Bibliographies thématiques / Energy Storage, Smart Grid

Littérature scientifique sur le sujet « Energy Storage, Smart Grid »

Auteur : Grafiati
Publié le 14 mars 2023

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Articles de revues sur le sujet "Energy Storage, Smart Grid"

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陶, 占良. « Energy Storage Technologies in Smart Grid ». Smart Grid 05, no 04 (2015) : 155–63. http://dx.doi.org/10.12677/sg.2015.54019.

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Komarnicki, Przemysław. « Energy storage systems : power grid and energy market use cases ». Archives of Electrical Engineering 65, no 3 (1 septembre 2016) : 495–511. http://dx.doi.org/10.1515/aee-2016-0036.

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Abstract Current power grid and market development, characterized by large growth of distributed energy sources in recent years, especially in Europa, are according energy storage systems an increasingly larger field of implementation. Existing storage technologies, e.g. pumped-storage power plants, have to be upgraded and extended by new but not yet commercially viable technologies (e.g. batteries or adiabatic compressed air energy storage) that meet expected demands. Optimal sizing of storage systems and technically and economically optimal operating strategies are the major challenges to the integration of such systems in the future smart grid. This paper surveys firstly the literature on the latest niche applications. Then, potential new use case and operating scenarios for energy storage systems in smart grids, which have been field tested, are presented and discussed and subsequently assessed technically and economically.
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Fu, Qiang, Chengxi Fu, Peng Fu et Yuke Deng. « Energy Storage Technology Used in Smart Grid ». Journal of Physics : Conference Series 2083, no 3 (1 novembre 2021) : 032067. http://dx.doi.org/10.1088/1742-6596/2083/3/032067.

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Abstract Energy storage is one of the main problems bothering the power system. The present research situation of energy storage is outlined. The working principles, development process and technical features of pumped storage, compressed air energy storage, flywheel energy storage, electromagnetic energy storage and chemical energy storage are described in detail. The application prospect of energy storage is proposed.
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Zame, Kenneth K., Christopher A. Brehm, Alex T. Nitica, Christopher L. Richard et Gordon D. Schweitzer III. « Smart grid and energy storage : Policy recommendations ». Renewable and Sustainable Energy Reviews 82 (février 2018) : 1646–54. http://dx.doi.org/10.1016/j.rser.2017.07.011.

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Bento, Fábio Ricardo de Oliveira, et Wanderley Cardoso Celeste. « A methodology for smart grid reconfiguration ». Latin American Journal of Energy Research 4, no 1 (27 août 2017) : 1–9. http://dx.doi.org/10.21712/lajer.2017.v4.n1.p1-9.

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In this work, it is presented a methodology for the reconfiguration of smart grids that is applied to a smart grid formed by two microgrids that can be electrically interconnected in contingency situations. Each microgrid is also connected to an Electric Power System (EPS) when operating in the normal state. Moreover, the smart grid includes energy storage devices (batteries) located at strategic points. Serious faults that isolated the microgrids of the EPS and, moreover, considerably reduced the generation capacity of such microgrids are simulated. The proposed methodology is applied to reconfiguration in scenarios involving cooperation between microgrids and/or the use of energy storage devices. Performance indices are also proposed to enable a quantitative analysis for each scenario. It is shown that intelligent cooperation between microgrids and the smart-use storage energy is the best option for reducing the impacts in a contingency scenarios.
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Salkuti, Surender Reddy. « Challenges, issues and opportunities for the development of smart grid ». International Journal of Electrical and Computer Engineering (IJECE) 10, no 2 (1 avril 2020) : 1179. http://dx.doi.org/10.11591/ijece.v10i2.pp1179-1186.

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The development smart grids have made the power systems planning and operation more efficient by the application of renewable energy resources, electric vehicles, two-way communication, self-healing, consumer engagement, distribution intelligence, etc. The objective of this paper is to present a detailed comprehensive review of challenges, issues and opportunities for the development of smart grid. Smart grids are transforming the traditional way of meeting the electricity demand and providing the way towards an environmentally friendly, reliable and resilient power grid. This paper presents various challenges of smart grid development including interoperability, network communications, demand response, energy storage and distribution grid management. This paper also reviews various issues associated with the development of smart grid. Local, regional, national and global opportunities for the development of smart grid are also reported in this paper.
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Zingales, Antonio. « Smart Storage the Key factor of Energy Transition ». IOP Conference Series : Earth and Environmental Science 1073, no 1 (1 septembre 2022) : 012014. http://dx.doi.org/10.1088/1755-1315/1073/1/012014.

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Abstract When it comes to evaluate the next-gen Energy Transition, it turns out there are so many new elements in the power grid that it’s hard to pick one as the k-factor. There is, however, one grid element that has been getting a great deal of next-gen interest: energy storage because it is the critical element for making wind and solar more grid friendly. Storage can be managed with smart characteristics (Digitally Smart EMS) able to solve problems of the grid, but to become profitable for investors, the local regulatory frame is to be considered. Today in the path to net zero emission, Storage is applied in the range of 1 to 4 h duration, but for the next step (to 70%, 100% RES) Energy Intensive Storage will become more relevant and LDES (Long Duration Energy Storage) will be necessary for the System. Because of this, we can say that Energy Storage System: digitally smart (for managing grid and revenues requirements), with proactive Regulatory frame (transparent for anticipating investment), with LDES option (Long Duration Energy Storage) technologically available – It can really be the key factor of Energy Transition and to drive the Net Zero agenda, see Fig.1.
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Sun, Qiu-ye, Da-shuang Li, En-hui Chu et Yi-bin Zhang. « Grid tie inverter energy stabilizing in smart distribution grid with energy storage ». Journal of Central South University 21, no 6 (juin 2014) : 2298–305. http://dx.doi.org/10.1007/s11771-014-2181-3.

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Fotopoulou, Maria, Dimitrios Rakopoulos et Orestis Blanas. « Day Ahead Optimal Dispatch Schedule in a Smart Grid Containing Distributed Energy Resources and Electric Vehicles ». Sensors 21, no 21 (2 novembre 2021) : 7295. http://dx.doi.org/10.3390/s21217295.

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This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers.
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Barbour, Edward, David Parra, Zeyad Awwad et Marta C. González. « Community energy storage : A smart choice for the smart grid ? » Applied Energy 212 (février 2018) : 489–97. http://dx.doi.org/10.1016/j.apenergy.2017.12.056.

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Thèses sur le sujet "Energy Storage, Smart Grid"

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Damnjanovic, Nenad. « Smart Grid Functionality of a PV-Energy Storage System ». Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3058.

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Renewable Energy will be the key to preserving the Earth's remaining resources and continuing this surge of technological progress that we have experienced this past century. New philosophies of how/when/where energy should be consumed and produced are attempting to improve upon the current grid infrastructure. The massive advancement in communications, renewable and control systems will allow this new-age electric grid to maximize its efficiency while reducing cost. Renewable, "green" energy is now at the forefront of innovation. As the world population increases, there will be a need to free ourselves from natural resources as much as possible. Advanced Energy Storage Systems (AESS) will play a vital and large role in this new-age infrastructure. Because renewable energy is not constant (aside from hydroelectricity), this energy needs to be conserved and used at appropriate times. The Sustainable Electric Energy Delivery System (SEEDS) project features an AESS made from Lithium-ion phosphate (LiFeP04) and a Photovoltaic (PV) source connected to the grid. Every current technology has different parameters, efficiency, charge/discharge rates, lifespan, etc. The current Li-FeP04 system will be used as an example and a model. This project acts as a pilot project for future large scale smart grid endeavors. This thesis is written in conjunction with the SEEDS project and will outline and discuss in detail the findings. For the PV system, the performance is analyzed. For the storage system, the round-trip efficiency (measured) and life cycle are broken down. The thesis concludes with a capacity sizing estimation of the storage system which is based on the renewable energy source (solar).
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Wang, Lu. « Optimization and control of energy storage in a smart grid ». Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/412630/.

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Environmental issues such as global warming, limited storage of fossil fuels and concerns about cost and energy efficiency are driving the development of the future smart grid. To reduce carbon emissions, it is expected that there will be a large-scale increase in the penetration of renewable generators (RGs), electric vehicles (EVs) and electrical heating systems. This will require new control approaches to ensure the balance of generation and consumption and the stability of the power grid. Energy storage can be used to support grid operations by controlling frequency and voltage, and alleviating thermal overload. This thesis makes three novel contributions to the field: optimal battery sizing; optimal dispatch of vehicle-to-grid batteries; and optimal coordination of EV batteries and RGs. Appropriate sizing of the energy storage is very important when using it to support the power system. In this thesis, an approach has been proposed to determine the capacity of a battery storage providing support during N-1 contingencies to relieve transmission line thermal overload. In addition, as the increasing use of EV is an inevitable trend in the future smart grid, the system's peak demand may increase significantly due to EV charging, causing serious overloading of some power system facilities such as transformers and cables in the grid if an effective EV battery dispatch strategy is not used. Therefore, this report presents a dispatch strategy for EV batteries based on the Analytic Hierarchy Process taking into account both vehicle users' and power system requirements and priorities, as well as the constraints of the battery system. However, using renewable power to charge EVs is the prerequisite of realizing clean transport. EVs can store the extra renewable power and feed it into the grid when needed via vehicle-to-grid operations to increase the utilization and integration of RGs in the power grid. Thus, the optimal dispatch of EVs and RGs to realize the synergy between them will be one of the key challenges. Two optimal agent-based coordinated dispatch strategies are developed in this thesis, respectively using dynamic programming and the A* search procedure (comparisons between these two algorithms are made and discussed), for the synergistic integration of EVs and RGs, so that the benefits of both EV users and power grid are maximized. Each of the proposed approaches was tested on an IEEE Reliability Test System or a modified UK generic distribution system (UKGDS) using MATLAB. The simulation results demonstrate the feasibility and efficacy of the proposed approaches.
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Östergård, Rickard. « Flywheel energy storage : a conceptucal study ». Thesis, Uppsala universitet, Elektricitetslära, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-164500.

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This master thesis was provided by ABB Cooperate Research in Västerås. This study has two major purposes: (1) to identify the characteristics of a flywheel energy storage system (FESS), (2) take the first steps in the development of a simulation model of a FESS. For the first part of this master thesis a literature reviews was conducted with focus on energy storage technologies in general and FESS in particular. The model was developed in the simulation environment PSCAD/EMTDC; with the main purpose to provide working model for future studies of the electrical dynamics of a flywheel energy storage system. The main conclusion of the literature review was that FESS is a promising energy storage solution; up to multiple megawatt scale. However, few large scale installations have so far been built and it is not a mature technology. Therefore further research and development is needed in multiple areas, including high strength composite materials, magnetic bearings and electrical machines. The model was implemented with the necessary control system and tested in a simulation case showing the operational characteristics.
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Barakat, Mahmoud. « Development of models for inegrating renewables and energy storage components in smart grid applications ». Thesis, Normandie, 2018. http://www.theses.fr/2018NORMC217/document.

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Cette thèse présente un modèle unique du MASG (Modèle d’Architecture du Smart Grid) en considérant l 'état de l’art des différentes directives de recherche du smart grid. Le système hybride de génération d'énergie active marine-hydrogène a été modélisé pour représenter la couche de composants du MASG. Le système intègre l'électrolyseur à membrane d’échange de proton (à l’échelle de méga watt) et les systèmes de piles à combustible en tant que composants principaux du bilan énergétique. La batterie LiFePO4 est utilisée pour couvrir la dynamique rapide de l'énergie électrique. En outre, la thèse analyse le système de gestion de l'énergie centralisé et décentralisé. Le système multi-agents représente le paradigme du système décentralisé. La plate-forme JADE est utilisée pour développer le système multi-agents, en raison de son domaine d'application général, de ses logiciels à licence libre, de son interface avec MATLAB et de sa calculabilité avec les standards de la Fondation des Agents Physiques Intelligentes. Le système de gestion d'énergie basé sur JADE équilibre l'énergie entre la génération (système de conversion d'énergie marine-courant) et la demande (profil de charge résidentielle) pendant les modes de fonctionnement autonome et connecté au réseau. Le modèle proposé du MASG peut être considéré comme une étude de cas pilote qui permet l'analyse détaillée et les applications des différentes directions de recherche du smart grid
This thesis presents a unique model of the SGAM (Smart Grid Architecture Model) with considering the state of the art of the different research directions of the smart grid and. The hybrid marine-hydrogen active power generation system has been modeled to represent the component layer of the SGAM. The system integrates the MW scale PEM electrolyzer and fuel cell systems as the main energy balance components. The LiFePO4 battery is used to cover the fast dynamics of the electrical energy. Moreover, the thesis analyzes the centralized and the decentralized energy management system. The MAS (Multi-Agent Systems) represents the paradigm of the decentralized system. The JADE platform is used to develop the MAS due to its general domain of application, open source and free license software, interface with MATLAB and the computability with the FIPA (Foundation of Intelligent Physical Agent) standards. The JADE based energy management system balances the energy between the generation (marine-current energy conversion system) and the demand side (residential load profile) during the stand-alone and the grid-connected modes of operation. The proposed model of the SGAM can be considered as a pilot case study that enables the detailed analysis and the applications of the different smart grid research directions
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Busuladzic, Ishak, et Marcus Tjäder. « Performance Indicators for Smart Grids : An analysis of indicators that measure and evaluate smart grids ». Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48902.

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Sweden has developed ambitious goals regarding energy and climate politics. One major goal is to change the entire electricity production from fossil fuels to sustainable energy sources, this will contribute to Sweden being one of the first countries in the world with non-fossil fuel in the electricity sector. To manage this, major changes need to be implemented and difficulties on the existing grid will occur with the expansion of digitalization, electrification and urbanization. By using smart grids, it is possible to deal with these problems and change the existing electricity grid to use more distributed power generation, contributing to flexibility, stability and controllability. The goal with smart grids is to have a sustainable electricity grid with low losses, security of supply, environmental-friendly generation and also have choices and affordable electricity for customers. The purpose of this project is to identify and evaluate several indicators for a smart grid, how they relate and are affected when different scenarios with different technologies are implemented in a test system. Smart grid indicators are quantified metrics that measure the smartness of an electrical grid. There are five scenarios where all are based on possible changes in the society and electricity consumption, these scenarios are; Scenario A – Solar power integration, Scenario B – Energy storage integration, Scenario C – Electric vehicles integration, Scenario D – Demand response and Scenario E – Solar power, Energy storage, Electric vehicles and Demand response integration. A model is implemented in MATLAB and with Monte Carlo simulations expected values, standard deviation and confidence interval were gained. Four selected indicators (Efficiency, capacity factor, load factor and relative utilization) was then analyzed. The results show that progress on indicators related to all smart grid characteristics is needed for the successful development of a smart grid. In scenario C, all four selected indicators improved. This shows that these indicators could be useful for promoting the integration of electric vehicles in an electricity grid. In Scenario A, solar power integration contributed to all indicators deteriorate, this means that, technical solutions that can stabilize the grid are necessary to implement when integrating photovoltaic systems. The load factor is a good indicator for evaluating smart grids. This indicator can incentivize for an even load and minimize the peak loads which contributes to a flexible and efficient grid. With the capacity factor, the utilization and free capacity can be measured in the grid, but it can counteract renewable energy integration if the indicator is used in regulation.
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Khasawneh, Hussam Jihad. « Sizing Methodology and Life Improvement of Energy Storage Systems in Microgrids ». The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429638668.

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Pantaleo, Gaetano. « Energy management di un sistema energetico ibrido on-grid ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Il livello di emissioni di inquinanti e gas serra negli ultimi decenni è salito vertiginosamente anche a causa della produzione di energia elettrica, nonostante la sempre crescente quota riguardante le rinnovabili, le fonti fossili e nucleari sono ancora le più utilizzate. Nell’ambito della gestione della risorsa elettrica è sempre più frequente l’utilizzo di sistemi ibridi off-grid ed on-grid come nel nostro caso. I sistemi HES e RES che possono essere combinati utilizzando fonti rinnovabili come solare ed eolico o geotermico o integrati a piccoli generatori nel caso di sistemi stand-alone. Nell’elaborato si valuta con l’implementazione in un software di calcolo di strategie di energy management la fattibilità economica, attraverso l’LCOE (Levelized cost of energy), di un impianto composto da pannelli fotovoltaici e sistema di accumulo installato presso la sede di Ingegneria dell’Università di Bologna di Via Terracini e la sede del Politecnico di Bari in Via Edoardo Orabona. L’analisi ha l’obiettivo di valutare la fattibilità economica dell’impianto e valutare quanto incidono le condizioni climatiche al contorno sul calcolo dell’LCOE. Il calcolo dell’LCOE verrà confrontato con i valori attuali del prezzo dell’energia elettrica e verranno valutate le differenze.
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Halawani, Mohanad. « An iterative analytical design framework for the optimal designing of an off-grid renewable energy based hybrid smart micro-grid : a case study in a remote area - Jordan ». Thesis, Abertay University, 2015. https://rke.abertay.ac.uk/en/studentTheses/40b75bc8-d237-4aaf-9668-797739f49f74.

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Creative ways of utilising renewable energy sources in electricity generation especially in remote areas and particularly in countries depending on imported energy, while increasing energy security and reducing cost of such isolated off-grid systems, is becoming an urgently needed necessity for the effective strategic planning of Energy Systems. The aim of this research project was to design and implement a new decision support framework for the optimal design of hybrid micro grids considering different types of different technologies, where the design objective is to minimize the total cost of the hybrid micro grid while at the same time satisfying the required electric demand. Results of a comprehensive literature review, of existing analytical, decision support tools and literature on HPS, has identified the gaps and the necessary conceptual parts of an analytical decision support framework. As a result this research proposes and reports an Iterative Analytical Design Framework (IADF) and its implementation for the optimal design of an Off-grid renewable energy based hybrid smart micro-grid (OGREH-SμG) with intra and inter-grid (μG2μG & μG2G) synchronization capabilities and a novel storage technique. The modelling design and simulations were based on simulations conducted using HOMER Energy and MatLab/SIMULINK, Energy Planning and Design software platforms. The design, experimental proof of concept, verification and simulation of a new storage concept incorporating Hydrogen Peroxide (H2O2) fuel cell is also reported. The implementation of the smart components consisting Raspberry Pi that is devised and programmed for the semi-smart energy management framework (a novel control strategy, including synchronization capabilities) of the OGREH-SμG are also detailed and reported. The hybrid μG was designed and implemented as a case study for the Bayir/Jordan area. This research has provided an alternative decision support tool to solve Renewable Energy Integration for the optimal number, type and size of components to configure the hybrid μG. In addition this research has formulated and reported a linear cost function to mathematically verify computer based simulations and fine tune the solutions in the iterative framework and concluded that such solutions converge to a correct optimal approximation when considering the properties of the problem. As a result of this investigation it has been demonstrated that, the implemented and reported OGREH-SμG design incorporates wind and sun powered generation complemented with batteries, two fuel cell units and a diesel generator is a unique approach to Utilizing indigenous renewable energy with a capability of being able to synchronize with other μ-grids is the most effective and optimal way of electrifying developing countries with fewer resources in a sustainable way, with minimum impact on the environment while also achieving reductions in GHG. The dissertation concludes with suggested extensions to this work in the future.
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Yang, You. « Privacy-enhancing and Cost-efficient Energy Management for an End-User Smart Grid in the Presence of an Energy Storage ». Thesis, KTH, Teknisk informationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214410.

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A smart grid is an energy network which manages the energy generation anddistribution more efficiently following the real-time energy demands of end-usersthrough control and communication technologies. Deploying smart grids canimprove the energy efficiency, enhance the network reliability, and reduce costsof both the energy provider and end-users. However, these benefits come withprivacy challenges. One of such challenges is the smart meter privacy problem.In a smart grid, the smart meter is used to record the real-time energy supplyand to feedback the records to the energy provider. Since the energy is suppliedon the demand, these smart meter records contain the information of energydemand profile of the end-user and therefore it brings the risk of compromisingconsumers’ privacy. Regarding this issue, a rechargeable energy storage canbe used to mitigate this risk by manipulating consumers’ energy consumptionprofile. However, privacy enhancement will lead to increasing the consumers’cost for purchasing energy, which violates the original cost-saving motivation forconsumers. In this work, we investigate the design of a privacy-enhancing andcost-efficient energy management strategy. In detail, dynamic pricing of energyis assumed so that the consumer has the opportunity to utilize the energy storageto reduce the energy cost. Furthermore, the Kullback-Leibler divergence rate isused as privacy measure, and the expected cost-saving rate is also evaluated. Tostudy the trade-off between privacy and cost, the proposed objective functionis a weighted sum of Kullback-Leibler divergence rate and expected cost-savingrate. We first decompose both Kullback-Leibler divergence rate and expectedcost-saving rate in additive forms over a finite horizon. Based on the predefinedbelief states, we express the overall objective function by state-actionpairs and reformulate the energy management design into an Markov decisionprocess (MDP), and the finite horizon optimal solution can be obtained by usingBellman dynamic programming. Finally, in the special case of independent andidentically distributed (i.i.d) demand, we explicitly characterize a stationarypolicy for the infinite horizon average cost by showing this policy can preserve acertain invariance property of the belief state. And we also show this stationarypolicy can achieve an optimal privacy leakage rate.
Ett smart nät är ett energinätverk som hanterar energigenerering och distributionmer effektivt efter slutanvändarnas energikrav i realtid genom kontrolloch kommunikationsteknik. Genom att distribuera smarta nät kan du förbättraenergieffektiviteten, förbättra nätverk säkerheten och minska kostnadernaför både energileverantören och slutanvändarna. Men dessa fördelar kommermed privata utmaningar. En av dessa utmaningar är problemet med smartamätare. I ett smart nät används den smarta mätaren för att registrera energitillförselni realtid och att återkoppla mätningarna till energileverantören.Eftersom energinlevereras efter begäran, innehåller dessa smarta mätarregisterinformationen om slutanvändarens energibehovs profil och därmed riskerardet att äventyra konsumenternas privatliv. När det gäller denna fråga kanen uppladdningsbar energilagring användas för att minska denna risk genomatt förändra konsumenternas energiförbruknings profil. Förbättringen av privatkommerdock att leda till att konsumenternas kostnad för inköp av energiökar, vilket strider mot den ursprungliga kostnads besparande motivationen förkonsumenterna. I detta arbete undersöker vi utformningen av en privatliv höjandeoch kostnads effektiv energihanterings strategi. I detalj antas dynamiskprissättning av energi så att konsumenten har möjlighet att utnyttja energilagringför att minska sin energikostnad. Vidare används Kullback-Leiblerdivergensvärde som privatliv metrisk, och den förväntade kostnads besparingsvärde utvärderas också. För att studera avvägningen mellan privatliv och kostnadär den föreslagna objektiv funktionen en viktad summa av Kullback-Leiblerdivergensvärde och förvÃďntad kostnads besparings värde. Vi bryter först itubåde Kullback-Leibler-divergens värde och den förväntade kostnads besparingeni additativa former över en finit horisont. Baserat på de fördefinierade antagandenauttrycker vi den övergripande objektiva funktionen med state-action-paroch omformulerar energistyrnings designen i en Markov-beslutsprocess (MDP),och den finita optimala lösningen kan erhållas genom att använda dynamiskBellman-programmering. Slutligen, i det speciella fallet med oberoende ochidentiskt distribuerad (i.i.d) efterfrågan karakteriserar vi uttryckligen en stationärpolitik för den oändliga horisontens genomsnittliga kostnad genom attvisa att denna policy kan bevara en viss invariant egenskap hos trosuppfattningen.Vi visar också att man med den här stationära principen kan uppnå ettoptimalt privatliv läckagevärde.
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Keerthisinghe, Chanaka. « Fast Solution Techniques for Energy Management in Smart Homes ». Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16033.

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In the future, residential energy users will seize the full potential of demand response schemes by using an automated smart home energy management system (SHEMS) to schedule their distributed energy resources. The underlying optimisation problem facing a SHEMS is a sequential decision making problem under uncertainty because the states of the devices depend on the past state. There are two major challenges to optimisation in this domain; namely, handling uncertainty, and planning over suitably long decision horizons. In more detail, in order to generate high quality schedules, a SHEMS should consider the stochastic nature of the photovoltaic (PV) generation and energy consumption. In addition, the SHEMS should accommodate predictable inter-daily variations over several days. Ideally, the SHEMS should also be able to integrate into an existing smart meter or a similar device with low computational power. However, extending the decision horizon of existing solution techniques for sequential stochastic decision making problems is computationally difficult and moreover, these approaches are only computationally feasible with a limited number of storage devices and a daily decision horizon. Given this, the research investigates, proposes and develops fast solution techniques for implementing efficient SHEMSs. Specifically, three novel methods for overcoming these challenges: a two-stage lookahead stochastic optimisation framework; an approximate dynamic programming (ADP) approach with temporal difference learning; and a policy function approximation (PFA) algorithm using extreme learning machines (ELM) are presented. Throughout the thesis, the performance of these solution techniques are benchmarked against dynamic programming (DP) and stochastic mixed-integer linear programming (MILP) using a range of residential PV-storage (thermal and battery) systems. We use empirical data collected during the Smart Grid Smart City project in New South Wales, Australia, to estimate the parameters of a Markov chain model of PV output and electrical demand using an hierarchical approach, which first cluster empirical data and then learns probability density functions using kernel regression (Chapter 2). The two-stage lookahead method uses deterministic MILP to solve a longer decision horizon, while its end-of-day battery state of charge is used as a constraint for a daily DP approach (Chapter 4). Here DP is used for the daily horizon as it is shown to provide close-to-optimal solutions when the state, decision and outcome spaces are finely discretised (Chapter 3). However, DP is computationally difficult because of the dimensionalities of state, decision and outcome spaces, so we resort to MILP to solve the longer decision horizon. The two-stage lookahead results in significant financial benefits compared to daily DP and stochastic MILP approaches (8.54% electricity cost savings for a very suitable house), however, the benefits decreases as the actual PV output and demand deviates from their forecast values. Building on this, ADP is proposed in Chapter 5 to implement a computationally efficient SHEMS. Here we obtain policies from value function approximations (VFAs) by stepping forward in time, compared to the value functions obtained by backward induction in DP. Similar to DP, we can use VFAs generated during the offline planning phase to generate fast real-time solutions using the Bellman optimality condition, which is computationally efficient compared to having to solve the entire stochastic MILP problem. The decisions obtained from VFAs at a given time-step are optimal regardless of what happened in the previous time-steps. Our results show that ADP computes a solution much faster than both DP and stochastic MILP, and provides only a slight reduction in quality compared to the optimal DP solution. In addition, incorporating a thermal energy storage unit using the proposed ADP-based SHEMS reduces the daily electricity cost by up to 57.27% for a most suitable home, with low computational burden. Moreover, ADP with a two-day decision horizon reduces the average yearly electricity cost by a 4.6% over a daily DP method, yet requires less than half of the computational effort. However, ADP still takes a considerable amount of time to generate VFAs in the off-line planning phase and require us to estimate PV and demand models. Given this, a PFA algorithm that uses ELM is proposed in Chapter 6 to overcome these difficulties. Here ELM is used to learn models that map input states and output decisions within seconds, without solving an optimisation problem. This off-line planning process requires a training data set, which has to be generated by solving the deterministic SHEMS problem over couple of years. Here we can use a powerful cloud or home computer as it is only needed once. PFA models can be used to make fast real-time decisions and can easily be embedded in an existing smart meter or a similar low power device. Moreover, we can use PFA models over a long period of time without updating the model and still obtain similar quality solutions. Collectively, ADP and PFA using ELM can overcome challenges of considering the stochastic variables, extending the decision horizon and integrating multiple controllable devices using existing smart meters or a device with low computational power, and represent a significant advancement to the state of the art in this domain.
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Livres sur le sujet "Energy Storage, Smart Grid"

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Zhi neng dian wang zhong de feng guang chu guan jian ji shu. Beijing Shi : Ji xie gong ye chu ban she, 2013.

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Carbone, Rosario. Energy storage in the emerging era of smart grids. Rijeka : InTech, 2011.

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Pelzer, Dominik. A Modular Framework for Optimizing Grid Integration of Mobile and Stationary Energy Storage in Smart Grids. Wiesbaden : Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-27024-7.

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Tomar, Anuradha, Rajkumar Viral, Divya Asija, U. Mohan Rao et Adil Sarwar. Smart Grids for Renewable Energy Systems, Electric Vehicles and Energy Storage Systems. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003311195.

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Research, California Energy Commission Public Interest Energy. Integrating new and emerging technologies into the California smart grid infrastructure : A report on a smart grid for California : PIER final project report. [Sacramento, Calif.] : California Energy Commission, 2008.

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(Firm), TheCapitol Net, dir. Smart grid : Modernizing electric power transmission and distribution ; energy independence, storage and security ; energy independence and security act of 2007 (EISA) ; improving electrical grid efficiency, communication, reliability, and resiliency ; integrating new and renewable energy sources. Alexandria, VA : TheCapitol.Net, 2009.

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Kumar, Ashwani, S. C. Srivastava et S. N. Singh, dir. Renewable Energy Towards Smart Grid. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7472-3.

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Momoh, James. Energy Processing and Smart Grid. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119521129.

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Robertson, Hope E. The charge for grid energy storage. Cambridge, MA : CERA, 2010.

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Kolhe, Mohan Lal, dir. Renewable Energy Systems in Smart Grid. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4360-7.

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Chapitres de livres sur le sujet "Energy Storage, Smart Grid"

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Belu, Radian. « Energy Storage in Future Power Systems ». Dans Smart Grid Fundamentals, 147–94. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9780429174803-5.

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Padmanaban, Sanjeevikumar, Mohammad Zand, Morteza Azimi Nasab, Mohamadmahdi Shahbazi et Heshmatallah Nourizadeh. « Energy Storage ». Dans Smart and Power Grid Systems – Design Challenges and Paradigms, 211–36. New York : River Publishers, 2023. http://dx.doi.org/10.1201/9781003339557-9.

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Barooah, Prabir. « Virtual Energy Storage from Flexible Loads : Distributed Control with QoS Constraints ». Dans Smart Grid Control, 99–115. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98310-3_6.

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Arif, Mohammad Taufiqul, Amanullah M. T. Oo et A. B. M. Shawkat Ali. « Energy Storage : Applications and Advantages ». Dans Smart Grids, 77–108. London : Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5210-1_4.

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Al-Hallaj, Said, Stephen Wilke et Ben Schweitzer. « Energy Storage Systems for Smart Grid Applications ». Dans Water, Energy & ; Food Sustainability in the Middle East, 161–92. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48920-9_8.

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Zhou, Youjie, Xudong Wang, Xiangjing Mu, Zhizhou Long, Changbo Lu et Lijie Zhou. « Energy Storage Techniques Applied in Smart Grid ». Dans Lecture Notes in Electrical Engineering, 2357–63. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_286.

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Rogers, Reese. « Smart Grid and Energy Storage Installations Rising ». Dans Vital Signs, 19–21. Washington, DC : Island Press/Center for Resource Economics, 2013. http://dx.doi.org/10.5822/978-1-61091-457-4_5.

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Guru Raghavendra, Kummara Venkata, et Kisoo Yoo. « Reliable Energy Storage System for the Grid Integration ». Dans Hybrid Intelligence for Smart Grid Systems, 27–46. Boca Raton : CRC Press, 2021. http://dx.doi.org/10.1201/9781003143802-2.

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Jaiswal, Supriya, et Sohit Sharma. « Electric vehicles and smart grid interactions ». Dans Smart Grids for Renewable Energy Systems, Electric Vehicles and Energy Storage Systems, 15–44. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003311195-2.

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Awasthi, Abhishek, V. Karthikeyan, Vipin Das, S. Rajasekar et Asheesh Kumar Singh. « Energy Storage Systems in Solar-Wind Hybrid Renewable Systems ». Dans Smart Energy Grid Design for Island Countries, 189–222. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50197-0_7.

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Actes de conférences sur le sujet "Energy Storage, Smart Grid"

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Pazouki, Samaneh, et Mahmoud-Reza Haghifam. « Impact of energy storage technologies on multi carrier energy networks ». Dans 2014 Smart Grid Conference (SGC). IEEE, 2014. http://dx.doi.org/10.1109/sgc.2014.7090854.

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Krasovsky, V., et P. Yatsko. « ENERGY STORAGE, SMART GRID WITH RENEWABLE ENERGY SOURCES ». Dans SAKHAROV READINGS 2020:ENVIRONMENTAL PROBLEMS OF THE XXI CENTURY. Minsk, ICC of Minfin, 2020. http://dx.doi.org/10.46646/sakh-2020-2-396-399.

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Al-Nory, Malak T., et Mohamed El-Beltagy. « Optimal selection of energy storage systems ». Dans 2015 Saudi Arabia Smart Grid (SASG). IEEE, 2015. http://dx.doi.org/10.1109/sasg.2015.7449273.

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Boksha, Victor V., Richard O. Foster, Alex N. Ignatiev, Clara Chow, Alexander P. Ryjov, Pavel M. Bulai, Chris Progler, Edward Hague et Ann Chai Wong. « From energy storage to EnerNet : Smart grid for abundant energy ». Dans 2014 Saudi Arabia Smart Grid (SASG). IEEE, 2014. http://dx.doi.org/10.1109/sasg.2014.7274284.

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Al-Hallaj, Said, Siddique Khateeb, Ahmed Aljehani et Mike Pintar. « Thermal energy storage for smart grid applications ». Dans PHYSICS OF SUSTAINABLE ENERGY IV (PSE IV) : Using Energy Efficiently and Producing it Renewably. Author(s), 2018. http://dx.doi.org/10.1063/1.5020287.

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Jin-hee Lee, Ui-kwon Son, Joon-young Choi, Jeong-min Lee et Jae-hong Lee. « Grid Energy Storage System for Smart-Renewable ». Dans 9th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.2134.

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Gharigh, M. R. Karimi, M. Seydali Seyf Abad, J. Nokhbehzaeem et A. Safdarian. « Optimal sizing of distributed energy storage in distribution systems ». Dans 2015 Smart Grid Conference (SGC). IEEE, 2015. http://dx.doi.org/10.1109/sgc.2015.7857391.

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Pazouki, Samaneh, Mahmoud-Reza Haghifam et Javad Olamaei. « Economical scheduling of multi carrier energy systems integrating Renewable, Energy Storage and Demand Response under Energy Hub approach ». Dans 2013 Smart Grid Conference (SGC). IEEE, 2013. http://dx.doi.org/10.1109/sgc.2013.6733803.

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Habibi, Mahdi, et Vahid Vahidinasab. « Emergency Services of Energy Storage Systems for Wind Ramp Events ». Dans 2019 Smart Grid Conference (SGC). IEEE, 2019. http://dx.doi.org/10.1109/sgc49328.2019.9056593.

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Geurin, S. O., A. K. Barnes et J. C. Balda. « Smart grid applications of selected energy storage technologies ». Dans 2012 IEEE PES Innovative Smart Grid Technologies (ISGT). IEEE, 2012. http://dx.doi.org/10.1109/isgt.2012.6175626.

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Rapports d'organisations sur le sujet "Energy Storage, Smart Grid"

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Tuffner, Francis K., et Christopher A. Bonebrake. Evaluation of Representative Smart Grid Investment Grant Project Technologies : Thermal Energy Storage. Office of Scientific and Technical Information (OSTI), février 2012. http://dx.doi.org/10.2172/1086926.

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Dallman, Ann Renee, Mohammad Khalil, Kaus Raghukumar, Jeremy Kasper, Craig Jones et Jesse D. Roberts. Improved Wave Energy Production Forecasts for Smart Grid Integration. Office of Scientific and Technical Information (OSTI), septembre 2018. http://dx.doi.org/10.2172/1481640.

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Barnes, Frank. Smart Grid Communications Security Project, U.S. Department of Energy. Office of Scientific and Technical Information (OSTI), septembre 2012. http://dx.doi.org/10.2172/1225224.

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Dallman, Ann Renee, Mohammad Khalil, Kaus Raghukumar, Jeremy Kasper, Craig Jones et Jesse D. Roberts. Improved Wave Energy Production Forecasts for Smart Grid Integration. Office of Scientific and Technical Information (OSTI), septembre 2018. http://dx.doi.org/10.2172/1531318.

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Magnuson, Brian L. DoD Installation Energy Security : Evolving to a Smart Grid. Fort Belvoir, VA : Defense Technical Information Center, mars 2012. http://dx.doi.org/10.21236/ada561398.

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Bibeault, Mark L. Modular Pumped Hydro for Grid Energy Storage. Office of Scientific and Technical Information (OSTI), février 2014. http://dx.doi.org/10.2172/1120713.

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Denholm, P., J. Jorgenson, M. Hummon, T. Jenkin, D. Palchak, B. Kirby, O. Ma et M. O'Malley. Value of Energy Storage for Grid Applications. Office of Scientific and Technical Information (OSTI), mai 2013. http://dx.doi.org/10.2172/1079719.

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Bowen, Thomas, Ilya Chernyakhovskiy, Kaifeng Xu, Sika Gadzanku et Kamyria Coney. USAID Grid-Scale Energy Storage Technologies Primer. Office of Scientific and Technical Information (OSTI), juillet 2021. http://dx.doi.org/10.2172/1808490.

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Twitchell, Jeremy, Jeffrey Taft, Rebecca O'Neil et Angela Becker-Dippmann. Regulatory Implications of Embedded Grid Energy Storage. Office of Scientific and Technical Information (OSTI), avril 2021. http://dx.doi.org/10.2172/1842842.

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Ton, Dan, Georgianne H. Peek, Charles Hanley et John Boyes. Solar energy grid integration systems - Energy storage (SEGIS-ES). Office of Scientific and Technical Information (OSTI), mai 2008. http://dx.doi.org/10.2172/1217673.

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