Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Optimum Distribution System Architectures for Efficient Operation of Hybrid AC/DC Power Systems Involving Energy Storage and Pulsed Loads

After more than a century of the ultimate dominance of AC in distribution systems, DC distribution is being re-considered. However, the advantages of AC systems cannot be omitted. This is mainly due to the cheap and efficient means of generation provided by the synchronous AC machines and voltage stepping up/down allowed by the AC transformers. As an intermediate solution, hybrid AC/DC distribution systems or microgrids are proposed. This hybridization of distribution systems, incorporation of heterogeneous mix of energy sources, and introducing Pulsed Power Loads (PPL) together add more complications and challenges to the design problem of distribution systems. In this dissertation, a comprehensive multi-objective optimization approach is presented to determine the optimal design of the AC/DC distribution system architecture. The mathematical formulation of a multi-objective optimal power flow problem based on the sequential power flow method and the Pareto concept is developed and discussed. The outcome of this approach is to answer the following questions: 1) the optimal size and location of energy storage (ES) in the AC/DC distribution system, 2) optimal location of the PPLs, 3) optimal point of common coupling (PCC) between the AC and DC sides of the network, and 4) optimal network connectivity. These parameters are to be optimized to design a distribution architecture that supplies the PPLs, while fulfilling the safe operation constraints and the related standard limitations. The optimization problem is NP-hard, mixed integer and combinatorial with nonlinear constraints. Four objectives are involved in the problem: minimizing the voltage deviation (ΔV), minimizing frequency deviation (Δf), minimizing the active power losses in the distribution system and minimizing the energy storage weight. The last objective is considered in the context of ship power systems, where the equipment’s weight and size are restricted. The utilization of Hybrid Energy Storage Systems (HESS) in PPL applications is investigated. The design, hardware implementation and performance evaluation of an advanced – low cost Modular Energy Storage regulator (MESR) to efficiently integrate ES to the DC bus are depicted. MESR provides a set of unique features: 1) It is capable of controlling each individual unit within a series/parallel array (i.e. each single unit can be treated, controlled and monitored separately from the others), 2) It is able to charge some units within an ES array while other units continue to serve the load, 3) Balance the SoC without the need for power electronic converters, and 4) It is able to electrically disconnect a unit and allow the operator to perform the required maintenance or replacement without affecting the performance of the whole array. A low speed flywheel Energy Storage System (FESS) is designed and implemented to be used as an energy reservoir in PPL applications. The system was based on a separately excited DC machine and a bi-directional Buck-Boost converter as the driver to control the charging/discharging of the flywheel. Stable control loops were designed to charge the FESS off the pulse and discharge on the pulse. All the developments in this dissertation were experimentally verified at the Smart Grid Testbed

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace