centre for Doctoral Training in Decarbonisation of the Built Environment (dCarb)
- Centre for Sustainable Energy Systems (SES)
- EPSRC Centre for Doctoral Training in Advanced Automotive Propulsion Systems (AAPS CDT)
- Institute for Advanced Automotive Propulsion Systems (IAAPS)
Person: Research & Teaching, Core staff, Affiliate staff