This paper investigates interaction among residential electricity users and utility company in a distribution network with the capability of two-way communication provided by smart grid. The energy consumption scheduling of electricity users is formulated as a game-theoretic problem within a group where all players are not totally selfish. Considering altruistic behavior of human decision-making, altruistic player action to other players actions can be influenced by recognizing the well being of others. The proposed model captures the empathy of electricity users in energy consumption scheduling and how this behavior affect peak demand and electricity prices. Numerical results demonstrate that both residential users and utility company can benefit through the channel of empathy.
In this paper, the problem of energy trading between smart grid prosumers, who can simultaneously consume and produce energy, and a grid power company is studied. The problem is formulated as a single-leader, multiple-follower Stackelberg game between the power company and multiple prosumers. In this game, the power company acts as a leader who determines the pricing strategy that maximizes its profits, while the prosumers act as followers who react by choosing the amount of energy to buy or sell so as to optimize their current and future profits. The proposed game accounts for each prosumer’s subjective decision when faced with the uncertainty of profits, induced by the random future price. In particular, the framing effect, from the framework of prospect theory (PT), is used to account for each prosumer’s valuation of its gains and losses with respect to an individual utility reference point. The reference point changes between prosumers and stems from their past experience and future aspirations of profits. The followers’ noncooperative game is shown to admit a unique pure-strategy Nash equilibrium (NE) under classical game theory which is obtained using a fully distributed algorithm. The results are extended to account for the case of PT using algorithmic solutions that can achieve an NE under certain conditions. Simulation results show that the total grid load varies significantly with the prosumers’ reference point and their loss-aversion level. In addition, it is shown that the power company’s profits considerably decrease when it fails to account for the prosumers’ subjective perceptions under PT. The increased uncertainty of the electric grid due to the penetration of renewable energy sources and deregulation of the electric market is aimed to be alleviated by demand response (DR) in the future smart grid. The demand-side resources can be incentivized to alter their consumption patterns by varying their electricity price over time. A major residential energy demand contribution is from electric heating, which, when combined with smart energy storage using water heaters, could be utilized to defer consumption to more inexpensive periods without affecting the customer's thermal quality of service. The objective is to optimize the consumer electricity price of electric storage space heating customers, in order to maximize the profit of the retailer. This approach of varying the customer electricity prices leads to a game-theoretic scenario, where the procurement and consumption profiles of the retailer and consumer agents are based on the set electricity price. The optimization of the consumer electricity price is shown to offer lesser expense for the retailer. In addition, hourly load-following can be improved by offering further discounts for the consumers. Demand Response Management (DRM) is a key component of the future smart grid that helps to reduce power peak load and variation. Different from most existing studies that focus on the scenario with a single utility company, this paper studies DRM with multiple utility companies. First, the interaction between utility companies and residential users is modeled as a two-level game. That is, the competition among the utility companies is formulated as a non-cooperative game, while the interaction among the residential users is formulated as an evolutionary game. Then, we prove that the proposed strategies are able to make both games converge to their own equilibrium. In addtion, the strategies for the utility companies and the residential users are implemented by distributed algorithms. Illustrative examples show that the proposed scheme is able to significantly reduce peak load and demand variation. This paper proposes a Stackelberg game approach to deal with demand response (DR) scheduling under load uncertainty based on real-time pricing (RTP) in a residential setup. We formulate the optimization problems for the service provider that acts as the leader and for the users who are the multiple followers, respectively. We derive the Stackelberg Equilibrium (SE) consisting of the optimal real-time electricity price and each user's optimal power consumption. Simulation results show the proposed DR scheduling not only can control the total power consumption, but also is beneficial to the service provider's revenue. Moreover, the load uncertainty increases the service provider's revenue and decreases each user's payoff. This paper proposes a real-time price (RTP)-based demand-response (DR) algorithm for achieving optimal load control of devices in a facility by forming a virtual electricity-trading process, where the energy management center of the facility is the virtual retailer (leader) offering virtual retail prices, from which devices (followers) are supposed to purchase energy. A one-leader, @math -follower Stackelberg game is formulated to capture the interactions between them, and optimization problems are formed for each player to help in selecting the optimal strategy. The existence of a unique Stackelberg equilibrium that provides optimal energy demands for each device was demonstrated. The simulation analysis showed that the Stackelberg game-based DR algorithm is effective for achieving the optimal load control of devices in response to RTP changes with a trivial computation burden. Demand Response Management (DRM) is a key component in the smart grid to effectively reduce power generation costs and user bills. However, it has been an open issue to address the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit. In this paper, we propose a Stackelberg game between utility companies and end-users to maximize the revenue of each utility company and the payoff of each user. We derive analytical results for the Stackelberg equilibrium of the game and prove that a unique solution exists. We develop a distributed algorithm which converges to the equilibrium with only local information available for both utility companies and end-users. Though DRM helps to facilitate the reliability of power supply, the smart grid can be succeptible to privacy and security issues because of communication links between the utility companies and the consumers. We study the impact of an attacker who can manipulate the price information from the utility companies. We also propose a scheme based on the concept of shared reserve power to improve the grid reliability and ensure its dependability.  In this paper, we consider the demand response problem in smart grid consisting of a retailer and multiple residential consumers, where the retailer determines consumers' payments based on their power consumption profile. Our aim is to propose a fully distributed algorithm that is able to optimize the aggregate cost, utility, and retailer's profit simultaneously. To this end, we first formulate the consumer-side trend as a constrained convex optimization problem and propose a fully distributed adaptive diffusion algorithm to solve it. In addition, we design a one-leader N-follower Stackelberg game to model interactions among the retailer and consumers. The proposed framework is able to continuously track the drifts resulting from the changes in the real-time pricing or the consumer preferences. Moreover, it is scalable and does not require network-wide information or rely on central controller. We provide comprehensive simulation results to show the effectiveness of the proposed framework.