Uncertain optimization decision of interruptible load in Demand Response program

In the Smart Grid, Demand Response (DR) programs have been developed rapidly, which change the fixed mindsets of satisfying the growing power demand merely by the development of power supply side, and utilize the demand side resources as alternative energy sources. Nevertheless, in application of DR programs, there are many uncertain factors which cannot be reflected in conventional optimization approaches. In this paper, the interruptible load optimization is carried out considering the uncertainties of customer response and total interruptible capacity requirement. The probability distribution of customer response can be deduced according to historical data. So the expected value of settling accounts as compensation or penalty to customers is calculated and minimized as one of the objective functions. The sum of variances is minimized as another objective function. The uncertainty of total interruptible capacity requirement is considered in the constraints, which is described as the confidence level. This optimization problem is called chance constrained programming because of the random variables in the constraints, which can be transformed to its deterministic equivalents. Example analysis demonstrates that the proposed optimization method can consider the coordination of economy and reliability in interrupting the customer loads and satisfy the confidence level.