Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

メタヒューリスティクスおよび機械学習を用いた建物・地域エネルギーシステムの最適化に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 大岡 龍三, 東京大学教授 加藤 信介, 東京大学教授 赤司 泰義, 東京大学教授 合原 一幸, 東京大学講師 菊本 英紀University of Tokyo(東京大学

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Renewable Energy

This book discusses renewable energy resources and systems as well as energy efficiency. It contains twenty-three chapters over six sections that address a multitude of renewable energy types, including solar and photovoltaic, biomass, hydroelectric, and geothermal. The information presented herein is a scientific contribution to energy and environmental regulations, quality and efficiency of energy services, energy supply security, energy market-based approaches, government interventions, and the spread of technological innovation

XVII Simposio CEA de Control Inteligente: Reunión anual del grupo de Control Inteligente del comité español de automática (CEA). Libro de Actas, León, 27-29 de junio de 2022

Al igual que en las ediciones anteriores, el XVII Simposio CEA de Control Inteligente ha tratado de mantener los objetivos propuestos por el Grupo Temático de CEA y desarrollar unas jornadas de convivencia en las que se han desarrollado actividades científicas de investigación, de formación de doctores, de relaciones con la industria y, por supuesto, actividades culturales y de relaciones sociales de todos los miembros que formamos esta comunidad científica. Este año, el lugar elegido para la celebración del Simposio ha sido la ciudad de León y le ha correspondido la organización del mismo al Grupo de Investigación SUPPRESS de la Universidad de León, dirigido por el profesor Manuel Domínguez. Con más de 90 asistentes en algunas de las actividades del Simposio, hemos conseguido batir récords de asistencia y generar un ambiente más que propicio para desarrollar distintas discusiones científicas de gran calado. Esto demuestra el interés que suscita nuestra disciplina en estos tiempos. Durante los últimos años el control inteligente está demostrando ser una herramienta esencial para contribuir a solucionar los grandes retos que se nos van a plantear en el futuro. Pero, hasta la fecha no habíamos experimentado, tan de primera mano, los efectos derivados del cambio climático, la falta de recursos energéticos y de materias primas, las pandemias, la falta de recursos hídricos, la ciberseguridad o los incendios. Por ello, más que nunca se antoja necesario reflexionar, reforzar nuestros vínculos o crear nuevas sinergias para contribuir y poner nuestro valioso conocimiento a disposición de nuestra sociedad. En este sentido nossentimos orgullosos de presentar las contribuciones tan valiosas que recoge este documento. Estas han superado todas nuestras expectativas, lo que da muestras del sentido de responsabilidad que tiene el Grupo Temático CEA de Control Inteligente con su tiemp

Renewable Energy

Renewable Energy is energy generated from natural resources - such as sunlight, wind, rain, tides and geothermal heat - which are naturally replenished. In 2008, about 18% of global final energy consumption came from renewables, with 13% coming from traditional biomass, such as wood burning. Hydroelectricity was the next largest renewable source, providing 3% (15% of global electricity generation), followed by solar hot water/heating, which contributed with 1.3%. Modern technologies, such as geothermal energy, wind power, solar power, and ocean energy together provided some 0.8% of final energy consumption. The book provides a forum for dissemination and exchange of up - to - date scientific information on theoretical, generic and applied areas of knowledge. The topics deal with new devices and circuits for energy systems, photovoltaic and solar thermal, wind energy systems, tidal and wave energy, fuel cell systems, bio energy and geo-energy, sustainable energy resources and systems, energy storage systems, energy market management and economics, off-grid isolated energy systems, energy in transportation systems, energy resources for portable electronics, intelligent energy power transmission, distribution and inter - connectors, energy efficient utilization, environmental issues, energy harvesting, nanotechnology in energy, policy issues on renewable energy, building design, power electronics in energy conversion, new materials for energy resources, and RF and magnetic field energy devices

Distributed anomaly detection models for industrial wireless sensor networks

Wireless Sensor Networks (WSNs) are firmly established as an integral technology that enables automation and control through pervasive monitoring for many industrial applications. These range from environmental applications and healthcare applications to major industrial monitoring applications such as infrastructure and structural monitoring. The key features that are common to such applications can be noted as involving large amounts of data, consisting of dynamic observation environments, non-homogeneous data distributions with evolving patterns and sensing functionality leading to data-driven control. Also in most industrial applications a major requirement is to have near real-time decision support. Accordingly there is a vital need to have a secure continuous and reliable sensing mechanism in integrated WSNs where integrity of the data is assured. However, in practice WSNs are vulnerable to different security attacks, faults and malfunction due to inherent resource constraints, openly commoditised wireless technologies employed and naive modes of implementation. Misbehaviour resulting from such threats manifest as anomalies in the sensed data streams in critically compromising the systems. Therefore, it is vital that effective techniques are introduced in accurately detecting anomalies and assuring the integrity of the data. This research focuses on investigating such models for large scale industrial wireless sensor networks. Focusing on achieving an anomaly detection framework that is adaptable and scalable, a hierarchical data partitioning approach with fuzzy data modelling is introduced first. In this model unsupervised data partitioning is performed in a distributed manner by adapting fuzzy c-means clustering in an incremental model over a hierarchical node topology. It is found that non-parametric and non-probabilistic determination of anomalies can be done by evaluating the fuzzy membership scores and inter-cluster distances adaptively over the node hierarchy. Considering heterogeneous data distributions with evolving patterns, a granular anomaly detection model that uses an entropy criterion to dynamically partition the data is proposed next. This successfully overcomes the issue of determining the proper number of expected clusters in a dynamic manner. In this approach the data is partitioned on to different cohesive regions using cumulative point-wise entropy directly. The effect of differential density distributions when relying on an entropy criterion is mitigated by introducing an average relative density measure to segregate isolated outliers prior to the partitioning. The combination of these two factors is shown to be significantly successful in determining anomalies adaptively in a fully dynamic manner. The need for near real-time anomaly evaluation is focused next on this thesis. Building upon the entropy based data partitioning model that is also proposed, a Point-of-View (PoV) entropy evaluation model is developed next. This employs an incremental data processing model as opposed to batch-wise data processing. Three unique points-of-view are introduced as the reference points over which point-wise entropy is computed in evaluating its relative change as the data streams evolve. Overall this thesis proposes efficient unsupervised anomaly detection models that employ distributed in-network data processing for accurate determination of anomalies. The resource constrained environment is taken in to account in each of the models with innovations made to achieve non-parametric and non-probabilistic detection