A Block Deepening Genetic Programming for Scheduling of Direct Load Control

A modified genetic programming (GP) called block deepening GP (BDGP) is proposed in this paper to optimize the scheduling of direct load control (DLC). The optimal scheduling obtained by BDGP is a both profit-based and fairness-based DLC scheduling strategy. The scheduling arranged by the BDGP not only individually satisfies the load to be shed at every time step while minimizes utility's revenue loss due to DLC, but also level off the accumulated shedding time of each load group, thus avoiding customers' complaints about fairness of scheduling. BDGP is composed of a master GP as well as a slave GP. As the master GP evaluates the status combination of all load groups at every time step, it calls upon the slave GP simultaneously looking ahead D more steps to evaluate the best load difference could result. The best status combinations in the following D steps associated with the status combination under evaluation are determined globally in the following D-steps block. Computer simulations are made to verify the effectiveness and efficiency of the proposed BDGP.