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  • Open Access


    Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

    Ximin Cao*, Xinglong Chen, He Huang, Yanchi Zhang, Qifan Huang

    Energy Engineering, Vol.121, No.4, pp. 1067-1089, 2024, DOI:10.32604/ee.2023.046783

    Abstract Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals. Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system, a multi-time scale optimal scheduling strategy based on model predictive control (MPC) is proposed under the consideration of load optimization. First, load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature, and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation… More >

  • Open Access


    An Accurate Dynamic Forecast of Photovoltaic Energy Generation

    Anoir Souissi1,*, Imen Guidara1, Maher Chaabene1, Giuseppe Marco Tina2, Moez Bouchouicha3

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1683-1698, 2022, DOI:10.32604/fdmp.2022.022051

    Abstract The accurate forecast of the photovoltaic generation (PVG) process is essential to develop optimum installation sizing and pragmatic energy planning and management. This paper proposes a PVG forecast model for a PVG/Battery installation. The forecasting strategy is built on a Medium-Term Energy Forecasting (MTEF) approach refined dynamically every hour (Dynamic Medium-Term Energy Forecasting (DMTEF)) and adjusted by means of a Short-Term Energy Forecasting (STEF) strategy. The MTEF predicts the generated energy for a day ahead based on the PVG of the last 15 days. As for STEF, it is a combination between PVG Short-Term (ST) More >

  • Open Access


    Energy Management of an Isolated Wind/Photovoltaic Microgrid Using Cuckoo Search Algorithm

    Hani Albalawi1,3, Ahmed M. Kassem2, Sherif A. Zaid1,3,4,*, Abderrahim Lakhouit5, Muhammed A. Arshad6

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2051-2066, 2022, DOI:10.32604/iasc.2022.026032

    Abstract This paper introduces a renewable-energy-based microgrid that includes Photovoltaic (PV) energy and wind energy generation units. Also, an energy storage system is present. The proposed microgrid is loaded with a constant load impedance. To improve the performance of the proposed microgrid, an optimal control algorithm utilizing Cuckoo Search Algorithm (CSA) is adapted. It has many merits such as fast convergence, simple tunning, and high efficiency. Commonly, the PV and wind energies are suitable for supplying loads under normal conditions. However, the energy storage system recovers the excess load demand. The load frequency and voltage are… More >

  • Open Access


    The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

    Dandan Xu1, Haijian Shao1,*, Xing Deng1,2, Xia Wang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 567-597, 2022, DOI:10.32604/cmes.2022.019245

    Abstract As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. More >

  • Open Access


    Conditional Probability Approach for Fault Detection in Photovoltaic Energy Farms

    Nagy I. Elkalashy1,*, Ibrahim B. M. Taha2

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1109-1120, 2022, DOI:10.32604/csse.2022.023509

    Abstract Detection of electric faults in photovoltaic (PV) farms enhances a sustainable service continuity of farm energy generation. In this paper, a probabilistic function is introduced to detect the faults in the PV farms. The conditional probability functions are adopted to detect different fault conditions such as internal string faults, string-to-string faults, and string-to-negative terminal faults. As the diodes are important to make the PV farms in-service safely during the faults, the distribution currents of these faults are evaluated with different concepts of diode consideration as well as without considering any diode installation. This part of… More >

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