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The paper proposes a new adaptive approach to power system model reduction for fast and accurate time-domain simulation. This new approach is a compromise between linear model reduction for faster simulation and nonlinear model reduction for better accuracy. During the simulation period, the approach adaptively switches among detailed and linearly or nonlinearly reduced models based on variations of the system state: it employs unreduced models for the fault-on period, uses weighted column norms of the admittance matrix to decide which functions to be linearized in power system differential-algebraic equations for large changes of the state, and adopts a linearly reduced model for small changes of the state. Two versions of the adaptive model reduction approach are introduced. The first version uses traditional power system partitioning where the model reduction is applied to a defined large external area in a power system and the other area defined as the study area keeps full detailed models. The second version applies the adaptive model reduction to the whole system. The paper also conducts comprehensive case studies comparing simulation results using the proposed adaptively reduced models with the linearly reduced model and coherency-based reduced model on the Northeast Power Coordinating Council 140-bus 48-machine system. [ABSTRACT FROM AUTHOR]

In this paper, we formulate and solve the ac optimal power flow (AC-OPF) as an equivalent circuit problem in terms of current, voltage, and admittance state variables. The generator models are represented using conductance and susceptance state variables, and the operational and network constraints are translated to corresponding current/voltage constraints without loss of accuracy or generality. To understand the physics behind the optimality conditions of the optimization problem, we extend the linear adjoint circuit theory to translate them to equivalent circuit domain. It is shown that the operating point that defines the equivalent circuit solution precisely represents an AC-OPF solution. We then further exploit the equivalent circuit representation to use power flow simulation techniques to robustly solve the optimization problem. The efficiency of our approach is demonstrated for several AC-OPF benchmark test cases (up to 70 k buses) under nominal and congested operating conditions, and the runtime and scalability properties are presented. [ABSTRACT FROM AUTHOR]

Simulation scheme constructed by transient models is widely used for interconnected power system studies. However, it has shortcomings when used in automatic generation control (AGC) and AGC relevant fields. In this paper, a novel simulation scheme designed for AGC and its relevant studies is presented. The scheme uses dynamic energy balancing equation and a single central frequency as core algorithm. Moreover, models described by algebraic equations are adopted instead of transient models described by differential equations. The simplified models reduce computational power requirement to about 3% of transient models and require fewer parameters. By ignoring transient dynamics and inter-area oscillations which are insignificant in AGC timescale, the presented scheme achieves high computational efficiency and acceptable accuracy making it acceptable for up to year-long simulation. At the end of the paper, comparative tests are performed to provide differences, validity and accuracy of proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, the power system with an excitation controller is represented as a class of large-scale, uncertain, interconnected nonlinear continuous-time system in strict-feedback form. Subsequently, dynamic surface control (DSC)-based adaptive neural network (NN) controller is designed to overcome the repeated differentiation of the control input that is observed in the conventional backstepping approach. The NNs are utilized to approximate the unknown subsystem and the interconnection dynamics. By using novel online NN weight update laws with quadratic error terms, the closed-loop signals are shown to be locally asymptotically stable via Lyapunov stability analysis, even in the presence of NN approximation errors in contrast with other NN techniques where a bounded stability is normally assured. Simulation results on the IEEE 14-bus power system with generator excitation control are provided to show the effectiveness of the approach in damping oscillations that occur after disturbances are removed. The end result is a nonlinear decentralized adaptive state-feedback excitation controller for damping power systems oscillations in the presence of uncertain interconnection terms. [ABSTRACT FROM PUBLISHER]

Matpower is an open-source Matlab-based power system simulation package that provides a high-level set of power flow, optimal power flow (OPF), and other tools targeted toward researchers, educators, and students. The OPF architecture is designed to be extensible, making it easy to add user-defined variables, costs, and constraints to the standard OPF problem. This paper presents the details of the network modeling and problem formulations used by Matpower, including its extensible OPF architecture. This structure is used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits. Simulation results are presented for a number of test cases comparing the performance of several available OPF solvers and demonstrating Matpower's ability to solve large-scale AC and DC OPF problems. [ABSTRACT FROM AUTHOR]