Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

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.

http://www.dtc.umn.edu/s/resources/spincom8181.pdf

Distributed Robust Power System State Estimation

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 4,200-bus benchmarks. Simulations demonstrate that the attainable accuracy can be reached within a few inter-area exchanges, while largest residual tests are outperformed.

This paper presents a new distributed state estimation method for multiarea power systems. Each area performs its own state estimation, using local measurements, and exchanges border information (estimated boundary states and measurements) at a coordination state estimator, which computes the system-wide state. Furthermore, observability and bad data analysis are accomplished in a distributed manner. The proposed method is illustrated with the IEEE 14-bus system. Test results with the IEEE 118-bus system are given.

A Distributed Multiarea State Estimation

This paper describes a fast and robust method for identifying the leverage points of a linearized power system state estimation model. These are measurements whose projections on the space spanned by the row vectors of the weighted Jacobian matrix, the so-called factor space, do not follow the pattern of the bulk of the point cloud. In other words, their projections are outliers in the factor space. The proposed method is implemented through a new version of the projection algorithm that accounts for the sparsity of the Jacobian matrix. It assigns to each data point a projection statistic defined as the maximum of the standardized projections of the point cloud on some directions passing through the origin. Based on these projection statistics, a robustly weighted Schweppe-type GM-estimator is defined, which can be computed by a reweighted least squares algorithm. The computational efficiency and the robustness of the method are demonstrated on the IEEE-14 bus and the 118-bus systems.

Robust state estimation based on projection statistics [of power systems]

The authors present an approach to the linear programming state estimation problem that is suitable for large-scale interconnected power systems. It uses the Dantzig-Wolfe decomposition algorithm. The performance of the method is enhanced by using a multiple column strategy and several other modifications to the original algorithm. Scaling techniques are used to guarantee a reliable solution. Test results on standard test systems are used to illustrate the usefulness of the method and its viability for parallel processor implementation. >

A decomposed state estimation technique suitable for parallel processor implementation

The authors present a fast and efficient technique for solving the power system state estimation problem using linear programming (LP). The dual formulation of the original problem is described. This formulation improves the solution time significantly. Four LP state estimators are presented and test results on the IEEE 30, 57, and 118 bus standard systems are included. Both the full and decoupled dual problems were solved. Test results indicate that the dual estimators are superior to the primal estimators. >

Fast linear programming state estimation using the dual formulation

The authors present an extensive review of the work to date on the subject of parallel state estimation. Modifications to an existing multilevel state estimator for power systems, based on the method of least squares, are proposed. These modifications are expected to improve the quality of the overall estimated system state. >

http://ieeexplore.ieee.org/iel2/283/3748/00138149.pdf

Parallel state estimation in power systems

The linear programming (LP) approach is discussed as an alternative to the weighted least squares method of estimating the state of power systems. The theory behind the approach is presented, the problems associated with its practical implementation are addressed, and suggestions for overcoming these problems are made. Test results which reveal the viability of the LP state estimators are included. >

H. Singh

Papers

A decomposed state estimation technique suitable for parallel processor implementation

Fast linear programming state estimation using the dual formulation

Parallel state estimation in power systems

Linear programming based state estimation in power systems