8,713 research outputs found

    A Microgrid Energy Management System Based on Non-Intrusive Load Monitoring via Multitask Learning

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    Multi Agent Systems for the Active Management of Electrical Distribution Networks

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    This Thesis presents an investigation on the technical impacts caused by the steady state operation of Small-Scale Embedded Generators (SSEGs) and also introduces the Small Scale Energy Zone (SSEZ) concept which aims to remove the technical barriers associated with SSEGs through intelligent coordination of large numbers of customerowned SSEGs, energy storage units and controllable loads. This approach represents a move away from the conventional passive, “fit-and-forget” philosophy under which the majority of Low Voltage (LV) distribution networks are currently operated and towards a higher degree of network operational management. The employment of a distributed management and control approach for an SSEZ, realised through the Multi Agent Systems (MAS) technology, is proposed due to the advantages that can potentially be realised in the areas of: (i) scalability and openness, (ii) reliability and resilience and (iii) communications efficiency. A FIPA-compliant MAS-based control approach is designed, developed and evaluated based on the specific SSEZ control requirements. The MAS is composed of three types of agents: direct control agents, indirect control agents and utility agents, exchanging information through the employment of a common ontology. In addition, a relational database management system is also designed and developed in order to be coupled with the developed MAS for data management purposes

    Advanced wind energy convertors using electronic power conversion.

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    SIGLEAvailable from British Library Document Supply Centre-DSC:DXN013000 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

    A novel power management and control design framework for resilient operation of microgrids

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    This thesis concerns the investigation of the integration of the microgrid, a form of future electric grids, with renewable energy sources, and electric vehicles. It presents an innovative modular tri-level hierarchical management and control design framework for the future grid as a radical departure from the ‘centralised’ paradigm in conventional systems, by capturing and exploiting the unique characteristics of a host of new actors in the energy arena - renewable energy sources, storage systems and electric vehicles. The formulation of the tri-level hierarchical management and control design framework involves a new perspective on the problem description of the power management of EVs within a microgrid, with the consideration of, among others, the bi-directional energy flow between storage and renewable sources. The chronological structure of the tri-level hierarchical management operation facilitates a modular power management and control framework from three levels: Microgrid Operator (MGO), Charging Station Operator (CSO), and Electric Vehicle Operator (EVO). At the top level is the MGO that handles long-term decisions of balancing the power flow between the Distributed Generators (DGs) and the electrical demand for a restructure realistic microgrid model. Optimal scheduling operation of the DGs and EVs is used within the MGO to minimise the total combined operating and emission costs of a hybrid microgrid including the unit commitment strategy. The results have convincingly revealed that discharging EVs could reduce the total cost of the microgrid operation. At the middle level is the CSO that manages medium-term decisions of centralising the operation of aggregated EVs connected to the bus-bar of the microgrid. An energy management concept of charging or discharging the power of EVs in different situations includes the impacts of frequency and voltage deviation on the system, which is developed upon the MGO model above. Comprehensive case studies show that the EVs can act as a regulator of the microgrid, and can control their participating role by discharging active or reactive power in mitigating frequency and/or voltage deviations. Finally, at the low level is the EVO that handles the short-term decisions of decentralising the functioning of an EV and essential power interfacing circuitry, as well as the generation of low-level switching functions. EVO level is a novel Power and Energy Management System (PEMS), which is further structured into three modular, hierarchical processes: Energy Management Shell (EMS), Power Management Shell (PMS), and Power Electronic Shell (PES). The shells operate chronologically with a different object and a different period term. Controlling the power electronics interfacing circuitry is an essential part of the integration of EVs into the microgrid within the EMS. A modified, multi-level, H-bridge cascade inverter without the use of a main (bulky) inductor is proposed to achieve good performance, high power density, and high efficiency. The proposed inverter can operate with multiple energy resources connected in series to create a synergized energy system. In addition, the integration of EVs into a simulated microgrid environment via a modified multi-level architecture with a novel method of Space Vector Modulation (SVM) by the PES is implemented and validated experimentally. The results from the SVM implementation demonstrate a viable alternative switching scheme for high-performance inverters in EV applications. The comprehensive simulation results from the MGO and CSO models, together with the experimental results at the EVO level, not only validate the distinctive functionality of each layer within a novel synergy to harness multiple energy resources, but also serve to provide compelling evidence for the potential of the proposed energy management and control framework in the design of future electric grids. The design framework provides an essential design to for grid modernisation

    Smart Metering System: Developing New Designs to Improve Privacy and Functionality

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    This PhD project aims to develop a novel smart metering system that plays a dual role: Fulfil basic functions (metering, billing, management of demand for energy in grids) and protect households from privacy intrusions whilst enabling them a degree of freedom. The first two chapters of the thesis will introduce the research background and a detailed literature review on state-of-the-art works for protecting smart meter data. Chapter 3 discusses theory foundations for smart meter data analytics, including machine learning, deep learning, and information theory foundations. The rest of the thesis is split into two parts, ‘Privacy’ and ‘Functionality’, respectively. In the ‘Privacy’ part, the overall smart metering system, as well as privacy configurations, are presented. A threat/adversary model is developed at first. Then a multi-channel smart metering system is designed to reduce the privacy risks of the adversary. Each channel of the system is responsible for one functionality by transmitting different granular smart meter data. In addition, the privacy boundary of the smart meter data in the proposed system is also discovered by introducing a data mining algorithm. By employing the algorithm, a three-level privacy boundary is concluded. Furthermore, a differentially private federated learning-based value-added service platform is designed to provide flexible privacy guarantees to consumers and balance the trade-off between privacy loss and service accuracy. In the ‘Functionality’ part, three feeder-level functionalities: load forecasting, solar energy separation, and energy disaggregation are evaluated. These functionalities will increase thepredictability, visibility, and controllability of the distributed network without utilizing household smart meter data. Finally, the thesis will conclude and summarize the overall system and highlight the contributions and novelties of this project

    Demand Response Load Following of Source and Load Systems

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    Thermal Baths as Quantum Resources: More Friends than Foes?

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    In this article we argue that thermal reservoirs (baths) are potentially useful resources in processes involving atoms interacting with quantized electromagnetic fields and their applications to quantum technologies. One may try to suppress the bath effects by means of dynamical control, but such control does not always yield the desired results. We wish instead to take advantage of bath effects, that do not obliterate "quantumness" in the system-bath compound. To this end, three possible approaches have been pursued by us: (i) Control of a quantum system faster than the correlation time of the bath to which it couples: Such control allows us to reveal quasi-reversible/coherent dynamical phenomena of quantum open systems, manifest by the quantum Zeno or anti-Zeno effects (QZE or AZE, respectively). Dynamical control methods based on the QZE are aimed not only at protecting the quantumness of the system, but also diagnosing the bath spectra or transferring quantum information via noisy media. By contrast, AZE-based control is useful for fast cooling of thermalized quantum systems. (ii) Engineering the coupling of quantum systems to selected bath modes: This approach, based on field -atom coupling control in cavities, waveguides and photonic band structures, allows to drastically enhance the strength and range of atom-atom coupling through the mediation of the selected bath modes. More dramatically, it allows us to achieve bath-induced entanglement that may appear paradoxical if one takes the conventional view that coupling to baths destroys quantumness. (iii) Engineering baths with appropriate non-flat spectra: This approach is a prerequisite for the construction of the simplest and most efficient quantum heat machines (engines and refrigerators). We may thus conclude that often thermal baths are "more friends than foes" in quantum technologies.Comment: 27 pages, 17 figure

    Power Management for Energy Systems

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    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
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