Search

In this paper, an advertising differential game model is proposed to describe the competition among different power retailers for attracting users in continuous time. After the equilibrium of advertising differential game is obtained, a multi-leader–multi-follower game model is developed to study the real-time pricing scheme in smart community. The power retailers calculate the optimal prices in response to the users' power consumption and obtain the non-cooperation Nash equilibrium, and then the multi-leader–multi-follower game equilibrium is found. The numerical simulation is performed to discuss the influence of different factors on the payoff of power retailers and users and to verify the rationality of the proposed pricing scheme. The results show that both advertising efficiency and user scale have positive effects on the revenues of the power retailers. Besides, the rise of electricity price has negative effect on users' utilities, meanwhile it makes the revenues of power retailers increase at first and then decrease. Moreover, power retailers will not transfer advertising costs to the users in the form of incremental electricity price. [ABSTRACT FROM AUTHOR]

As a powerful tool of Demand Response (DR) techniques in smart grid market, Real-time Pricing (RTP) may optimize the electricity consumption pattern of users and improve the efficiency of electricity market. In this paper, a multi-leader-follower Stackelberg Game (SG) based on RTP is established to model the strategic interaction behavior between multiple electricity retailers and multiple users while simultaneously considering the power load uncertainty of users and the price competition among electricity retailers. In the game model, electricity retailers aim to seek their revenue maximization while the optimal power consumption competition among the users is taken into account. Lagrange multiplier method is utilized to solve the Nash Equilibriums (NE) of two non-cooperative games, and the closed-form optimal solution is obtained, then the Stackelberg Equilibrium (SE) consisting of the optimal real-time prices of electricity retailers and the power consumption of users is given. Finally, the numerical analysis results verify that the proposed scheme can reduce the real-time electricity price and increase the users' satisfaction under feasible constraint, which shows the effectiveness and better performance of proposed RTP scheme. [ABSTRACT FROM AUTHOR]

This paper considers the worst-case regret portfolio optimization problem when the distributions of the asset returns are uncertain. In general, the solution to this problem is NP hard and approximation methods that minimise the difference between the maximum return and the sum of each portfolio return are often proposed. Applying the duality of semi-infinite programming, the worst-case regret portfolio optimization problem with uncertain distributions can be equivalently reformulated to a linear optimization problem, and the established solution approaches for linear optimization can then be applied. An example of a portfolio optimization problem is provided to show the efficiency of our method and the results demonstrate that our method can satisfy the portfolio risk diversification property under the uncertain distributions of the returns. [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]

Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation of power stations and ensure users' electricity consumption. As a good forecasting tool, Gated Recurrent Unit method has been widely used in different forecasting areas. However, the existing studies ignore the impact of data fluctuations on prediction accuracy, to fill the gaps and enhance prediction accuracy, several different data smoothing techniques are introduced and compared to reduce fluctuations, Random Forest method is used for feature selection, and RepeatVector layer extended by attribute dimensions and TimeDistributed layer with full connectivity are utilized to optimize the Gated Recurrent Unit model. A real-world case from the photovoltaic power plant in Xuhui District, Shanghai, China, is adopted to evaluate the performance of proposed method. The comparing results with Recurrent Neural Networks and Long Short-Term Memory, and the actual data as well, show that the proposed prediction method can effectively improve the prediction accuracy of photovoltaic power generation. We also use the daily and monthly data of The Desert Knowledge Australia Solar Centre in Australia to investigate whether the proposed method is suitable for short-term or medium and long-term prediction. The results indicate that our method is more appropriate for short-term prediction. • Introducing and comparing different data smoothing technologies to reduce the data fluctuation. • Using Random Forest to extract the characteristics of natural factors. • Adding RepeatVector layer and TimeDistributed layer into the GRU to improve prediction accuracy. • Utilizing the dataset from Shanghai, China and three prediction models to verify the accuracy. • Utilizing dataset from Alice Springs to verify that our method is more suitable for short-term forecasting. [ABSTRACT FROM AUTHOR]

Summary: The advertising differential game is introduced to depict the competition among electricity retailers in continuous time. A Stackelberg game model with one leader and multiple followers is established to study the real‐time pricing scheme in a smart community. By solving the optimization problem on users' side with budget constraint and the noncooperative differential game among retailers, the Stackelberg equilibrium between the retailers and the users is obtained. Finally, numerical simulation is carried out to discuss the influence of different factors on the payoff of electricity retailers and users and verify the rationality of the pricing scheme. [ABSTRACT FROM AUTHOR]