2,316 research outputs found

    1. Helgoland Power and Energy Conference - 24. Dresdener Kreis 2023

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    Der Sammelband "1. Helgoland Power and Energy Conference" beinhaltet neben einem kurzen Bericht zum 24. Treffen des Dresdener Kreises 2023 wissenschaftliche Beiträge von Doktoranden der beteiligten Hochschulinstitute zum Thema Elektroenergieversorgung. Der Dresdener Kreis setzt sich aus der Professur für Elektroenergieversorgung der Technischen Universität Dresden, dem Fachgebiet Elektrische Anlagen und Netze der Universität Duisburg-Essen, dem Fachgebiet Elektrische Energieversorgung der Leibniz Universität Hannover und dem Lehrstuhl Elektrische Netze und Erneuerbare Energie der Otto-von-Guericke Universität Magdeburg zusammen und trifft sich einmal im Jahr zum fachlichen Austausch an einer der beteiligten Universitäten

    Energy storage design and integration in power systems by system-value optimization

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    Energy storage can play a crucial role in decarbonising power systems by balancing power and energy in time. Wider power system benefits that arise from these balancing technologies include lower grid expansion, renewable curtailment, and average electricity costs. However, with the proliferation of new energy storage technologies, it becomes increasingly difficult to identify which technologies are economically viable and how to design and integrate them effectively. Using large-scale energy system models in Europe, the dissertation shows that solely relying on Levelized Cost of Storage (LCOS) metrics for technology assessments can mislead and that traditional system-value methods raise important questions about how to assess multiple energy storage technologies. Further, the work introduces a new complementary system-value assessment method called the market-potential method, which provides a systematic deployment analysis for assessing multiple storage technologies under competition. However, integrating energy storage in system models can lead to the unintended storage cycling effect, which occurs in approximately two-thirds of models and significantly distorts results. The thesis finds that traditional approaches to deal with the issue, such as multi-stage optimization or mixed integer linear programming approaches, are either ineffective or computationally inefficient. A new approach is suggested that only requires appropriate model parameterization with variable costs while keeping the model convex to reduce the risk of misleading results. In addition, to enable energy storage assessments and energy system research around the world, the thesis extended the geographical scope of an existing European opensource model to global coverage. The new build energy system model ‘PyPSA-Earth’ is thereby demonstrated and validated in Africa. Using PyPSA-Earth, the thesis assesses for the first time the system value of 20 energy storage technologies across multiple scenarios in a representative future power system in Africa. The results offer insights into approaches for assessing multiple energy storage technologies under competition in large-scale energy system models. In particular, the dissertation addresses extreme cost uncertainty through a comprehensive scenario tree and finds that, apart from lithium and hydrogen, only seven energy storage are optimizationrelevant technologies. The work also discovers that a heterogeneous storage design can increase power system benefits and that some energy storage are more important than others. Finally, in contrast to traditional methods that only consider single energy storage, the thesis finds that optimizing multiple energy storage options tends to significantly reduce total system costs by up to 29%. The presented research findings have the potential to inform decision-making processes for the sizing, integration, and deployment of energy storage systems in decarbonized power systems, contributing to a paradigm shift in scientific methodology and advancing efforts towards a sustainable future

    High Voltage DC-biased Oil Type Medium Frequency Transformer; A Green Solution for Series DC Wind Park Concept

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    The electric energy generated by remote offshore wind parks is transported to the consumers using high voltage submarine cables. On the generation site, such transmissions are realized today by collecting the energy produced by several wind turbines in a bulky and expensive transformer placed on a dedicated platform. An alternative solution has been proposed recently, which allows to reduce the installation and maintenance costs by eliminating such a platform. It is suggested to equip each wind turbine in the wind park by an individual DC/DC converter and connect them in series to reach the DC voltage level required for an efficient HVDC energy transportation to the shore. The DC/DC converter is supposed to be a Dual Active Bridge (DAB) converter, which can be made reasonably small to be placed on the wind turbine tower or even in its nacelle. The key element of the converter defining its size and mass is a special transformer, which operates at voltages comprising a high (switching) frequency component superimposed on a high DC offset voltage. DC insulation design of such a transformer and investigation of the effects of a high DC insulation level on the other electromagnetic properties of the transformer is the subject of the present research.In order to verify the concept a prototype of the transformer was built, and its evaluation presented. The unit has been manufactured for the rated power of 50 kW and rated voltages 0.4/5 kV including DC offset of 125 kV and square-shaped oscillations with the frequency of 5 kHz. The magnetic system was made of ferrite material and consisted of 10 shell-type core segments. The magnetic properties have been verified by measuring magnetization and losses at various frequencies in the range 1-10 kHz to cover the operational range of the DAB. The types and dimensions of the windings and their conductors were chosen to minimize the proximity and eddy current effects at higher frequencies. To reduce the size of the transformer and to allow for its efficient cooling, the active part was immersed in oil and cellulose-based materials (paper and pressboard) were used to build the high voltage insulation system. The principles for dimensioning the insulation of the transformer are discussed. The criteria used for selecting insulating distances were based on the consideration of the electric field strength obtained from FEM simulations and using the non-linear Maxwell-Wagner model accounting for local variations of the electric field caused by accumulation of interfacial charges induced by DC stresses. The properties of the materials needed for the calculations were obtained by measuring their dielectric constants and electric conductivities. The methodology used for the measurements conducted for conventional mineral oil and eco-friendly biodegradable transformer oils and, respectively, for oil-impregnated paper/pressboard, is presented. The methodologies used for obtaining parameters of the built transformer prototype needed for its integration in the power electric circuit of the DAB are introduced. A method developed for accurate calculations of the leakage inductance for the shell-type multi core transformers with circular windings is described. Two innovative methods for evaluations of parasitic capacitances based on high frequency equivalent circuits of the transformer are presented. The results of their verifications against performed Frequency Response Analysis measurements and FEM calculations as well as their accuracy are discussed.Thermal performance of the developed transformer prototype is analysed based on the results of computer simulations of heat transfer in its active part under rated load. Identified hot spots and solutions for their elimination are presented.Finally, the expected dimensions, weight, and efficiency of an actual DC/DC converter with the rated parameters corresponding to a 6 MW, 1.8 kV real wind turbine having a 250 kV offset DC voltage are estimated assuming that the developed transformer prototype is scalable. It is shown that the proposed solution allows for installing the full-scale converter having 2.2 Tons in weight and 1.8 m3 in volume on the bottom of the wind turbine’s tower

    DEEP LEARNING BASED POWER SYSTEM STABILITY ASSESSMENT FOR REDUCED WECC SYSTEM

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    Power system stability is the ability of power system, for a giving initial operating condition, to reach a new operation condition with most of the system variables bounded in normal range after subjecting to a short or long disturbance. Traditional power system stability mainly uses time-domain simulation which is very time consuming and only appropriate for offline assessment. Nowadays, with increasing penetration of inverter based renewable, large-scale distributed energy storage integration and operation uncertainty brought by weather and electricity market, system dynamic and operating condition is more dramatic, and traditional power system stability assessment based on scheduling may not be able to cover all the real-time dispatch scenarios, also online assessment and self-awareness for modern power system becomes more and more important and urgent for power system dynamic security. With the development of fast computation resources and more available online dataset, machine learning techniques have been developed and applied to many areas recently and could potentially applied to power system application. In this dissertation, a deep learning-based power system stability assessment is proposed. Its accurate and fast assessment for power system dynamic security is useful in many places, including day-ahead scheduling, real-time operation, and long-term planning. The simplified Western Electricity Coordinating Council (WECC) 240-bus system with renewable penetration up to 49.2% is used as the study system. The dataset generation, model training and error analysis are demonstrated, and the results show that the proposed deep learning-based method can accurately and fast predict the power system stability. Compared with traditional time simulation method, its near millisecond prediction makes the online assessment and self-awareness possible in future power system application

    Nonlinear Modeling of Power Electronics-based Power Systems for Control Design and Harmonic Studies

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    The massive integration of power electronics devices in the modern electric grid marked a turning point in the concept of stability, power quality and control in power systems. The evolution of the grid toward a converter-dominated network motivates a deep renovation of the classical power system theory developed for machine-dominated networks. The high degree of controllability of power electronics converters, furthermore, paves the way to the investigation of advanced control strategies to enhance the grid stability, resiliency and sustainability. This doctoral dissertation explores four cardinal topics in the field of power electronics-based power systems: dynamic modeling, stability analysis, converters control, and power quality with particular focus on harmonic distortion. In all four research areas, a particular attention is given to the implications of the nonlinearity of the converter models on the power system

    Hierarchical Coordinated Fast Frequency Control using Inverter-Based Resources for Next-Generation Power Grids

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    The proportion of inverter-connected renewable energy resources (RES) in the grid is expanding, primarily displacing conventional synchronous generators. This shift significantly impacts the objective of maintaining grid stability and reliable operations. The increased penetration of RESs contributes to the variability of active power supply and a decrease in the rotational inertia of the grid, resulting in faster system dynamics and larger, more frequent frequency events. These emerging challenges could make traditional centralized frequency control strategies ineffective, necessitating the adoption of modern, high-bandwidth control schemes. In this thesis, we propose a novel hierarchical and coordinated real-time frequency control scheme. It leverages advancements in grid monitoring and communication infrastructure to employ local, flexible inverter-based resources for promptly correcting power imbalances in the system. We solve two research problems that, when combined, yield a practical, real-time, next-generation frequency control scheme. This scheme blends localized control with high-bandwidth wide-area coordination. For the first problem, we propose a layered architecture where control, estimation, and optimization tasks are efficiently aggregated and decentralized across the system. This layered control structure, comprising decentralized, distributed, and centralized assets, enables fast, localized control responses to local power imbalances, integrated with wide- area coordination. For the second problem, we propose a data-driven extension to the framework to enhance model flexibility. Achieving high accuracy in system models used for control design is a considerable challenge due to the increasing scale, complexity, and evolving dynamics of the power system. In our proposed approach, we leverage collected data to provide direct data-driven controller designs for fast frequency regulation. The devised scheme ensures swift and effective frequency control for the bulk grid by accurately re-dispatching inverter-based resources (IBRs) to compensate for unmeasured net-load changes. These changes are computed in real-time using frequency and area tie power flow measurements, alongside collected historical data, thus eliminating reliance on proprietary power system models. Validated through detailed simulations under various scenarios such as load increase, generation trips, and three-phase faults, the scheme is practical, provides rapid, localized frequency control, safeguards data privacy, and eliminates the need for system models of the increasingly complex power system

    Accurate Battery Modelling for Control Design and Economic Analysis of Lithium-ion Battery Energy Storage Systems in Smart Grid

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    Adoption of lithium-ion battery energy storage systems (Li-ion BESSs) as a flexible energy source (FES) has been rapid, particularly for active network management (ANM) schemes to facilitate better utilisation of inverter based renewable energy sources (RES) in power systems. However, Li-ion BESSs display highly nonlinear performance characteristics, which are based on parameters such as state of charge (SOC), temperature, depth of discharge (DOD), charge/discharge rate (C-rate), and battery-aging conditions. Therefore, it is important to include the dynamic nature of battery characteristics in the process of the design and development of battery system controllers for grid applications and for techno-economic studies analyzing the BESS economic profitability. This thesis focuses on improving the design and development of Li-ion BESS controllers for ANM applications by utilizing accurate battery performance models based on the second-order equivalent-circuit dynamic battery modelling technique, which considers the SOC, C-rate, temperature, and aging as its performance affecting parameters. The proposed ANM scheme has been designed to control and manage the power system parameters within the limits defined by grid codes by managing the transients introduced due to the intermittence of RESs and increasing the RES penetration at the same time. The validation of the ANM scheme and the effectiveness of controllers that manage the flexibilities in the power system, which are a part of the energy management system (EMS) of ANM, has been validated with the help of simulation studies based on an existing real-life smart grid pilot in Finland, Sundom Smart Grid (SSG). The studies were performed with offline (short-term transient-stability analysis) and real-time (long-term transient analysis) simulations. In long-term simulation studies, the effect of battery aging has also been considered as part of the Li-ion BESS controller design; thus, its impact on the overall power system operation can be analyzed. For this purpose, aging models that can determine the evolving peak power characteristics associated with aging have been established. Such aging models are included in the control loop of the Li-ion BESS controller design, which can help analyse battery aging impacts on the power system control and stability. These analyses have been validated using various use cases. Finally, the impact of battery aging on economic profitability has been studied by including battery-aging models in techno-economic studies.Aurinkosähköjärjestelmien ja tuulivoiman laajamittainen integrointi sähkövoimajärjestelmän eri jännitetasoille on lisääntynyt nopeasti. Uusiutuva energia on kuitenkin luonteeltaan vaihtelevaa, joka voi aiheuttaa nopeita muutoksia taajuudessa ja jännitteessä. Näiden vaihteluiden hallintaan tarvitaan erilaisia joustavia energiaresursseja, kuten energiavarastoja, sekä niiden tehokkaan hyödyntämisen mahdollistaviea älykkäitä ja aktiivisia hallinta- ja ohjausjärjestelmiä. Litiumioniakkuihin pohjautuvien invertteriliitäntäisten energian varastointijärjestelmien käyttö joustoresursseina aktiiviseen verkonhallintaan niiden pätö- ja loistehon ohjauksen avulla on lisääntynyt nopeasti johtuen niiden kustannusten laskusta, modulaarisuudesta ja teknisistä ominaisuuksista. Litiumioniakuilla on erittäin epälineaariset ominaisuudet joita kuvaavat parametrit ovat esimerkiksi lataustila, lämpötila, purkaussyvyys, lataus/ purkausnopeus ja akun ikääntyminen. Akkujen ominaisuuksien dynaaminen luonne onkin tärkeää huomioida myös akkujen sähköverkkoratkaisuihin liittyvien säätöjärjestelmien kehittämisessä sekä teknis-taloudellisissa kannattavuusanalyyseissa. Tämä väitöstutkimus keskittyy ensisijaisesti aktiiviseen verkonhallintaan käytettävien litiumioniakkujen säätöratkaisuiden parantamiseen hyödyntämällä tarkkoja, dynaamisia akun suorituskykymalleja, jotka perustuvat toisen asteen ekvivalenttipiirien akkumallinnustekniikkaan, jossa otetaan huomioon lataustila, lataus/purkausnopeus ja lämpötila. Työssä kehitetyn aktiivisen verkonhallintajärjestelmän avulla tehtävät akun pätö- ja loistehon ohjausperiaatteet on validoitu laajamittaisten simulointien avulla, esimerkiksi paikallista älyverkkopilottia Sundom Smart Gridiä simuloimalla. Simuloinnit tehtiin sekä lyhyen aikavälin offline-simulaatio-ohjelmistoilla että pitkän aikavälin simulaatioilla hyödyntäen reaaliaikasimulointilaitteistoa. Pitkän aikavälin simulaatioissa akun ikääntymisen vaikutus otettiin huomioon litiumioniakun ohjauksen suunnittelussa jotta sen vaikutusta sähköjärjestelmän kokonaistoimintaan voitiin analysoida. Tätä tarkoitusta varten luotiin akun ikääntymismalleja, joilla on mahdollista määrittää akun huipputehon muutos sen ikääntyessä. Akun huipputehon muutos taas vaikuttaa sen hyödynnettävyyteen erilaisten pätötehon ohjaukseen perustuvien joustopalveluiden tarjoamiseen liittyen. Lisäksi väitöstutkimuksessa tarkasteltiin akkujen ikääntymisen vaikutusta niiden taloudelliseen kannattavuuteen sisällyttämällä akkujen ikääntymismalleja teknis-taloudellisiin tarkasteluihin.fi=vertaisarvioitu|en=peerReviewed

    Learning, future cost and role of offshore renewable energy technologies in the North Sea energy system

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    The pace of cost decline of offshore renewable energy technologies significantly impacts their role in the North Sea energy transition. However, a good understanding of their remains a critical knowledge gap in the literature. Therefore, this thesis aims to quantify the future role of offshore renewables in the North Sea energy transition and assess the impact of cost development on their optimal deployments. The following findings were observed in this thesis, 1) Fixed-bottom offshore wind is well established in the North Sea region and is already competitive with onshore renewables 2) Floating wind is emerging and their current costs are high, but it can reach about 40 EUR/MWh by early 2040 and would require 44 billion EUR of learning investment.3) Grid connection costs will become a major factor as wind farm moves further away. Policy actions and innovation is needed in this space to avoid increasing integration costs. 4) Offshore wind (fixed-bottom and floating) can play a significant role in the North Sea energy system, comprising 498 GW of deployments in 2050 (222 GW of fixed-bottom and 276 GW of floating wind) and contributing up to a maximum of 51% of total power generation in the North Sea power system. 5) The role of the investigated low-TRL offshore renewables, including the tidal stream, wave technology, and bioethanol, was limited in all scenarios considered, as they remain expensive compared to other mature technologies in the system

    Power System Dynamic Control and Performance Improvement Based on Reinforcement Learning

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    This dissertation investigates the feasibility and effectiveness of using Reinforcement Learning (RL) techniques for power system dynamic control, particularly voltage and frequency control. The conventional control strategies used in power systems are complex and time-consuming due to the complicated high-order nonlinearities of the system. RL, which is a type of neural network-based technique, has shown promise in solving these complex problems by fitting any nonlinear system with the proper network structure. The proposed RL algorithm, called Guided Surrogate Gradient-based Evolution Strategy (GSES) determines the weights of the policy (which generates the action for our control reference signal) without back-propagation process for gradient update using a simultaneous perturbation stochastic approximation approach comparing to many other RL algorithms, thus it achieves a much faster and more robust learning convergence. It is introduced and implemented in three different power system scenarios: High Voltage Direct Current (HVDC) based inter-area oscillation damping system, Doubly-fed Induction Generator (DFIG) based Fault-Ride-Through (FRT) system, and modified IEEE-39 Bus based frequency regulation system. In the case of the HVDC-based system, the proposed GSES-based power oscillation damping control approach overcomes the challenges of setting optimal controller parameters of the HVDC under various system transient events. This approach is also shown to be superior to conventional power oscillation damping methods. Further, the GSES algorithm is found to be effective in controlling the DFIG power and capacitor DC-link voltage, which helps prevent the rotor of DFIG from over-current risk and maintain the grid-connected operation. Finally, the proposed RL-based solution for frequency response in wind farms is tested on a modified IEEE-39 bus system and is found to reliably support the frequency of the power system and prevent unnecessary load shedding. Overall, this dissertation shows the potential of RL-based techniques in power system dynamic control, particularly frequency control, and provides evidence for the effectiveness of the GSES algorithm in various power system scenarios. The use of RL in power systems could lead to more efficient and effective control strategies during contingencies, which is crucial in maintaining the stability of today’s large, high-order nonlinear dynamic power systems
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