Learning from residential load data: Impacts on LV network planning and operation

The deployment of advanced metering infrastructure has already started in many countries around the world in order to facilitate the transition towards low-carbon economies, to improve electricity billing, to decrease distribution network operational costs, and to empower householders. In addition, the adoption of photovoltaic panels, electric vehicles and smart appliances, already being encouraged by governments, will change the way households consume and generate electricity. However, in order to adequately assess the impacts from these low-carbon technologies it is required a much better understanding of how electricity is currently consumed. This work firstly studies the effects of load characterization on the optimal selection of the conductors from the planning perspective based on a high granularity model for UK residential consumers that mimics data that could eventually be available through smart meters. Then, from the operational point of view, the benefits of load shifting (i.e., demand side management) to reduce peak demand are also investigated. The latter study is applied to a real LV network the North West of England. Results clearly indicate the potential benefits on LV network planning from high granularity data, as well as the important insights that could be gained from modeling load shifting schemes using such a data.