Two‐stage load‐scheduling model for the incentive‐based demand response of industrial users considering load aggregators

Abstract

The demand response (DR) resources provided by modern industrial users tend to be diversified because of the popularisation of renewable energy. In this context, for the problem of satisfaction and uncertainty in the DR, a load-scheduling model considering load aggregators (LAs) is presented. In the multi-level DR scheduling system, the users and aggregator simultaneously participate in the decision; thus, the proposed methodology is a two-stage optimisation process. The first-stage optimisation considers the load interruption strategies of complex industrial processes, where a multi-objective optimisation model is established to coordinate the user benefits and satisfaction. This model is solved by non-dominated sorting genetic algorithm-II (NSGA-II) and the entropy weight double-base point method to obtain the optional interruptible load (IL) contracts for production processes. The second-stage optimisation maximises the economic returns of the LA considering the uncertainty of the resource's response, where chance-constraint programming is applied to solve the problem and select the appropriate IL contract. The effectiveness of the proposed methodology is examined according to the actual industrial production process. The multi-objective coordination effect of production load-scheduling is shown in an example. Finally, the effects of joint resource scheduling and different confidence levels on the profit of the aggregator are analysed.