Search

It is sometimes difficult to explain how a power system works, especially for people who have not studied electrical engineering. This article shows how the interactions of a power system and its components can be explained using the parallel with a tandem bicycle. The active power balance in the power system is modeled by the parallel of keeping a constant speed of the bike, while the reactive power balance is represented with the fact that the bike has to be kept on the road without overturning. The parallel gives a good visualization of phenomena such as active power balance, frequency control, asynchronous machine slip, reactive power balance, and voltage control. [ABSTRACT FROM PUBLISHER]

Presents the 2022 author/subject index for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents the front cover for this issue of the publication. [ABSTRACT FROM AUTHOR]

Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. It would be difficult to develop a universal forecasting model dominating over other alternative models because of the inherent stochastic nature of wind power. Therefore, a novel multi-model combination (MMC) approach for probabilistic wind power forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models those provide different kinds of probability density functions to improve the performance of probabilistic forecasting. Three probabilistic forecasting models based on the sparse Bayesian learning, kernel density estimation, and beta distribution fitting are used to form the combined model. The parameters of the MMC model are solved by two-step optimization. Comprehensive numerical studies illustrate the effectiveness of the proposed MMC approach. [ABSTRACT FROM AUTHOR]

The uncertainties of renewable energy may bring impacts to power system reliability. This paper proposes a novel generation rescheduling algorithm, which adjusts generation outputs to mitigate the variations of branch power flows and relieve the overload probability. This algorithm helps prevent power congestion and balance the aggregate renewable generations and loads in power systems. Besides, the paper also proposes a probabilistic method evaluating the system uncertainty issue in the power system considering generation rescheduling operation. The proposed generation rescheduling algorithm has been tested on the Arizona transmission system. The generation rescheduling method enhances power system reliability analysis with a high penetration of renewable energy resources. [ABSTRACT FROM PUBLISHER]

Wind generation forecast approaches are typically based on fixed model structures, which might fit well with existing data but fall short in forecasting out-of-sample data. The reason lies in that predetermined models cannot always capture the actual dynamic features of wind farms, resulting in unavoidable model errors. In this paper, a novel ultra-short-term wind generation forecast approach based on multivariate empirical dynamic modeling is proposed. The time series of wind generation and the explanatory variables are applied for attractor reconstruction according to Takens’ theorem. By this means, the dynamics of the original wind generation system can be accurately captured by the trajectories of the state variables in the reconstructed space. Then, using a simplex projection approach, wind generation can be directly forecasted with the trajectories. A unique feature of the proposed approach is that its effectiveness depends solely on the potential context hidden in the sequences of the state variables rather than the correctness of the predetermined models. Consequently, this approach can faithfully depict the dynamics of wind farms and improve the forecast accuracy. Case studies illustrate that the proposed approach can obtain advanced forecast results comparing with the benchmark methods. [ABSTRACT FROM AUTHOR]

This paper presents an analytical method to model the output of large wind farms for power system reliability assessment. The model considers the variability of wind, random failures (forced outages) of turbines, as well as the correlation between turbine outputs that results from the turbines on a farm being subjected to similar incident wind speeds. Due to this correlation, the outputs of individual turbines cannot be modeled as independent random variables. The problem is addressed here by separately modeling the independent outages of wind turbines and the dependency on wind speed, and then convolving the two distributions. The resulting model includes both probability and frequency distributions of the power output of the wind farm. The proposed method is demonstrated on the IEEE Reliability Test System. Monte Carlo simulation is used to validate the results. [ABSTRACT FROM PUBLISHER]