Libros y Capítulos de libros

URI permanente para esta colección

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

Examinar

Examinando Libros y Capítulos de libros por Materia "Microgrids"

Mostrando 1 - 2 de 2
  • 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
    Renewable power generation price prediction and forecasting using machine learning
    (Wiley, 2024-05-28) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
    In the power market, electricity price forecasting (EPF) is a crucial consideration for decision-making. The need to optimize earnings by adjusting bids in day-ahead power markets is becoming more and more important to different market participants. Prior information is required for marketers to have an advantage over the competition while controlling the risk of pricing fluctuation. However, not all marketers must accurately predict the worth of future pricing when making decisions. To make a choice, it is necessary to determine whether the cost will be prohibitive. Thus, in order to determine the prices that have an impact on marketers, electricity price classification is first performed. Based on a threshold value, prices are categorized as low class prices and high class pricing. In order to determine the precise value of prices for utility maximization, the EPF has been explored next. In order to maximize advantage or utility, buying and selling bidding techniques rely on the accurate projections of prices for the following day. Effective forecasting models can improve the performance of producers and consumers, who play important roles in the electrical markets. The EPF approaches now in use produce complicated models and are not generalizable.The best method for forecasting prices with superior generalization performance, kernel functions, and distributive prediction is to use machine learning (ML)-based models. In order to forecast prices, machine learning-based electricity price forecasting has been studied. On the markets of Ontario, Austria, and India, the ML models have been put to the test. Neural networks, support vector machines, core vector machines, information vector machines, and relevance vector machines (RVM) have all been used with EPF, and their performances have been assessed in order to determine the optimum model. When all of these models are examined, it becomes clear that RVM is the most effective and trustworthy ML approach for various markets. The price of power fluctuates a lot, with both typical costs and spikes that might be tens to hundreds of times higher than the average price range. The prediction for the overall price of energy is created by adding together average prices and price peaks. A good and precise price spike forecast is required because price spikes have a big influence on the electrical market. As a result, a threshold value has also been used to forecast the spike value. The potential for more research in this field has also been emphasized.