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This textbook introduces the fundamentals of renewable electrical power systems examining their direct relationships with the environment. It covers conventional power systems and opportunities for increased efficiencies and friendlier environmental interactions. While presenting state-of-the-art technology, the author uses a practical interdisciplinary approach explaining electrical, thermodynamics, and environmental topics within every chapter. This approach allows students to feel comfortable moving across these disciplines. The added value are the examples of software programs using open source systems which serve as learning tools for the concepts and techniques described in the book-- Provided by publisher.

Introduction to Renewable Power Systems and the Environment with R showcases the fundamentals of electrical power systems while examining their relationships with the environment. To address the broad range of interrelated problems that come together when generating electricity, this reference guide ties together multiple engineering disciplines with applied sciences. The author merges chapters on thermodynamics, electricity, and environmental systems to make learning fluid and comfortable for students with different backgrounds. Additionally, this book provides users with the opportunity to execute computer examples and exercises that use the open source R system. Functions of the renpow R package have been described and used in this book in the context of specific examples. The author lays out a clear understanding of how electricity is produced around the world and focuses on the shift from carbon-based energy conversions to other forms including renewables. Each energy conversion system is approached both theoretically and practically to provide a comprehensive guide. Electrical circuits are introduced from the simplest circumstances of direct current (DC), progressing to more complex alternating current (AC) circuits, single phase and three-phase, and electromagnetic devices including generators and transformers. Thermodynamics are employed to understand heat engines and a variety of processes in electrochemical energy conversion, such as fuel cells. The book emphasizes the most prevalent renewable energy conversions in use today: hydroelectrical, wind, and solar. This book is an invaluable for students as a resource to help them understand those aspects of environment systems that motivate the development and utilization of renewable power systems technology. Miguel F. Acevedo has 40 years of academic experience, the last 24 of these at the University of North Texas (UNT) where he is currently a faculty member. His career has been interdisciplinary and especially at the interface of science and engineering. He has served UNT as faculty member in the department of Geography, the Graduate Program in Environmental Sciences of the Biology department, and more recently in the Electrical Engineering department. He obtained his Ph.D. degree in Biophysics from the University of California, Berkeley (1980) and master degrees in Electrical Engineering and Computer Science from Berkeley (M.E., 1978) and the University of Texas at Austin (M.S., 1972). Before joining UNT, he was at the Universidad de Los Andes, Merida, Venezuela, where he taught since 1973 in the School of Systems Engineering, the graduate program in Tropical Ecology, and the Center for Simulation and Modeling. He has served on the Science Advisory Board of the U.S. Environmental Protection Agency and on many review panels of the U.S. National Science Foundation. He has received numerous research grants, and written several textbooks, numerous journal articles, as well as many book chapters and proceeding articles. UNT has recognized him with the Regents Professor rank, the Citation for Distinguished Service to International Education, and the Regent’s Faculty Lectureship. Basics of environmental systems, thermodynamics, and electric circuits; Carbon cycle, magnetic circuits, and entropy; Steam processes and three-phase circuits; Natural gas, Brayton cycle, and power quality; The electric power grid; Solar resources and power from PV; Wind resources and wind power; Hydroelectric power generation; Other major sources: geothermal and nuclear sources ; Off the grid, rural areas, and standalone applications; Fuel cells; Distributed generation; Biomass, agriculture, and food; Other topics, applications, and prospects; Economics and Financing; Introduction to R

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

\"With special relation to smart grids, this book provides clear and comprehensive explanation of how Digital Signal Processing (DSP) and Computational Intelligence (CI) techniques can be applied to solve problems in the power system.Its unique coverage bridges the gap between DSP, electrical power and energy engineering systems, showing many different techniques applied to typical and expected system conditions with practical power system examples.Surveying all recent advances on DSP for power systems, this book enables engineers and researchers to understand the current state of the art and to develop new tools. It presents: an overview on the power system and electric signals, with description of the basic concepts of DSP commonly found in power system problems the application of several signal processing tools to problems, looking at power signal estimation and decomposition, pattern recognition techniques, detection of the power system signal variations description of DSP in relation to measurements, power quality, monitoring, protection and control, and wide area monitoring a companion website with real signal data, several Matlab codes with examples, DSP scripts and samples of signals for further processing, understanding and analysis Practicing power systems engineers and utility engineers will find this book invaluable, as will researchers of electrical power and energy systems, postgraduate electrical engineering students, and staff at utility companies\"--

Smart grids collect real-time power consumption reports that are then forwarded to the utility service providers over the public communication channels. Compared with the traditional power grids, smart grids integrate information and communication technologies, cyber physical systems, power generation and distribution domains to enhance flexibility, efficiency, transparency and reliability of the electric power systems. However, this integration of numerous heterogeneous technologies and devices increases the attack surface. Therefore, a myriad of security techniques have been introduced based on technologies such as public key cryptosystems, blockchain, bilinear pairing and elliptic curve cryptography. However, majority of these protocols have security challenges while the others incur high complexities. Therefore, they are not ideal for some of the smart grid components such as smart meters which are resource-constrained. In this paper, a protocol that leverages on digital certificates, signatures, elliptic curve cryptography and blockchain is developed. The formal verification using Real-Or-Random (ROR) model shows that the derived session keys are secure. In addition, semantic security analysis shows that it is robust against typical smart grid attacks such as replays, forgery, privileged insider, side-channeling and impersonations. Moreover, the performance evaluation shows that our protocol achieves a 17.19% reduction in the computation complexity and a 46.15% improvement in the supported security and privacy features.

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

For complex mechanical transmission equipment, shaft bearings are usually enclosed together with the shaft in the internal space of the housing to maintain good sealing and reliability. However, it is difficult to monitor the status of the shaft bearing through external sensors on the housing, while internal sensors face challenges in energy supply and data transmission. Therefore, a piezoelectric transducer ring-based energy harvesting microelectromechanical system (PTR-EH-MEMS) is proposed for the condition monitoring of shaft bearings. Specifically, the piezoelectric transducer ring is designed to convert mechanical vibrations into electrical energy, which simultaneously acts as a self-powered monitoring sensor through energy harvesting. In addition, the MEMS is embedded for piezoelectric data processing and condition monitoring of the shaft bearings. To verify the proposed PTR-EH-MEMS, an experimental investigation is implemented under different conditions. The experimental results demonstrate that the system can achieve the maximum DC output of 0.8 V and the root mean square power of 43.979 μW within 128 s, which can effectively identify early-stage bearing faults frequency through a self-powered mode. By combining energy harvesting with condition monitoring capability, the PTR-EH-MEMS offers a compact and sustainable approach for predictive maintenance in rotating machinery, reducing the reliance on external power sources and enhancing the reliability of industrial systems.

This study intends to optimize the carbon footprint management model of power enterprises through artificial intelligence (AI) technology to help the scientific formulation of carbon emission reduction strategies. Firstly, a carbon footprint calculation model based on big data and AI is established, and then machine learning algorithm is used to deeply mine the carbon emission data of power enterprises to identify the main influencing factors and emission reduction opportunities. Finally, the driver-state-response (DSR) model is used to evaluate the carbon audit of the power industry and comprehensively analyze the effect of carbon emission reduction. Taking China Electric Power Resources and Datang International Electric Power Company as examples, this study uses the comprehensive evaluation method of entropy weight- technique for order preference by similarity to ideal solution (TOPSIS). China Electric Power Resources Company has outstanding performance in promoting renewable energy, with its comprehensive evaluation index rising from 0.5458 in 2020 to 0.627 in 2022, while the evaluation index of Datang International Electric Power Company fluctuated and dropped to 0.421 in 2021. The research conclusion reveals the actual achievements and existing problems of power enterprises in energy saving and emission reduction, and provides reliable carbon information for the government, enterprises, and the public. The main innovation of this study lies in: using artificial intelligence technology to build a carbon footprint calculation model, combining with the data of International Energy Agency Carbon Dioxide (IEA CO 2 ) emission database, and using machine learning algorithm to deeply mine the important factors in carbon emission data, thus putting forward a carbon audit evaluation system of power enterprises based on DSR model. This study not only fills the blank of carbon emission management methods in the power industry, but also provides a new perspective and basis for the government and enterprises to formulate carbon emission reduction strategies.

Hydropower is among the most efficient technologies to produce renewable electrical energy. Hydropower systems present multiple advantages since they provide sustainable and controllable energy. However, hydropower plants’ effectiveness is affected by multiple factors such as river/reservoir inflows, temperature, electricity price, among others. The mentioned factors make the prediction and recommendation of a station’s operational output a difficult challenge. Therefore, reliable and accurate energy production forecasts are vital and of great importance for capacity planning, scheduling, and power systems operation. This research aims to develop and apply artificial neural network (ANN) models to predict hydroelectric production in Ecuador’s short and medium term, considering historical data such as hydropower production and precipitations. For this purpose, two scenarios based on the prediction horizon have been considered, i.e., one-step and multi-step forecasted problems. Sixteen ANN structures based on multilayer perceptron (MLP), long short-term memory (LSTM), and sequence-to-sequence (seq2seq) LSTM were designed. More than 3000 models were configured, trained, and validated using a grid search algorithm based on hyperparameters. The results show that the MLP univariate and differentiated model of one-step scenario outperforms the other architectures analyzed in both scenarios. The obtained model can be an important tool for energy planning and decision-making for sustainable hydropower production.

As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model’s ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect R2 value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting.