Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System

González-Longatt, Francisco; Acosta, Martha N.; Chamorro, Harold R.; Topić, Danijel

doi:10.1109/sst49455.2020.9264087

Cited by 14 publications

(16 citation statements)

References 19 publications

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“…This method uses the concept of a linear combination of past events/values by identifying the dependency of observation and residual errors ( t ). In an ARIMA model, the process (Z t = Y t − Y t-d ) is modeled as Z t = µ + t , where the residual errors can be described with Equation ( 16) [25] and the forecasting of the time series predictors (Y t ) can be performed with the autoregressive method as given in Equation (17). In the equations, L is the lag operator, θi is the moving average parameters, p is the order of the lagged observation, d is the degree of difference, and u t is the white noise defined by (u t~N ormal (0, σ 2 )).…”

Section: Arima Approachmentioning

confidence: 99%

“…A number of studies have been conducted to forecast the short-term time series data of load as an indicator for a power system [14][15][16]. A previous paper from the authors presented a structural time series-based model to forecast the kinetic energy of a power system for a short period, which concluded that the identified value of kinetic energy can be used to estimate the system inertia on a real-time basis [17]. This research article is based on further investigation of that research article and presents a new forecasting method to estimate system performance indicators.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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Section: Arima Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…As the system inertia is evolving from a relatively controllable variable to a time-varying variable affected by external uncertainties, more accurate inertia forecasting methods, need to be developed with the consideration of recessive-related data such as date, weather, and renewable generation information [5]. In recent years, data-driven methods such as ML have been applied to power system inertia forecasting [5], [6], [7], [8], [9]. The authors in [5] designed a power system inertia forecasting tool based on artificial neural network (ANN) under a scenario with high penetration of wind generation.…”

Section: Introductionmentioning

confidence: 99%

Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack

Chen

Sun

Chu

et al. 2023

IEEE Trans. Smart Grid

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With the increasing penetration of renewables, the power system is facing unprecedented challenges of low-inertia levels. The inherent ability of the system to defense disturbance and power imbalance through inertia response is degraded, and thus, system operators need to make faster and more efficient scheduling operations. As one of the most promising solutions, machine learning (ML) methods have been investigated and employed to realize effective inertia forecasting with considerable accuracy. Nevertheless, it is yet to understand its vulnerability with the growing threat of cyberattacks. To this end, this paper proposes a methodological framework to explore the vulnerability of ML-based inertia forecasting models, with a special focus on data integrity attacks. In particular, a cost-oriented false data injection attack is proposed, for the first time, with the primary objective to significantly increase the system operation cost while retaining the stealthiness of the attack via minimizing the differences between the pre-perturbed and after-perturbed inertia forecasts. Moreover, we propose four vulnerability assessment metrics for the ML-based inertia forecasting models. Case studies on the GB power system demonstrate the vulnerability and impact of the ML-based inertia forecasting models, as well as the stealthiness and transferability of the proposed cost-oriented data integrity attacks.

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“…System operators face numerous and increasing challenges to maintain and optimize their grids since the power network are being stressed by the adoption of renewables [2]. The natural consequence is that timescales for operational decision making have decreased [3]. Therefore, it is essential to rapidly foresee undesirable situations, and have forecasting analysis on grid contingencies of short-coming situations as a functionality that helps the Transmission System Operators (TSOs), to resolve risks and secure the continuous grid stability for the current and future operation [4,5].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

et al. 2021

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Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

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Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System

Cited by 14 publications

References 19 publications

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

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

Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

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