In this paper, we consider the energy management problem for smart cyber-physical power systems with conventional utility companies, microgrids (MGs) and customers. We have adopted a game-theoretical approach to model the problem as a hierarchical game, which takes the interactions and interconnections among utility companies, MGs and customers into consideration. We have considered two completely different situations depending on whether utility companies can enforce their strategies upon MGs and customers, and have modelled the problem as a two-stage Stackelberg game and three-stage Stackelberg game, respectively. The backward induction method is used to analyse the proposed games and closed-form expressions are derived for optimum strategies. We prove that there exists a unique Nash equilibrium for each non-cooperative price competition game, and these Nash equilibria constitute the Stackelberg equilibrium. Simulation results show the effectiveness of the proposed algorithm and the relationships among system parameters.

The introduction of microgrids (MGs) into the utility grid poses new challenges in the energy management design due to the intermittent characteristics of renewable energy sources and limited storage capacity. In this paper, we proposed a distributed energy management algorithm by taking into consideration the the interactions and interconnections among utility companies, MGs, and customers. We model the energy management problem as a two-stage Stackelberg game, in which utility companies and MGs are game leaders, and customers are game followers. Utility companies and MGs make decisions about what price to offer their electricity to customers. Customers adjust their electricity procurement amounts based on the prices offered by utility companies and MGs. We prove that a Nash equilibrium exists in the proposed two-stage Stackelberg game, and the optimum solutions obtained by the distributed energy management algorithm is exactly the Nash equilibrium. We have analyzed and verified the relationships among utility functions, electricity prices, electricity demands, electricity procurement amounts, and pollutant parameters through computer simulations. We have also compared the performance of the proposed distributed algorithm with the centralized algorithm under different simulation conditions.

In this paper, we consider the energy management issues in smart grid with a dominated electricity provider, i.e., the utility company, and multiple microgrids (MGs) and customers. The utility company has much lower electricity generation cost, higher generation capacity, and first-mover advantage compared to MGs. On the other hand, MGs which are based on renewable energy sources have lower pollutant emission cost and higher customer preference. The interactions and interconnections among the utility company, MGs, and customers are taken into consideration. We model the energy management problem as a three-stage Stackelberg game, in which the utility company is the game leader, and MGs and customers are game followers. The backward induction method is used to analyze the proposed game and closed-form expressions are derived. Simulation results show the effectiveness of the proposed algorithm and the relationships among system parameters.

Device-to-device (D2D) communications have gained great attentions due to the potential and numerous benefits for cellular networks. However,it also brings tremendous resource allocation challenges for the sake of the constraint of battery life. Up to now, there are limited works attempt to prolong the battery life by improving the energy efficiency (EE). In this paper, we study how to perform resource allocation to increase EE in a interference limited environment under a noncooperative game model. Each D2D pair can reuse all or part of the channel resources allocated to cellular users. An energy-efficient joint power allocation and channel selection is proposed by employing the nonlinear fractional programming. We obtain the optimal power allocation and channel selection through an iterative algorithm called Dinkelbach method. Finally, the algorithm proposed in this paper is verified by simulation.