An Immune Genetic Algorithm Based Combinatorial Auction Model in Smart Grid

The application of distributed energy and renewable energy in the smart grid has broadened power sources and increased the diversity of power supply and demand. To meet the needs for combined electric energy, and to achieve the balance of power supply and demand, this paper chooses the transaction mechanism of the combinatorial auction to realize optimal energy scheduling. In the combinatorial auction, bidders can bid for any combination of all energies, which meets the diversified power needs of users. A combinatorial auction model to maximize the overall auction proceeds is constructed considering the needs of both the user and generation side. For the electricity trading optimal scheduling problem, this paper employs a genetic algorithm to solve the combinatorial auction model, and the immune operator is added to the algorithm to address the problem of degeneration and better extract features of the auction problem. Moreover, a large number of simulations are performed to verify the feasibility, effectiveness, and practicability of the immune genetic algorithm. The adaptive value of the population is improved and population degeneration is prevented.