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Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE). Implementing a centralized estimator though is practically infeasible due to the complexity scale of an interconnection, the communication bottleneck in real-time monitoring, regional disclosure policies, and reliability issues. In this context, distributed PSSE methods are treated here under a unified and systematic framework. A novel algorithm is developed based on the alternating direction method of multipliers. It leverages existing PSSE solvers, respects privacy policies, exhibits low communication load, and its convergence to the centralized estimates is guaranteed even in the absence of local observability. Beyond the conventional least-squares based PSSE, the decentralized framework accommodates a robust state estimator. By exploiting interesting links to the compressive sampling advances, the latter jointly estimates the state and identifies corrupted measurements. The novel algorithms are numerically evaluated using the IEEE 14-, 118-bus, and a 4200-bus benchmarks. Simulations demonstrate that the attainable accuracy can be reached within a few inter-area exchanges, while largest residual tests are outperformed. [ABSTRACT FROM PUBLISHER]

This article examines the effect of plant entry and exit in a deregulated 'energy only' electricity market. A partial equilibrium framework is presented that determines the optimal portfolio of base, intermediate and peaking plant for a given electricity load curve. An optimal result for Queensland is compared against the actual plant stock. Analysis of the portfolio indicates that deregulation is failing a key objective, namely enhancing dynamic efficiency, because too much base plant has been delivered. The research presents scenarios of structural corrections, using the theory of the generalised war of attrition to develop the cases. Results from simulation experiments are clear, consumers will secure lower electricity prices in the short run. But oversupply of base plant may suppress prices to such an extent that they fail to signal timely entry of peaking plant, the consequence of this failure being eventual price shocks and, potentially, load shedding. [ABSTRACT FROM AUTHOR]

Deregulation of electricity markets is occurring all over the world. This trend introduces new risks and uncertainties into the electricity industry, the most significant being price risk. The spot price of electricity is highly volatile, and the ability to price risk management contracts on this commodity is contingent on a robust and realistic model of the underlying price process. One key driver of electricity spot price is the forced outages of generating plants in the system. The current paper describes a system aggregate model of short-term generating capacity that can be adapted to any generating system of interest. After describing the model, we test it using the IEEE Reliability Test System (RTS). [ABSTRACT FROM AUTHOR]

Focuses on issues and topics related to the deregulation of electricity in the U.S. Contention of economists that consumers do not benefit from regulated markets; Characteristics of the electricity markets that make it difficult to manage through property rights and contracts; Advantages of achieving the right balance between private incentives and public responsibilities.

It is time to stop regulating the electricity industry as if it were a natural monopoly. Technology has moved on and competition must not be discouraged by regulation. Competition is not only necessary for lower prices to consumers: the particular technological developments that have taken place in recent years mean that competition can diversify supply sources and contribute to increased security of supply. [ABSTRACT FROM AUTHOR]

The electric power industry is going through deregulation. As a result, the load on the generating units of a utility is becoming increasingly unpredictable. Furthermore, electric utilities may need to buy power or sell their production to a power pool that serves as a spot market for electricity. These trading activities expose utilities to volatile electricity prices. In this paper, we present a stochastic model for the unit commitment that incorporates power trading into the picture. Our model also accounts for fuel constraints and prices that may vary with electricity prices and demand. The resulting model is a mixed-integer program that is solved using Lagrangian relaxation and Bender's decomposition. Using this solution approach, we solve problems with 729 demand scenarios on a single processor to within 0.1% of the optimal solution in less than 10 minutes. Our numerical results indicate that significant savings can be achieved when the spot market is entered into the problem and when stochastic policy is adopted instead of a deterministic one. [ABSTRACT FROM AUTHOR]