Stochastic Household Load Modelling From A Smart Grid Planning Perspective

Changes in the residential load warrant the investigation into more advanced planning methods for distribution grids. Smart grid alternatives require detailed information on network loading, and a risk based evaluation of different planning options, via a probabilistic approach. To limit the increased computational burden associated with this increased complexity, the required level of detail in modelling the household load is assessed. The effect of different aggregation levels of household load curves on the error in estimated voltage deviations is demonstrated, as well as the impact of varying degrees of availability for data regarding demand-side management (DSM) on the expected peak load reduction. The required level of load curve aggregation is determined depending on the feeder characteristics and the grid operators risk appetite. We show that incorporating DSM in network planning requires a high level of data availability, as the amount of expected DSM drops significantly when less measurement data is available.