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Resilience of polygeneration systems for residential buildings in Spain

Pinto, Edwin S., Canals Casals, Lluc·2026·Energy and Buildings
DOI 10.1016/j.enbuild.2025.116750
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Research works printed but not linked: 41

  1. Gonzalez, 2025, Challenges of variable energy resource integration and power system security: lessons from the 2025 iberian system blackout, Trans. Energy Syst. Eng. Appl., 6, 1, 10.32397/tesea.vol6.n1.911 · doi:10.32397/tesea.vol6.n1.911
  2. Deason, 2019, Electrification of buildings: potential, challenges, and outlook, Current Sustain./Renew. Energy Rep., 6, 131, 10.1007/s40518-019-00143-2 · doi:10.1007/s40518-019-00143-2
  3. Rubin, 2019, Behavioural and psychological responses of the public during a major power outage: a literature review, Int. J. Disaster Risk Reduct., 38, 10.1016/j.ijdrr.2019.101226 · DOI
  4. Kulor, 2024, Invisible threats: an investigation of electrical hazards and safety practices among residential electricity consumers, Heliyon, 10, 10.1016/j.heliyon.2024.e34470 · doi:10.1016/j.heliyon.2024.e34470
  5. Gatto, 2020, A taxonomy of energy resilience, Energy Policy, 136, 10.1016/j.enpol.2019.111007 · DOI
  6. Francis, 2014, A metric and frameworks for resilience analysis of engineered and infrastructure systems, Reliab. Eng. Syst. Saf., 121, 90, 10.1016/j.ress.2013.07.004 · DOI · Paper page
  7. Hamborg, 2020, Rethinking resilience: a cross-epistemic resilience framework for interdisciplinary energy research, Energy Res. Soc. Sci., 59, 10.1016/j.erss.2019.101285 · DOI
  8. Hasselqvist, 2022, Household energy resilience: shifting perspectives to reveal opportunities for renewable energy futures in affluent contexts, Energy Res. Soc. Sci., 88, 10.1016/j.erss.2022.102498 · DOI
  9. Kuang, 2024, Household energy resilience in extreme weather events: an investigation of energy service importance, HVAC usage behaviors, and willingness to pay, Appl. Energy, 363, 10.1016/j.apenergy.2024.123051 · DOI
  10. Kalina, 2023, The quest for game changers - Review of new trends and innovations in the design of large-scale energy systems, Energy, 277, 10.1016/j.energy.2023.127750 · DOI
  11. Jana, 2017, Polygeneration as a future sustainable energy solution – a comprehensive review, Appl. Energy, 202, 88, 10.1016/j.apenergy.2017.05.129 · DOI · Paper page
  12. Serra, 2009, Polygeneration and efficient use of natural resources, Energy, 34, 575, 10.1016/j.energy.2008.08.013 · doi:10.1016/j.energy.2008.08.013
  13. Pinto, 2022, Energy communities approach applied to optimize polygeneration systems in residential buildings: case study in zaragoza, spain, Sustain. Cities Soc., 10.1016/j.scs.2022.103885 · DOI
  14. Wang, 2024, Incorporate robust optimization and demand defense for optimal planning of shared rental energy storage in multi-user industrial park, Energy, 301, 10.1016/j.energy.2024.131721 · doi:10.1016/j.energy.2024.131721
  15. Kim, 2022, Development and evaluation of an integrated operation strategy for a poly-generation system with electrical and thermal storage systems, Energy Convers. Manage., 256, 10.1016/j.enconman.2022.115384 · doi:10.1016/j.enconman.2022.115384
  16. Barberis, 2022, Heat pump integration in a real poly-generative energy district: a techno-economic analysis, Energy Conver. Manag. X, 15
  17. Jung, 2024, Multi-criteria evaluation of poly-generation system applied to commercial buildings with various prime movers and building types, Energy Build., 317, 10.1016/j.enbuild.2024.114388 · doi:10.1016/j.enbuild.2024.114388
  18. Pinto, 2022, Design of affordable sustainable energy supply systems for residential buildings: a case study, Int. J. Energy Res., 46, 7556, 10.1002/er.7660 · DOI
  19. Rong, 2017, Polygeneration systems in buildings: a survey on optimization approaches, Energy Build., 151, 439, 10.1016/j.enbuild.2017.06.077 · doi:10.1016/j.enbuild.2017.06.077
  20. Domínguez-Muñoz, 2011, Selection of typical demand days for CHP optimization, Energy Build., 43, 3036, 10.1016/j.enbuild.2011.07.024 · doi:10.1016/j.enbuild.2011.07.024
  21. Zatti, 2019, K-MILP: a novel clustering approach to select typical and extreme days for multi-energy systems design optimization, Energy, 181, 1051, 10.1016/j.energy.2019.05.044 · doi:10.1016/j.energy.2019.05.044
  22. Schütz, 2018, Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis, Renew. Energy, 129, 570, 10.1016/j.renene.2018.06.028 · doi:10.1016/j.renene.2018.06.028
  23. Kotzur, 2018, Impact of different time series aggregation methods on optimal energy system design, Renew. Energy, 117, 474, 10.1016/j.renene.2017.10.017 · DOI · Paper page
  24. Pinto, 2020, Evaluation of methods to select representative days for the optimization of polygeneration systems, Renew. Energy, 151, 10.1016/j.renene.2019.11.048 · doi:10.1016/j.renene.2019.11.048
  25. Fthenakis, 2017, 7 – environmental Life-cycle assessment of photovoltaic systems, Perf. Photovolt. Syst., 209
  26. Bonou, 2016, Life cycle assessment of onshore and offshore wind energy-from theory to application, Appl. Energy, 180, 327, 10.1016/j.apenergy.2016.07.058 · DOI · Paper page
  27. Fleck, 2009, Comparative life-cycle assessment of a small wind turbine for residential off-grid use, Renew. Energy, 34, 2688, 10.1016/j.renene.2009.06.016 · DOI
  28. Beccali, 2016, A simplified LCA tool for solar heating and cooling systems, Energy Procedia, 91, 317, 10.1016/j.egypro.2016.06.226 · DOI
  29. Peters, 2017, The environmental impact of li-Ion batteries and the role of key parameters – a review, Renew. Sustain. Energy Rev., 67, 491, 10.1016/j.rser.2016.08.039 · DOI · Paper page
  30. Dufo-López, 2014, Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems, Appl. Energy, 115, 242, 10.1016/j.apenergy.2013.11.021 · DOI · Paper page
  31. Mancarella, 2014, MES (multi-energy systems): an overview of concepts and evaluation models, Energy, 65, 1, 10.1016/j.energy.2013.10.041 · DOI · Paper page
  32. J. Uche, I. Zabalza, L.G. Gesteira, A. Martínez-Gracia, S. Usón, A sustainable polygeneration system for a residential building, 2022, 10.3390/app122412992. · doi:10.3390/app122412992
  33. Hai, 2023, Enhancing efficiency and reducing CO2 emission of a geothermal-driven polygeneration system: environmental analysis and optimization, Process Saf. Environ. Protect., 174, 95, 10.1016/j.psep.2023.03.055 · doi:10.1016/j.psep.2023.03.055
  34. Rosales-Asensio, 2023, Peak load reduction and resilience benefits through optimized dispatch, heating and cooling strategies in buildings with critical microgrids, J. Build. Eng., 68
  35. C. Hachem-Vermette, S. Yadav, Impact of Power Interruption on Buildings and Neighborhoods and Potential Technical and Design Adaptation Methods, 2023, 10.3390/su152115299. · DOI
  36. Canals Casals, 2025, The potential of V2G considering EV charging behaviors, battery lifespan, and distribution networks, Sustain. Energy Grids Netw., 42
  37. Song, 2025, Fuzzy optimal scheduling of hydrogen-integrated energy systems with uncertainties of renewable generation considering hydrogen equipment under multiple conditions, Appl. Energy, 393, 10.1016/j.apenergy.2025.126047 · doi:10.1016/j.apenergy.2025.126047
  38. Lu, 2025, Collaborative planning of integrated hydrogen energy chain multi-energy systems: a review, Appl. Energy, 393, 10.1016/j.apenergy.2025.126019 · DOI
  39. Cheng, 2023, Multi-occupant dynamic thermal comfort monitoring robot system, Build. Environ., 234, 10.1016/j.buildenv.2023.110137 · DOI
  40. Zhang, 2025, A dynamic carbon flow traceability framework for integrated energy systems, J. Clean. Prod., 518, 10.1016/j.jclepro.2025.145878 · DOI
  41. Valor, 2001, Daily air temperature and electricity load in Spain Enric Valor, Vicente Meneu, and Vicente Caselles, Am. Meteorol. Soc., 1413

Metadata

Authors
Pinto, E. S., & Canals Casals, L.
Venue
Energy and Buildings
Publisher
Elsevier BV
Published
2026-02
Volume
352
Pages
116750
ISSN
0378-7788
DOI
10.1016/j.enbuild.2025.116750
Cited by
0
Language
en
Licence
Funders
  • Agència de Gestió d'Ajuts Universitaris i de Recerca
  • Generalitat de Catalunya (2021 SGR 00226)

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CoChat. (2026). Citation check: Resilience of polygeneration systems for residential buildings in Spain. https://app.cochat.ai/papers/10.1016/j.enbuild.2025.116750
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Pinto, E. S., & Canals Casals, L. (2026). Resilience of polygeneration systems for residential buildings in Spain. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2025.116750

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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