Typical Scenario Reactive Power Optimization of Active Distribution Network Based on Limit Theory

With the power output of new energy farm in active distribution network is highly uncertain, the forecast error may lead to the insufficient reactive power capacity of the power grid when scheduling mechanism formulates the scheduling strategy according to the predctive power of new energy farm. The limit theory is introduced to establish the relationship between the active output power and the reactive power range of new energy farm. The power output of new energy is divide into different typical scenario based on the K_means clustering method, and the probability of each scenario is determined according to the historical data. The adjustable capacity range of new energy reactive power under each scenario is determined based on the limit theory. A typical reactive power optimization model, which considers the uncertainty, is established. The simulation result obtained by a actual 48 nodes network show that the proposed method can more accurately determine the reactive power range, and it can reduce the power loss of network and make the voltage level reasonable when regarding the new energy farm as a reactive power source.