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 consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances 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.

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Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.

Demand response and smart grids—A survey

Cyber-physical systems are ubiquitous in power systems, transportation networks, industrial control processes, and critical infrastructures. These systems need to operate reliably in the face of unforeseen failures and external malicious attacks. In this paper: (i) we propose a mathematical framework for cyber-physical systems, attacks, and monitors; (ii) we characterize fundamental monitoring limitations from system-theoretic and graph-theoretic perspectives; and (ii) we design centralized and distributed attack detection and identification monitors. Finally, we validate our findings through compelling examples.

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Attack Detection and Identification in Cyber-Physical Systems

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

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Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

With the increase in use of information technology in advanced demand side management and given the growth in power consumption in the computation and communications sectors, a new class of cyberintrusion plans is emerging that aims to alter the load through the Internet and by means of automatic and distributed software intruding agents. These attacks work by compromising direct load control command signals, demand side management price signals, or cloud computation load distribution algorithms to affect the load at the most crucial locations in the grid in order to cause circuit overflow or other malfunctions and damage the power system equipment. To gain insights into these less-examined yet important intrusion strategies, in this paper, we identify a variety of practical loads that can be volnurable to Internet-based load altering attacks. In addition, we overview a collection of defense mechanisms that can help in blocking these attacks or minimizing the damage caused by them. Our simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that our proposed cost-efficent load protection strategy can significantly reduce the cost of load protection while it guarantees that no Internet-based load altering attack may overload the power distribution system.

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Distributed Internet-Based Load Altering Attacks Against Smart Power Grids

Wireless links are often unreliable and prone to transmission error, particularly when network users are mobile. These can degrade the performance in wireless networks, particularly for applications with tight quality-of-service requirements. A common remedy to this problem is channel coding. However, this per-link solution can compromise the link data rate, leading to an undesired end-to-end performance. In this paper, we show that this shortcoming can be mitigated if the end-to-end transmission rates and channel code rates are properly selected over multiple routing paths. We formulate a joint channel coding and end-to-end data rate allocation problem in multipath wireless networks with max-min fairness as the objective function. Our goal is to maximize the minimum throughput available among the network users. To cope with the fast and frequent changes in dynamic environments that are typical for vehicular networks, we address both adaptive and nonadaptive channel coding scenarios. Unlike similar formulations in single-path routing networks, in the multipath routing case, we face an optimization problem that is nonconvex and usually difficult to solve. We tackle the nonconvexity by using function approximation and iterative techniques from signomial programming. Simulation results confirm that by using channel coding jointly with multipath routing, we can significantly improve the end-to-end network performance compared with the case when only one of them is used in the network. Nonadaptive channel coding is also shown to achieve a high degree of optimality with much less complexity.

A-H Mohsenian-Rad

Papers

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Distributed Internet-Based Load Altering Attacks Against Smart Power Grids

Optimal Data Transmission and Channel Code Rate Allocation in Multipath Wireless Networks