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  • Open Access

    ARTICLE

    Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

    Yuanjun Dai, Haonan Li, Baohua Li*

    Energy Engineering, Vol.121, No.6, pp. 1607-1636, 2024, DOI:10.32604/ee.2024.047255

    Abstract This paper addresses the micro wind-hydrogen coupled system, aiming to improve the power tracking capability of micro wind farms, the regulation capability of hydrogen storage systems, and to mitigate the volatility of wind power generation. A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction, the hydrogen storage state division interval, and the daily scheduled output of wind power generation. The control strategy maximizes the power tracking capability, the regulation capability of the hydrogen storage system, and the fluctuation of the joint output of the wind-hydrogen… More >

  • Open Access

    ARTICLE

    A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

    Farhan Ullah1, Xuexia Zhang1,*, Mansoor Khan2, Muhammad Abid3,*, Abdullah Mohamed4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656

    Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

  • Open Access

    ARTICLE

    A Wind Power Prediction Framework for Distributed Power Grids

    Bin Chen1, Ziyang Li1, Shipeng Li1, Qingzhou Zhao1, Xingdou Liu2,*

    Energy Engineering, Vol.121, No.5, pp. 1291-1307, 2024, DOI:10.32604/ee.2024.046374

    Abstract To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on More >

  • Open Access

    ARTICLE

    Investigating Load Regulation Characteristics of a Wind-PV-Coal Storage Multi-Power Generation System

    Zhongping Liu1, Enhui Sun2,*, Jiahao Shi2, Lei Zhang2, Qi Wang1, Jiali Dong1

    Energy Engineering, Vol.121, No.4, pp. 913-932, 2024, DOI:10.32604/ee.2023.043973

    Abstract There is a growing need to explore the potential of coal-fired power plants (CFPPs) to enhance the utilization rate of wind power (wind) and photovoltaic power (PV) in the green energy field. This study developed a load regulation model for a multi-power generation system comprising wind, PV, and coal energy storage using real-world data. The power supply process was divided into eight fundamental load regulation scenarios, elucidating the influence of each scenario on load regulation. Within the framework of the multi-power generation system with the wind (50 MW) and PV (50 MW) alongside a CFPP… More > Graphic Abstract

    Investigating Load Regulation Characteristics of a Wind-PV-Coal Storage Multi-Power Generation System

  • Open Access

    ARTICLE

    The Short-Term Prediction of Wind Power Based on the Convolutional Graph Attention Deep Neural Network

    Fan Xiao1, Xiong Ping1, Yeyang Li2,*, Yusen Xu2, Yiqun Kang1, Dan Liu1, Nianming Zhang1

    Energy Engineering, Vol.121, No.2, pp. 359-376, 2024, DOI:10.32604/ee.2023.040887

    Abstract The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale. Therefore, wind power forecasting plays a key role in improving the safety and economic benefits of the power grid. This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data. Based on the graph attention network and attention mechanism, the method extracts spatial-temporal characteristics from the data of multiple wind farms. Then, combined with a deep… More >

  • Open Access

    ARTICLE

    A Temporary Frequency Response Strategy Using a Voltage Source-Based Permanent Magnet Synchronous Generator and Energy Storage Systems

    Baogang Chen1, Fenglin Miao2,*, Jing Yang1, Chen Qi2, Wenyan Ji1

    Energy Engineering, Vol.121, No.2, pp. 541-555, 2024, DOI:10.32604/ee.2023.028327

    Abstract Energy storage systems (ESS) and permanent magnet synchronous generators (PMSG) are speculated to be able to exhibit frequency regulation capabilities by adding differential and proportional control loops with different control objectives. The available PMSG kinetic energy and charging/discharging capacities of the ESS were restricted. To improve the inertia response and frequency control capability, we propose a short-term frequency support strategy for the ESS and PMSG. To this end, the weights were embedded in the control loops to adjust the participation of the differential and proportional controls based on the system frequency excursion. The effectiveness of More >

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal

    Shumin Sun1, Peng Yu1, Jiawei Xing1, Yan Cheng1, Song Yang1, Qian Ai2,*

    Energy Engineering, Vol.120, No.12, pp. 2761-2782, 2023, DOI:10.32604/ee.2023.042635

    Abstract Wind power prediction is very important for the economic dispatching of power systems containing wind power. In this work, a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and (long short-term memory) LSTM neural network is proposed and studied. First, the original data is prepossessed including removing outliers and filling in the gaps. Then, the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model. In addition, this study conducts seasonal classification… More >

  • Open Access

    ARTICLE

    Capacity Optimization Configuration of Hydrogen Production System for Offshore Surplus Wind Power

    Yanshan Lu1, Binbin He1, Jun Jiang1, Ruixiao Lin2,*, Xinzhen Zhang2, Zaimin Yang3, Zhi Rao3, Wenchuan Meng3, Siyang Sun3

    Energy Engineering, Vol.120, No.12, pp. 2803-2818, 2023, DOI:10.32604/ee.2023.042328

    Abstract To solve the problem of residual wind power in offshore wind farms, a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power. By studying the mathematical model of wind power output and calculating surplus wind power, as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank, an innovative capacity optimization allocation model was established. The objective of the model was to achieve the lowest total net present value over the entire life cycle.… More >

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on Combinatorial Neural Networks

    Tusongjiang Kari1, Sun Guoliang2, Lei Kesong1, Ma Xiaojing1,*, Wu Xian1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

    Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used… More >

  • Open Access

    ARTICLE

    An Investigation of Battery Energy Storage Aided Wind-Coal Integrated Energy System

    Enhui Sun1,2, Jiahao Shi1,2, Lei Zhang1,2,*, Hongfu Ji1,2, Qian Zhang1,2, Yongyi Li1,2

    Energy Engineering, Vol.120, No.7, pp. 1583-1602, 2023, DOI:10.32604/ee.2023.027790

    Abstract This paper studies the feasibility of a supply-side wind-coal integrated energy system. Based on grid-side data, the load regulation model of coal-fired power and the wind-coal integrated energy system model are established. According to the simulation results, the reasons why the wind-coal combined power supply is difficult to meet the grid-side demand are revealed through scenario analysis. Based on the wind-coal combined operation, a wind-coal-storage integrated energy system was proposed by adding lithium-iron phosphate battery energy storage system (LIPBESS) to adjust the load of the system. According to the four load adjustment scenarios of grid-side… More >

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