Solar Energy Forecasting and Resource Assessment


Book Description

Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore. Scrutiny from solar project developers and their financiers on the accuracy of long-term resource projections and grid operators' concerns about variable short-term power generation have made the field of solar forecasting and resource assessment pivotally important. This volume provides an authoritative voice on the topic, incorporating contributions from an internationally recognized group of top authors from both industry and academia, focused on providing information from underlying scientific fundamentals to practical applications and emphasizing the latest technological developments driving this discipline forward. - The only reference dedicated to forecasting and assessing solar resources enables a complete understanding of the state of the art from the world's most renowned experts. - Demonstrates how to derive reliable data on solar resource availability and variability at specific locations to support accurate prediction of solar plant performance and attendant financial analysis. - Provides cutting-edge information on recent advances in solar forecasting through monitoring, satellite and ground remote sensing, and numerical weather prediction.







Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems


Book Description

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.




Renewable Energy Forecasting


Book Description

Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects. - Offers comprehensive coverage of wind, solar, wave, and hydropower forecasting in one convenient volume - Addresses a topic that is growing in importance, given the increasing penetration of renewable energy in many countries - Reviews state-of-the-science techniques for renewable energy forecasting - Contains chapters on operational applications










Artificial Intelligence and Machine Learning in Smart City Planning


Book Description

Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques. - Reviews the smart city concept and teaches how it can contribute to achieving urban development priorities - Explains soft computing techniques for smart city applications - Describes how to model problems for effective analysis, intelligent decision making, and optimal operation and control in the smart city paradigm - Teaches how to carry out independent projects using soft computing techniques in a vast range of areas in diverse fields like engineering, management, and sciences




Low-Carbon Oriented Improvement Strategy for Flexibility and Resiliency of Multi-Energy Systems


Book Description

Due to the inherent volatility and randomness, the increasing share of energy from renewable resources presents a challenge to the operation of multi-energy systems with heterogeneous energy carriers such as electricity, heat, hydrogen, etc. These factors will make the systems hard to adjust their supply and demand flexibly to maintain energy balance to ensure reliability. Further, this hinders the development of a low-carbon and economically viable energy system. By making full use of the synergistic interaction of generation, transmission, load demand, and energy storage, a three-fold approach focused on quantifying demand flexibility, evaluating supply capabilities, and enhancing resilience can unlock the flexibility potential across various sectors of new energy systems. This approach provides an effective means of facilitating the transition from conventional energy systems to low-carbon, clean-energy-oriented paradigms. However, huge challenges arising from renewable energy pose great obstacles to the aforementioned solution pathway. The main objectives of this Research Topic are: 1. Develop advanced carbon emission accounting and measurement techniques for emerging multi-energy systems 2. Design effective methods for predicting renewable electricity generation 3. Proposed efficient methods for quantitative assessment of uncertainty from renewables and loads 4. Put forward advanced evaluation, optimization, and planning strategies incorporating diverse flexibility resources 5. Design multifaceted market mechanisms and collaborative frameworks balancing economics and low carbon footprint 6. Develop operational control and resilience-enhancement techniques for distribution networks under large-scale distributed energy integration




Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs


Book Description

This book describes the common ground between electricity markets (EMs) and software agents (or artificial intelligence generally). It presents an up-to-date introduction to EMs and intelligent agents, and offers a comprehensive description of the research advances and key achievements related to existing and emerging market designs to reliably and efficiently manage the potential challenges of variable generation (VG). Most EMs are unique in their complex relationships between economics and the physics of energy, but were created without the notion that large penetrations of variable generation (VG) would be part of the supply mix. An advanced multi-agent approach simulates the behavior of power markets over time, particularly markets with large-scale penetrations of renewable resources. It is intended as a reference book for researchers, academics and industry practitioners, but given the scope of the chapters and the highly accessible style, the book also provides a coherent foundation for several different graduate courses.




AI Approaches to Smart and Sustainable Power Systems


Book Description

Today, the global power demand relies on a delicate balance between conventional and renewable energy systems, necessitating both efficient power generation and the effective utilization of these energy resources through appropriate energy storage solutions. Integrating microgrid systems into the utility grid has become a critical facet of modern power systems. The intermittent and unpredictable nature of these energy sources poses a formidable challenge for academic scholars and researchers. This compels them to explore under-investigated areas, including energy source estimation, storage elements, load pattern prediction, coordination among distributed sources, and the development of energy management algorithms for precise and efficient control. AI Approaches to Smart and Sustainable Power Systems tackles these issues using cutting-edge AI techniques. It examines the most effective methods to optimize voltage, frequency, power, fault diagnosis, component health, and overall power system quality and reliability. AI empowers predictive and preventive maintenance for a sustainable energy future. The book focuses on emerging research areas, including renewable energy, power flow calculations, demand scheduling, real-time performance validation, and AI integration into modern power systems, accompanied by insightful case studies.