Multi-time slots real-time pricing strategy with power fluctuation caused by operating continuity of smart home appliances.

Demand side management aims to match power demand to supply through cutting the peak and filling the valley, is one of the most important factors in smart grid. The real-time pricing (RTP) mechanism is an ideal method to adjust power balance between supply and demand. Its implementation has a profound impact on users’ behavior, and on operation and management of the power grid. In this research, we propose an expectation social welfare maximization model, considering the classification of the smart home appliances (SHA) and the correlation of power consumption of multi-time slots. Users can arrange their appliances more profitable and more closely to reality with the advantage of multi-time slots RTP strategy. The constraint in the model reflects the fluctuation (uncertainty) of power consumption caused by operating continuity of the SHA. By introducing probabilistic constraints, the uncertainty optimization model is transformed into a convex optimization problem. The existence and uniqueness of the optimal solution are shown, and its properties are further analyzed. Considering the convex optimization problem is separable in dual domain, this study proposes a decentralized online RTP algorithm to determine each user’s demand and energy supplier’s supply simultaneously. By utilizing Armijo line search to instead of fixed step size of the dual subgradient method, the decentralized online RTP algorithm proposed in this research can overcome the defects of slow convergence and even no convergence from the original dual subgradient method. Finally, the simulation validates the rationality and feasibility of optimization model by the decentralized online RTP algorithm.