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Power system simulations are greatly affected by uncertainties arising from models and parameters. The conventional methods of uncertainty analysis involve many thousand rounds of computation, which are both time and effort demanding. Based on the stochastic response surface method and the trajectory sensitivity method, this article proposes a new solution to reduce the number of uncertain inputs and to expedite the calculation of uncertainty. The trajectory sensitivity method is first applied to narrow down a set of parameters with high sensitivity, and the stochastic response surface method is then applied on this set to analyze the uncertainties, which can greatly speed up the computation process. The uncertainties estimated this way are close to those by 105 Monte Carlo simulations in case studies of the NE power grid of China.

Load model is one of the most important elements in power system simulation and control. Based on more than 20 years of practice using load characteristic recorders in the measurement-based load modeling, this paper proposes the expectation composite load model to predict unseen data. The methods for load bus classification and parameter identification are also provided. The generalization capability of the proposed load modeling is validated by the measured load dynamics and system dynamics during two three-phase short circuit tests of the NE power grid in China. In order to evaluate the measurement-based load model, the probabilistic collocation method (PCM) is applied to quantitatively analyze uncertainties of the simulation responses raised by the parameters.

A load model is one of the most important elements in power system simulation and control. Recently, the constant impedance, constant current, and constant power load in combination with the induction motor load have been widely used as the composite load model, whose parameters are all identified from the field measurements in measurement-based load modeling practices so far. However, there is virtually no research conducted on whether all these parameters could really be identified. This paper investigates the possibility on reducing the number of composite load model parameters to be identified from field measurements. This paper first shows that direct application of the IEEE load motor parameters in the composite load model may be inadequate on describing the load dynamics over different operating status. Then the perturbation method is used to derive the trajectory sensitivities of the equivalent motor parameters, based on which the reduction on the identified parameters of the composite load model is presented. Two cases of measurement-based load modeling in North China and Northeast China are studied to illustrate the validity of the reduction method. It is shown that the reduction does not lose the model's capability on describing the load dynamics. The reduction on the number of identified parameters not only provides a possible way to solve the multi-valued load model problem based on the current practices on measurement-based load modeling, but it also facilitates building of the load model with more components included in it. Meanwhile, reducing the identified parameters reduces the identification time; thus, the proposed strategy significantly enhances the efficiency of the load modeling work.

Load model is one of the most important element power system simulation and control. However, quantitatively analyzing uncertainty of key parameter in load model is difficult, due to the high computation cost of the traditional uncertainty analysis methods. Through Probabilistic Collocation Method (PCM), the quantitative uncertainty analysis of load models in dynamic stability is studied. Based on the measurements of artificial three-phase short circuit test in 2005 in North East Power Grid of China, this paper analyzes and compares the uncertainties of the composite load model and static load model, by using second-order PCM approximation function. Cases study indicates that PCM is suitable and feasible for uncertainty analysis in dynamics of power system.

It has been well recognized that the load model has great effects on the power system analysis and control. However, it is also widely known that the load modeling is a quite difficult problem due to its special properties such as time-variant characteristics of the load. In this paper, the measurement-based load modeling approach and multi-curve identification technique are applied in Hushitai power station in Northeast China. In order to build practical load models that can describe the actual load characteristics without changing the model parameters too often, cluster analysis is used to classify the loads. Two main kinds of eigenvectors used in cluster analysis are also compared. Finally, based on the load classification, a new aggregation method is proposed to build load models with good generalization capability. Case studies show the efficiency of the proposed method.

Load modeling is very important to power system operation and control. Measurement-based load modeling has been widely practiced in recent years. Mathematically, measurement-based load modeling problem are closely related to the parameter identification area. Consequently, an efficient optimization method is needed to derive the load model parameters based on the feedback of estimation errors between the measurements and model outputs. This paper reports our work on applying genetic algorithms on measurement-based load modeling research. Due to its robustness to the initial guesses on the load model parameters, genetic algorithms are very suitable for load model parameter identification. Two cases including both the real measurement in a power station and the digital simulation are studied in the paper. For comparison purpose, the classical nonlinear least square estimation method is also applied to find the load model parameters. The simulated outputs from the load model confirm the efficiency of genetic algorithms in measurement-based load modeling analysis. Future work will focus on fastening the converging speed of the genetic algorithms, and/or utilizing more efficient evolutionary computation methods.

Summary form only given. The accuracy of the load model has great effects on power system stability analysis and control. Based on our practice in China on modeling load from field measurements, this paper systematically develops a measurement-based composite load model. Principles guiding the load modeling practice are discussed based on detailed analysis on stochastic characteristics of modeling procedure. The structure of the measurement-based composite load model is presented. A multi-curve identification technique is described to derive parameters. The generalization capability of thus built load model is also investigated in the paper. Two cases are studied to illustrate the accuracy of the developed load model on describing the load dynamic characteristics in the actual power system.

Load characteristics have important effects on dynamic performance of power system. The failure to represent loads in sufficient detail may produce results that miss significant phenomena. The existing load model was inadequate to simulate real process of power system, therefore development and use of more accurate dynamic load models is highly desirable. To get load model for different physical loads in different places in large-scale power grid, measurement devices must be set to record the load dynamic characteristic. This has no problem in theory, but actually, with the power grid bigger and bigger, it's unacceptable in project to mount so many devices, for the cost on manpower, material resource and money. At present load model which used in Chinese power system simulation is rough, for example, load model in north-east grid is 50% static ZIP which is composed by constant impedance, constant power and constant current load, and 50% induction motor which represents the dynamic characteristic of load, load model in north China grid is 40% constant impedance load and 60% constant power load.

For power purchase type of electric power market, unit commitment is the important technology method to get the result of spot transaction. In order to explain the phenomenon of transaction distortion, the analysis is given in considering of the result fairness and the process fairness. The technical index USI is defining to describe the result fairness, and unit commitment model is established considering the result bounded fairness. Process fairness is studied in combination with unit commitment solution.

A load model derived from measurement around the operating point in the field, for long term dynamic simulation, has been defined and experimented for discrete loads in laboratory tests. But network conditions are different and more complex than those in a laboratory. A linear discrete-dynamic system is proposed for describing network random phenomena. Transition matrix elements are studied one by one, and simplifications are proposed. Load model parameters identification is replaced by the identification of transition matrix elements of a linear discrete-dynamic system.