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.
This paper explores the use of distributed intelligence to assist the integration of the demand as a flexible resource, to mitigate the emerging uncertainty in the power system, while fulfilling the customer's local needs, i.e., comfort management. More exactly, our contribution is twofold. First, we propose a novel cooperative and decentralized reinforcement learning method, dubbed extended joint action learning (eJAL). Second, we perform a comparison between eJAL to noncooperative decentralized decision making strategies, i.e., Q-learning, and a centralized game theoretic approach, i.e., Nash n-player game. This comparison has been conducted on the basis of grid support effectiveness and the loss of comfort for each customer. Various metrics were used to analyze the advantages and disadvantages of each method. We demonstrated that a range of flexibility requests can be met by providing an optimal energy portfolio of buildings without substantially violating comfort constraints. Moreover, we showed that the proposed eJAL method achieves the highest fairness index. This paper proposes a cooperative demand response scheme for price-anticipating buildings in smart grid. The cooperative demand response scheme is formulated as a constrained social optimization problem. We develop a cooperative strategy and obtain a Pareto-optimal solution from the constrained social optimization problem. Comparing with the Nash equilibrium obtained from the one-stage demand management game, the Pareto-optimal solution reduces the electricity costs to all the building managers. We further align this Pareto-optimal solution with the subgame perfect Nash equilibrium of a repeated demand management game and develop an incentive-compatible trigger-and-punishment mechanism to avoid the noncooperative behavior of the building managers. Numerical results demonstrate that the cooperative demand response scheme can reduce the electricity costs, the electricity price, and the total energy consumption. 