{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T22:16:10Z","timestamp":1778019370916,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia (FCT) and C-MAST (Centre for Mechanical and Aerospace Science and Technologies)","doi-asserted-by":"publisher","award":["UIDB\/00151\/2020"],"award-info":[{"award-number":["UIDB\/00151\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>This paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the Micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the Micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution\u2019s effectiveness was validated through simulations, with results demonstrating the Micro-cogeneration system\u2019s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems.<\/jats:p>","DOI":"10.3390\/en16135215","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T01:57:09Z","timestamp":1688695029000},"page":"5215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Intelligent Micro-Cogeneration Systems for Residential Grids: A Sustainable Solution for Efficient Energy Management"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6165-5348","authenticated-orcid":false,"given":"Daniel","family":"Cardoso","sequence":"first","affiliation":[{"name":"Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"},{"name":"C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilh\u00e3, Portugal"}]},{"given":"Daniel","family":"Nunes","sequence":"additional","affiliation":[{"name":"C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilh\u00e3, Portugal"}]},{"given":"Jo\u00e3o","family":"Faria","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"},{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade da Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]},{"given":"Paulo","family":"Fael","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"},{"name":"C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-1709","authenticated-orcid":false,"given":"Pedro D.","family":"Gaspar","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, Rua Marqu\u00eas d\u2019\u00c1vila e Bolama, 6201-001 Covilh\u00e3, Portugal"},{"name":"C-MAST-Centre for Mechanical and Aerospace Science and Technologies, 6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Faria, J., Pombo, J., Mariano, S., and Rosario Calado, M.D. (2018, January 12\u201315). Power Management Strategy for Standalone PV Applications with Hybrid Energy Storage System. Proceedings of the 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC\/I&CPS Europe), Palermo, Italy.","DOI":"10.1109\/EEEIC.2018.8494598"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.aej.2016.09.001","article-title":"Energy, economic and environmental performance simulation of a hybrid renewable Microgeneration system with neural network predictive control","volume":"57","author":"Entchev","year":"2018","journal-title":"Alex. Eng. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.applthermaleng.2013.10.051","article-title":"Exploring the potential synergy between Micro-cogeneration and electric vehicle charging","volume":"71","author":"Ribberink","year":"2014","journal-title":"Appl. Therm. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101047","DOI":"10.1016\/j.est.2019.101047","article-title":"A review of energy storage types, applications, and recent developments","volume":"27","author":"Rosen","year":"2020","journal-title":"J. Energy Storage"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kang, E.-C., Lee, E.-J., Ghorab, M., Yang, L., Entchev, E., Lee, K.-S., and Lyu, N.-J. (2016). Investigation of energy and environmental potentials of a renewable trigeneration system in a residential application. Energies, 9.","DOI":"10.3390\/en9090760"},{"key":"ref_6","first-page":"101533","article-title":"Internal combustion and reciprocating engine systems for small and Micro combined heat and power (CHP) applications","volume":"Volume 125\u2013146","author":"Mikalsen","year":"2011","journal-title":"Small and Micro Combined Heat and Power (CHP) Systems"},{"key":"ref_7","unstructured":"Frangopoulos, C.A. (2001). EDUCOGEN, The European Educational Tools on Cogeneration, European Commission."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.rser.2004.07.005","article-title":"Residential cogeneration systems: Review of the current technology","volume":"105","author":"Onovwiona","year":"2006","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_9","unstructured":"Orlando, J.A. (1996). Cogeneration Design Guide, ASHRAE."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.energy.2007.10.006","article-title":"Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part I: Models and indicators","volume":"33","author":"Chicco","year":"2008","journal-title":"Energy"},{"key":"ref_11","unstructured":"Tanaka, H., Suzuki, A., Yamamoto, K., Yamamoto, I., Yoshimura, M., and Togawa, K. (2011, January 19\u201321). New Ecowill\u2014A new generation gas engine Micro-CHP. Proceedings of the International Gas Union Research Conference 2011, Seoul, Republic of Korea."},{"key":"ref_12","unstructured":"Japan\u2019s Smallest Gas Engine Cogeneration System (2001). CADDET Energy Efficiency, IEA\/OECD."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jpowsour.2005.11.041","article-title":"Fuel cells for chemicals and energy cogeneration","volume":"153","author":"Alcaide","year":"2006","journal-title":"J. Power Sources"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1080\/23744731.2017.1296301","article-title":"Review and analysis of fuel cell-based, Micro-cogeneration for residential applications: Current state and future opportunities","volume":"23","author":"Milcarek","year":"2018","journal-title":"Sci. Technol. Built Environ."},{"key":"ref_15","first-page":"173","article-title":"Volume I: Main Text","volume":"Volume 1","author":"Little","year":"2000","journal-title":"Opportunities for Micropower and Fuel Cell\/Gas Turbine Hybrid Systems in Industrial Applications"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2236","DOI":"10.1016\/j.rser.2017.06.034","article-title":"A comprehensive review of cogeneration system in a Microgrid: A perspective from architecture and operating system","volume":"81","author":"Isa","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.apenergy.2013.03.088","article-title":"Higher-capacity lithium ion battery chemistries for improved residential energy storage with Micro-cogeneration","volume":"111","author":"Darcovich","year":"2013","journal-title":"Appl. Energy"},{"key":"ref_18","unstructured":"Desideri, U., Cinti, G., Discepoli, G., Sisani, E., and Penchini, D. (2012). Proceedings of the 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012, Firenze University Press."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Quaschning, V. (2016). Understanding Renewable Energy Systems, Routledge.","DOI":"10.4324\/9781315769431"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.jobe.2016.04.010","article-title":"Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers","volume":"6","author":"Mahmoudi","year":"2016","journal-title":"J. Build. Eng."},{"key":"ref_21","first-page":"188","article-title":"Prediction of remaining service life of pavement using an optimized support vector machine","volume":"13","author":"Karballaeezadeh","year":"2019","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_22","first-page":"438","article-title":"Computational intelligence approach for modeling hydrogen production: A review","volume":"12","author":"Najafi","year":"2018","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., and Varkonyi-Koczy, A. (2019). State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. Energies, 12.","DOI":"10.3390\/en12071301"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1109\/78.388860","article-title":"Wavelet neural networks for function learning","volume":"43","author":"Zhang","year":"1995","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.jobe.2017.08.008","article-title":"A novel enhanced exergy method in analyzing HVAC system using soft computing approaches: A case study on mushroom growing hall","volume":"13","author":"Najafi","year":"2017","journal-title":"J. Build. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.renene.2015.12.032","article-title":"A sizing methodology based on Levelized Cost of Supplied and Lost Energy for off-grid rural electrification systems","volume":"89","author":"Mandelli","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.energy.2017.03.032","article-title":"Optimal sizing of stand-alone photovoltaic systems in residential buildings","volume":"126","author":"Okoye","year":"2017","journal-title":"Energy"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Li, Y., Shang, Y., Duan, B., Cui, N., and Zhang, C. (2019). A Fractional-Order Kinetic Battery Model of Lithium-Ion Batteries Considering a Nonlinear Capacity. Electronics, 8.","DOI":"10.3390\/electronics8040394"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rodrigues, L., Montez, C., Moraes, R., Portugal, P., and Vasques, F. (2017). A Temperature-Dependent Battery Model for Wireless Sensor Networks. Sensors, 17.","DOI":"10.3390\/s17020422"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/0038-092X(93)90060-2","article-title":"Lead acid battery storage model for hybrid energy systems","volume":"50","author":"Manwell","year":"1993","journal-title":"Solar Energy"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Amarasinghe, K., Marino, D.L., and Manic, M. (2017, January 19\u201321). Deep neural networks for energy load forecasting. Proceedings of the 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK.","DOI":"10.1109\/ISIE.2017.8001465"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, H.-T., Xu, F.-Y., and Zhou, L. (2010, January 11\u201314). Artificial neural network for load forecasting in smart grid. Proceedings of the 2010 International Conference on Machine Learning and Cybernetics, Qingdao, China.","DOI":"10.1109\/ICMLC.2010.5580713"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lai, L.L., Subasinghe, H., Rajkumar, N., Vaseekar, E., Gwyn, B.J., and Sood, V.K. (1999, January 24\u201327). Object-oriented genetic algorithm based artificial neural network for load forecasting. Proceedings of the Simulated Evolution and Learning: Second Asia-Pacific Conference on Simulated Evolution and Learning, SEAL\u201998, Canberra, Australia.","DOI":"10.1007\/3-540-48873-1_59"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.neucom.2019.05.030","article-title":"Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting","volume":"358","author":"Bento","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_35","first-page":"504","article-title":"Comparison of conventional and modern load forecasting techniques based on artificial intelligence and expert systems","volume":"8","author":"Islam","year":"2011","journal-title":"Int. J. Comput. Sci. 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