A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Abstract

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

Comparative Performance of Inverted-Based Generation using Synchonverter during Transient Stability Conditions

“…The limited overcurrent period following the fault clearing is caused by the amount of reactive power required to recover the voltage during the post contingency condition. The SynC controller tries to mimic the dynamic performance of the synchronous machine; to do so, it follows a second-order linear differential equation to imitate the rotor angle and frequency of a fictitious synchronous generator, which is presented in (2). The model includes an acceleration time (Tacel) of the identical value of the SG1 for comparative purposes.…”

“…Current power networks are undergoing substantial transformation, which is tied to all aspects of the electrical sector [1], [2], [3]: (i) generation-side: deployment and use of more environmentally friendly and weather dependent technologies [4], (ii) grid-side: integration of more flexible assets where the use of direct current (DC) and other technologies (FACTS and storage) are vital elements [5], (iii) demand-side: the rise of novel technologies behind the meter such as small-scale battery energy storage (BESS), electric cars, and the huge transformation of the costumer evolving for a very passive role and the emergence of the concept of the prosumer, prosumers concept is a reality in various nations across the world [6], [7]. The aforementioned power system transformation shares numerous characteristics, but the unifying denominator is the massive integration of IBG [8], [9].…”

Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting

“…In developed countries, electricity distribution is fixed to a day ahead, which in real time is managed using demand-side management strategies, resulting in the reduction of high operation costs. However, a lack of accuracy in load forecasting can have a huge impact on system reliability [6]. The complex behavior and fluctuating nature of electricity demand is a major challenge when developing a robust model.…”

Parametric Sensitivity Analysis of Rotor Angle Stability Indicators

“…The modern power system faces complex transformation in all levels, such as generation, transmission, distribution, and utilization [1]. With this rapid transformation, the modern power system faces significant changes in the power system dynamics; one of the main reasons behind this is reduction of system inertia because of these PEC-based technologies [2]. In the conventional power system, most of the power generators were of the synchronous type; they are characterized as the source of rotational inertia in a power system, whereas in a modern power system, the proportion of synchronous generators is at a decreasing rate [3].…”