Design of Degradation-Conscious Control Schemes for Energy Storage Systems in Grid-connected Microgrid of High PV Generation

The integration of high PV-penetrated prosumers into the distribution system is not without challenges due to the uncertain PV power. This investigation examines a hierarchical HESS scheme that incorporates both distributed and centralized storages. The primary objective is to present a direct methodology for determining the capacities and control strategies of centralized and distributed hybrid storage scheme. Thus, the thesis proposes a degradation-conscious battery control for ESS scheme while the grid constraints are sufficiently met

Evolutionary Approach to Efficient Provisioning and Self-organization in Wireless Sensor Networks (WSN)

Advances in low-power digital integration and microelectro-mechanical systems (MEMS) have paved the way for micro-sensors. These sensors are equipped with data processing capabilities along with sensory circuits. Sensor data are processed on these individual sensors and transmitted to the target (sink). Lowcost integration and small sizes of these sensors have generated special interest in the area of disposable-sensors and large scale platform management. Queries to these sensors are addressed to nodes which have data satisfying the same condition. However, these sensors may be constrained in energy, bandwidth, storage, and processing capabilities. Large number of such sensors along with these constraints creates a sensor-management problem. At the network layer it amounts to setting up the efficient route that transmits the non-redundant data from source to the sink in order to maximize one or more sensor objectives (e.g. battery (and sensor's) life, Sensor-Data yield). This is done while adapting to changing connectivity due to failure of some nodes and new nodes powering up. First part of the thesis propose a reduced-complexity genetic algorithm (GA) for optimization of multi-hop battery-constrained sensor networks. The goal of the system is to generate optimal number of sensor-clusters with cluster-heads. It results in minimization of the power consumption of the sensor system while maximizing the sensor objectives (coverage and exposure). The genetic algorithm is used to adaptively create various components such as cluster-members, cluster-heads, and next-cluster. These components are then used to evaluate the average fitness of the system based on the sequence of communication links towards the sink. We then enhance the genetic algorithm (GA) approach for secure deployment of resource constrained multi-hop sensor networks. The goal in this case is to achieve secure coverage and improve battery life by dynamically optimizing security attributes (Like authentication and encryption). Further, we augment the GA approach for intrusion detection of resource constrained multi-hop sensor networks. Traditional intrusion detection mechanisms have limited applicability to the sensor networks due to scarce battery and processing resources. Therefore, we propose an effective scheme that would offer a power efficient and lightweight approach to identify malicious attacks. We evaluate sensor node attributes by measuring the perceived threat and its suitability to host local monitoring node (LMN) that acts as trusted proxy agent for the sink and capable of securely monitoring its neighbors. Security attributes in conjunction with genetic algorithm jointly optimizes the selection of monitoring nodes (i.e., LMN) by dynamically evaluating node fitness by profiling workloads patterns, packet statistics, utilization data, battery status, and quality-of-service compliance. Second part of the thesis delves into application of Information Technology (and Industrial) Systems and devices where the use of sensor networks can deliver non-intrusive and effective telemetry for group-based server management. These systems (Like Data Centers or Shipment tracking) face major challenges in seamless integration of telemetry and control data that is essential to various autonomic management functions related to power, thermal, reliability, predictability, survivability, locality and adaptability. Such systems that are supported by a dense network of sense-points operating in noisy environment (Metals, Cables) are required to deliver reliable trends, measurements and analysis in a timely fashion. The traditional approaches to provide distributed observability and control using wired solutions are static, expensive, and nonscalable. We apply the proposed GA approach for this unique environment that replaces static wired sensors with dynamically reconfigurable battery-powered wireless sensors. The proposed technique employs machine learning approach to optimize sensor node function assignment, clustering decisions, route establishment and data collection trees for improved throughput that results in effective controls

Smart Metering Technology and Services

Global energy context has become more and more complex in the last decades; the raising prices of fuels together with economic crisis, new international environmental and energy policies that are forcing companies. Nowadays, as we approach the problem of global warming and climate changes, smart metering technology has an effective use and is crucial for reaching the 2020 energy efficiency and renewable energy targets as a future for smart grids. The environmental targets are modifying the shape of the electricity sectors in the next century. The smart technologies and demand side management are the key features of the future of the electricity sectors. The target challenges are coupling the innovative smart metering services with the smart meters technologies, and the consumers' behaviour should interact with new technologies and polices. The book looks for the future of the electricity demand and the challenges posed by climate changes by using the smart meters technologies and smart meters services. The book is written by leaders from academia and industry experts who are handling the smart meters technologies, infrastructure, protocols, economics, policies and regulations. It provides a promising aspect of the future of the electricity demand. This book is intended for academics and engineers who are working in universities, research institutes, utilities and industry sectors wishing to enhance their idea and get new information about the smart meters

Driving Sustainability through Engineering Management and Systems Engineering

Despite the ongoing impact of the COVID-19 pandemic, the challenge of realizing sustainability across the triple bottom line of social, environmental, and economic development remains an urgent priority. If anything, it is now imperative that we work towards achieving the United Nations Sustainable Development Goals (SDGs). However, the global challenges are significant. Many of the societal challenges represent complex problems that require multifaceted solutions drawing on multidisciplinary approaches. Engineering management involves the management of people and projects related to technological or engineering systems—this includes project management, engineering economy, and technology management, as well as the management and leadership of teams. Systems engineering involves the design, integration, and management of complex systems over the full life cycle—this includes requirements capture, integrated system design, as well as modelling and simulation. In addition to the theoretical underpinnings of both disciplines, they also provide a range of tools and techniques that can be used to address technological and organisational complexity. The disciplines of engineering management and systems engineering are therefore ideally suited to help tackle both the challenges and opportunities associated with realising a sustainable future for all. This book provides new insights on how engineering management and systems engineering can be utilised as part of the journey towards sustainability. The book includes discussion of a broad range of different approaches to investigate sustainability through utilising quantitative, qualitative and conceptual methodologies. The book will be of interest to researchers and students focused on the field of sustainability as well as practitioners concerned with devising strategies for sustainable development

Driving Sustainability through Engineering Management and Systems Engineering

Despite the ongoing impact of the COVID-19 pandemic, the challenge of realizing sustainability across the triple bottom line of social, environmental, and economic development remains an urgent priority. If anything, it is now imperative that we work towards achieving the United Nations Sustainable Development Goals (SDGs). However, the global challenges are significant. Many of the societal challenges represent complex problems that require multifaceted solutions drawing on multidisciplinary approaches.Engineering management involves the management of people and projects related to technological or engineering systems—this includes project management, engineering economy and technology management, as well as the management and leadership of teams. Systems engineering involves the design, integration and management of complex systems over the full life cycle—this includes requirements capture and integrated system design, as well as modelling and simulation. In addition to the theoretical underpinnings of both disciplines, they also provide a range of tools and techniques that can be used to address technological and organisational complexity. The disciplines of engineering management and systems engineering are therefore ideally suited to help tackle both the challenges and the opportunities associated with realising a sustainable future for all.This book provides new insights on how engineering management and systems engineering can be utilised as part of the journey towards sustainability. The book includes a discussion of a broad range of different approaches to investigate sustainability through utilising quantitative, qualitative and conceptual methodologies. The book will be of interest to researchers and students focused on the field of sustainability as well as practitioners concerned with devising strategies for sustainable development

Efficient Learning Machines

Computer scienc

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer