In smart grid, customers have access to the electricity consumption and the price data via smart meters; thus, they are able to participate in the demand response (DR) programs. In this paper, we address the interaction among multiple utility companies and multiple customers in smart grid by modeling the DR problem as two noncooperative games: the supplier and customer side games. In the first game, supply function bidding mechanism is employed to model the utility companies' profit maximization problem. In the proposed mechanism, the utility companies submit their bids to the data center, where the electricity price is computed and is sent to the customers. In the second game, the price anticipating customers determine optimal shiftable load profile to maximize their daily payoff. The existence and uniqueness of the Nash equilibrium in the mentioned games are studied and a computationally tractable distributed algorithm is designed to determine the equilibrium. Simulation results demonstrate the superior performance of the proposed DR method in increasing the utility companies' profit and customers' payoff, as well as in reducing the peak-to-average ratio in the aggregate load demand. Finally, the algorithm performance is compared with a DR method in the literature to demonstrate the similarities and differences.Index Terms-Bidding mechanism, demand response (DR), Nash equilibrium, noncooperative game.

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The integration of energy hubs -as an important component of future energy networks that will employ demand-side management techniques -has a key role in the process of efficiency improvement and reliability enhancement of power grids. In such power grids, energy hub operators need to optimally schedule the consumption, conversion, and storage of available resources based on their own utility functions. In sufficiently large networks, scheduling an individual hub can affect the utility of the other energy hubs. In this paper, the interaction between energy hubs is modeled as a potential game. Each energy hub operator (player) participates in a dynamic energy pricing market and tries to maximize his own payoff with regard to energy consumption satisfaction. We propose a distributed algorithm based on a potential game, which guarantees the existence of a Nash equilibrium. Furthermore, two different types of signaling are developed and simulation results are compared. Simulation results show that with the implementation of either setup the peak-to-average ratio between electricity networks and natural gas networks diminishes.An analysis of the results shows that either setup can have superiority over the other one with regard to generation costs, convergence rate, price level, and the stability perspective. Hence, energy providers and consumers can choose a favorable setup based on their respective needs.

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Massive increases in Internet data traffic over the last years have led to rapidly rising electricity demand and CO2 emissions, giving rise to environmental externalities and network congestion costs. One particular concern is the rise in data traffic generated by video-streaming services. We analyze the electricity-saving potential related to video streaming in Europe from 2020 to 2030. To this end, three trend scenarios (Business-as-usual, Gray, and Green) are considered and modeled bottom-up, taking specific energy consumption (and trends) of data transmission networks, end-use devices, and data centers into account. Using these scenarios, we examine in more detail the approximate energy-saving impact that regulatory interventions and technical standards can have on the electricity consumption of end-users, network operators, and data centers. The model results reveal that regulatory intervention can have a significant impact on energy consumption and CO2 emissions. As technical regulation carries the risk of stymieing innovation and dynamic efficiency, we propose economic regulation in terms of a mandatory transit fee as a long-term solution.