Modelling, Design, and Control of Energy Systems: A Data-Driven Approach

In 2018, nearly two-thirds of newly installed global power generation has come from renewable energy sources. Distributed installations of solar photovoltaic (PV) panels have been at the forefront of this global energy transition. In many places, the cost of solar power has dropped below the cost of fossil fuels such as coal. The main challenge in incorporating this growing source of clean and cheap energy is its high variability; it must often be used in conjunction with an expensive energy storage system to help match electricity supply and demand. Despite the growing focus on energy storage and its role in helping meet the ambitious renewable energy targets set by climate-conscious policy makers, the relatively high capital cost of combined PV-storage systems has limited their widespread adoption. The high cost of PV-storage systems may be offset by the value they provide to system owners. The combination of PV panel and energy storage components adds complexity and flexibility to an energy system where both supply and demand are stochastic and depend on many factors, and this contributes to the technical challenges of system design and operation. Practical methods for increasing the value of PV-storage systems through effective system design and operation are the focus of this dissertation. The proposed approach to solving these problems involves the use of system models and data describing the system's operating environment. Our research consists of two main components: the theoretical component, i.e., modelling, and the practical component, i.e., analysis of energy systems on the basis of models and data. In this thesis, we construct new battery models that enable simulation and optimization studies of PV-storage systems. These models are then used to develop several advanced methods for designing and operating PV-storage systems based on available solar generation and electricity consumption data. We study the problem of determining the combined sizes of PV panel and storage components to meet a given system load target at the lowest possible cost. We also study the problem of system operation, with the objective of increasing system value via minimization of operating expenses. The sizing and control methods developed in this thesis are based primarily on system simulation, mathematical programming, and neural networks, and are evaluated on datasets of PV generation and electricity consumption measurements of buildings. Among our contributions are an accurate battery model for simulation studies that can be easily calibrated. We further derive models for optimization studies with various degrees of accuracy and complexity, including a linear model with higher accuracy than existing linear models. For system sizing, we develop a novel approach for sizing a PV-storage system to reliably meet a load performance target at the lowest possible cost. For system operation, we develop a set of algorithms which achieve high performance with low information requirements, a mixed-timescale approach to reduce the online computational complexity of model predictive control, and a system controller designed to encode a deep neural network with a model predictive control policy and capable of refining its performance over time while adapting to changes in the operating environment

Network capacity charge for sustainability and energy equity: A model-based analysis

© 2020 Elsevier Ltd It is long known that the afternoon peak demand accounts for over-investment in the electricity network assets. This results in a high price of delivered electricity which does not fairly differentiate between peak and non-peak users. Energy tariff is proven to be one of the best demand-side management (DSM) tools for shaping consumers’ behaviour. While electricity pricing models, such as inclining block and time-of-use tariffs, have received decent attention as successful mechanisms, there are little discussions about another efficient tariff known as a rollover network capacity charge. It is a penalty for the highest recorded power usage over the previous reading cycle (or year) which is introduced to commercial users in some jurisdictions. With recent price reduction in distributed generation and storage (DGS) systems, the interest has increased in devising policies for directing the household and commercial consumers’ behaviour towards using DGS systems in line with DSM objectives. In this paper, we have integrated the rollover network capacity charge into DGS systems investment analysis. The introduced optimisation formulation can consider capacity charge for both energy import and export. The results from a few case studies show the positive impact of capacity charge in directing the peak-consumers’ investment decisions towards DSM tools (e.g., energy storage) to curb their peak demands. This not only improves the resilience of the network but also promises as an effective mechanism in energy-justice nexus by avoiding the transfer of the associated costs of peak demand to all users

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point

Battery Management in Electric Vehicles: Current Status and Future Trends

Lithium-ion batteries are an indispensable component of the global transition to zero-carbon energy and are instrumental in achieving COP26's objective of attaining global net-zero emissions by the mid-century. However, their rapid expansion comes with significant challenges. The continuous demand for lithium-ion batteries in electric vehicles (EVs) is expected to raise global environmental and supply chain concerns, given that the critical materials required for their production are finite and predominantly mined in limited regions worldwide. Consequently, significant battery waste management will eventually become necessary. By implementing appropriate and enhanced battery management techniques in electric vehicles, the performance of batteries can be improved, their lifespan extended, secondary uses enabled, and the recycling and reuse of EV batteries promoted, thereby mitigating global environmental and supply chain concerns. Therefore, this reprint was crafted to update the scientific community on recent advancements and future trajectories in battery management for electric vehicles. The content of this reprint spans a spectrum of EV battery advancements, ranging from fundamental battery studies to the utilization of neural network modeling and machine learning to optimize battery performance, enhance efficiency, and ensure prolonged lifespan

Review of soft computing models in design and control of rotating electrical machines

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Power Management for Energy Systems

The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences

2015 Projects Day Booklet

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Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles