Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models

Lu, Jun; Wang, Bo; Ren, Hui; Zhao, Daqian; Wang, Fei; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.3390/en10101518

Cited by 17 publications

(6 citation statements)

References 23 publications

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“…Statistical methods can be divided into two categories. One category establishes linear or nonlinear mapping of wind power system input and output by curve fitting and parameter estimation based on previous wind power, including the autoregressive moving average (ARMA) model [7], autoregressive integrated moving average (ARIMA) model [8], and generalized autoregressive conditional heterosked (GARCH) model [9]. The other category builds the regression model between meteorological observations and wind power.…”

Section: Introductionmentioning

confidence: 99%

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

Zheng

2019

Applied Sciences

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Large-scale wind power access may cause a series of safety and stability problems. Wind power forecasting (WPF) is beneficial to dispatch in advance. In this paper, a new extreme gradient boosting (XGBoost) model with weather similarity analysis and feature engineering is proposed for short-term wind power forecasting. Based on the similarity among historical days’ weather, k-means clustering algorithm is used to divide the samples into several categories. Additionally, we also create some time features and drop unimportant features through feature engineering. For each category, we make predictions using XGBoost. The results of the proposed model are compared with the back propagation neural network (BPNN) and classification and regression tree (CART), random forests (RF), support vector regression (SVR), and a single XGBoost model. It is shown that the proposed model produces the highest forecasting accuracy among all these models.

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Section: Introductionmentioning

confidence: 99%

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

Zheng

2019

Applied Sciences

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“…Works that used ARMA can be found in [33][34][35]. One drawback of the ARMA is that the time series needs to be stationary, that is, their properties do not depend on the time at which the series is observed.…”

Section: Solar Output Forecastingmentioning

confidence: 99%

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Pham,

Shah,

Dao

et al. 2024

Preprint

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The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined.

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“…In order to address the static voltage stability issue and suppress the voltage fluctuation caused by the increasing integration of wind farms and solar photovoltaic (PV) power plants, Lu et al proposed a two-tier reactive power and voltage control strategy based on ARMA power forecasting models for wind and solar plants [16]. Ergin and Shi proposed four approaches based on the autoregressive moving average (ARMA) method to forecasting short-term wind speed and direction tuple [17].…”

Section: Arma Model In Wind Power Industry Forecastingmentioning

confidence: 99%

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Luo

Zeng

et al. 2021

Journal of Mathematics

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The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

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Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models

Cited by 17 publications

References 23 publications

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

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