Demand-side management and control for a class of smart grids based on game theory

With the help of evolutionary game theory, demand-side management (DSM) problem of a new class of networked smart grids is presented and solved in this paper. Some communities selected as controllers are cooperative with grid providers, while others pursue their individual benefits in the considered smart grids. Unconditional Imitation and Blind Imitation rules are introduced as strategy updating rules (SURs) of the uncooperative communities. The electricity price of each grid varies with the number of grid users. Based on semi-tensor product (STP) technique, the problem can be converted into control networked evolutionary game (CNEG). The objective in this paper is to select some communities as controllers and then design appropriate control sequences for them such that the minimal common benefit can be obtained and maintained. A nonlinear binary optimization is formulated to minimize total cost in the transient process of considered game.