Methods for incorporating digital controllers in power system dynamic simulations

Nonlinearity of power system is always one of the difficulties when dealing with dynamic simulation of power systems. Solving differential-algebraic equations representing power systems are difficult without losing nonlinearity, especially for large power systems. This thesis shows an alternative method to solve nonlinear dynamical power system by producing a purely differential representation of the power systems. This new representation converts the algebraic equations to differential equations in order to have an absolute differential system. By using Runge-Kutta algorithm to solve this differential system, the results of the power system simulations are compared to trapezoidal integration algorithm commonly used to solve the differential-algebraic equations. In this thesis, IEEE 14-bus system and IEEE 118-bus system are tested with both classical generator model generator model and two-axis generator model in MATLAB. The proposed algorithm shows significantly faster convergence comparted to trapezoidal integration method in larger power systems. It is a great improvement to shorten the simulation time in while keeping the same accuracy in large power systems.

This paper describes two novel methods for distributed simulation of nonlinear power system dynamics, which are named two-level integration and interaction-level integration. An underlying assumption is that the simulation of dynamic response is distributed spatially, i.e. each power system component and/or each group of components perform numerical integration of their own states while occasionally communicating relevant information with their neighbors. The novelty of the proposed methods is in exploiting the structure of dynamic equations of typical power system components to shorten the total simulation time in this distributed setting. The proposed methods consist of a number of state transformations on the dynamical power system model which bring this model into a form favored for distributed numerical integration. These transformations, proposed first in [1] and [2] for control design, separate internal component dynamics from dynamics of its interactions with neighboring components. It is further shown on a simple example that it is sufficient to exchange the information between components at a rate determined by the dynamics of the interaction variables to successfully perform numerical integration of the entire power system model with a significant improvement in simulation time. Since the proposed methods are based on the state transformation of the power system model, this approach could be used together with other numerical and machine-level programming optimization techniques to further enhance performance of the power system dynamics simulator.

With the rapid development of artificial intelligence (AI), it is foreseeable\nthat the accuracy and efficiency of dynamic analysis for future power system\nwill be greatly improved by the integration of dynamic simulators and AI. To\nexplore the interaction mechanism of power system dynamic simulations and AI, a\ngeneral design of an AI-oriented power system dynamic simulator is proposed,\nwhich consists of a high-performance simulator with neural network\nsupportability and flexible external and internal application programming\ninterfaces (APIs). With the support of APIs, simulation-assisted AI and\nAI-assisted simulation form a comprehensive interaction mechanism between power\nsystem dynamic simulations and AI. A prototype of this design is implemented\nand made public based on a highly efficient electromechanical simulator. Tests\nof this prototype are carried out under four scenarios including sample\ngeneration, AI-based stability prediction, data-driven dynamic component\nmodeling, and AI-aided stability control, which prove the validity,\nflexibility, and efficiency of the design and implementation of the AI-oriented\npower system dynamic simulator.\n

The increasing capacities of wind power bring influence on power system stability because wind power can produce stochastic behaviors due to special fluctuant characteristics of natural wind. The dynamic simulation method and software considering effect of wind power mainly concentrated on electromagnetic transient and electromechanic transient process, whereas the analysis for the long term dynamic simulation for power system including wind farms is relatively scarce at present. This paper adopts mathematic dynamic model of induction wind generating set and double-fed induction generators respectively, and makes use of dynamic simulation method based on variables grouping and seceding grouping strategy, realizes long term dynamic simulation for power system including wind farms under different wind condition and operating mode based on a modified IEEE-30 bus network. The numerical example shows that dynamic simulation method being adopted is suitable for long term dynamic simulation of power system including wind farms, and also shows that doubly fed induction wind turbines have better stability characteristic than asynchronous induction wind turbines and can improve system stability characteristic.

Large power system frequency/voltage fluctuations due to severe faults in trunk transmission lines or major generating units may trip‐off other facilities and result in large‐scale power system blackout. To prevent such blackout, development of an accurate and fast long‐term power system dynamic simulation tool is required. With respect to the development stage, selection of a numerical integration method available for step‐size adjustment, and the development of a scheme for the adjustment are principal subjects.The trapezoidal and the second‐order Gear methods were selected as possible candidates for the step‐size adjustment from the viewpoint of numerical stability. The trapezoidal method is selected from the viewpoint of accuracy and applicability to power system dynamic simulations.A new scheme for the automatic step‐size adjustment is proposed for the trapezoidal method to achieve fast simulation of the long‐term power system dynamics. The validity of the proposed scheme is verified through simulation studies on a single‐machine‐to‐infinite bus system.

The integration of uncertain photovoltaics (PVs) and flexible loads leads to uncertainties in the power system dynamic simulation results. Furthermore, geographically close PV farms are correlated and may exhibit nonlinear correlations. This article proposes a copula-based sparse polynomial chaos expansion (PCE) framework for quantifying the impacts of uncertain dynamic PVs and loads on power system dynamic simulations and stability. The dynamics include both PV and load stochasticity and those governed by differential and algebraic equations. The copula statistics are utilized to accurately characterize the dependence structure of PVs and further used to develop the copula-PCE for quantifying the impacts of uncertain PVs and loads. A probabilistic TSI is also developed to assess the uncertainties from PVs and loads on the system stability. To address the cases, where both stable and unstable conditions coexist, a preprocessing step via sample classification is proposed. The effects of different dependence structures of PVs and different numbers of uncertain sources are investigated. Comparison results with other methods on the modified IEEE 39- and 118-bus systems, including the Monte Carlo method, Latin hypercube sampling, and traditional PCE without consideration of uncertain input correlations show that the proposed method is able to accurately quantify the uncertain dynamic simulations and transient system stability while being computationally efficient.

Models have been developed for simulating the plant dynamics of light-water-reactor (PWR and BWR) plants under both small and large power system disturbances. The model permits precise and effective analysis of the dynamics of the nuclear plant response to power system faults by combining the plant model with the conventional power system dynamics simulation program. The modeling of the nuclear plant is described, and the application of the model to power system dynamics simulation is outlined. >

A tendency to erect ever more wind turbines can be observed in order to reduce the environmental consequences of electric power generation. As a result of this, in the near future, wind turbines may start to influence the behavior of electric power systems by interacting with conventional generation and loads. Therefore, wind turbine models that can be integrated into power system simulation software are needed. In this contribution, a model that can be used to represent all types of variable speed wind turbines in power system dynamics simulations is presented. First, the modeling approach is commented upon and models of the subsystems of which a variable speed wind turbine consists are discussed. Then, some results obtained after incorporation of the model in PSS/E, a widely used power system dynamics simulation software package, are presented and compared with measurements.

As a result of increasing environmental concern, increasing amounts of electricity are generated from renewable sources. One way of generating electricity from renewable sources is to use wind turbines. A tendency to erect more wind turbines can be observed. As a result of this, in the near future wind turbines may start to influence the behavior of electrical power systems. To investigate the impact of increasing wind turbine penetration, power system dynamics studies need to be carried out. To this end, power system dynamics simulation software is used, in which wind turbine models must be integrated to enable the investigation of increasing wind turbine penetration on power system behavior. If wind turbine models are to be integrated into power system dynamics simulation software, it must be possible to calculate the initial conditions of the dynamic model from load flow data to be able to initialize the dynamic simulation correctly. In this paper, a solution to this problem is presented.

The authors address the problem of slow transient and dynamic simulation of power systems on personal computers. An approach is presented for the systematic description and organization of the information and data pertaining to these studies, such as control system configuration and parameters, output selection, simulation scenario description, etc., in order to provide the basis for user interactive analysis programs. The techniques, applied to the EMTDC program, result in an autonomous executable module that permits the user to conduct the simulation studies completely through input data files. This development eliminates the need for coding, compilation, and linking and constitutes the necessary step towards creating user interactive software on personal computers for power system studies. The final product includes the modified EMTDC main module and is linked with the EMTDC libraries MAIN, UTIL, VALG, and SVG plus additional subroutines to facilitate the simulation description process.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Adaptive PSS is an intelligent power system stabilizer based on adaptive control theory. More inexpensive and more flexible control equipment will be needed in the environment of the deregulation of the electric power market, and adaptive PSS is considered as a software-based effective controller. In our research, power system dynamic simulations were carried out using an analog-type real-time simulator in order to show the effectiveness of the proposed adaptive PSS with local identifier. So far, some digital simulations have been done for single adaptive PSS, but there are few studies on multiple adaptive PSS. As a result, power system damping in the case of multiple adaptive PSS installation is improved more than that in the single PSS case. This means that even an adaptive PSS with local identifier can serve as a good controller if multiple PSS are installed.

Real-time co-simulation of adjustable-speed pumped storage hydro for transient stability analysis

In this paper we present the development of an energy and power system modelling, simulation, and analysis (ePowerSim.jl) package in Julia programming language. ePowerSim.jl is designed to present a uniform data interface for static, quasi-static, dynamic analysis, as well as network operation optimisation. It provides a co-simulation framework for the further development and experimentation of various types of models of electric power systems components or abstract entities that have mathematical formalism or data representation. ePowerSim.jl makes extensive use of cutting edge packages such as DifferentialEquations.jl, Dataframes.jl, NamedTupleTools.jl, Helics.jl, ForwardDiff.jl, JuMP.jl, and BifurcationKit just to mention a few in the Julia ecosystem. Models of synchronous generator, synchronous condenser, excitation systems, and governors developed in the package were used to model IEEE 9 bus and IEEE 14 bus test networks and subsequently validated by a real-time digital simulator of electric power systems (RTDS). The results obtains for static and dynamic models simulation in ePowerSim.jl show a close match with a simulation of the same system in RTDS. A maximum error of 0.00001 pu and 0.0001 pu were obtained for steady states and transient state respectively. Similarly, a maximum deviation of 0.0001 pu was obtained during validation for voltage magnitude during transient state at buses in the network.

Models of different parts of the DC power system including the energy storage devices—supercapacitors (SCs) are discussed in this paper. Charging and discharging voltage thresholds and SOC (State of Charge) should be taken into consideration when establishing the SCs model. Also the algorithm to calculate the power flow of the system changes after the addition of SCs. Combined with the states of traction network, the control strategy of SCs is analyzed in detail. The dynamic simulation is based on the Chain—rule Reduction of Ladder Circuit Jacobian Matrices method. To compare the energy saving and voltage stabilization effects, three equipment installation conditions—with braking resistors (BRs) only, with SCs only, with neither of them, are set in this paper. The simulation program is edited in Matlab with m language. Finally with this program the simulation results of a certain metro line in three conditions were compared and analyzed, and all effects were achieved.

Load model plays important role in the dynamic simulation of power system. The time-varying property of load in short-time appears to be stochastic in long-term. Thus the approach in this paper to study the statistical regularity of load characteristic vector, based on probability distribution and statistics, gives references to the choice and application of load model and its parameters. This paper proposes the following approaches. First, the differential equation model is transformed into continuous state space model, and is solved for the time-domain response curve against step input. Second, the characteristic parameters of response curve, represented by [k/sub t/ T/sub f/,k/sub s/], are distilled as the characteristic vector of load property. 69 sets of effective sampling data from Yuzhuang substation of Cangzhou region in China were studied for the character parameters of load model, from which useful conclusions of the load model variety regularity have been got.