Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.

This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power forecasting and proposes a novel scheme for the situation-dependent aggregation of forecasting models. For performance measures, error scores for deterministic as well as probabilistic forecasts are compared, and their characteristics are shown in detail. For the evaluation of deterministic forecasts, a categorization by basic error measure and normalization technique is introduced that simplifies the process of choosing an appropriate error measure for certain forecasting tasks. Furthermore, a scheme for the common evaluation of different forms of probabilistic forecasts is proposed. Based on the analysis of the error scores, a novel hierarchical aggregation technique for both deterministic and probabilistic forecasting models is proposed that dynamically weights individual forecasts using multiple weighting factors such as weather situation and lead time dependent weighting. In the experimental evaluation it is shown that the forecasting quality of the proposed technique is able to outperform other state of the art forecasting models and ensembles.

A guide to a multi-disciplinary approach that includes perspectives from noted experts in the energy and utilities fields Advances in Energy Systems offers a stellar collection of articles selected from the acclaimed journal Wiley Interdisciplinary Review: Energy and Environment. The journalcovers all aspects of energy policy, science and technology, environmental and climate change. The book covers a wide range of relevant issues related to the systemic changes for large-scale integration of renewable energy as part of the on-going energy transition. The book addresses smart energy systems technologies, flexibility measures, recent changes in the marketplace and current policies. With contributions from a list of internationally renowned experts, the book deals with the hot topic of systems integration for future energy systems and energy transition. This important resource: Contains contributions from noted experts in the field Covers a broad range of topics on the topic of renewable energy Explores the technical impacts of high shares of wind and solar power Offers a review of international smart-grid policies Includes information on wireless power transmission Presents an authoritative view of micro-grids Contains a wealth of other relevant topics Written forenergy planners, energy market professionals and technology developers, Advances in Energy Systems is an essential guide with contributions from an international panel of experts that addresses the most recent smart energy technologies.

This book brings together state-of-the-art advances in intelligent data analytics as driver of the future evolution of PaE systems. In the modern power and energy (PaE) domain, the increasing penetration of renewable energy sources (RES) and the consequent empowerment of consumers as a central and active solution to deal with the generation and development variability are driving the PaE system towards a historic paradigm shift. The small-scale, diversity, and especially the number of new players involved in the PaE system potentiate a significant growth of generated data. Moreover, advances in communication (between IoT devices and M2M: machine to machine, man to machine, etc.) and digitalization hugely increased the volume of data that results from PaE components, installations, and systems operation. This data is becoming more and more important for PaE systems operation, maintenance, planning, and scheduling with relevant impact on all involved entities, from producers, consumer,s and aggregators to market and system operators. However, although the PaE community is fully aware of the intrinsic value of those data, the methods to deal with it still necessitate substantial enhancements, development and research. Intelligent data analytics is thereby playing a fundamental role in this domain, by enabling stakeholders to expand their decision-making method and achieve the awareness on the PaE environment. The editors also included demonstrated codes for presented problems for better understanding for beginners.

Integration of Distributed Energy Resources in Power Systems: Implementation, Operation and Control covers the operation of power transmission and distribution systems and their growing difficulty as the share of renewable energy sources in the world's energy mix grows and the proliferation trend of small scale power generation becomes a reality. The book gives students at the graduate level, as well as researchers and power engineering professionals, an understanding of the key issues necessary for the development of such strategies. It explores the most relevant topics, with a special focus on transmission and distribution areas. Subjects such as voltage control, AC and DC microgrids, and power electronics are explored in detail for all sources, while not neglecting the specific challenges posed by the most used variable renewable energy sources. - Presents the most relevant aspects of the integration of distributed energy into power systems, with special focus on the challenges for transmission and distribution - Explores the state-of the-art in applications of the most current technology, giving readers a clear roadmap - Deals with the technical and economic features of distributed energy resources and discusses their business models

The smart grid initiative, integrating advanced sensing technologies, intelligent control methods, and bi-directional communications into the contemporary electricity grid, offers excellent opportunities for energy efficiency improvements and better integration of distributed generation, coexisting with centralized generation units within an active network. A large share of the installed capacity for recent renewable energy sources already comprises insular electricity grids, since the latter are preferable due to their high potential for renewables. However, the increasing share of renewables in the power generation mix of insular power systems presents a significant challenge to efficient management of the insular distribution networks, mainly due to the variability and uncertainty of renewable generation. More than other electricity grids, insular electricity grids require the incorporation of sustainable resources and the maximization of the integration of local resources, as well as specific solutions to cope with the inherent characteristics of renewable generation. Insular power systems need a new generation of methodologies and tools to face the new paradigm of large-scale renewable integration. Smart and Sustainable Power Systems: Operations, Planning, and Economics of Insular Electricity Grids discusses the modeling, simulation, and optimization of insular power systems to address the effects of large-scale integration of renewables and demand-side management. This practical book: Describes insular power systems, renewable energies, uncertainty, variability, reserves, and demand response Examines state-of-the-art forecasting techniques, power flow calculations, and scheduling models Covers probabilistic and stochastic approaches, scenario generation, and short-term operation Includes comprehensive testing and validation of the mathematical models using real-world data Explores electric price signals, competitive operation of distribution networks, and network expansion planning Smart and Sustainable Power Systems: Operations, Planning, and Economics of Insular Electricity Grids provides a valuable resource for the design of efficient methodologies, tools, and solutions for the development of a truly sustainable and smart grid.

Renewable Energy Integration for Bulk Power Systems: ERCOT and the Texas Interconnection looks at the practices and changes introduced in the Texas electric grid to facilitate renewable energy integration. It offers an informed perspective on solutions that have been successfully demonstrated, tested, and validated by the Electric Reliability Council of Texas (ERCOT) to meet the key challenges which engineers face in integrating increased levels of renewable resources into existing electric grids while maintaining reliability. Coverage includes renewable forecasting, ancillary services, and grid and market operations. Proved methods and their particular use scenarios, including wind, solar, and other resources like batteries and demand response, are also covered. The book focuses on a real-world context that will help practicing engineers, utility providers, and researchers understand the practical considerations for developing renewable integration solutions and inspire the future development of more innovative strategies and theoretical underpinnings.

New Technologies for Power System Operation and Analysis considers the very latest developments in renewable energy integration and system operation, including electricity markets and wide-area monitoring systems and forecasting. Helping readers quickly grasp the essential information needed to address renewable energy integration challenges, this new book looks at basic power system mathematical models, advanced renewable integration and system optimizations from transmission and distribution system sides. Sections cover wind, solar, gas and petroleum, making this a useful reference for all engineers interested in power system operation. - Includes codes in MATLAB® and Python - Provides a complete analysis of all new and relevant power system technologies - Covers the impact on existing power system operations at the advanced level, with detailed technical insights

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

This book outlines the challenges that increasing amounts of renewable and distributed energy represent when integrated into established electricity grid infrastructures, offering a range of potential solutions that will support engineers, grid operators, system planners, utilities, and policymakers alike in their efforts to realize the vision of moving toward greener, more secure energy portfolios. Covering all major renewable sources, from wind and solar, to waste energy and hydropower, the authors highlight case studies of successful integration scenarios to demonstrate pathways toward overcoming the complexities created by variable and distributed generation.