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.
By the introduction of smart grid, the access to potential capabilities of DR programs becomes much more viable because of two-way exchange of information between users and utility company. In such environment, the utility and users can interact by exchange of supply and demand information in order to make mutually beneficial decisions @cite_24 . Different approaches have been employed to study demand response in smart gird paradigm and game theory is one of the most promising approaches due to the dynamic nature of interactive decision-making between users and utility. In the following, we explore several studies related to game-theoretic DR within three different groups. However, first we introduce some terms which are used frequently hereafter: denote decision makers of the game such as electricity users and utility. describe expected benefits that each player obtains which could be maximizing revenue for utility or minimizing billing cost for user. are decisions made by each player like scheduling energy consumption for user and setting electricity prices for utility company.
@cite_24
2204963357 2001386890 2010739835 2055947209
Demand-response (DR) is regarded as a promising solution for future power grids. Here we use a Stackelberg game approach, and describe a novel DR model for electricity trading between one utility company and multiple users, which is aimed at balancing supply and demand, as well as smoothing the aggregated load in the system. The interactions between the utility company (leader) and users (followers) are formulated into a 1-leader, N-follower Stackelberg game, where optimization problems are formed for each player to help select the optimal strategy. A pricing function is adopted for regulating real-time prices (RTP), which then act as a coordinator, inducing users to join the game. An iterative algorithm is proposed to derive the Stackelberg equilibrium, through which optimal power generation and power demands are determined for the utility company and users respectively. Numerical results indicate that the proposed method can efficiently reshape users’ demands, including flattening peak demands and filling the vacancy of valley demands, and significantly reduce the mismatch between supply and demand. In this paper, we study Demand Response (DR) problematics for different levels of information sharing in a smart grid. We propose a dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and study how close they can get to an ideal flat profile depending on how much information they share. When customers can share all their load profiles, we provide a distributed algorithm, set up as a cooperative game between consumers, which significantly reduces the total cost and peak-to-average ratio (PAR) of the system. In the absence of full information sharing (for reasons of privacy), when users have only access to the instantaneous total load on the grid, we provide distributed stochastic strategies that successfully exploit this information to improve the overall load profile. Simulation results confirm that these solutions efficiently benefit from information sharing within the grid and reduce both the total cost and PAR. 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. 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.