Libros y Capítulos de libros

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https://repositorio.uft.cl/handle/20.500.12254/2438

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Examinando Libros y Capítulos de libros por Materia "Optimization"

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  • Miniatura
    Ítem
    Chapter 8: Impacts of energy storage system on PV prosumer based on household load profiles
    (Taylor & Francis Group, 2024-09) Srikanth, Bevara; Sahoo, Sarat Kumar; Yanine, Fernando
    The development of renewable energy generation in power grids has reduced greenhouse emissions due to fossil fuels. Renewable energy sources take part in electricity generation, which affects the economy, environment, national security, and human health. Photovoltaic generating units have increased dramatically worldwide. Even high-generation PV could not meet high demands in the dawn and dusk as it could not produce power regularly. A duck curve always forms with the demand dip in the midday period. This leads to the curtailment of PV power, which in turn affects the environmental and economic advantages of renewable sources. To ensure dependability and stability, most hybrid systems still need a traditional generator or a connection to the main grid. Energy Storage Systems (ESS) are useful in this regard because they encourage the use of renewable energy from the charge and discharge cycles of power with respect to all time horizons. In recent years, the PV consumer market with BESS (battery energy storage systems) and super capacitors has expanded greatly, which has filled the gap between household load and PV power profiles. This chapter highlights the available combinations of storage systems and methodologies for the enhancement of PV prosumer profitability.
  • Miniatura
    Ítem
    Forecasting Electric Power Generation in a Photovoltaic Power Systems for Smart Energy Management
    (Institute of Electrical and Electronics Engineers (IEEE), 2022-08-25) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
    Solar electricity is generated using photovoltaic (PV) systems all over the world. Solar power sources are irregular in nature since PV system output power is intermittent and highly dependent on environmental conditions. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. The uncertainty in photovoltaic generating, it's crucial to plan ahead for solar power generation. Solar power forecasting is required for electric grid supply and demand planning. Because solar power generation is weather-dependent and unregulated, this forecast is complicated and difficult. Selective developed to this goal. Traditional approaches such as statistics, autoregressive moving average, regression, and others were used to forecast PV power before the widespread usage variables are assessed for prediction models based on Artificial Neural Networks (ANN) and regression models. Several PV forecasting algorithms have been of machine learning technologies. Artificial Neural Networks, Support Vector Machines, and hybrid techniques have grown popular as a result of recent advances in machine learning methodologies and access to huge data. This study examines the impacts of numerous environmental conditions on PV system output, as well as the working principle and application of various PV forecasting approaches, in order to better comprehend the insights of PV prediction. Furthermore, the important parameters influencing PV generation are calculated using real-time data.