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A real-time, personalized consumption-based pricing scheme can induce electricity users to change their purchase behaviors, thus becoming an important issue in exploring the management mechanisms of electricity markets. To stabilize electricity prices, increase operators' revenues, and balance market demands, we consider the pricing scheme in a smart grid market where traditional and renewable energies are available for sales. Under the scheme, we develop a leader-follower game to characterize the strategic interactions between a demand side management center and residential users, and show that there exists a unique Stackelberg equilibrium. Our numerical analysis indicates that the real-time pricing scheme makes the electricity price difference between valley and peak times within 0.4 cents, thereby achieving the goal of mitigating peak loads and stabilizing electricity prices. We reveal that the renewable energy loads dominate traditional energy loads even when the price of renewable energy is higher than that of traditional energy. We also perform sensitivity analysis and find that an increase in a user's dissatisfaction with the electricity supply can raise electricity price for the user and two different electricity loads. Moreover, the demand side management center's revenue changes with a concave appearance. [Display omitted] [ABSTRACT FROM AUTHOR]

Summary: As the key component of demand response management in smart grid, real‐time pricing has been an open issue to address. In this paper, a Stackelberg game with multiple power retailers and multiple users is proposed to maximize the revenue of each retailer and the payoff of each user in power retail market. The existence of Stackelberg equilibrium is analyzed and proved. A distributed algorithm is developed to ensure the convergence of Stackelberg equilibrium for retailers and users with only local information. Additionally, we design an incentive factor that adjusts the price information from retailers to guarantee the grid operation and assure supply‐demand balance since the smart grid still can be susceptible to the issues about privacy and security. The simulation results show the effectiveness of algorithm and the impact of incentive factor. [ABSTRACT FROM AUTHOR]

As an essential characteristic of smart grid, demand response may reduce the power consumption of users and the operating expense of power suppliers. Real-time pricing is the key component of demand response that encourages power utilization in an efficient and economical way. In this paper, we study the real-time pricing scheme in smart grid with multiple retailers and multiple residential users using Stackelberg game. Additionally, the price competition among power retailers is formulated as a non-cooperative game, while the coordination among the residential users is formulated as an evolutionary game considering the private information of power retailers and residential users. The existence of Stackelberg equilibrium is proved. Moreover, two special algorithms are developed to solve the equilibrium. Numerical results show the convergence of algorithms, and also confirm the efficiency and effectiveness of proposed real-time pricing scheme. [ABSTRACT FROM AUTHOR]

An accurate power load prediction in smart grid plays an important role in maintaining the balance between power supply and demand and thus ensuring the safe and stable operation of power system. In this paper we develop a hybrid power load prediction method, which involves three main steps: data decomposition with the empirical mode decomposition method, data processes with the minimal redundancy maximal relevance method and the weighted gray relationship projection algorithm, and support vector machine prediction, whose parameters are optimized through the particle swarm optimization algorithm with a second-order oscillation and repulsive force factor. Moreover, we predict the power load with our hybrid forecasting method based on the real dataset from the electricity market in Singapore, and also compare our prediction results with those by using other forecasting methods. Our comparison results show that our novel hybrid method possesses a high accuracy in both the level and directional predictions. • A novel hybrid forecasting method for power load is proposed. • The EMD method is used to decompose the original load data. • The mRMR and WGRP algorithms are used to extract features and process holiday load. • The parameters of SVM are optimized by the improved PSO algorithm. • The accuracy of level prediction and direction prediction are both measured. [ABSTRACT FROM AUTHOR]