37,069 research outputs found

    Challenges of Primary Frequency Control and Benefits of Primary Frequency Response Support from Electric Vehicles

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    As the integration of wind generation displaces conventional plants, system inertia provided by rotating mass declines, causing concerns over system frequency stability. This paper implements an advanced stochastic scheduling model with inertia-dependent fast frequency response requirements to investigate the challenges on the primary frequency control in the future Great Britain electricity system. The results suggest that the required volume and the associated cost of primary frequency response increase significantly along with the increased capacity of wind plants. Alternative measures (e.g. electric vehicles) have been proposed to alleviate these concerns. Therefore, this paper also analyses the benefits of primary frequency response support from electric vehicles in reducing system operation cost, wind curtailment and carbon emissions

    Benefits of demand-side response in providing frequency response service in the future GB power system

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    The demand for ancillary service is expected to increase significantly in the future Great Britain (GB) electricity system due to high penetration of wind. In particular, the need for frequency response, required to deal with sudden frequency drops following a loss of generator, will increase because of the limited inertia capability of wind plants. This paper quantifies the requirements for primary frequency response and analyses the benefits of frequency response provision from demand-side response (DSR). The results show dramatic changes in frequency response requirements driven by high penetration of wind. Case studies carried out by using an advanced stochastic generation scheduling model suggest that the provision of frequency response from DSR could greatly reduce the system operation cost, wind curtailment, and carbon emissions in the future GB system characterized by high penetration of wind. Furthermore, the results demonstrate that the benefit of DSR shows significant diurnal and seasonal variation, whereas an even more rapid (instant) delivery of frequency response from DSR could provide significant additional value. Our studies also indicate that the competing technologies to DSR, namely battery storage, and more flexible generation could potentially reduce its value by up to 35%, still leaving significant room to deploy DSR as frequency response provider

    Designing effective frequency response patterns for flexible thermostatic loads

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    Value of thermostatic loads in future low-carbon Great Britain system

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    This paper quantifies the value of a large population of heterogeneous thermostatically controlled loads (TCLs). The TCL dynamics are regulated by means of an advanced demand side response model (DSRM). It optimally determines the flexible energy/power consumption and simultaneously allocates multiple ancillary services. This model explicitly incorporates the control of dynamics of the TCL recovery pattern after the provision of the selected services. The proposed framework is integrated in a mixed integer linear programming formulation for a multi-stage stochastic unit commitment. The scheduling routine considers inertia-dependent frequency response requirements to deal with the drastic reduction of system inertia under future low-carbon scenarios. Case studies focus on the system operation cost and CO2 emissions reductions for individual TCLs for a) different future network scenarios, b) different frequency requirements, c) changes of TCL parameters (e.g. coefficient of performance, thermal insulation etc.)

    Scenarios for the development of smart grids in the UK: literature review

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    Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

    Parallel detrended fluctuation analysis for fast event detection on massive PMU data

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    ("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

    Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

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    In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe
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