TOWARDS OPTIMAL OPERATION AND CONTROL OF EMERGING ELECTRIC DISTRIBUTION NETWORKS

The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and PVs, an improved three-layer predictive hierarchical power management framework is presented to provide economic operation and cyber-physical security while reducing uncertainties. This scheme providessuperior frequency regulation capability and maintains low system operating costs. Third, a decentralized strategy for coordinating adaptive controls of PVs and battery energy storage systems (BESSs) in islanded DC nanogrids is presented. Finally, for transient stability evaluation (TSE) of emerging electric distribution networks dominated by EV supercharging stations, a data-driven region of attraction (ROA) estimation approach is presented. The proposed data-driven method is more computationally efficient than traditional model-based methods, and it also allows for real-time ROA estimation for emerging electric distribution networks with complex dynamics

Model based forecasting for demand response strategies

The incremental deployment of decentralized renewable energy sources in the distribution grid is triggering a paradigm change for the power sector. This shift from a centralized structure with big power plants to a decentralized scenario of distributed energy resources, such as solar and wind, calls for a more active management of the distribution grid. Conventional distribution grids were passive systems, in which the power was flowing unidirectionally from upstream to downstream. Nowadays, and increasingly in the future, the penetration of distributed generation (DG), with its stochastic nature and lack of controllability, represents a major challenge for the stability of the network, especially at the distribution level. In particular, the power flow reversals produced by DG cause voltage excursions, which must be compensated. This poses an obstacle to the energy transition towards a more sustainable energy mix, which can however be mitigated by using a more active approach towards the control of the distribution networks. Demand side management (DSM) offers a possible solution to the problem, allowing to actively control the balance between generation, consumption and storage, close to the point of generation. An active energy management implies not only the capability to react promptly in case of disturbances, but also to ability to anticipate future events and take control actions accordingly. This is usually achieved through model predictive control (MPC), which requires a prediction of the future disturbances acting on the system. This thesis treat challenges of distributed DSM, with a particular focus on the case of a high penetration of PV power plants. The first subject of the thesis is the evaluation of the performance of models for forecasting and control with low computational requirements, of distributed electrical batteries. The proposed methods are compared by means of closed loop deterministic and stochastic MPC performance. The second subject of the thesis is the development of model based forecasting for PV power plants, and methods to estimate these models without the use of dedicated sensors. The third subject of the thesis concerns strategies for increasing forecasting accuracy when dealing with multiple signals linked by hierarchical relations. Hierarchical forecasting methods are introduced and a distributed algorithm for reconciling base forecasters is presented. At the same time, a new methodology for generating aggregate consistent probabilistic forecasts is proposed. This method can be applied to distributed stochastic DSM, in the presence of high penetration of rooftop installed PV systems. In this case, the forecasts' errors become mutually dependent, raising difficulties in the control problem due to the nontrivial summation of dependent random variables. The benefits of considering dependent forecasting errors over considering them as independent and uncorrelated, are investigated. The last part of the thesis concerns models for distributed energy markets, relying on hierarchical aggregators. To be effective, DSM requires a considerable amount of flexible load and storage to be controllable. This generates the need to be able to pool and coordinate several units, in order to reach a critical mass. In a real case scenario, flexible units will have different owners, who will have different and possibly conflicting interests. In order to recruit as much flexibility as possible, it is therefore importan

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Energy Development for Sustainability

Recently, energy development has received significant attention through the promising results of technology development, experimentation, computational modeling, and validation. However, it remains a persistent challenge to produce the needed energy while significantly reducing the environmental effects, such as the emission of greenhouse gases, which lead to climate change. Moreover, technological and economic limitations may also hinder energy development for sustainability. This book entitled Energy Development for Sustainability covers technologies, products, equipment, and devices as well as energy services based on software and data protected by patents and/or trademarks. This book will serve as a collection of the latest scientific and technological approaches to various energy development initiatives for sustainability encompassing novel sonocatalytic application and integrated algal and sludge-based wastewater treatment system, energy storage, sustainable building, gas absorption, organosolv pretreatment, energy usage and CO2 emission in transportation, coal regulation for energy, solar photovoltaic system, torrefaction for fuel production, energy management system, clean energy incubator, biofuels from microalgae, and the influence of COVID-19 on climate change. Overall, this book addresses researchers, advanced students, technical consultants, as well as decision-makers in industries and politics. This book contains comprehensive overview and in-depth technical research papers addressing recent progress in the area of energy development for sustainability. We hope the readers will enjoy this book

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios