Hierarchical Evaluation of Distribution Network Access Capacity Taking Into Various Prosumer Group Load Forecasts

Now the measurement means of access capacity at all levels of distribution network are insufficient and it is difficult to calculate effectively based on limited data. This paper proposes a hierarchical evaluation method of access capacity of distribution network with multiple prosumer group load forecasting. Firstly, a load dynamic prediction model is constructed. According to the typical load model of historical data mining, a unit prediction model of typical users is established by using the conjugate gradient RBF neural network learning algorithm. Then, according to the charge-discharge dynamic model of the energy storage power station and the load dynamic prediction results, an assessment model of access capacity at the 10kV side was constructed. By analyzing the known load type, load type ratio and transformer capacity on the low-voltage side, the conversion coefficient of 10kV side load to 0.4kV side transformer capacity is obtained by weighting different types of loads proportionally. Then, access distribution capacity of different prosumer group can be obtained based on conversion coefficient. It can ensure the effective measurement of access capacity at all levels of distribution network under the premise of meeting N-1 safety criteria. Finally, an example is given to demonstrate the effectiveness of the proposed method.