A flexible antibody memory based immune optimization algorithm with application to automatic demand response

Automatic demand response is an important realization means of achieving information and energy interaction between smart grid and users. In application of spot price based automatic demand response, to solve the problem of integrated power planning for a user with various loads, a mathematical model of optimization was established. According to this model, a flexible antibody memory based immune optimization algorithm was proposed. To provide the user several alternative feasible solutions after the optimization process, a refresh mechanism according to double affinity threshold detection was designed. The accuracy of this algorithm was improved by use of prior knowledge vaccines inoculation. The convergence of this algorithm was proved by the transition probability analysis of antibody population best values' Markov chain. Then the feasibility of proposed algorithm was verified by the optimization result of a practical example. Comparison result shows that the proposed algorithm has a better performance of global optimization and searching efficiency than other algorithms. © 2014 Chinese Society for Electrical Engineering.