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.