102 research outputs found

    Binary Search Algorithm for Mixed Integer Optimization: Application to energy management in a microgrid

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    This paper presents a binary search algorithm to deal with binary variables in mixed integer optimization problems. One example of this kind of problem is the optimal operation of hydrogen storage and energy sale and purchase into a microgrids context. In this work was studied a system composed by a microgrid that has a connection with the external electrical network and a charging station for electric cars. The system modeling was carried out by the Energy Hubs methodology. The proposed algorithm transforms the MIQP (Mixed Integer Quadratic Program) problem into a QP (Quadratic Program) that is easier to solve. In this way the overall control task is carried out the electricity purchase and sale to the power grid, maximizes the use of renewable energy sources, manages the use of energy storages and supplies the charge of the parked vehicles.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RUniversidad de Sevilla CNPq401126/2014-5Universidad de Sevilla CNPq303702/2011-

    Model predictive control of interconnected microgrids and electric vehicles

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    [ES] La microrred como elemento agregador de fuentes de generación, cargas y sistemas de almacenamiento de energía aparece como tecnología clave para dotar a los sistemas eléctricos de suficiente flexibilidad para una transición energética basada en fuentes renovables. Sin embargo, el problema de control para la gestión de energía se vuelve complejo cuando se incrementa el número de sistemas conectados a una misma microrred. De igual forma, se requiere flexibilidad para integrar a los vehículos eléctricos. La interacción entre las distintas microrredes y los vehículos hacen necesarias herramientas avanzadas de control para resolver el problema de optimización. El objeto del presente trabajo es presentar distintas herramientas de control predictivo basado en el modelo (Model Predictive Control, MPC) para resolver el problema de control asociado a este tipo de sistemas. En concreto, se abordan dos problemas: la conexión de vehículos eléctricos a la microrred y la interconexión de varias microrredes. Para el primer caso se analizan dos escenarios, según que el intercambio de energía sea uni o bidireccional y se presenta la forma de optimizar la operación usando MPC. En el segundo caso se aborda el problema usando técnicas de control distribuido.[EN] Microgrids, as aggregators of sources, loads and energy storage systems, appear as key technology to provide the required flexibility to electric power systems to carry out an energy transition based on renewable sources. Nevertheless, the control problem becomes complex when the number of connected components to the same microgrid increases. Also, the system requires flexibility to integrate electric vehicles. The complexity given by the associated control problem to optimize the energy exchange between microgrids and the electric vehicles makes necessary the development of advanced control tools. In this work, dierent Model Predictive Control (MPC) strategies are introduced in order to face the challenge of the control problem formulation of this kind of systems. Specifically, two problems are addressed: the connection of electric vehicles to the microgrid and the interconnection of several microgrids. For the first case, two scenarios are analyzed, depending on whether the energy exchange is uni or bidirectional, the way to optimize the operation using MPC is presented and examples of use are shown. For the second case, the problem isaddressed using distributed control techniques.Este trabajo ha sido realizado parcialmente gracias al apoyo del Ministerio de Econom´ıa, Industria y Competitividad de Espana mediante el proyecto CONFIGURA (DPI2016-78338-R) y por la Comision Europea, en el proyecto AGERAR (0076- ´ AGERAR-6-E), dentro del programa Interreg Spain-Portugal (POCTEP).Bordons, C.; Garcia-Torres, F.; Ridao, M. (2020). Control predictivo en microrredes interconectadas y con vehículos eléctricos. Revista Iberoamericana de Automática e Informática industrial. 17(3). https://doi.org/10.4995/riai.2020.13304OJS253173Alvarado, I., Limon, D., de la Peña, D. M., Maestre, J., Ridao, M., Scheu, H., Marquardt, W., Negenborn, R., Schutter, B. D., Valencia, F., Espinosa, J., 2011. A comparative analysis of distributed mpc techniques applied to the hd-mpc four-tank benchmark. Journal of Process Control 21 (5), 800 - 815, special Issue on Hierarchical and Distributed Model Predictive Control. https://doi.org/10.1016/j.jprocont.2011.03.003Bashash, S., Fathy, H., 2011. Robust demand-side plug-in electric vehicle load control for renewable energy management. American Control Conference (ACC), 929-934. https://doi.org/10.1109/ACC.2011.5990856Bazmohammadi, N., Tahsiri, A., Anvari-Moghaddam, A., Guerrero, J. M., 2019. A hierarchical energy management strategy for interconnected microgrids considering uncertainty. International Journal of Electrical Power & Energy Systems 109, 597-608. https://doi.org/10.1016/j.ijepes.2019.02.033Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., 2016. Diagnosis and Fault-Tolerant Control. Springer. https://doi.org/10.1007/978-3-662-47943-8Bordons, C., Garcia-Torres, F., Ridao, M. A., 2020. Model Predictive Control of Microgrids. Springer, Londres. https://doi.org/10.1007/978-3-030-24570-2Bordons, C., Garcia-Torres, F., Valverde, L., 2015. Optimal energy management for renewable energy microgrids. Revista Iberoamericana de Automatica e Informatica industrial 12 (2), 117-132. https://doi.org/10.1016/j.riai.2015.03.001Bouzid, A., Guerrero, J. M., Cheriti, A., Bouhamida, M., Sicard, P., Benghanem, M., 2015. A survey on control of electric power distributed generation systems for microgrid applications. Renewable and Sustainable Energy Reviews 44, 751 - 766. https://doi.org/10.1016/j.rser.2015.01.016Camponogara, E., Jia, D., Krogh, B. H., Talukdar, S., 2002. Distributed model predictive control. IEEE Control Systems 22 (1), 44-52. https://doi.org/10.1109/37.980246Cheng, P., Shi, L., Sinopoli, B., 2017. Special issue on secure control of cyberphysical systems. IEEE Trans on Control of Network Systems 4 (1). https://doi.org/10.1109/TCNS.2017.2667233Colson, C. M., Nehrir, M. H., 2013. Comprehensive real-time microgrid power management and control with distributed agents. IEEE Transactions on Smart Grid 4 (1), 617-627. https://doi.org/10.1109/TSG.2012.2236368Deilami, S., Masoum, A. S., Moses, P. S., Masoum, M., 2011. Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Trans. on Smart Grid 2 (3), 456-467. https://doi.org/10.1109/TSG.2011.2159816Duarte-Mermoud, M. A., Milla, F., 2018. Estabilizador de sistemas de potencia usando control predictivo basado en modelo. Revista Iberoamericana de Automática e Informática industrial 15 (3), 286-296. https://doi.org/10.4995/riai.2018.10056Fathi, M., Bevrani, H., 2013. Statistical cooperative power dispatching in interconnected microgrids. IEEE Transactions on Sustainable Energy 4 (3), 586-593. https://doi.org/10.1109/TSTE.2012.2232945Galus, M. D., Andersson, G., Art, S., 2012. A hierarchical, distributed pev charging control in low voltage distribution grids to ensure network security. Power and Energy Society General Meeting, 2012 IEEE, 1-8. https://doi.org/10.1109/PESGM.2012.6345024Garcia-Torres, F., Vilaplana, D. G., Bordons, C., Roncero-Sanchez, P., Ridao, M. A., 2018. Optimal management of microgrids with external agents including battery/fuel cell electric vehicles. IEEE Transactions on Smart Grid, 1-1.Gautschi, M., Scheuss, O., Schluchter, C., 2009. Simulation of an agent based vehicle-to-grid (v2g) implementation. Electric Power Systems Research 120, 177 - 183.Giorgio, A. D., Liberati, F., Canale, S., 2014. Electric vehicle charging control in smartgrids: A model predictive control approach. Control Engineering Practice 22, 147-162. https://doi.org/10.1016/j.conengprac.2013.10.005Guerrero, J. M., Chandorkar, M., Lee, T.-L., Loh, P. C., 2012a. Advanced control architectures for intelligent microgrids?part i: Decentralized and hierarchical control. IEEE Transactions on Industrial Electronics 60 (4), 1254-1262. https://doi.org/10.1109/TIE.2012.2194969Guerrero, J. M., Loh, P. C., Lee, T.-L., Chandorkar, M., 2012b. Advanced control architectures for intelligent microgrids?part ii: Power quality, energy storage, and ac/dc microgrids. IEEE Transactions on Industrial Electronics 60 (4), 1263-1270. https://doi.org/10.1109/TIE.2012.2196889Hu, J., You, S., Lind, M., Østergaard, J., 2014. Coordinated charging of electric vehicles for congestion prevention in the distribution grid. IEEE Transactions on Smart Grid 5 (2), 703-711. https://doi.org/10.1109/TSG.2013.2279007Issermann, R., 2006. Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Springer.Ito, A., Kawashima, A., Suzuki, T., Inagaki, S., Yamaguchi, T., Zhou, Z., 2018. Model predictive charging control of in-vehicle batteries for home energy management based on vehicle state prediction. IEEE Transactions on Control Systems Technology 26 (1), 51-64. https://doi.org/10.1109/TCST.2017.2664727Lasseter, R. H., 2002. Microgrids. IEEE Power Engineering Society Winter Meeting 1, 305-308.Lasseter, R. H., 2011. Smart distribution: Coupled microgrids. Proceedings of the IEEE 99 (6), 1074-1082. https://doi.org/10.1109/JPROC.2011.2114630Maestre, J. M., Negenborn, R. R., 2014. Distributed Model Predictive Control Made Easy. Springer-Verlag, London. https://doi.org/10.1007/978-94-007-7006-5Mendes, P., Valverde, L., Bordons, C., Normey-Rico, J., 2016. Energy management of an experimental microgrid coupled to a v2g system. Journal of Power Sources 327, 702 - 713. https://doi.org/10.1016/j.jpowsour.2016.07.076Mesbah, A., Dec 2016. Stochastic model predictive control: An overview and perspectives for future research. IEEE Control Systems Magazine 36 (6), 30-44. https://doi.org/10.1109/MCS.2016.2602087Mohsenian-Rad, H., et al., 2015. Optimal charging of electric vehicles with uncertain departure times: A closed-form solution. IEEE Transactions on Smart Grid 6 (2), 940-942. https://doi.org/10.1109/TSG.2014.2367242Mou, Y., Xing, H., Lin, Z., Fu, M., 2015. Decentralized optimal demand-side management for phev charging in a smart grid. IEEE Transactions on Smart Grid 6 (2), 726-736. https://doi.org/10.1109/TSG.2014.2363096Mwasilu, F., Justo, J. J., Kim, E.-K., Do, T. D., Jung, J.-W., 2014. Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renewable and sustainable energy reviews 34, 501-516. https://doi.org/10.1016/j.rser.2014.03.031N. Nikmehr and S. N. Ravadanegh, 2016. Reliability evaluation of multimicrogrids considering optimal operation of small scale energy zones under load-generation uncertainties. International Journal of Electrical Power & Energy Systems 78, 80-87. https://doi.org/10.1016/j.ijepes.2015.11.094Negenborn, R. R., Houwing, M., Schutter, B. D., Hellendoorn, J., 2009. Model predictive control for residential energy resources using a mixed-logical dynamic model. International Conference on Networking, Sensing and Control, 702-707. https://doi.org/10.1109/ICNSC.2009.4919363Nunna, H. K., Doolla, S., 2012. Multiagent-based distributed-energy-resource management for intelligent microgrids. IEEE Transactions on IndustrialElectronics 60 (4), 1678-1687. https://doi.org/10.1109/TIE.2012.2193857Ouammi, A., Dagdougui, H., Sacile, R., 2015. Optimal control of power flows and energy local storages in a network of microgrids modeled as a system of systems. IEEE Transactions on Control Systems Technology 23 (1), 128-138. https://doi.org/10.1109/TCST.2014.2314474Pahasa, J., Ngamroo, I., 2015. Phevs bidirectional charging/discharging and soc control for microgrid frequency stabilization using multiple mpc. IEEE Transactions on Smart Grid 6 (2), 526-533. https://doi.org/10.1109/TSG.2014.2372038Parisio, A., Rikos, E., Glielmo, L., 2014. A model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology 22 (5), 1813-1827. https://doi.org/10.1109/TCST.2013.2295737Parisio, A., Rikos, E., Glielmo, L., 2016. Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study. Journal of Process Control 43, 24 - 37. https://doi.org/10.1016/j.jprocont.2016.04.008Reddy, S. S., Sandeep, V., Jung, C., Jun 2017. 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Distributed Model Predictive Control of Large-Scale Systems Assessment and Future Directions. Springer-Verlag, Berlin.Wang, G., Zhao, J., Wen, F., Xue, Y., Ledwich, G., 2015. Dispatch strategy of phevs to mitigate selected patterns of seasonally varying outputs from renewable generation. IEEE Transactions on Smart Grid 6 (2), 627-639. https://doi.org/10.1109/TSG.2014.2364235Wu, J., Guan, X., 2013. Coordinated multi-microgrids optimal control algorithm for smart distribution management system. IEEE Transactions on Smart Grid 4 (4), 2174-2181. https://doi.org/10.1109/TSG.2013.2269481Xu, J., Zou, Y., Niu, Y., 2013. Distributed predictive control for energy management of multi-microgrids systems. In: 13th IFAC Symposium on Large Scale Complex Systems: Theory ans Applications, Shanghai, China. pp. 551-556. https://doi.org/10.3182/20130708-3-CN-2036.00090Yazdanian, M., Mehrizi-Sani, A., 2014. Distributed control techniques in microgrids. IEEE Transactions on Smart Grid 5 (6), 2901-2909. https://doi.org/10.1109/TSG.2014.233783

    Dual-active bridge series resonant electric vehicle charger: A self-tuning method

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    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents a new self-tuning loop for a bidirectional dual-active bridge (DAB) series resonant converter (SRC). For different loading conditions, the two active bridges can be controlled with a minimum time displacement between them to assure zero voltage switching (ZVS) and minimum circulation current conditions. The tuning loop can instantly reverse the power direction with a fast dynamics. Moreover, the tuning loop is not sensitive to series resonant tank tolerances and deviations, which makes it a robust solution for power tuning of the SRCs. For simplicity, the power is controlled based on the power-frequency control method with a fixed time displacement between the active bridges. The main design criteria of the bidirectional SRC are the time displacement, operating frequency bandwidth, and the minimum and maximum power, which are simply derived and formulated based on the self-tuning loop’s parameters. Based on the parameters of the tuning loop, a simplified power equation and power control method is proposed for DAB-SRCs. The proposed control method is simulated in static and dynamic conditions for different loadings. The analysis and simulation results show the effectiveness of the new tuning method

    Towards Open and Equitable Access to Research and Knowledge for Development

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    Leslie Chan and colleagues discuss the value of open access not just for access to health information, but also for transforming structural inequity in current academic reward systems and for valuing scholarship from the South

    Productivity trends and collaboration patterns: A diachronic study in the eating disorders field

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    [EN] Objective The present study seeks to extend previous bibliometric studies on eating disorders (EDs) by including a time-dependent analysis of the growth and evolution of multi-author collaborations and their correlation with ED publication trends from 1980 to 2014 (35 years). Methods Using standardized practices, we searched Web of Science (WoS) Core Collection (WoSCC) (indexes: Science Citation Index-Expanded [SCIE], & Social Science Citation Index [SSCI]) and Scopus (areas: Health Sciences, Life Sciences, & Social Sciences and Humanities) to identify a large sample of articles related to EDs. We then submitted our sample of articles to bibliometric and graph theory analyses to identify co-authorship and social network patterns. Results We present a large number of detailed findings, including a clear pattern of scientific growth measured as number of publications per five-year period or quinquennium (Q), a tremendous increase in the number of authors attracted by the ED subject, and a very high and steady growth in collaborative work. Conclusions We inferred that the noted publication growth was likely driven by the noted increase in the number of new authors per Q. Social network analyses suggested that collaborations within ED follow patters of interaction that are similar to well established and recognized disciplines, as indicated by the presence of a ¿giant cluster¿, high cluster density, and the replication of the ¿small world¿ phenomenon¿the principle that we are all linked by short chains of acquaintances.This work was performed with a subsidy from Universidad Catolica de Valencia "San Vicente Martir" to resarch group INDOTEI: Evaluacion de la Ciencia, for the years 2016-2017. This work is benefited from Spanish Government assistance through Government Delegation for the National Drugs Plan of the Ministry of Health, Social Services and Equality (project 2016/028); and National R+D+I (projects: CS02012-39632-C02-01 and CS02015-65594-C2-2-R) and 2015-Networks of Excellence Call (project CS02015-71867-REDT) of the Ministry of Economy and Competitiveness.Valderrama Zurian, JC.; Aguilar-Moya, R.; Cepeda-Benito, A.; Melero-Fuentes, D.; Navarro-Moreno, MÁ.; Gandía-Balaguer, A.; Aleixandre-Benavent, R. (2017). Productivity trends and collaboration patterns: A diachronic study in the eating disorders field. PLoS ONE. 12(8):1-17. https://doi.org/10.1371/journal.pone.0182760S117128McClelland, J., Bozhilova, N., Campbell, I., & Schmidt, U. (2013). A Systematic Review of the Effects of Neuromodulation on Eating and Body Weight: Evidence from Human and Animal Studies. 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Revista española de Documentación Científica, 34(3), 301-333. doi:10.3989/redc.2011.3.804Valderrama-Zurián, J.-C., Aguilar-Moya, R., Melero-Fuentes, D., & Aleixandre-Benavent, R. (2015). A systematic analysis of duplicate records in Scopus. Journal of Informetrics, 9(3), 570-576. doi:10.1016/j.joi.2015.05.002Guardiola-Wanden-Berghe, R., Sanz-Valero, J., & Wanden-Berghe, C. (2012). Medical subject headings versus American Psychological Association Index Terms: indexing eating disorders. Scientometrics, 94(1), 305-311. doi:10.1007/s11192-012-0866-7Soh, N., Walter, G., Touyz, S., Russell, J., Malhi, G. S., & Hunt, G. E. (2012). Food for thought: Comparison of citations received from articles appearing in specialized eating disorder journals versus general psychiatry journals. International Journal of Eating Disorders, 45(8), 990-994. doi:10.1002/eat.22036Theander, S. S. (2004). 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    Understanding Crowd-Powered Search Groups: A Social Network Perspective

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    Background: Crowd-powered search is a new form of search and problem solving scheme that involves collaboration among a potentially large number of voluntary Web users. Human flesh search (HFS), a particular form of crowd-powered search originated in China, has seen tremendous growth since its inception in 2001. HFS presents a valuable test-bed for scientists to validate existing and new theories in social computing, sociology, behavioral sciences, and so forth. Methodology: In this research, we construct an aggregated HFS group, consisting of the participants and their relationships in a comprehensive set of identified HFS episodes. We study the topological properties and the evolution of the aggregated network and different sub-groups in the network. We also identify the key HFS participants according to a variety of measures. Conclusions: We found that, as compared with other online social networks, HFS participant network shares the power-law degree distribution and small-world property, but with a looser and more distributed organizational structure, leading to the diversity, decentralization, and independence of HFS participants. In addition, the HFS group has been becoming increasingly decentralized. The comparisons of different HFS sub-groups reveal that HFS participants collaborated more often when they conducted the searches in local platforms or the searches requiring a certain level of professional knowledge background. On the contrary, HFS participants did not collaborate much when they performed the search tas

    Towards a consolidation of worldwide journal rankings - A classification using random forests and aggregate rating via data envelopment analysis

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    AbstractThe question of how to assess research outputs published in journals is now a global concern for academics. Numerous journal ratings and rankings exist, some featuring perceptual and peer-review-based journal ranks, some focusing on objective information related to citations, some using a combination of the two. This research consolidates existing journal rankings into an up-to-date and comprehensive list. Existing approaches to determining journal rankings are significantly advanced with the application of a new classification approach, ‘random forests’, and data envelopment analysis. As a result, a fresh look at a publication׳s place in the global research community is offered. While our approach is applicable to all management and business journals, we specifically exemplify the relative position of ‘operations research, management science, production and operations management’ journals within the broader management field, as well as within their own subject domain
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