Probabilistic Analysis for Capacity Planning in Smart Grid at Residential Low Voltage Level by Monte-Carlo Method

AbstractSmart Grid integrates sustainable energy sources and allows mutual communications between electricity distribution operators and electricity consumers. Electricity demand and supply becomes more complex in Smart Grid. It is more challenging for DNOs in grid asset capacity planning, especially at low voltage level. In this research, probabilistic analysis is presented aiming at finding more accurately peak loads then deterministic method, and also it allows estimation of probabilities of overloads, which are crucial factors in grid asset capacity planning process. Monte-Carlo simulation generates stochastic demand and supply profiles, including normal load profiles at households, EVs charging profiles, solar PVs’ generation profiles, and micro wind-turbine generation profiles. Through Monte-Carlo simulation, all the impacts of the uncertainties to the grid capacities are integrated. And mutual responses between DNOs and consumers in influencing electricity load profiles can also be simulated. The research results provide deeper insights of the impacts of technical uncertainties in Smart Grid, supporting DNOs in residential grid capacity planning process