A new mechanism aimed at misleading a power system control center about the source of a data attack is proposed. As a man-in-the-middle state attack, a data framing attack is proposed to exploit the bad data detection and identification mechanisms currently in use at most control centers. In particular, the proposed attack frames meters that are providing correct data as sources of bad data such that the control center will remove useful measurements that would otherwise be used by the state estimator. The optimal design of a data framing attack is formulated as a quadratically constrained quadratic program. It is shown that the proposed attack is capable of perturbing the power system state estimate by an arbitrary degree controlling only half of a critical set of measurements that are needed to make a system unobservable. Implications of this attack on power system operations are discussed, and the attack performance is evaluated using benchmark systems.

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.

Electrical Vehicles are gaining increasing attention, due to the opportunities and challenges they present for the energy market. On the one hand, they will allow to drastically reduce the need for oil; on the other hand they may require a significant shift in the day to day management of the electricity generation. This paper is concerned with finding appropriate models for residential load in light of a widespread penetration of electric vehicles. The analysis is aimed at finding a SmartGrid solution that would enable us to optimize the generation dispatch in real time and allow to plug cars in any SmartGrid enabled plug. The key idea is to discriminate between regular load and the load due to the EVs, gathering in real time aggregate information about the sensed EV arrivals and their associated charging times in a demand matrix, that can be readily used to optimize the dispatch, while updating without real time constraints the billing record for the EV.

Malicious data attacks to the real-time electricity market are studied. In particular, an adversary launches an attack by manipulating data from a set of meters with the goal of influencing revenues of a real-time market. The adversary must deal with the tradeoff between avoiding being detected by the control center and making maximum profit from the real time market. Optimal attacking strategy is obtained through an optimization of a quasi-concave objective function. It is shown that the probability of detection of optimal attack will always be less than 0.5. Attack performance is evaluated using simulations on the IEEE 14-bus system.

The restructuring of the electric power grid has created new control and monitoring requirements for which classical technologies may be inadequate. The most obvious way of building such systems, using TCP connections to link monitoring systems with data sources, gives poor scalability and exhibits instability precisely when information is most urgently required. Astrolabe, Bimodal Multicast, and Gravitational Gossip, technologies of our own design, seek to overcome these problems using what are called "epidemic" communication protocols. This paper evaluates a hypothetical power monitoring scenario involving the New York State grid, and concludes that the technology is well matched to the need.

Transmission systems in the United States and around the world have become more difficult to plan under deregulation. Uncertainties in generation type, location, and pricing coupled with market mechanisms that encourage transfers not designed for in the legacy transmission system have made the transmission planner's life difficult. This paper describes the planning process under regulation, some of the issues encountered during the present transition to a deregulated industry, and some of the open questions that need to be addressed in the United States.

Abstract We describe a formulation of the thermal unit commitment problem that includes AC network constraints, which allows taking into account VAr production as a criterion for generator commitment for the first time. The formulation makes it possible to use the Lagrangian Relaxation technique as a solution algorithm even though the network constraints are not linear. Although the solution method is computationally intensive, the problem exhibits a separation structure and a price coordination schedule that suggest several ways of improving the performance. Preliminary test results are reported.

This paper describes the architecture and uses of an Internet-based software platform called PowerWeb. PowerWeb was designed and implemented as a simulation environment for evaluating various power exchange auction markets through experimental investigation. It is designed to host simulations of a competitive `day-ahead' electric energy market in which the participants interact by submitting bids or offers into a market via a web-based user interface. The PowerWeb environment is meant to be flexible so as to allow investigation of various market mechanisms. In this paper we describe its interactive, distributed and web-based character.

Abstract The general availability of government‐sponsored studies on scientific and technical information (STI) provides an opportunity to trace the development of “marketing research” in a specific industry. A review of this research tradition revealed two major orientations; a series of “user”‐oriented studies, followed by “systems”‐oriented studies. It is suggested that traditional STI user studies may have produced “myopic” research results, and systems studies may have produced “macropic” research results for STI policy decisions. An emerging “managerial” orientation is identified which espouses the definition of a more meaningful unit of analysis as the focus of future research efforts.