Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica
Ilustraciones
- Autores:
-
Amador Soto, Gerardo José
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86405
- Palabra clave:
- 620 - Ingeniería y operaciones afines::621 - Física aplicada
Consumo de energía
Eficiencia energética
Sistema dinámico
Eficiencia energética
Sistema energético multidominio
Modelado unificado basado en energía
Control basado en comportamiento
Enfoque comportamental para sistemas abiertos e interconectados
Aprendizaje evolutivo de trayectorias
Energy efficiency
Multidomain energy system
Energy-based unified modeling
Behavior-based control
Bbehavioral approach for open and interconnected systems
Evolutionary learning of trajectorie
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| id |
UNACIONAL2_1a415319b919abb9b8e8b32e87bb49ff |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/86405 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica |
| dc.title.translated.eng.fl_str_mv |
Intensification of energy efficiency for a multidomain energy system through direct intervention in its dynamics |
| title |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica |
| spellingShingle |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica 620 - Ingeniería y operaciones afines::621 - Física aplicada Consumo de energía Eficiencia energética Sistema dinámico Eficiencia energética Sistema energético multidominio Modelado unificado basado en energía Control basado en comportamiento Enfoque comportamental para sistemas abiertos e interconectados Aprendizaje evolutivo de trayectorias Energy efficiency Multidomain energy system Energy-based unified modeling Behavior-based control Bbehavioral approach for open and interconnected systems Evolutionary learning of trajectorie |
| title_short |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica |
| title_full |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica |
| title_fullStr |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica |
| title_full_unstemmed |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica |
| title_sort |
Intensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámica |
| dc.creator.fl_str_mv |
Amador Soto, Gerardo José |
| dc.contributor.advisor.none.fl_str_mv |
Hernández Riveros, Jesús Antonio |
| dc.contributor.author.none.fl_str_mv |
Amador Soto, Gerardo José |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Investigación en Inteligencia Computacional |
| dc.contributor.orcid.spa.fl_str_mv |
Amador Soto, Gerardo Jose [0009000197374812] |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::621 - Física aplicada |
| topic |
620 - Ingeniería y operaciones afines::621 - Física aplicada Consumo de energía Eficiencia energética Sistema dinámico Eficiencia energética Sistema energético multidominio Modelado unificado basado en energía Control basado en comportamiento Enfoque comportamental para sistemas abiertos e interconectados Aprendizaje evolutivo de trayectorias Energy efficiency Multidomain energy system Energy-based unified modeling Behavior-based control Bbehavioral approach for open and interconnected systems Evolutionary learning of trajectorie |
| dc.subject.lemb.none.fl_str_mv |
Consumo de energía Eficiencia energética Sistema dinámico |
| dc.subject.proposal.spa.fl_str_mv |
Eficiencia energética Sistema energético multidominio Modelado unificado basado en energía Control basado en comportamiento Enfoque comportamental para sistemas abiertos e interconectados Aprendizaje evolutivo de trayectorias |
| dc.subject.proposal.eng.fl_str_mv |
Energy efficiency Multidomain energy system Energy-based unified modeling Behavior-based control Bbehavioral approach for open and interconnected systems Evolutionary learning of trajectorie |
| description |
Ilustraciones |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-05T16:15:58Z |
| dc.date.available.none.fl_str_mv |
2024-07-05T16:15:58Z |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
| format |
http://purl.org/coar/resource_type/c_db06 |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/86405 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/86405 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.indexed.spa.fl_str_mv |
LaReferencia |
| dc.relation.references.spa.fl_str_mv |
Ajitha, A. R., & Radhika, S. (2023). A Comprehensive Review of Demand Response Strategies and Role of Emergent Technologies for a Sustainable Home Energy Management Systems. International Journal of Ambient Energy, 1–55. https://doi.org/10.1080/01430750.2023.2233522 Andrei Morch, Marialaura Di Somma, Christina Papadimitriou, Hanne Sæle, Valeria Palladino, Jesús Fraile Ardanuy, Giuseppe Conti, Mosè Rossi, & Gabriele Comodi. (2023). Technologies enabling evolution of Integrated Local Energy Communities. https://doi.org/10.36227/techrxiv.22133294 Arronategui, U., Bañares, J. Á., & Colom, J. M. (2023). A Framework to Support Decision-Making Based on AI and Simulation of Large-Scale Models. Lecture Notes in Computer Science, 148–152. https://doi.org/10.1007/978-3-031-29315-3_14 Åström, K. J., Albertos, P., Blanke, M., Isidori, A., Schaufelberger, W., & Sanz, R. (2012). Control of Complex Systems. https://doi.org/10.1007/978-1-4471-0349-3 Ayres, R. U. (2016). Energy and Technology. 231–253. https://doi.org/10.1007/978-3-319-30545-5_8 Azieva, R. K. (2023). Impact of energy efficiency on activities of enterprise. 2023(1), 28–35. https://doi.org/10.24143/2073-5537-2023-1-28-35 Barbeito, I., Zaragoza, S., Tarrío-Saavedra, J., & Naya, S. (2017). Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data. Applied Energy, 190, 1–17. https://doi.org/https://doi.org/10.1016/j.apenergy.2016.12.100 Batlle, C., & Doria-Cerezo, A. (2006). Energy-based modelling and simulation of the interconnection of a back-to-back converter and a doubly-fed induction machine. 2006 American Control Conference, 6 pp. http://doi.org/10.1109/ACC.2006.1656489 Bejarano, G. (2017). Optimization and multivariable control of refrigeration systems. Universidad de Sevilla. Bjørn H. Samset. (2016). Energy and Climate: Vision for the Future. Blenk, T., & Weindl, C. (2023). Fundamentals of State-Space Based Load Flow Calculation of Modern Energy Systems †. Energies, 16(13). https://doi.org/10.3390/en16134872 Borutzky, W. (2011). Bond Graph Modelling of Engineering Systems: Theory, Applications and Software Support. Borutzky, W. (2016). Bond graphs for modelling, control and fault diagnosis of engineering systems. In Bond Graphs for Modelling, Control and Fault Diagnosis of Engineering Systems, Second Edition. https://doi.org/10.1007/978-3-319-47434-2 Broenink, J. F. (1999). Introduction to physical systems modelling with bond graphs. http://www.menet.umn.edu/~lixxx099/ME8287_S11/BondGraphsV2.pdf Cavallaro, A., Gennaro, F. Di, Euzenat, J., Peters-Anders, J., & Osello, A. (2014). Vision of energy systems for smart cities. 35. https://hal.archives-ouvertes.fr/hal-01180924/document Chaudhary, P. K., & Dubey, D. S. P. (2016). Efficiency Optimization of Induction Motor Drive in Steady- State using Artificial Neural Network. International Conference on Computation of Power, 2, 295–302. http://doi.org/10.1109/ICCPEIC.2016.7557246 Cheng, Q., Goh, B. W., & Kim, J. B. (2018). Internal Control and Operational Efficiency. Contemporary Accounting Research, 35(2), 1102–1139. https://doi.org/10.1111/1911-3846.12409 Choudhary, P. K., & Choudhary, U. K. (2017). Efficiency optimization of pump motor drive at steady-state condition. 2017 {IEEE} {International} {Conference} on {Power}, {Control}, {Signals} and {Instrumentation} {Engineering} ({ICPCSI}), 342–347. https://doi.org/10.1109/ICPCSI.2017.8392312 Chuang, H.-C., Chan, C.-C., Weng, W.-B., & Lee, C.-T. (2018). Energy saving technique for natural stone cutting machine by constant speed control in stone industry. Journal of the Chinese Institute of Engineers, 41(1), 61–68. https://doi.org/10.1080/02533839.2017.1410450 Cicchella, A. (2023). Human Power Production and Energy Harvesting. Encyclopedia, 3(2), 698–704. https://doi.org/10.3390/encyclopedia3020050 Cichy, M., Kropiwnicki, J., & Kneba, Z. (2015). A Model of Thermal Energy Storage According to the Convention of Bond Graphs (Bg) and State Equations (Se). Polish Maritime Research, 22(4), 41–47. https://doi.org/10.1515/pomr-2015-0069 Cinar, S., & Cimen, H. (2012). On the Investigation of the Energy Efficiency Using PID and Fuzzy Logic Controllers in a Marble Machine, 5(4), 73–80. Coelho, R. L. (2014). On the Concept of Energy: Eclecticism and Rationality. Science Education, 23(6), 1361–1380. https://doi.org/10.1007/S11191-013-9634-1 Craig B. Smith, & Kelly E. Parmenter. (2016). Energy Management Principles: Applications, Benefits, Savings (second). Crespo, E. A., & Coutinho, J. A. P. (2022). Insights into the Limitations of Parameter Transferability in Heteronuclear SAFT-type Equations of State. Dubois, D. (1987). An application of fuzzy arithmetic to the optimization of industrial machining processes. Mathematical Modelling, 9(6), 461–475. http://doi.org/10.1016/0270-0255(87)90512-4 Dwyer, A. O. (2006). Reducing energy costs by optimizing controller tuning, 0–6. Dzyuba, A. P., Solovyeva, I., & Semikolenov, A. V. (2023). Raising the Resilience of Industrial Manufacturers through Implementing Natural Gas-Fired Distributed Energy Resource Systems with Demand Response. Sustainability, 15(10), 8241. https://doi.org/10.3390/su15108241 Fong, B., Sobocinski, P., & Rapisarda, P. (2016). A categorical approach to open and interconnected dynamical systems. 495–504. https://doi.org/10.1145/2933575.2934556 Frigo, G. (2017). Energy ethics, homogenization, and hegemony: A reflection on the traditional energy paradigm. Energy Research and Social Science, 30, 7–17. https://doi.org/10.1016/J.ERSS.2017.06.030 Gavrilova, A. A., & Salov, A. (2019). System Analysis and Modeling of the Infrastructure of Production Activities of Generating Companies. https://doi.org/10.1109/CSCMP45713.2019.8976680 Gavrilova, A. A., & Salov, A. G. (2020). Systemic Analysis of Energy Systems in the New Economy. https://doi.org/10.1109/FAREASTCON50210.2020.9271638 Ghiaus, C. (1999). Fault diagnosis of air conditioning systems based on qualitative bond graph. Energy and Buildings. https://doi.org/https://doi.org/10.1016/S0378-7788(98)00070-X Gillingham, K., Huang, P., Buehler, C., Peccia, J., & Gentner, D. R. (2021). The climate and health benefits from intensive building energy efficiency improvements. Science Advances, 7(34). https://doi.org/10.1126/SCIADV.ABG0947 Gupta, J., & Chakraborty, M. (2021). Energy efficiency in buildings. 457–480. https://doi.org/10.1016/B978-0-12-822989-7.00016-0 Han, S., Keel, L. H., & Bhattacharyya, S. P. (2023). Robust Decoupling, Disturbance Rejection and Linearization of Unknown Nonlinear Systems. 606–618. https://doi.org/10.1007/978-3-031-28076-4_44 Han, X. (2012). Intelligent efficient engine and control method. Haoye, S., Hongzhe, Q., & Guozheng, T. (2020). Intelligent control system for high-efficiency aluminum electrolysis production. Hasan, S., Zeyad, M. T., Ahmed, S. M. M., & Anubhove, Md. S. T. (2023). Optimization and planning of renewable energy sources based microgrid for a residential complex. Environmental Progress & Sustainable Energy. https://doi.org/10.1002/ep.14124 Hazi, A., Badea, A., Hazi, G., Necula, H., & Grigore, R. (2009). Increase of Paper Mill Energy Efficiency by Optimization Energy Supply System Industry. Romania, 1–5. He, W., & Huang, Y. (2021). Real-time Energy Optimization of Hybrid Electric Vehicle in Connected Environment Based on Deep Reinforcement Learning. IFAC-PapersOnLine, 54(10), 176–181. https://doi.org/https://doi.org/10.1016/j.ifacol.2021.10.160 Hermes, C. J. L., & Melo, C. (2008). A first-principles simulation model for the start-up and cycling transients of household refrigerators. International Journal of Refrigeration, 31(8), 1341–1357. https://doi.org/10.1016/j.ijrefrig.2008.04.003 Hewitt, S. (2022a). History of Energy. 72–130. https://doi.org/10.53478/tuba.978-625-8352-00-9.ch02 Hewitt, S. (2022b). History of Energy. 72–130. https://doi.org/10.53478/tuba.978-625-8352-00-9.ch02 Holdren, J. P. (2007). Energy and Sustainability. Science, 315(5813), 737. https://doi.org/10.1126/SCIENCE.1139792 Hoyler, T. L. (2023). Intelligent energy management systems: a review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-023-10441-3 Hroncová, D., & Gmiterko, A. (2013). Bond Graphs of the Electrical RLC Circuit. American Journal of Mechanical Engineering, 1(7), 318–323. https://doi.org/10.12691/AJME-1-7-33 Hu, X., Wang, H., & Tang, X. (2017). Cyber-physical control for energy-saving vehicle following with connectivity. IEEE Transactions on Industrial Electronics, 64(11), 8578–8587. https://doi.org/10.1109/TIE.2017.2703673 Hybrid Physics and Data-Driven Method for Modeling and Analysis of Electricity–Heat Integrated Energy Systems. (2023). IEEE Systems Journal, 17(2), 2847–2857. https://doi.org/10.1109/jsyst.2022.3213048 Ibrahim Dincer, Marc A. Rosen, & Pouria Ahmadi. (2017). Optimization of Energy Systems. Innocent, K., Leila, B., & Atieh, D. (2023). Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions. https://doi.org/10.20944/preprints202305.1021.v1 International Energy Agency, I. (2022). Energy Efficiency 2022. www.iea.org Ishankhodjayev, G., & Sultanov, M. (2022). Development of Information Support for Decision-Making in Intelligent Energy Systems. 1–5. https://doi.org/10.1109/ICISCT55600.2022.10146931 Jahangeer, K. A., Tay, A. A. O., & Raisul Islam, M. (2011). Numerical investigation of transfer coefficients of an evaporatively-cooled condenser. Applied Thermal Engineering, 31(10), 1655–1663. https://doi.org/10.1016/j.applthermaleng.2011.02.007 Jia, S. Q., Xu, J. J., Pan, Y., Tian, L., & Gu, R. (2022). Operation optimization study of integrated energy system based on multi-energy complementary. Journal of Physics: Conference Series, 2237(1), 12031. https://doi.org/10.1088/1742-6596/2237/1/012031 Jing, R., Ani, V., & Bone, J. (2023). Integrated energy system. 165–176. https://doi.org/10.1016/b978-0-12-821204-2.00138-0 Jordan, M., Corry, J., & Jaques, I. (2017). Energy efficiency : a key enabler for energy access. 1–13. Junsheng, L. (2016). Intelligent energy efficiency optimizing control system for central air conditioning system and control method of intelligent energy efficiency optimizing control system. Kaya, A. (2022). Queue models Evaluation of Simulation for Managerial Decision Support Systems: Application of Two-Stage Production Control. İzmir Sosyal Bilimler Dergisi, 4(2), 89–96. https://doi.org/10.47899/ijss.1193183 Kiptoo, M. K., Adewuyi, O. B., Howlader, H. O. R., Nakadomari, A., & Senjyu, T. (2023). Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies. Energies, 16(10), 4147. https://doi.org/10.3390/en16104147 Koshti, A. M. (2023). Transfer function models and sensitivity analysis. 12488, 124880T-124880T. https://doi.org/10.1117/12.2652138 Koskinen, K. U. (2013). Systemic View and Systems Thinking. 13–30. https://doi.org/10.1007/978-3-319-00104-3_3 Kypuros, J. A. (2013). System dynamics and control with bond graph modeling. https://doi.org/10.1201/b14676 L Pei, L., ML, M., & XW, X. W. (2023). Integrated Energy Systems towards Carbon Neutrality. https://doi.org/10.3390/books978-3-0365-6804-1 Laimon, M., Yusaf, T., Mai, T., Goh, S., & Alrefae, W. (2022). A systems thinking approach to address sustainability challenges to the energy sector. International Journal of Thermofluids, 15, 100161. https://doi.org/10.1016/j.ijft.2022.100161 Lawrence E. Jones. (2017). Renewable Energy Integration Practical Management of Variability, Uncertainty, and Flexibility in Power Grids. Lin, Y. (1999). General Systems Theory: A Mathematical Approach. Springer US. https://doi.org/10.1007/b116863 Lobontiu, N. (2018). Chapter 7 – Transfer Function Approach. 323–378. https://doi.org/10.1016/B978-0-12-804559-6.00007-5 Lopes, M. B., Antunes, C. H., & Martins, N. (2015). Towards more effective behavioural energy policy: An integrative modelling approach to residential energy consumption in Europe. Energy Research and Social Science, 7, 84–98. https://doi.org/10.1016/J.ERSS.2015.03.004 Maheedhar, T. D. (2023). Advanced Technologies in Integrated Energy Systems. 105–117. https://doi.org/10.1002/9781119847564.ch8 Makarov, A. (2022). Energy of the “Damned Side of Things.” Logos et Praxis, 3, 89–94. https://doi.org/10.15688/lp.jvolsu.2022.3.10 Mashrur Chowdhury, Amy Apon, & Kakan Dey. (2017). Data Analytics for Intelligent Transportation Systems. Moezzi, M., & Lutzenhiser, L. (2020). Beyond energy behaviour: A broader way to see people for climate change technology planning. 89–106. https://doi.org/10.1016/B978-0-12-818567-4.00003-X Marine, C. (2020). Energy efficiency, the overlooked climate emergency solution. Economic Policy, 15(2), 48–67. Neil A. Duffie. (2022). Control Theory Applications for Dynamic Production Systems: Time and Frequency Methods for Analysis and Design. Wiley. Peña, M., Biscarri, F., Personal, E., & León, C. W. A. (2022). Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach. Sensors, 22(4), 1380. https://doi.org/10.3390/s22041380 Pillai, H. K., Shankar, S., Pillai, H. K., & Shankar, S. (1999). A Behavioral Approach to Control of Distributed Systems. Siam Journal on Control and Optimization. https://doi.org/10.1137/s0363012997321784 Pothitou, M. (2015). Linking energy behaviour, attitude and habits, and social practices with environmental predisposition and knowledge:what are the factors with influence on environmental behaviour? Pothitou, M., Kolios, A., Varga, L., & Gu, S. (2016). A framework for targeting household energy savings through habitual behavioural change. International Journal of Sustainable Energy, 35(7), 686–700. https://doi.org/10.1080/14786451.2014.936867 Qin, C., Zhao, J., & Wang, W. (2023). Hybrid Physics and Data-Driven Method for Modeling and Analysis of Electricity–Heat Integrated Energy Systems. IEEE Systems Journal, 17, 2847–2857. https://doi.org/10.1109/JSYST.2022.3213048 Rane, D., Sharma, S., & Verma, A. (2022). Multi-Objective Building Energy Management for Integrated Energy Systems Considering Greenhouse Gas Emissions. 530–535. https://doi.org/10.1109/NPSC57038.2022.10068981 Rosen, M. A., & Farsi, A. (2022). Sustainability and sustainable energy. 107–132. https://doi.org/10.1016/b978-0-323-99872-7.00007-3 Sass, L. L. (2004). Symbolic modeling of electromechanical multibody, (January 2004). Retrieved from http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-0102004-172924/ Schné, T., Jaskó, S., & Simon, G. (2015). Dynamic models of a home refrigerator. MACRo 2015- 5th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, 105–114. https://doi.org/10.1515/macro-2015-0010 Schné, T., Jaskó, S., & Simon, G. (2018). Embeddable adaptive model predictive refrigerator control for cost-efficient and sustainable operation. Journal of Cleaner Production, 190, 496–507. http://doi.org/10.1016/j.jclepro.2018.04.137 Seleem, M. S. (2023). Energy Transition: Changing the Brazilian Landscape Over Time. 1–15. https://doi.org/10.1007/978-3-031-21033-4_1 Selva, J. A. N. (2010). A systemic vision of belief systems and ideologies. ViXra. https://rua.ua.es/dspace/bitstream/10045/24798/1/Tesis_Nescolarde.pdf Sellami, A., Aridhi, E., Mzoughi, D., & Mami, A. (2018). Performance of the bond graph approach for the detection and localization of faults of a refrigerator compartment containing an ice quantity. https://doi.org/10.1142/S2010132518500281 Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., & Bemporad, A. (2018). Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: problem formulation, applications and opportunities. Energies, 11(3). https://doi.org/10.3390/en11030631 Shaikh, P. H., Nor, N. B. M., Nallagownden, P., Elamvazuthi, I., & Ibrahim, T. (2016). Intelligent multi-objective control and management for smart energy efficient buildings. International Journal of Electrical Power & Energy Systems, 74, 403–409. https://doi.org/https://doi.org/10.1016/j.ijepes.2015.08.006 Shin, S.-J., Woo, J., & Rachuri, S. (2017). Energy efficiency of milling machining: {Component} modeling and online optimization of cutting parameters. Journal of Cleaner Production, 161, 12–29. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.05.013 Sievers, J., & Blank, T. (2023). A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems. Energies, 16(4), 1688. https://doi.org/10.3390/en16041688 Soto., G. J. A., & Hernandez-Riveros., J.-A. (2019). Evolutionary split range controller for a refrigeration system. https://doi.org/10.5220/0007930803410351 Soto, G. J. A., López, J. M. G., & Hernández-Riveros, J. A. (2018). Coupled evolutionary tuning of PID controllers for the benchmark on vapor compression refrigeration. https://doi.org/10.1016/j.ifacol.2018.06.146 Stuart Borlase. (2013). Smart Grids: Infrastructure, Technology, and Solutions" . Sundqvist, J.-O., Baky, A., Björklund, A., Carlsson, M., Eriksson, O., Frostell, B., Granath, J., & Thyselius, L. (1999). Systemanalys av energiutnyttjande från avfall - utvärdering av energi, miljö och ekonomi : Översiktsrapport. Surana, K., Chikkatur, A. P., & Sagar, A. D. (2013). Technology Innovation and Energy. 27–43. https://doi.org/10.1016/B978-0-12-409548-9.09059-X Tian, H., Wang, K., Cui, X.-F., Chen, Z., & Zhao, E. (2023). Multi-objective planning of microgrid based on renewable energy sources and energy storage system. Journal of Energy Storage, 68, 107803. https://doi.org/10.1016/j.est.2023.107803 Trentelman, H. L., & Willems, J. C. (2003). The behavioral approach as a paradigm for modeling interconnected systems. 9, 296–306. https://doi.org/10.3166/EJC.9.296-306 Ullah, Z., Wang, S. R., Elkadeem, M. R., & Kotb, K. M. (2023). Optimal Capacity Planning and Analysis of a Sustainable Solar/Wind Microgrid in Rural Areas. 299–303. https://doi.org/10.1109/GreenTech56823.2023.10173808 Uparikar, Miss. U. A., & Jumale, N. V. (2020). Review on supercapacitor and battery power management in electric vehicle application. International Journal of Advance Research and Innovative Ideas in Education, 6(2), 1645–1649. van Schoor, G., Uren, K. R., van Wyk, M. A., van Vuuren, P. A., & du Rand, C. P. (2014). An energy perspective on modelling, supervision, and control of large-scale industrial systems: {Survey} and framework. IFAC Proceedings Volumes, 47(3), 6692–6703. https://doi.org/10.3182/20140824-6-ZA-1003.02190 Vinatier, I. (2022). Analysis of Transfer Function Models. 17–24. https://doi.org/10.1201/9781003336891-2 Wang, B., & Wang, S. (2003). Parameter optimization in complex industrial process control based on improved fuzzy-GA. Machine Learning and …, (November), 2–5. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1259936 Wangsupphaphol, A., Phichaisawat, S., Idris, N. R. N., Jusoh, A., & Muhamad, N. D. (2023). A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications. Sustainability. https://doi.org/10.3390/su151411200 Wellstead, P., & Cloutier, M. (2011). An energy systems approach to Parkinson’s disease. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(1), 1–6. https://doi.org/10.1002/WSBM.107 Willems, J. C. (1997). On interconnections, control, and feedback. IEEE Transactions on Automatic Control, 42(3), 326–339. https://doi.org/10.1109/9.557576 Willems, J. C. (2007b). The Behavioral Approach to Open and Interconnected Systems. IEEE Control Systems Magazine, 27(6), 46–99. https://doi.org/10.1109/MCS.2007.906923 Xia, X., & Zhang, L. (2016). Industrial energy systems in view of energy efficiency and operation control. Annual Reviews in Control, 0, 1–10. https://doi.org/10.1016/j.arcontrol.2016.09.009 Yan, Y., Bao, J., & Huang, B. (2021a). Behavioural Approach to Distributed Control of Interconnected Systems. http://arxiv.org/abs/2103.10063 Yan, Y., Bao, J., & Huang, B. (2021b). Behavioural Approach to Distributed Control of Interconnected Systems. http://arxiv.org/pdf/2103.10063.pdf Yu, W. Y., Patros, P., Young, B., Klinac, E., & Walmsley, T. G. (2022). Energy digital twin technology for industrial energy management: Classification, challenges and future. Renewable & Sustainable Energy Reviews, 161, 112407. https://doi.org/10.1016/j.rser.2022.112407 Zhang, R., Wu, S., Lu, R., & Gao, F. (2014). Predictive control optimization based PID control for temperature in an industrial surfactant reactor. Chemometrics and Intelligent Laboratory Systems, 135, 48–62. http://doi.org/10.1016/j.chemolab.2014.03.021 Zhang, S., Hu, X., He, X., Tang, S., & Zhang, D. (2023). Dynamic coupling across energy forms and hybrid simulation of the multi-energy system. Frontiers in Energy Research, 11. https://doi.org/10.3389/fenrg.2023.1209845 Zhao, A., Ma, J., Zhou, X., Cong, L., Bai, B., Zeng, M., & Zhang, X. (2021). The Optimal Operation Model of Electric-Thermal-Gas Integrated Energy System Considering Multi-Energy Complementarity. 781(4), 042021. https://doi.org/10.1088/1755-1315/781/4/042021 Ziyou Song. (2022). Battery/Supercapacitor hybrid energy storage system in vehicle applications. 165–192. https://doi.org/10.1016/b978-0-08-102888-9.00007-0 Zubidi, A. N., Ismail, B., Hamrounni, I. M. A. Al, Rahman, N. H. A., & Rozlan, M. H. H. M. (2023). The Impact of Integrating Multi-Microgrid System with FACTS Devices for Voltage Profile Enhancement and Real Power Loss Reduction in Power System: A Review. Pertanika Journal of Science and Technology, 31(2), 633–653. https://doi.org/10.47836/pjst.31.2.01 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
103 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Medellín - Minas - Doctorado en Ingeniería - Sistemas Energéticos |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Minas |
| dc.publisher.place.spa.fl_str_mv |
Medellín, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Medellín |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/86405/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/86405/3/574193.2024.pdf https://repositorio.unal.edu.co/bitstream/unal/86405/4/574193.2024.pdf.jpg |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a 03c632baa5fa4e30d4784c513050f9cc a4411783d992df35f2c76f3dea891042 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814089431528767488 |
| spelling |
Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hernández Riveros, Jesús Antoniof918aaba185b5ef877c6e5877463b08cAmador Soto, Gerardo José7091b2ccb76fb721e236900aa7be81fbGrupo de Investigación en Inteligencia ComputacionalAmador Soto, Gerardo Jose [0009000197374812]2024-07-05T16:15:58Z2024-07-05T16:15:58Z2024https://repositorio.unal.edu.co/handle/unal/86405Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesEl uso eficiente de la energía es actualmente un objetivo global para mejorar la calidad de vida y promover el progreso económico y social. Los sistemas dinámicos de múltiples dominios energéticos integran diversas formas de energía (mecánica, eléctrica, térmica, neumática y química) para satisfacer variadas necesidades de producción y consumo. Estos sistemas complejos, presentes en equipos y maquinaria de todo tipo, se caracterizan por sus múltiples componentes altamente interrelacionados. Tradicionalmente, el análisis de estos sistemas bajo un enfoque reduccionista motivado por la simplificación propendió a la omisión de sus dinámicas internas, limitando el desarrollo de nuevas estrategias operativas basadas en su naturaleza dinámica y compleja. Este trabajo propone una estrategia para intensificar la eficiencia energética de estos sistemas, considerando su manifestación física real. Mediante una estructura de control inteligente basada exclusivamente en comportamiento medible, se evalúa y proponen nuevas trayectorias de comportamiento disponibles bajo condicionantes de operación sujetas a influencias del entorno. Los resultados demuestran la efectividad del método al lograr con precisión los objetivos operativos deseados, utilizando menos energía de la fuente de inyección de potencia del sistema.Efficient energy use is currently a global objective to improve quality of life and promote economic and social progress. Dynamic multi-domain energy systems integrate various forms of energy (mechanical, electrical, thermal, pneumatic, and chemical) to meet diverse production and consumption needs. These complex systems, present in equipment and machinery of all types, are characterized by their multiple highly interrelated components. Traditionally, the analysis of these systems under a reductionist approach motivated by simplification tended to omit their internal dynamics, limiting the development of new operational strategies based on their dynamic and complex nature. This work proposes a strategy to intensify the energy efficiency of these systems, considering their real physical manifestation. Through an intelligent control structure based exclusively on measurable behavior, new available behavioral trajectories are evaluated and proposed under operating conditions subject to environmental influences. The results demonstrate the effectiveness of the method by accurately achieving the desired operational objectives while using less energy from the system's power injection source.DoctoradoDoctor en IngenieríaEficiencia EnergéticaÁrea curricular de Ingeniería Química e Ingeniería de Petróleos103 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - Sistemas EnergéticosFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::621 - Física aplicadaConsumo de energíaEficiencia energéticaSistema dinámicoEficiencia energéticaSistema energético multidominioModelado unificado basado en energíaControl basado en comportamientoEnfoque comportamental para sistemas abiertos e interconectadosAprendizaje evolutivo de trayectoriasEnergy efficiencyMultidomain energy systemEnergy-based unified modelingBehavior-based controlBbehavioral approach for open and interconnected systemsEvolutionary learning of trajectorieIntensificación de la eficiencia energética para un sistema energético multidominio por intervención directa en su dinámicaIntensification of energy efficiency for a multidomain energy system through direct intervention in its dynamicsTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDLaReferenciaAjitha, A. R., & Radhika, S. (2023). A Comprehensive Review of Demand Response Strategies and Role of Emergent Technologies for a Sustainable Home Energy Management Systems. International Journal of Ambient Energy, 1–55. https://doi.org/10.1080/01430750.2023.2233522Andrei Morch, Marialaura Di Somma, Christina Papadimitriou, Hanne Sæle, Valeria Palladino, Jesús Fraile Ardanuy, Giuseppe Conti, Mosè Rossi, & Gabriele Comodi. (2023). Technologies enabling evolution of Integrated Local Energy Communities. https://doi.org/10.36227/techrxiv.22133294Arronategui, U., Bañares, J. Á., & Colom, J. M. (2023). A Framework to Support Decision-Making Based on AI and Simulation of Large-Scale Models. Lecture Notes in Computer Science, 148–152. https://doi.org/10.1007/978-3-031-29315-3_14Åström, K. J., Albertos, P., Blanke, M., Isidori, A., Schaufelberger, W., & Sanz, R. (2012). Control of Complex Systems. https://doi.org/10.1007/978-1-4471-0349-3Ayres, R. U. (2016). Energy and Technology. 231–253. https://doi.org/10.1007/978-3-319-30545-5_8Azieva, R. K. (2023). Impact of energy efficiency on activities of enterprise. 2023(1), 28–35. https://doi.org/10.24143/2073-5537-2023-1-28-35Barbeito, I., Zaragoza, S., Tarrío-Saavedra, J., & Naya, S. (2017). Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data. Applied Energy, 190, 1–17. https://doi.org/https://doi.org/10.1016/j.apenergy.2016.12.100Batlle, C., & Doria-Cerezo, A. (2006). Energy-based modelling and simulation of the interconnection of a back-to-back converter and a doubly-fed induction machine. 2006 American Control Conference, 6 pp. http://doi.org/10.1109/ACC.2006.1656489Bejarano, G. (2017). Optimization and multivariable control of refrigeration systems. Universidad de Sevilla.Bjørn H. Samset. (2016). Energy and Climate: Vision for the Future.Blenk, T., & Weindl, C. (2023). Fundamentals of State-Space Based Load Flow Calculation of Modern Energy Systems †. Energies, 16(13). https://doi.org/10.3390/en16134872Borutzky, W. (2011). Bond Graph Modelling of Engineering Systems: Theory, Applications and Software Support.Borutzky, W. (2016). Bond graphs for modelling, control and fault diagnosis of engineering systems. In Bond Graphs for Modelling, Control and Fault Diagnosis of Engineering Systems, Second Edition. https://doi.org/10.1007/978-3-319-47434-2Broenink, J. F. (1999). Introduction to physical systems modelling with bond graphs. http://www.menet.umn.edu/~lixxx099/ME8287_S11/BondGraphsV2.pdfCavallaro, A., Gennaro, F. Di, Euzenat, J., Peters-Anders, J., & Osello, A. (2014). Vision of energy systems for smart cities. 35. https://hal.archives-ouvertes.fr/hal-01180924/documentChaudhary, P. K., & Dubey, D. S. P. (2016). Efficiency Optimization of Induction Motor Drive in Steady- State using Artificial Neural Network. International Conference on Computation of Power, 2, 295–302. http://doi.org/10.1109/ICCPEIC.2016.7557246Cheng, Q., Goh, B. W., & Kim, J. B. (2018). Internal Control and Operational Efficiency. Contemporary Accounting Research, 35(2), 1102–1139. https://doi.org/10.1111/1911-3846.12409Choudhary, P. K., & Choudhary, U. K. (2017). Efficiency optimization of pump motor drive at steady-state condition. 2017 {IEEE} {International} {Conference} on {Power}, {Control}, {Signals} and {Instrumentation} {Engineering} ({ICPCSI}), 342–347. https://doi.org/10.1109/ICPCSI.2017.8392312Chuang, H.-C., Chan, C.-C., Weng, W.-B., & Lee, C.-T. (2018). Energy saving technique for natural stone cutting machine by constant speed control in stone industry. Journal of the Chinese Institute of Engineers, 41(1), 61–68. https://doi.org/10.1080/02533839.2017.1410450Cicchella, A. (2023). Human Power Production and Energy Harvesting. Encyclopedia, 3(2), 698–704. https://doi.org/10.3390/encyclopedia3020050Cichy, M., Kropiwnicki, J., & Kneba, Z. (2015). A Model of Thermal Energy Storage According to the Convention of Bond Graphs (Bg) and State Equations (Se). Polish Maritime Research, 22(4), 41–47. https://doi.org/10.1515/pomr-2015-0069Cinar, S., & Cimen, H. (2012). On the Investigation of the Energy Efficiency Using PID and Fuzzy Logic Controllers in a Marble Machine, 5(4), 73–80.Coelho, R. L. (2014). On the Concept of Energy: Eclecticism and Rationality. Science Education, 23(6), 1361–1380. https://doi.org/10.1007/S11191-013-9634-1Craig B. Smith, & Kelly E. Parmenter. (2016). Energy Management Principles: Applications, Benefits, Savings (second).Crespo, E. A., & Coutinho, J. A. P. (2022). Insights into the Limitations of Parameter Transferability in Heteronuclear SAFT-type Equations of State.Dubois, D. (1987). An application of fuzzy arithmetic to the optimization of industrial machining processes. Mathematical Modelling, 9(6), 461–475. http://doi.org/10.1016/0270-0255(87)90512-4Dwyer, A. O. (2006). Reducing energy costs by optimizing controller tuning, 0–6.Dzyuba, A. P., Solovyeva, I., & Semikolenov, A. V. (2023). Raising the Resilience of Industrial Manufacturers through Implementing Natural Gas-Fired Distributed Energy Resource Systems with Demand Response. Sustainability, 15(10), 8241. https://doi.org/10.3390/su15108241Fong, B., Sobocinski, P., & Rapisarda, P. (2016). A categorical approach to open and interconnected dynamical systems. 495–504. https://doi.org/10.1145/2933575.2934556Frigo, G. (2017). Energy ethics, homogenization, and hegemony: A reflection on the traditional energy paradigm. Energy Research and Social Science, 30, 7–17. https://doi.org/10.1016/J.ERSS.2017.06.030Gavrilova, A. A., & Salov, A. (2019). System Analysis and Modeling of the Infrastructure of Production Activities of Generating Companies. https://doi.org/10.1109/CSCMP45713.2019.8976680Gavrilova, A. A., & Salov, A. G. (2020). Systemic Analysis of Energy Systems in the New Economy. https://doi.org/10.1109/FAREASTCON50210.2020.9271638Ghiaus, C. (1999). Fault diagnosis of air conditioning systems based on qualitative bond graph. Energy and Buildings. https://doi.org/https://doi.org/10.1016/S0378-7788(98)00070-XGillingham, K., Huang, P., Buehler, C., Peccia, J., & Gentner, D. R. (2021). The climate and health benefits from intensive building energy efficiency improvements. Science Advances, 7(34). https://doi.org/10.1126/SCIADV.ABG0947Gupta, J., & Chakraborty, M. (2021). Energy efficiency in buildings. 457–480. https://doi.org/10.1016/B978-0-12-822989-7.00016-0Han, S., Keel, L. H., & Bhattacharyya, S. P. (2023). Robust Decoupling, Disturbance Rejection and Linearization of Unknown Nonlinear Systems. 606–618. https://doi.org/10.1007/978-3-031-28076-4_44Han, X. (2012). Intelligent efficient engine and control method.Haoye, S., Hongzhe, Q., & Guozheng, T. (2020). Intelligent control system for high-efficiency aluminum electrolysis production.Hasan, S., Zeyad, M. T., Ahmed, S. M. M., & Anubhove, Md. S. T. (2023). Optimization and planning of renewable energy sources based microgrid for a residential complex. Environmental Progress & Sustainable Energy. https://doi.org/10.1002/ep.14124Hazi, A., Badea, A., Hazi, G., Necula, H., & Grigore, R. (2009). Increase of Paper Mill Energy Efficiency by Optimization Energy Supply System Industry. Romania, 1–5.He, W., & Huang, Y. (2021). Real-time Energy Optimization of Hybrid Electric Vehicle in Connected Environment Based on Deep Reinforcement Learning. IFAC-PapersOnLine, 54(10), 176–181. https://doi.org/https://doi.org/10.1016/j.ifacol.2021.10.160Hermes, C. J. L., & Melo, C. (2008). A first-principles simulation model for the start-up and cycling transients of household refrigerators. International Journal of Refrigeration, 31(8), 1341–1357. https://doi.org/10.1016/j.ijrefrig.2008.04.003Hewitt, S. (2022a). History of Energy. 72–130. https://doi.org/10.53478/tuba.978-625-8352-00-9.ch02Hewitt, S. (2022b). History of Energy. 72–130. https://doi.org/10.53478/tuba.978-625-8352-00-9.ch02Holdren, J. P. (2007). Energy and Sustainability. Science, 315(5813), 737. https://doi.org/10.1126/SCIENCE.1139792Hoyler, T. L. (2023). Intelligent energy management systems: a review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-023-10441-3Hroncová, D., & Gmiterko, A. (2013). Bond Graphs of the Electrical RLC Circuit. American Journal of Mechanical Engineering, 1(7), 318–323. https://doi.org/10.12691/AJME-1-7-33Hu, X., Wang, H., & Tang, X. (2017). Cyber-physical control for energy-saving vehicle following with connectivity. IEEE Transactions on Industrial Electronics, 64(11), 8578–8587. https://doi.org/10.1109/TIE.2017.2703673Hybrid Physics and Data-Driven Method for Modeling and Analysis of Electricity–Heat Integrated Energy Systems. (2023). IEEE Systems Journal, 17(2), 2847–2857. https://doi.org/10.1109/jsyst.2022.3213048Ibrahim Dincer, Marc A. Rosen, & Pouria Ahmadi. (2017). Optimization of Energy Systems.Innocent, K., Leila, B., & Atieh, D. (2023). Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions. https://doi.org/10.20944/preprints202305.1021.v1International Energy Agency, I. (2022). Energy Efficiency 2022. www.iea.orgIshankhodjayev, G., & Sultanov, M. (2022). Development of Information Support for Decision-Making in Intelligent Energy Systems. 1–5. https://doi.org/10.1109/ICISCT55600.2022.10146931Jahangeer, K. A., Tay, A. A. O., & Raisul Islam, M. (2011). Numerical investigation of transfer coefficients of an evaporatively-cooled condenser. Applied Thermal Engineering, 31(10), 1655–1663. https://doi.org/10.1016/j.applthermaleng.2011.02.007Jia, S. Q., Xu, J. J., Pan, Y., Tian, L., & Gu, R. (2022). Operation optimization study of integrated energy system based on multi-energy complementary. Journal of Physics: Conference Series, 2237(1), 12031. https://doi.org/10.1088/1742-6596/2237/1/012031Jing, R., Ani, V., & Bone, J. (2023). Integrated energy system. 165–176. https://doi.org/10.1016/b978-0-12-821204-2.00138-0Jordan, M., Corry, J., & Jaques, I. (2017). Energy efficiency : a key enabler for energy access. 1–13.Junsheng, L. (2016). Intelligent energy efficiency optimizing control system for central air conditioning system and control method of intelligent energy efficiency optimizing control system.Kaya, A. (2022). Queue models Evaluation of Simulation for Managerial Decision Support Systems: Application of Two-Stage Production Control. İzmir Sosyal Bilimler Dergisi, 4(2), 89–96. https://doi.org/10.47899/ijss.1193183Kiptoo, M. K., Adewuyi, O. B., Howlader, H. O. R., Nakadomari, A., & Senjyu, T. (2023). Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies. Energies, 16(10), 4147. https://doi.org/10.3390/en16104147Koshti, A. M. (2023). Transfer function models and sensitivity analysis. 12488, 124880T-124880T. https://doi.org/10.1117/12.2652138Koskinen, K. U. (2013). Systemic View and Systems Thinking. 13–30. https://doi.org/10.1007/978-3-319-00104-3_3Kypuros, J. A. (2013). System dynamics and control with bond graph modeling. https://doi.org/10.1201/b14676L Pei, L., ML, M., & XW, X. W. (2023). Integrated Energy Systems towards Carbon Neutrality. https://doi.org/10.3390/books978-3-0365-6804-1Laimon, M., Yusaf, T., Mai, T., Goh, S., & Alrefae, W. (2022). A systems thinking approach to address sustainability challenges to the energy sector. International Journal of Thermofluids, 15, 100161. https://doi.org/10.1016/j.ijft.2022.100161Lawrence E. Jones. (2017). Renewable Energy Integration Practical Management of Variability, Uncertainty, and Flexibility in Power Grids.Lin, Y. (1999). General Systems Theory: A Mathematical Approach. Springer US. https://doi.org/10.1007/b116863Lobontiu, N. (2018). Chapter 7 – Transfer Function Approach. 323–378. https://doi.org/10.1016/B978-0-12-804559-6.00007-5Lopes, M. B., Antunes, C. H., & Martins, N. (2015). Towards more effective behavioural energy policy: An integrative modelling approach to residential energy consumption in Europe. Energy Research and Social Science, 7, 84–98. https://doi.org/10.1016/J.ERSS.2015.03.004Maheedhar, T. D. (2023). Advanced Technologies in Integrated Energy Systems. 105–117. https://doi.org/10.1002/9781119847564.ch8Makarov, A. (2022). Energy of the “Damned Side of Things.” Logos et Praxis, 3, 89–94. https://doi.org/10.15688/lp.jvolsu.2022.3.10Mashrur Chowdhury, Amy Apon, & Kakan Dey. (2017). Data Analytics for Intelligent Transportation Systems.Moezzi, M., & Lutzenhiser, L. (2020). Beyond energy behaviour: A broader way to see people for climate change technology planning. 89–106. https://doi.org/10.1016/B978-0-12-818567-4.00003-XMarine, C. (2020). Energy efficiency, the overlooked climate emergency solution. Economic Policy, 15(2), 48–67.Neil A. Duffie. (2022). Control Theory Applications for Dynamic Production Systems: Time and Frequency Methods for Analysis and Design. Wiley.Peña, M., Biscarri, F., Personal, E., & León, C. W. A. (2022). Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach. Sensors, 22(4), 1380. https://doi.org/10.3390/s22041380Pillai, H. K., Shankar, S., Pillai, H. K., & Shankar, S. (1999). A Behavioral Approach to Control of Distributed Systems. Siam Journal on Control and Optimization. https://doi.org/10.1137/s0363012997321784Pothitou, M. (2015). Linking energy behaviour, attitude and habits, and social practices with environmental predisposition and knowledge:what are the factors with influence on environmental behaviour?Pothitou, M., Kolios, A., Varga, L., & Gu, S. (2016). A framework for targeting household energy savings through habitual behavioural change. International Journal of Sustainable Energy, 35(7), 686–700. https://doi.org/10.1080/14786451.2014.936867Qin, C., Zhao, J., & Wang, W. (2023). Hybrid Physics and Data-Driven Method for Modeling and Analysis of Electricity–Heat Integrated Energy Systems. IEEE Systems Journal, 17, 2847–2857. https://doi.org/10.1109/JSYST.2022.3213048Rane, D., Sharma, S., & Verma, A. (2022). Multi-Objective Building Energy Management for Integrated Energy Systems Considering Greenhouse Gas Emissions. 530–535. https://doi.org/10.1109/NPSC57038.2022.10068981Rosen, M. A., & Farsi, A. (2022). Sustainability and sustainable energy. 107–132. https://doi.org/10.1016/b978-0-323-99872-7.00007-3Sass, L. L. (2004). Symbolic modeling of electromechanical multibody, (January 2004). Retrieved from http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-0102004-172924/Schné, T., Jaskó, S., & Simon, G. (2015). Dynamic models of a home refrigerator. MACRo 2015- 5th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, 105–114. https://doi.org/10.1515/macro-2015-0010Schné, T., Jaskó, S., & Simon, G. (2018). Embeddable adaptive model predictive refrigerator control for cost-efficient and sustainable operation. Journal of Cleaner Production, 190, 496–507. http://doi.org/10.1016/j.jclepro.2018.04.137Seleem, M. S. (2023). Energy Transition: Changing the Brazilian Landscape Over Time. 1–15. https://doi.org/10.1007/978-3-031-21033-4_1Selva, J. A. N. (2010). A systemic vision of belief systems and ideologies. ViXra. https://rua.ua.es/dspace/bitstream/10045/24798/1/Tesis_Nescolarde.pdfSellami, A., Aridhi, E., Mzoughi, D., & Mami, A. (2018). Performance of the bond graph approach for the detection and localization of faults of a refrigerator compartment containing an ice quantity. https://doi.org/10.1142/S2010132518500281Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., & Bemporad, A. (2018). Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: problem formulation, applications and opportunities. Energies, 11(3). https://doi.org/10.3390/en11030631Shaikh, P. H., Nor, N. B. M., Nallagownden, P., Elamvazuthi, I., & Ibrahim, T. (2016). Intelligent multi-objective control and management for smart energy efficient buildings. International Journal of Electrical Power & Energy Systems, 74, 403–409. https://doi.org/https://doi.org/10.1016/j.ijepes.2015.08.006Shin, S.-J., Woo, J., & Rachuri, S. (2017). Energy efficiency of milling machining: {Component} modeling and online optimization of cutting parameters. Journal of Cleaner Production, 161, 12–29. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.05.013Sievers, J., & Blank, T. (2023). A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems. Energies, 16(4), 1688. https://doi.org/10.3390/en16041688Soto., G. J. A., & Hernandez-Riveros., J.-A. (2019). Evolutionary split range controller for a refrigeration system. https://doi.org/10.5220/0007930803410351Soto, G. J. A., López, J. M. G., & Hernández-Riveros, J. A. (2018). Coupled evolutionary tuning of PID controllers for the benchmark on vapor compression refrigeration. https://doi.org/10.1016/j.ifacol.2018.06.146Stuart Borlase. (2013). Smart Grids: Infrastructure, Technology, and Solutions" .Sundqvist, J.-O., Baky, A., Björklund, A., Carlsson, M., Eriksson, O., Frostell, B., Granath, J., & Thyselius, L. (1999). Systemanalys av energiutnyttjande från avfall - utvärdering av energi, miljö och ekonomi : Översiktsrapport.Surana, K., Chikkatur, A. P., & Sagar, A. D. (2013). Technology Innovation and Energy. 27–43. https://doi.org/10.1016/B978-0-12-409548-9.09059-XTian, H., Wang, K., Cui, X.-F., Chen, Z., & Zhao, E. (2023). Multi-objective planning of microgrid based on renewable energy sources and energy storage system. Journal of Energy Storage, 68, 107803. https://doi.org/10.1016/j.est.2023.107803Trentelman, H. L., & Willems, J. C. (2003). The behavioral approach as a paradigm for modeling interconnected systems. 9, 296–306. https://doi.org/10.3166/EJC.9.296-306Ullah, Z., Wang, S. R., Elkadeem, M. R., & Kotb, K. M. (2023). Optimal Capacity Planning and Analysis of a Sustainable Solar/Wind Microgrid in Rural Areas. 299–303. https://doi.org/10.1109/GreenTech56823.2023.10173808Uparikar, Miss. U. A., & Jumale, N. V. (2020). Review on supercapacitor and battery power management in electric vehicle application. International Journal of Advance Research and Innovative Ideas in Education, 6(2), 1645–1649.van Schoor, G., Uren, K. R., van Wyk, M. A., van Vuuren, P. A., & du Rand, C. P. (2014). An energy perspective on modelling, supervision, and control of large-scale industrial systems: {Survey} and framework. IFAC Proceedings Volumes, 47(3), 6692–6703. https://doi.org/10.3182/20140824-6-ZA-1003.02190Vinatier, I. (2022). Analysis of Transfer Function Models. 17–24. https://doi.org/10.1201/9781003336891-2Wang, B., & Wang, S. (2003). Parameter optimization in complex industrial process control based on improved fuzzy-GA. Machine Learning and …, (November), 2–5. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1259936Wangsupphaphol, A., Phichaisawat, S., Idris, N. R. N., Jusoh, A., & Muhamad, N. D. (2023). A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications. Sustainability. https://doi.org/10.3390/su151411200Wellstead, P., & Cloutier, M. (2011). An energy systems approach to Parkinson’s disease. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(1), 1–6. https://doi.org/10.1002/WSBM.107Willems, J. C. (1997). On interconnections, control, and feedback. IEEE Transactions on Automatic Control, 42(3), 326–339. https://doi.org/10.1109/9.557576Willems, J. C. (2007b). The Behavioral Approach to Open and Interconnected Systems. IEEE Control Systems Magazine, 27(6), 46–99. https://doi.org/10.1109/MCS.2007.906923Xia, X., & Zhang, L. (2016). Industrial energy systems in view of energy efficiency and operation control. Annual Reviews in Control, 0, 1–10. https://doi.org/10.1016/j.arcontrol.2016.09.009Yan, Y., Bao, J., & Huang, B. (2021a). Behavioural Approach to Distributed Control of Interconnected Systems. http://arxiv.org/abs/2103.10063Yan, Y., Bao, J., & Huang, B. (2021b). Behavioural Approach to Distributed Control of Interconnected Systems. http://arxiv.org/pdf/2103.10063.pdfYu, W. Y., Patros, P., Young, B., Klinac, E., & Walmsley, T. G. (2022). Energy digital twin technology for industrial energy management: Classification, challenges and future. Renewable & Sustainable Energy Reviews, 161, 112407. https://doi.org/10.1016/j.rser.2022.112407Zhang, R., Wu, S., Lu, R., & Gao, F. (2014). Predictive control optimization based PID control for temperature in an industrial surfactant reactor. Chemometrics and Intelligent Laboratory Systems, 135, 48–62. http://doi.org/10.1016/j.chemolab.2014.03.021Zhang, S., Hu, X., He, X., Tang, S., & Zhang, D. (2023). Dynamic coupling across energy forms and hybrid simulation of the multi-energy system. Frontiers in Energy Research, 11. https://doi.org/10.3389/fenrg.2023.1209845Zhao, A., Ma, J., Zhou, X., Cong, L., Bai, B., Zeng, M., & Zhang, X. (2021). The Optimal Operation Model of Electric-Thermal-Gas Integrated Energy System Considering Multi-Energy Complementarity. 781(4), 042021. https://doi.org/10.1088/1755-1315/781/4/042021Ziyou Song. (2022). Battery/Supercapacitor hybrid energy storage system in vehicle applications. 165–192. https://doi.org/10.1016/b978-0-08-102888-9.00007-0Zubidi, A. N., Ismail, B., Hamrounni, I. M. A. Al, Rahman, N. H. A., & Rozlan, M. H. H. M. (2023). The Impact of Integrating Multi-Microgrid System with FACTS Devices for Voltage Profile Enhancement and Real Power Loss Reduction in Power System: A Review. Pertanika Journal of Science and Technology, 31(2), 633–653. https://doi.org/10.47836/pjst.31.2.01InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86405/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL574193.2024.pdf574193.2024.pdfTesis de Doctorado en Ingeniería - Sistemas Energéticosapplication/pdf4273004https://repositorio.unal.edu.co/bitstream/unal/86405/3/574193.2024.pdf03c632baa5fa4e30d4784c513050f9ccMD53THUMBNAIL574193.2024.pdf.jpg574193.2024.pdf.jpgGenerated Thumbnailimage/jpeg4306https://repositorio.unal.edu.co/bitstream/unal/86405/4/574193.2024.pdf.jpga4411783d992df35f2c76f3dea891042MD54unal/86405oai:repositorio.unal.edu.co:unal/864052024-08-26 23:10:23.827Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |