Relevant bibliographies by topics / Optimization of power management

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Optimization of power management'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Optimization of power management"

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Benini, L., A. Bogliolo, G. A. Paleologo, and G. De Micheli. "Policy optimization for dynamic power management." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 18, no. 6 (June 1999): 813–33. http://dx.doi.org/10.1109/43.766730.

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Riaz, Muhammad, Sadiq Ahmad, Irshad Hussain, Muhammad Naeem, and Lucian Mihet-Popa. "Probabilistic Optimization Techniques in Smart Power System." Energies 15, no. 3 (January 24, 2022): 825. http://dx.doi.org/10.3390/en15030825.

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Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.
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Devika, B., and P. N. Sudha. "Power optimization in MANET using topology management." Engineering Science and Technology, an International Journal 23, no. 3 (June 2020): 565–75. http://dx.doi.org/10.1016/j.jestch.2019.07.008.

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Al-Falahi, Monaaf D. A., Kutaiba S. Nimma, Shantha D. G. Jayasinghe, Hossein Enshaei, and Josep M. Guerrero. "Power management optimization of hybrid power systems in electric ferries." Energy Conversion and Management 172 (September 2018): 50–66. http://dx.doi.org/10.1016/j.enconman.2018.07.012.

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Aman, Nurlely. "A Sustainable Forest Management Optimization Model to Preserve Water Allocation for Hydroelectric Power Plant." Indian Journal of Applied Research 3, no. 9 (October 1, 2011): 15–18. http://dx.doi.org/10.15373/2249555x/sept2013/181.

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Wang, Weida, Changle Xiang, Hui Liu, and Shipeng Jia. "A model-predictive-control-based power management strategy for a power-split electromechanical transmission." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 230, no. 14 (August 5, 2016): 1987–2001. http://dx.doi.org/10.1177/0954407016630911.

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A power management strategy is a key necessity for power-split electromechanical transmission systems. A model predictive control strategy which is based on finite-horizon optimization and can combine the advantages of instantaneous optimization and global optimization is a good solution for online optimization of the power management. Therefore, a model-predictive-control-based power management strategy is proposed for a two-mode electromechanical transmission. A model predictive control strategy consists of two parts: a predictive model and a receding-horizon optimization algorithm. A predictive model is used for predicting future information on the electromechanical transmission states, and real-time receding-horizon optimization with a finite horizon is adopted for optimal decision making. First, the predictive model, including the battery state and the transmission output torque, which provides a priori knowledge for optimal calculation, is proposed. Then, to ensure optimal operating areas of the engine and the motors, a novel overall efficiency calculation method for the whole powertrain including the engine, the motors, the power-split coupled machine and the battery is proposed and regarded as the optimization objective. The overall efficiency not only is focused on the engine fuel economy but also determines the power loss of the motors, the battery and the planetary gears together, which enhances the fuel economy and the transmission efficiency significantly. Based on the predictive model and receding-horizon optimization, the MPC strategy is established and tested by hardware-in-the-loop simulations under Urban Dynamometer Driving Schedule and New European Driving Cycle conditions. The test results showed that the power management strategy can enhance the fuel economy and proved to be a potential real-time optimization method for power distribution in the electromechanical transmission system; this strategy can provide theoretical support for actual application of electromechanical transmission systems.
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Dolgov, Arseny, Regan Zane, and Zoya Popovic. "Power Management System for Online Low Power RF Energy Harvesting Optimization." IEEE Transactions on Circuits and Systems I: Regular Papers 57, no. 7 (July 2010): 1802–11. http://dx.doi.org/10.1109/tcsi.2009.2034891.

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Al-Falahi, Monaaf, Tomasz Tarasiuk, Shantha Jayasinghe, Zheming Jin, Hossein Enshaei, and Josep Guerrero. "AC Ship Microgrids: Control and Power Management Optimization." Energies 11, no. 6 (June 5, 2018): 1458. http://dx.doi.org/10.3390/en11061458.

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Niu, Dong Xiao, and Qian Zhang. "A Robust Revenue Optimization Approach for Power Plant Revenue Management." Applied Mechanics and Materials 373-375 (August 2013): 1784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1784.

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This paper examines optimization of revenue of power plants facing stochastic demand with varied prices. A network optimization model is proposed for power plant revenue management under an uncertain environment. The network optimization has a stochastic programming formulation designed to capture the randomness of the unknown demand. A novel approach of robust optimization and PSO are applied to solve the problem on a scenario-basis. Decision-makers risk aversion is considered in the objective function. Mean absolute value is used to measure risk of deviation of revenue from its expected value.
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Iwamura, Kazuaki, Yosuke Nakanishi, Udom Lewlomphaisarl, Noel Estoperez, and Abraham Lomi. "Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, S (October 12, 2021): 101–7. http://dx.doi.org/10.53560/ppasa(58-sp1)742.

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This paper describes the architecture and the utilization for a facility planning optimization platform called GGOD, “Grid of Grids Optimal Designer” and applies it to expandable cluster-type micro-grid installations and operations. The expandable cluster-type micro-grid is defined as a group of micro-grids that are connected by bi-directional power transfer networks. Furthermore, power sources are also networked. Especially, by networking among power sources, powers necessary for social activities in-demand areas are secured. The proposed architecture is based on service-oriented architecture, meaning that optimization functions are executed as services. For flexibility, these services are executed by requests based on extensible mark-up language texts. The available optimizations are written in meta-data, which are accessible to end-users from the meta-data database system called clearinghouse. The meta-data are of two types, one for single optimization and the other for combined optimization. The processes in GGOD are conducted by the management function which interprets descriptions in meta-data. In meta-data, the names of optimization functions and activation orders are written. The basic executions follow sequential, branch, or loop flow processes, which execute combined optimizations, compare more than two kinds of optimization processes, and perform iterative simulations, respectively. As an application of the proposed architecture, the power generation sites and transmission networks are optimized in a geospatial integrated-resource planning scenario. In this application, a structure and a method for the combination of component functions in GGOD are exemplified. Moreover, GGOD suggests promotions of a lot of applications by effective combinations of basic optimization functions.
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Dissertations / Theses on the topic "Optimization of power management"

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Besnard, Francois. "On Optimal Maintenance Management for Wind Power Systems." Licentiate thesis, KTH, Electromagnetic Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11793.

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Sound maintenance strategies and planning are of crucial importance for wind power systems, and especially for offshore locations. In the last decades, an increased awareness of the impact of human living on the environment has emerged in the world. The importance of developing renewable energy is today highly recognized and energy policies have been adopted towards this development. Wind energy has been the strongest growing renewable source of energy this last decade. Wind power is now developing offshore where sites are available and benefits from strong and steady wind. However, the initial investments are larger than onshore, and operation and maintenance costs may be substantially higher due to transportation costs for maintenance and accessibility constrained by the weather.

Operational costs can be significantly reduced by optimizing decisions for maintenance strategies and maintenance planning. This is especially important for offshore wind power systems to reduce the high economic risks related to the uncertainties on the accessibility and reliability of wind turbines.

This thesis proposes decision models for cost efficient maintenance planning and maintenance strategies for wind power systems. One model is proposed on the maintenance planning of service maintenance activities. Two models investigate the benefits of condition based maintenance strategies for the drive train and for the blades of wind turbines, respectively. Moreover, a model is proposed to optimize the inspection interval for the blade. Maintenance strategies for small components are also presented with simple models for component redundancy and age replacement.

The models are tested in case studies and sensitivity analyses are performed for parameters of interests. The results show that maintenance costs can be significantly reduced through optimizing the maintenance strategies and the maintenance planning.

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Hilber, Patrik. "Maintenance optimization for power distribution systems." Doctoral thesis, Stockholm : Electrical Engineering, Elektrotekniska system, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4686.

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Wang, Baochao. "Intelligent control and power flow optimization of microgrid : energy management strategies." Thesis, Compiègne, 2013. http://www.theses.fr/2013COMP2122/document.

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La production intermittente et aléatoire des énergies renouvelables, sources photovoltaïques et éoliennes, est toujours un problème pour leur intégration massive dans le réseau public. L'une des solutions est de grouper des sources renouvelables, des sources traditionnelles, des dispositifs de stockage et des charges locales, et les traiter comme une seule unité dans le réseau public. Il s'agit du concept "micro-réseau". Un micro-réseau a des potentiels pour mieux répondre aux besoins de l'utilisateur final et du réseau public, et il facilite la mise en œuvre de futur smart grid, soit le réseau intelligent.Basé sur un micro-réseau représentatif en zone urbaine et intégré aux bâtiments, cette thèse propose une supervision multicouche, afin d'effectuer une étude systémique en mettant en exergue un verrou scientifique concernant l'implémentation d'une optimisation dans l'exploitation en temps réel.La supervision traite un ensemble d’opérations telles que : l'équilibré des puissances,l'optimisation des coûts énergétiques, utilisation de métadonnées, et échange d'informations avec le réseau intelligent et avec l'utilisateur final. Cette supervision a été validée par des tests expérimentaux. Malgré les incertitudes concernant les prévisions météorologiques, la faisabilité d'implémentation de l'optimisation dans l'exploitation réelle est vérifiée. La supervision proposée est en mesure de gérer efficacement les flux en assurant l'équilibre des puissances dans tous les cas. Néanmoins, la performance d'optimisation est liée aux précisions de prédiction. Ce problème peut être amélioré dans les travaux futurs par la mise à jour des résultats d'optimisation en temps réel
The intermittent and random production of renewable sources, such as photovoltaic and wind turbine, is always a problem for their large-scale integration in the utility grid. One of the solutions is to group renewable sources, traditional sources, storage and local consumption and treat it as a single unit in the utility grid. This is the concept of microgrid. A microgrid has the potentials of better responding both grid and end-user requirement, it facilitate the implementation of future smart grid. Based on a representative microgrid in urban area and integrated in buildings, this thesis proposes a multi-layer supervision, in order to realise a systemic study while particularly attempting to cover the research gap of implementing optimisation in realtimeoperation. The supervision handles together power balancing, energetic cost optimisation, metadata using, and information exchanges from both end-users and the smart grid. The supervision has been validated by experimental tests. The feasibility of implementing optimisation in real-time operation is validated even with uncertainties. The supervision is able to manage efficiently the power flow while maintaining power balancing in any case. Nevertheless, optimization effect relies on prediction precision. This problem can be improved in future works by updating optimization in real-time
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Loeffler, Benjamin Haile. "Modeling and optimization of a thermosiphon for passive thermal management systems." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45960.

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An optimally designed thermosiphon for power electronics cooling is developed. There exists a need for augmented grid assets to facilitate power routing and decrease line losses. Power converter augmented transformers (PCATs) are critically limited thermally. Conventional active cooling system pumps and fans will not meet the 30 year life and 99.9% reliability required for grid scale implementation. This approach seeks to develop a single-phase closed-loop thermosiphon to remove heat from power electronics at fluxes on the order of 10 - 15 W/cm2. The passive thermosiphon is inherently a coupled thermal-fluid system. A parametric model and multi-physics design optimization code will be constructed to simulate thermosiphon steady state performance. The model will utilize heat transfer and fluid dynamic correlations from literature. A particle swarm optimization technique will be implemented for its performance with discrete domain problems. Several thermosiphons will be constructed, instrumented, and tested to verify the model and reach an optimal design.
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Zhang, Yu. "Implementation of Reliability Centered Asset Management method on Power Systems." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-201717.

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Asset management is getting increasingly important in nearly all fields, especially inthe electric power engineering. It is mainly due to the following two reasons. First isthe high investment cost include the design cost, construction cost, equipment costand the high maintenance cost. Another reason is that there is always a high penaltyfee for the system operator if an interruption happened in the system. Besides, due tothe deregulation of electricity market in these years, the electricity utilities are payingmore attentions to the investment and maintenance cost. And one of their main goalsis to maximize the maintenance performance. So the challenge for the systems is toprovide high-reliability power to the customs and meanwhile be cost-effective for thesuppliers. Reliability Centered Asset Management (RCAM) is one of the bestmethods to solve this problem.The basic RCAM method is introduced first in this thesis. The model includes themaintenance strategy definition, the maintenance cost calculation and an optimizationmodel. Based on the basic model some improvements are added and a new model isproposed. The improvements include the new improvement maintenance strategy,increasing failure rate and a new objective function. The new model is also able toprovide a time-based maintenance plan.The simulation is done to a Swedish distribution system-Birka system by GAMS. Theresults and a sensitivity analysis is presented. A maintenance strategy for 58components and in 120 months is finally found. The impact on the changing failurerate is also shown for the whole peroid.
Kapitalförvaltning har inom alla områdem blivit allt viktigare, speciellt inomelkraftsteknik. Det beror i huvudsak av två orsaker. Den första är storinvesteringskostnad, vilket inkluderar design, konstruktion, utrustning och underhåll.Den andra är den höga straffavgiften för system operatören vid elavbrott. Dessutom,på grund av den nyligen avreglerade elmarknaden, så fäster elföretagen meruppmärksamhet på investerings och underhållskostnader. En av deras huvudmål är attmaximera underhållsprestandan. Så utmaningen för operatörerna är att levereratillförlitlig elkraft till kunder, samtidigt vara kostnadseffektiva mot leveratörer.Reliability Centered Asset Management (RCAM) är bland de bästa metoderna för attlösa detta problem. En enklare RCAM metod är introducerad först i denna rapport.Modellen inkluderar en underhållsstrategi-definition, underhållskostnad-kalkyl och enIIoptimiserings modell. Grundad på denna enklare modell, andra förbättringar ärtillagda och en ny modell är föreslagen. Förbättringarna inrymmer en nyunderhållsstrategi, ökad felfrekvens och en ny målfunktion. Den nya modellentillhandahåller också en tidsbaserad underhållsplan.
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Endo, Fumihiro, Masaki Kanamitsu, Ryo Shiomi, Hiroki Kojima, Naoki Hayakawa, and Hitoshi Okubo. "Optimization of Asset Management and Power System Operation Based on Equipment Performance." IEEE, 2008. http://hdl.handle.net/2237/11798.

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Rector, R. Blake. "Generalized Differential Calculus and Applications to Optimization." PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/3627.

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This thesis contains contributions in three areas: the theory of generalized calculus, numerical algorithms for operations research, and applications of optimization to problems in modern electric power systems. A geometric approach is used to advance the theory and tools used for studying generalized notions of derivatives for nonsmooth functions. These advances specifically pertain to methods for calculating subdifferentials and to expanding our understanding of a certain notion of derivative of set-valued maps, called the coderivative, in infinite dimensions. A strong understanding of the subdifferential is essential for numerical optimization algorithms, which are developed and applied to nonsmooth problems in operations research, including non-convex problems. Finally, an optimization framework is applied to solve a problem in electric power systems involving a smart solar inverter and battery storage system providing energy and ancillary services to the grid.
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Khargharia, Bithika. "Adaptive Power and Performance Management of Computing Systems." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/193653.

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With the rapid growth of servers and applications spurred by the Internet economy, power consumption in today's data centers is reaching unsustainable limits. This has led to an imminent financial, technical and environmental crisis that is impacting the society at large. Hence, it has become critically important that power consumption be efficiently managed in these computing power-houses of today. In this work, we revisit the issue of adaptive power and performance management of data center server platforms. Traditional data center servers are statically configured and always over-provisioned to be able to handle peak load. We transform these statically configured data center servers to clairvoyant entities that can sense changes in the workload and dynamically scale in capacity to adapt to the requirements of the workload. The over-provisioned server capacity is transitioned to low-power states and they remain in those states for as long as the performance remains within given acceptable thresholds. The platform power expenditure is minimized subject to performance constraints. This is formulated as a performance-per-watt optimization problem and solved using analytical power and performance models. Coarse-grained optimizations at the platform-level are refined by local optimizations at the devices-level namely - the processor & memory subsystems. Our adaptive interleaving technique for memory power management yielded about 48.8% (26.7 kJ) energy savings compared to traditional techniques measured at 4.5%. Our adaptive platform power and performance management technique demonstrated 56.25% energy savings for memory-intensive workload, 63.75% savings for processor-intensive workload and 47.5% savings for a mixed workload while maintaining platform performance within given acceptable thresholds.
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Kalsing, Arthur. "Power-Intent Management During RTL Optimizations." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT115.

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Répondre aux exigences de la conception de puces basse consommation constitue un véritable défi pour l’industrie des semi-conducteurs. Au cours de ces dernières années, de nouvelles méthodologies ont été développées pour aider les ingénieurs à traiter la complexité croissante des puces. L’une de ces méthodologies traite l'unification des descriptions dites power-intent dans la norme IEEE-1801 en définissant un langage standard structuré pour annoter le power-intent dans la description des circuits. Tout en permettant de nombreuses améliorations lors de la conception, la vérification et la mise en œuvre de circuits à faible consommation d'énergie, la norme amène également de nouveaux défis, en particulier son intégration dans les flots de conception existants. Nous présentons l'évolution d'un flot de conception traditionnel vers un flot de conception intégrant des stratégies pour la basse consommation et exploitant d'une synthèse à l’état de l’art. Dans cette thèse, nous soulignons les raisons et les choix qui ont donné forme au flot de conception actuel, et qui nous amènent aux défis rencontrés aujourd'hui.Cette thèse propose deux méthodologies intégrant la gestion du power-intent dans les flots existants pour faire face aux problèmes rencontrés dans l’industrie. Plus spécifiquement, il aborde le domaine de l'optimisation au niveau RTL au travers de cette thèse CIFRE (partenariat académique – industriel). Tout d'abord, nous présentons une méthodologie de vérification de la cohérence entre une description UPF (IEEE-1801) et les langages de description matérielle (HDL). Cette méthodologie a été mise en œuvre et validée par un outil, spécifiquement développé, qui nous a servi de preuve de concept. Enfin, nous avons étendu nos recherches en proposant une méthode automatisée préservant la cohérence du power-intent entre une description UPF et RTL lors de la modification du design. Nous avons présenté et modélisé les principes théoriques d’une optimisation RTL et ses effets de bord dans les descriptions de power-intent. Cette méthodologie est accompagnée d'une pléthore de cas d’usage décrivant les étapes pour préserver chaque spécification du power-intent
Meeting the requirements of low-power design is a real challenge in the semiconductor industry. In the past few years, new methodologies have been introduced to help engineers dealing with the growing complexity of chip design. One of such methodologies is the unification of power-intent descriptions into the IEEE-1801 standard, defining a structured standard language to annotate power-intent to a design. While enabling many improvements in low-power design, verification and implementation, the standard also introduces new challenges, in particular its integration into existing design flows. We present the evolution from a traditional design flow to a power-aware design flow, accompanied by a state-of-art low-power design synthesis. In this PhD work, we highlight the reasons and choices that shaped the current design flow, contributing to the challenges seen today.This thesis proposes two methodologies to cope with the issues commonly faced by the industry while integrating power-intent management into existing flows. More specifically, it addresses the field of RTL design optimizations, due to the industrial context of this CIFRE PhD (academical–industrial partnership). First, we present a tool agnostic methodology highly correlating UPF (IEEE-1801) and Hardware Description Languages (HDL) in order to track power-intent inconsistencies due to modifications in either of the descriptions. The consistency check methodology is validated by the implementation of a proof-of-concept tool. Finally, we extend the research by proposing an automation methodology preserving a consistent power-intent between UPF and RTL when modifying the design. We model and present the theoretical principles of RTL optimizations and their effects in the power-intent descriptions. This methodology is accompanied by a plethora of small, but comprehensive, use cases, depicting the steps to preserve each of the main power-intent specifications
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Nilsson, Julia. "On maintenance management of wind and nuclear power plants." Licentiate thesis, Stockholm : Skolan för elektro- och systemteknik, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11321.

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Books on the topic "Optimization of power management"

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Chris, Mi, and SpringerLink (Online service), eds. Vehicle Power Management: Modeling, Control and Optimization. London: Springer-Verlag London Limited, 2011.

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Valdma, Mati. Optimization of thermal power plants operation. Tallinn: TUT Press, 2009.

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Göttlich, Simone, Michael Herty, and Anja Milde, eds. Mathematical Modeling, Simulation and Optimization for Power Engineering and Management. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62732-4.

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International, Conference on Power Control and Optimization (2nd 2009 Bali Indonesia). Power control and optimization: Proceedings of the 2nd Global Conference on Power Control and Optimization, Bali, Indonesia 1-3 June 2009. Melville, N.Y: American Institute of Physics, 2009.

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International, Conference on Power Control and Optimization (3rd 2010 Gold Coast Qld ). Power control and optimization: Proceedings of the 3rd Global Conference on Power Control and Optimization, Gold Coast, Australia, 2-4 February 2010. Melville, N.Y: American Institute of Physics, 2010.

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Hassan, Mantawy Abdel-Aal, and SpringerLink (Online service), eds. Modern Optimization Techniques with Applications in Electric Power Systems. New York, NY: Springer Science+Business Media, LLC, 2012.

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A, Soliman S., ed. Optimal long-term operation of electric power systems. New York: Plenum Press, 1988.

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International Conference on Power Control and Optimization (2008 Chiang Mai, Thailand). International Conference on Power Control and Optimization: Innovation in power control for optimal industry : Chiang Mai, Thailand 18-20 July 2008. Edited by Barsoum Nader N, Uatrongjit Sermsak, and Vasant Pandian. Melville, N.Y: American Institute of Physics, 2008.

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International Conference on Power Control and Optimization (2008 Chiang Mai, Thailand). International Conference on Power Control and Optimization: Innovation in power control for optimal industry : Chiang Mai, Thailand 18-20 July 2008. Edited by Barsoum Nader N, Uatrongjit Sermsak, and Vasant Pandian. Melville, N.Y: American Institute of Physics, 2008.

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International Conference on Power Control and Optimization (2008 Chiang Mai, Thailand). International Conference on Power Control and Optimization: Innovation in power control for optimal industry : Chiang Mai, Thailand 18-20 July 2008. Edited by Barsoum Nader N, Uatrongjit Sermsak, and Vasant Pandian. Melville, N.Y: American Institute of Physics, 2008.

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Book chapters on the topic "Optimization of power management"

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Raghunathan, Anand, Niraj K. Jha, and Sujit Dey. "Power Management." In High-Level Power Analysis and Optimization, 81–114. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5433-2_4.

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Amaris, Hortensia, Monica Alonso, and Carlos Alvarez Ortega. "Reactive Power Optimization." In Reactive Power Management of Power Networks with Wind Generation, 55–76. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4667-4_4.

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Zhang, Xi, and Chris Mi. "HEV Component Design and Optimization for Fuel Economy." In Vehicle Power Management, 287–301. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-736-5_9.

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Kim, Younghyun, and Naehyuck Chang. "Joint Optimization with Power Sources." In Design and Management of Energy-Efficient Hybrid Electrical Energy Storage Systems, 55–84. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07281-4_5.

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Benmessaoud, Mohammed Tarik, A. Boudghene Stambouli, Pandian Vasant, S. Flazi, H. Koinuma, and M. Tioursi. "New Smart Power Management Hybrid System Photovoltaic-Fuel Cell." In Intelligent Computing & Optimization, 476–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00979-3_50.

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Fiorenzani, Stefano. "Financial Optimization of Power Generation Activity." In Quantitative Methods for Electricity Trading and Risk Management, 111–26. London: Palgrave Macmillan UK, 2006. http://dx.doi.org/10.1057/9780230598348_10.

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P.-Vaisband, Inna, Renatas Jakushokas, Mikhail Popovich, Andrey V. Mezhiba, Selçuk Köse, and Eby G. Friedman. "Power Optimization Based on Link Breaking Methodology." In On-Chip Power Delivery and Management, 413–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29395-0_25.

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Bizon, Nicu. "Optimization Algorithms and Energy Management Strategies." In Optimization of the Fuel Cell Renewable Hybrid Power Systems, 57–105. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40241-9_3.

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Helen Catherine, Rathinadurai Louis, A. Soundarrajan, and Josephine Rathinadurai Louis. "Cost Optimization of a Ring Frame Unit." In Advances in Power Systems and Energy Management, 107–17. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4394-9_11.

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Hirata, Kenji. "Real-Time Pricing and Decentralized Optimization Strategy for Power Flow Balancing in EV/PHV Storage Management." In Power Electronics and Power Systems, 87–106. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33672-1_5.

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Conference papers on the topic "Optimization of power management"

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Macko, Dominik, Katarina Jelemenska, and Pavel Cicak. "Power-efficient power-management logic." In 2014 24th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS). IEEE, 2014. http://dx.doi.org/10.1109/patmos.2014.6951881.

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Husagic-Selman, Alma, and Tarik Namas. "Power distribution management system software deployment." In 2016 International Conference Multidisciplinary Engineering Design Optimization (MEDO). IEEE, 2016. http://dx.doi.org/10.1109/medo.2016.7746548.

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Paleologo, G. A., L. Benini, A. Bogliolo, and G. De Micheli. "Policy optimization for dynamic power management." In the 35th annual conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/277044.277094.

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Moon, Ayaz Hassan, Junaid Ahmed, and G. Mohiuddin Bhat. "CPLD based power management system for low power devices." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755380.

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Siddiqui, Anwar Shahzad, Md Sarwar, and Shahzad Ahsan. "Congestion management using improved inertia weight particle swarm optimization." In 2014 6th IEEE Power India International Conference (PIICON). IEEE, 2014. http://dx.doi.org/10.1109/poweri.2014.7117641.

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Bodden, David, Bob Eller, and Scott Clements. "Integrated Electrical and Thermal Management Sub-system Optimization." In Power Systems Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2010. http://dx.doi.org/10.4271/2010-01-1812.

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Vasant, Pandian, Nader Barsoum, Abdul Halim Hakim, Pandian Vasant, and Nader Barsoum. "HYBRID SIMULATED ANNEALING AND GENETIC ALGORITHMS FOR INDUSTRIAL PRODUCTION MANAGEMENT PROBLEMS." In POWER CONTROL AND OPTIMIZATION: Proceedings of the Second Global Conference on Power Control and Optimization. AIP, 2009. http://dx.doi.org/10.1063/1.3223938.

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Zhi xu Chen, Li zi Zhang, and Jun Shu. "Congestion management based on particle swarm optimization." In 2005 International Power Engineering Conference. IEEE, 2005. http://dx.doi.org/10.1109/ipec.2005.207057.

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Mohamed, Faisal A., and Heikki N. Koivo. "MicroGrid Online Management and Balancing Using Multiobjective Optimization." In 2007 IEEE Power Tech. IEEE, 2007. http://dx.doi.org/10.1109/pct.2007.4538391.

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Zolfaghari, Ahmad, Hamid Minuchehr, Ali Noroozy, Peymaan Makarachi, and F. Koshahval. "PWR Nuclear Power Plants Fuel Management Optimization." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75924.

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The objective of this paper is to develop a new hybrid mutation in genetic algorithm (GA) for designing the loading pattern (LP) in pressurized water reactors. Because of huge number of possible combinations for the fuel assemblies (FA’s) loading in a core, finding the optimum solution is truly a complex problem. In common genetic algorithm the mutation and crossover techniques are used to optimize an objective function but in this paper a new hybrid mutation is presented. In this study flattening of power inside a reactor core is chosen as an objective function. To obtain optimal FA arrangement, a core reload package code, MAKNOGA, based on well established MAKGA code is developed. This code is applicable for all types of PWR core having different geometries and designs with an unlimited number of FA types. The result is well improved in comparison with pattern proposed by designer.
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Reports on the topic "Optimization of power management"

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Howard, Heidi, Chad Helmle, Raina Dwivedi, and Daniel Gambill. Stormwater Management and Optimization Toolbox. Engineer Research and Development Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/39480.

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As stormwater regulations for hydrologic and water quality control become increasingly stringent, Department of Defense (DoD) facilities are faced with the daunting task of complying with multiple laws and regulations. This often requires facilities to plan, design, and implement structural best management practices (BMPs) to capture, filter, and/or infiltrate runoff—requirements that can be complicated, contradictory, and difficult to plan. This project demonstrated the Stormwater Management Optimization Toolbox (SMOT), a spreadsheet-based tool that effectively analyzes and plans for compliance to the Energy Independence and Security Act (EISA) of 2007 pre-hydrologic conditions through BMP implementation, resulting in potential cost savings by reducing BMP sizes while simultaneously achieving compliance with multiple objectives. SMOT identifies the most cost-effective modeling method based on an installation’s local conditions (soils, rainfall patterns, drainage network, and regulatory requirements). The work first demonstrated that the Model Selection Tool (MST) recommendation accurately results in the minimum BMP cost for 45 facilities of widely varying climatic and regional conditions, and then demonstrated SMOT at two facilities.
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Jackson, L. T. High performance magnet power supply optimization. Office of Scientific and Technical Information (OSTI), January 1988. http://dx.doi.org/10.2172/6841772.

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Yi, Qing. Power-Aware Datacenter Networking and Optimization. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5358.

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Arora, Jasbir S. Database Design and Management in Engineering Optimization. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada193325.

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Lesieutre, Bernard C., and Daniel K. Molzahn. Optimization and Control of Electric Power Systems. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1159823.

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Sanders, David. Land Warrior Power Management. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada414902.

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Backlund, Peter, Darryl J. Melander, Adam J. Pierson, John Andrew Flory, Alexander Dessanti, Stephen Michael Henry, and John H. Gauthier. Schedule Management Optimization (SMO) Domain Model: Version 1.2. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1467776.

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Detamore, J. A., M. H. Raudenbush, R. W. Wolaver, and G. A. Hastings. TRU Waste Management Program. Cost/schedule optimization analysis. Office of Scientific and Technical Information (OSTI), October 1985. http://dx.doi.org/10.2172/5471332.

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Detamore, J. A., M. H. Raudenbush, R. W. Wolaver, and G. A. Hastings. TRU Waste Management Program cost/schedule optimization analysis. Office of Scientific and Technical Information (OSTI), October 1985. http://dx.doi.org/10.2172/5142784.

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Bent, Russell W., Michael Chertkov, and Scott Backhaus. Optimization and control theory for smart (power) grids. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1092469.

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