Selecting and prioritizing the electricity customers for participating in demand response programs

Demand response (DR) provides an opportunity for customers to play important role in the operation of electricity grid by reducing or shifting their usage during peak periods. However, selecting participate DR programs is challenging. To solve this problem, typical load profiles should be characterized data mining techniques such as clustering algorithms. Traditional algorithms manually determine centre clusters and that selected may fall into a local optimum. Here, overcome these issues, new algorithm based on Density Peak Clustering (DPC) Artificial Bee Colony (ABC) which called A-DPC, implemented optimally representative curves. Moreover, introducing priority index, eligible residential are participating programs. Also, meet sufficient reduction event, proposed approach suggests plenty number called. The result evidence A-DPC has stronger global search ability select if compared other