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This paper describes the application of the continuous Newton's method to the power flow problem. This method basically consists in formulating the power flow problem as a set of autonomous ordinary differential equations. Based on this formal analogy, we propose an entire family of numerically efficient algorithms for solving ill-conditioned or badly-initialized power flow cases. The paper also shows that the classical Newton-Raphson's method and most robust power flow techniques proposed in the literature are particular cases of the proposed formulation. An example based on a 1254-bus model of the UCTE system is presented and discussed. [ABSTRACT FROM AUTHOR]

This paper describes a method of developing wide-band multi-port system equivalents for use with real-time digital power system simulators. The proposed equivalent combines a frequency dependent network equivalent (FDNE) for the high frequency electromagnetic transients and a transient stability analysis (TSA) type simulation block for the electromechanical transients. The frequency dependent characteristic for FDNE is obtained by curve-fitting frequency domain admittance characteristics using the vector fitting method. The paper also introduces an approach for approximating the frequency dependent characteristic of large power networks from readily available typical power-flow data. The paper shows how the TSA algorithm can be adapted to a real-time platform. The validity of this method is confirmed with examples, including the study of a multi infeed HVDC system based network. [ABSTRACT FROM AUTHOR]

The power oscillation damping (POD) controllers implemented in the two thyristor controlled series compensators of the Brazilian North-South (NS) interconnection, in the year 1999, were solely intended to damp the low-frequency NS oscillation mode. These controllers are still under operation and are derived from the modulus of the active power flow in the NS line that is phase-lagged at the frequency of the NS mode and may experience relatively large excursions generated by exogenous disturbances. This paper utilizes the same 1999 data to compare the performance of a proposed robust POD controller design with those of two conventional designs. A recent robust control synthesis algorithm used in this work is based on a nonsmooth optimization technique and has the capability to handle various controller structures, including reduced-order, and to deal with time-domain constraints on both controlled and measured outputs. Moreover, the nonsmooth design technique encompasses multiple operating conditions subject to various test signals, hence building a truly time-domain multi-scenarios approach. According to the results discussed hereafter, this is a key advantage in the industrial context of increasing demand for performance and robustness. The described results relate to a large-scale system model used in the feasibility studies for that interconnection. [ABSTRACT FROM AUTHOR]

Traditional online security assessment determines whether the system is secure or not, but how secure or insecure is not explicitly indicated. This paper develops probabilistic indices, risk, to assess real-time power system security level. Risk captures not only event likelihood, but also consequence. System security level associated with low voltage and overload can be optimally controlled, using the NSGA multiobjective optimization method. A security diagram is used to visualize operating conditions in a way that enables both risk-based and traditional deterministic views. An index for cascading overloads is used to evaluate the Pareto optimal solutions. This paper shows that the multiobjective approach results in less risky and less costly operating conditions, and it provides a practical algorithm for implementation. The IEEE 24-bus RTS-1996 system is analyzed to show that risk-based system security control results in lower risk, lower cost, and less exposure to cascading outages. [ABSTRACT FROM AUTHOR]

Parallel and distributed processing has been broadly applied to scientific and engineering computing, including various aspects of power system analysis. This paper first presents a distributed processing approach of reliability index assessment (RIA) for distribution systems. Then, this paper proposes a balanced task partition approach to achieve better efficiency. Next, the distributed processing of RIA is applied to reliability-based network reconfiguration (NR), which employs an algorithm combining local search and simulated annealing to optimize system reliability. Testing results are presented to demonstrate the speeded execution of RIA and NR with distributed processing. [ABSTRACT FROM AUTHOR]

In power distribution networks the load varies within any given time frame. It may, therefore, seem that a good approach to reduce losses would be the solving of a network reconfiguration problem to suit each of the significant load variations. However, frequent changes in configuration can trigger outages or cause transient problems; they are best avoided. A recent formulation of this problem explicitly considers load variations and proposes to restrain frequent reconfigurations by assuming that network topologies will remain unchanged for a given planning period. This formulation leads to a much larger optimization problem than that traditionally used for network reconfiguration; moreover, it requires a new approach to optimization which is capable of dealing with energy flows instead of only instantaneous power flows. Such an approach is proposed in this paper, which discusses the design of an adaptive hybrid genetic algorithm that fulfills these new requirements. Key concepts in evolutionary computation and analysis of distribution systems are explored to develop this new algorithm. Application to real case studies certifies its benefits. [ABSTRACT FROM AUTHOR]

In this paper, dead band control algorithms using a performance index of the load tap changing (LTC) tap position are proposed to reduce the tap changing operation times. The performance index is defined as the customer voltage quality with the tap position of LTC transformer. In addition, dead band control algorithms using the load diversity values of the feeders are proposed. The mathematical formulations of the proposed dead band control algorithms are introduced. A sample case study is shown to verify the effectiveness of the proposed dead band control algorithms. [ABSTRACT FROM AUTHOR]

In a competitive electricity market, price forecasts are important for market participants. However, electricity price is a complex signal due to its nonlinearity, nonstationarity, and time variant behavior. In spite of much research in this area, more accurate and robust price forecast methods are still required. In this paper, a combination of a feature selection technique and cascaded neuro-evolutionary algorithm (CNEA) is proposed for this purpose. The feature selection method is an improved version of the mutual information (MI) technique. The CNEA is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithm (EA). An iterative search procedure is also incorporated in our solution strategy to fine-tune the adjustable parameters of both the MI technique and CNEA. The price forecast accuracy of the proposed method is evaluated by means of real data from the Pennsylvania-New Jersey-Maryland (PJM) and Spanish electricity markets. The method is also compared with some of the most recent price forecast techniques. [ABSTRACT FROM AUTHOR]

As power systems becoming more closely interconnected and are being deregulated in energy markets, distributed simulations among different dispatch centers are highly required for online full system analysis and control applications. In this paper a new algorithm for distributed transient stability simulation of interconnected power systems is presented. Based on a Jacobian-free Newton-GMRES(m) method, this algorithm requires only exchanges of states of boundary buses among different regions. Therefore, it has strong scalability in distributed computing environments built on heterogeneous computing resources. Moreover, several accelerating methods are developed to enhance its efficiency, including continuous preconditioning with adaptive preconditioners, predicting boundary conditions and multistep coordination. The standard IEEE 39-bus system and a real power system with 1165 buses were used as test systems. The test results show that these accelerating methods greatly enhance the convergence rate of the proposed algorithm and reduce communication costs remarkably, which proves the novel algorithm is feasible and can be adopted in wide area networks with high-latency. [ABSTRACT FROM AUTHOR]

Many applications in modern distribution management systems (DMS) need the support of robust and reliable radial power flow analysis. In this connection, although radial power flow solution algorithms are widely proposed in the literature, their application is often complicated by the presence of uncertainties affecting the distribution network operation. The effect of these uncertainties could affect the power flow solution to a considerable extent. A comprehensive tolerance analysis is therefore required in order to incorporate the effect of data uncertainties into power flow analysis. To address this problem, in this paper the employment of interval constraint propagation (ICP) is proposed. ICP is an effective technique for refining enclosures to solutions of nonlinear systems of equations by merging interval mathematic and constraint propagation techniques. Several numerical results are presented and discussed in order to assess the effectiveness of the proposed methodology as an alternative to sampling-based technique, in radial power flow analysis. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of predicting hourly load demand using adaptive artificial neural networks (ANNs). A particle swarm optimization (PSO) algorithm is employed to adjust the network's weights in the training phase of the ANNs. The advantage of using a PSO algorithm over other conventional training algorithms such as the back-propagation (BP) is that potential solutions will be flown through the problem hyperspace with accelerated movement towards the best solution. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. Data are wavelet transformed during the preprocessing stage and then inserted into the neural network to extract redundant information from the load curve. This results in better load characterization which creates a more reliable forecasting model. The transformed data of historical load and weather information were trained and tested over various periods of time. The generalized error estimation is done by using the reverse part of the data as a "test" set. The results were compared with traditional BP algorithm and offered a high forecasting precision. [ABSTRACT FROM AUTHOR]

The ability of using dispersed generation (DG) in the distribution system restoration service in the context of smart networks is presented in this paper. The objectives are to reduce the consequences due to a major blackout in terms of the out-of-service load volume, and the duration of restoration process. Based on knapsack problem formulation and network represented graph modeling, a new restoration procedure for distribution network is proposed. An adapted branch-and-bound algorithm is then used to solve the problem. It maximizes the restored loads in distribution by using the DG availability. Simulation results on a study case will be shown to illustrate the proposed procedure and quantify the benefit of using DG in critical situations. [ABSTRACT FROM AUTHOR]