Libros y Capítulos de libros

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

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Chapter 12: Demand side energy management algorithms integrated with the IoT framework in the PV smart grid system
(Academic Press, 2024-01-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
The smart grid revolution in the electric power sector will play a major role in the future. In the electric power system, the combination of new technology and communication infrastructure makes the grid smarter. To incorporate intelligence into the grid, many technological challenges must be solved, including those posed by energy storage systems, the integration of renewable sources, communication, protection, control, and demand-side management with customer involvement. Considering the rising need for electricity, one of the primary operational difficulties in the power system is balancing power generation to the constantly shifting load. Under the smart grid, utilities have realized that through demand side management and different demand response (DR) efforts, customer participation may be efficiently used for this balancing mechanism. The two-way communication between supply and demand can be successfully implemented with the help of smart grid intervention. Customers participate in DR schemes by actively reducing or shifting loads from peak to nonpeak hours concerning the pricing scheme. Therefore, it is essential to develop new demand response strategies for the smart grid, taking into consideration all features of the utility provider and the customers.
Chapter 14: Roles and Challenges of 6G for the Human–Computer Interface
(Wiley, 2024-12-02) Priyabrata, Dash; Akankshya, Patnaik; Sahoo, Sarat Kumar; Yanine, Fernando
Needless to say, technology flies like time. Within a relatively short span of about 30 years, we have been able to visualize the advent of 2G, 3G, 4G, and, finally today, the whole world is fondly using 5G. It is without a doubt that the 5G network is successfully satisfying customers’ needs, but dealing with the flow of excessive data and massive network densification all around the world has made things difficult for telecommunications and industry digitalization. Thus, the evolution of wireless communication beyond 5G (B5G) or 6G may not only be necessary for future industry growth, encompassing massive digital transformation of several industries, but also certainly plausible. In this context, we will show, for example, that 6G is expected to deal with multi-sensory technologies in order to create new ways for people to interact with each other and with other technologies, in an effort to be updated with the upcoming global sustainability and fairness trends. 6G is believed to be a self-content application of Artificial Intelligence. Thus, as researchers, it is the right time to investigate and discover upcoming 6G technology deliverables, especially for the human–computer interface (HCI). HCI is a multidisciplinary topic of investigation and also a broad term that connects Computer Science, Cognitive Science, and Human Factors Engineering. HCI no longer focuses on the behavior of individual or generic users, but it broadens its base towards organizational and social computing. This chapter aims to clarify the proposed road map that lies ahead, which identifies certain roles of 6G technology for the human–computer interface. It also explores its role in current businesses and organizations. The chapter also unearths key tenets of the literature on the subject as well as helps researchers to discover both identified and unidentified challenges of 6G for HCI. The chapter also intends to identify potential technology challenges that may lie ahead and provide a solution for many upcoming problems and open a new horizon for imminent research on the matter, in order to advance knowledge in these areas. The main purpose of this chapter is to analyze and deliberate the various potentials and flaws of 6G for human–computer interfaces.
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.
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.
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.

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