320 IEEE TRANSACTIONS ON SMART GRID, VOL. 1, NO. 3, DECEMBER 2010 Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid Amir-Hamed Mohsenian-Rad, Member, IEEE, Vincent W. S.Wong, Senior Member, IEEE, Juri Jatskevich, Senior Member, IEEE, Robert Schober, Fellow, IEEE, and Alberto Leon-Garcia, Fellow, IEEE Abstract—Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy con- sumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appli- ances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user’s individual daily electricity charges. Index Terms—Demand-side management, distributed algo- rithms, energy consumption scheduling, energy pricing, game theory, market incentives, smart grid, smart meter. I. INTRODUCTION D EMAND-SIDE management (DSM) commonly refers to programs implemented by utility companies to control the energy consumption at the customer side of the meter Manuscript received April 07, 2010; revised July 05, 2010; accepted August 15, 2010. Date of current version November 19, 2010. Paper no. TSG-00045- 2010. A. H. Mohsenian-Rad was with the Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4 and with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada, M5S 2E4. He is now with the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA (e-mail: hamed.mohsenian-rad@ttu.edu). V. W. S. Wong, J. Jatskevich, and R. Schober are with the Department of Elec- trical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4 (e-mail: vincentw@ece.ubc.ca; jurij@ece.ubc.ca; rschober@ece.ubc.ca). A. Leon-Garcia is with the Department of Electrical and Computer Engi- neering, University of Toronto, Toronto, ON, Canada, M5S 2E4 (e-mail: al- berto.leongarcia@utoronto.ca). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSG.2010.2089069 [1]. These programs are employed to use the available energy more efficiently without installing new generation and trans- mission infrastructure. DSM programs include conservation and energy efficiency programs, fuel substitution programs, demand response programs, and residential or commercial load management programs [2]–[4]. Residential load management programs usually aim at one or both of the following design objectives: reducing consumption and shifting consumption [5]. The former can be achieved among users by encouraging energy-aware consumption patterns and by constructing more energy efficient buildings. However, there is also a need for practical solutions to shift the high-power household appliances to off-peak hours to reduce the peak-to-average ratio (PAR) in load demand. Appropriate load-shifting is foreseen to become even more crucial as plug-in hybrid electric vehicles (PHEVs) become popular. Most PHEVs need 0.2–0.3 KWh of charging power for one mile of driving [6]. This will represent a signifi- cant new load on the existing distribution system. In particular, during the charging time, the PHEVs can almost double the average household load and drastically exacerbate the already high PAR. Moreover, unbalanced conditions resulting from an increasing number of PHEVs may lead to further degradation of the power quality, voltage problems, and even potential damage to utility and consumer equipment if the system is not properly reinforced [6]. One approach in residential load management is direct load control (DLC) [7]–[10]. In DLC programs, based on an agreement between the utility company and the customers, the utility or an aggregator, which is managed by the utility, can remotely control the operations and energy consumption of certain appliances in a household. For example, it may control lighting, thermal comfort equipment (i.e., heating, ventilating, and air conditioning), refrigerators, and pumps. However, when it comes to residential load control and home automation, users’ privacy can be a major concern and even a barrier in implementing DLC programs [11]. An alternative for DLC is smart pricing, where users are en- couraged to individually and voluntarily manage their loads, e.g., by reducing their consumption at peak hours [12]–[14]. In this regard, critical-peak pricing (CPP), time-of-use pricing (ToUP), and real-time pricing (RTP) are among the popular op- tions. For example, in RTP tariffs, the price of electricity varies at different hours of the day. The prices are usually higher during the afternoon, on hot days in the summer, and on cold days in the winter [15]. RTP programs have been adopted in some places in 1949-3053/$26.00 © 2010 IEEE