The penetration of residential photovoltaic (PV) panels is increasing particularly in those countries with special incentives. The total PV installed capacity in the UK has increased from negligible to 1.2 GW since the Feed-In Tariff scheme was created in 2010. As a result, Distribution Network Operators (DNOs) are already experiencing voltage issues in low voltage (LV) feeders were clusters have appeared. This work proposes a Monte Carlo-based technique to assess the impacts of different PV penetrations on LV networks in order to estimate their corresponding hosting capabilities. Three-phase models of two real LV networks in the North West of England are studied considering 5-min resolution synthetic data for domestic load and PV generation. Voltage-related impacts are measured using the European Standard EN50160. Additionally, the importance of data granularity on the impact assessment is analyzed. Results for the studied LV networks indicate that feeders with greater lengths and larger number of customers tend to experience voltage issues with lower PV penetration levels. In terms of the granularity, it was found that hourly resolution analyses underestimate the voltage impacts of residential PV.Index Terms-low voltage networks, small-scale photovoltaic generation, DG impacts.
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.
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