A probabilistic load model based on chi-square method for distribution network

In the risk assessment and operation simulation process of power system, the uncertainty of risk is mainly caused by the load fluctuation. Therefore, the probability forecasting on load distribution becomes a significant issue as it apocalyptically influences the operation and planning process of the smart grid. The normal distribution has been applied frequently to predict the occurrence probability of the load or the peak value within a certain time period in previous research work. This paper statistically analyzes the historical load data at certain hours from a residential community site and uncovers the normal distribution is challenged by the real load data distribution observed from a distribution-level feeder. This paper explores the probabilistic distribution of time-series load in the distribution network level and develops a probabilistic distribution model by means of chi-square distribution theory to fit the statistical load data gathered from a residential community. The simulation results proves the proposed chi-square distribution model is better than the normal distribution and provide more accurate load data for the real-time simulation on the risk assessment in smart distribution networks.