Aggregate domestic demand modelling for the next day direct load control applications

Abstract

A large group of residential appliances can be controlled by an aggregator as a single entity to reduce their aggregate energy consumption at peak periods. The target of this study is concentrated on proposing a new strategy from the perspective of an aggregator that optimally schedules residential loads during the next day. In this research, Gaussian copula (GC) function and Gaussian mixture model (GMM) are investigated as new efficient tools to estimate the aggregate power demand of specific domestic appliances. Since copula function considers the correlation of the exact data in generating new data, the estimation would lead to much more accurate solutions in practice compared to other estimating functions. However, the GMM estimates data with about 5% reduction in prediction error compared to those of the GC. A semi-automated demand response is required for the aggregator to apply the proposed strategy to the flexible loads, including dish washer, washing machine and electrical vehicle. Moreover, a mathematical formulation for the objective function is also introduced in which both the peak demand and energy cost are treated by a stochastic non-linear programming. Finally, the proposed strategy was simulated with the GAMS as an optimisation tool.