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Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings

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  • Manfren, Massimiliano
  • Nastasi, Benedetto
  • Tronchin, Lamberto
  • Groppi, Daniele
  • Garcia, Davide Astiaso

Abstract

Smart energy services and technologies are key components of energy transition and decarbonisation strategies for the built environment. On the one hand, the technical potential of the building stock in terms of energy, emissions and cost savings is large and exploited only partially at present. On the other hand, the increasing availability of data generated by smart meters, smart devices, sensors and building management systems can help monitoring, verifying and tracking building energy performance improvements in a transparent way. In particular, energy modelling and data analytics can provide empirically grounded and tested methods to standardize the way energy performance is measured and reported. Further, techno-economic analysis is crucial to ensure the feasibility of innovative business models. For these reasons, this paper aims to address the role of techno-economic analysis and energy modelling as key enablers for next-generation energy services and technologies. In terms of methods, scientific literature selection criteria are derived from previous research and are focused on limitations and bottlenecks to the achievement of innovative business models, which are motivated, at their very basics, by energy, emission and cost savings. Additionally, besides these potential savings, smart energy services and technologies can provide multiple additional benefits such as improved Indoor Environmental Quality (IEQ) and energy flexibility on the demand side, with respect to energy infrastructures. First, the research identifies the key elements that are necessary to integrate and to streamline techno-economic analysis and energy modelling processes. After that, it highlights potential advances in the broad area of energy transitions and decarbonisation of the built environment that can be achieved as an evolution of current practices and processes. Finally, it envisions the creation of “eco-systems” of interacting models for the building sector that share common underlying principles.

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  • Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:rensus:v:150:y:2021:i:c:s1364032121007711
    DOI: 10.1016/j.rser.2021.111490
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    as
    1. Han, Dong & Zhang, Chengzhenghao & Ping, Jian & Yan, Zheng, 2020. "Smart contract architecture for decentralized energy trading and management based on blockchains," Energy, Elsevier, vol. 199(C).
    2. Jentsch, Mark F. & James, Patrick A.B. & Bourikas, Leonidas & Bahaj, AbuBakr S., 2013. "Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates," Renewable Energy, Elsevier, vol. 55(C), pages 514-524.
    3. Raillon, L. & Ghiaus, C., 2018. "An efficient Bayesian experimental calibration of dynamic thermal models," Energy, Elsevier, vol. 152(C), pages 818-833.
    4. Pfenninger, Stefan & Hirth, Lion & Schlecht, Ingmar & Schmid, Eva & Wiese, Frauke & Brown, Tom & Davis, Chris & Gidden, Matthew & Heinrichs, Heidi & Heuberger, Clara & Hilpert, Simon & Krien, Uwe & Ma, 2018. "Opening the black box of energy modelling: Strategies and lessons learned," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19, pages 63-71.
    5. Mazzoni, Stefano & Ooi, Sean & Nastasi, Benedetto & Romagnoli, Alessandro, 2019. "Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems," Applied Energy, Elsevier, vol. 254(C).
    6. Gans, Will & Alberini, Anna & Longo, Alberto, 2013. "Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland," Energy Economics, Elsevier, vol. 36(C), pages 729-743.
    7. Schmidt, Mischa & Åhlund, Christer, 2018. "Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 742-756.
    8. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    9. Lukas Lundström & Jan Akander & Jesús Zambrano, 2019. "Development of a Space Heating Model Suitable for the Automated Model Generation of Existing Multifamily Buildings—A Case Study in Nordic Climate," Energies, MDPI, vol. 12(3), pages 1-27, February.
    10. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    11. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    12. Deane, J.P. & Chiodi, Alessandro & Gargiulo, Maurizio & Ó Gallachóir, Brian P., 2012. "Soft-linking of a power systems model to an energy systems model," Energy, Elsevier, vol. 42(1), pages 303-312.
    13. Hoarau, Quentin & Perez, Yannick, 2019. "Network tariff design with prosumers and electromobility: Who wins, who loses?," Energy Economics, Elsevier, vol. 83(C), pages 26-39.
    14. Pomponi, Francesco & Moncaster, Alice, 2018. "Scrutinising embodied carbon in buildings: The next performance gap made manifest," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2431-2442.
    15. Massimiliano Manfren & Benedetto Nastasi, 2020. "Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings," Energies, MDPI, vol. 13(3), pages 1-14, February.
    16. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    17. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    18. Lindberg, Marie Byskov & Markard, Jochen & Andersen, Allan Dahl, 2019. "Policies, actors and sustainability transition pathways: A study of the EU’s energy policy mix," Research Policy, Elsevier, vol. 48(10).
    19. Lomas, K.J. & Oliveira, S. & Warren, P. & Haines, V.J. & Chatterton, T. & Beizaee, A. & Prestwood, E. & Gething, B., 2018. "Do domestic heating controls save energy? A review of the evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 52-75.
    20. Darby, Sarah J., 2020. "Demand response and smart technology in theory and practice: Customer experiences and system actors," Energy Policy, Elsevier, vol. 143(C).
    21. Pasichnyi, Oleksii & Wallin, Jörgen & Kordas, Olga, 2019. "Data-driven building archetypes for urban building energy modelling," Energy, Elsevier, vol. 181(C), pages 360-377.
    22. Reka, S. Sofana & Dragicevic, Tomislav, 2018. "Future effectual role of energy delivery: A comprehensive review of Internet of Things and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 90-108.
    23. Kaufmann, Simon & Künzel, Karoline & Loock, Moritz, 2013. "Customer value of smart metering: Explorative evidence from a choice-based conjoint study in Switzerland," Energy Policy, Elsevier, vol. 53(C), pages 229-239.
    24. Benedetto Nastasi & Massimiliano Manfren & Michel Noussan, 2020. "Open Data and Energy Analytics," Energies, MDPI, vol. 13(9), pages 1-3, May.
    25. Buchmann, Marius, 2020. "How decentralization drives a change of the institutional framework on the distribution grid level in the electricity sector – The case of local congestion markets," Energy Policy, Elsevier, vol. 145(C).
    26. Orehounig, Kristina & Evins, Ralph & Dorer, Viktor, 2015. "Integration of decentralized energy systems in neighbourhoods using the energy hub approach," Applied Energy, Elsevier, vol. 154(C), pages 277-289.
    27. Ribó-Pérez, David & Van der Weijde, Adriaan H. & Álvarez-Bel, Carlos, 2019. "Effects of self-generation in imperfectly competitive electricity markets: The case of Spain," Energy Policy, Elsevier, vol. 133(C).
    28. Arghandeh, Reza & von Meier, Alexandra & Mehrmanesh, Laura & Mili, Lamine, 2016. "On the definition of cyber-physical resilience in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1060-1069.
    29. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    30. Krese, Gorazd & Lampret, Žiga & Butala, Vincenc & Prek, Matjaž, 2018. "Determination of a Building's balance point temperature as an energy characteristic," Energy, Elsevier, vol. 165(PB), pages 1034-1049.
    31. Tronchin, Lamberto & Manfren, Massimiliano & James, Patrick AB., 2018. "Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building," Energy, Elsevier, vol. 165(PA), pages 26-40.
    32. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    33. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    34. Filippo Busato & Renato M. Lazzarin & Marco Noro, 2012. "Energy and economic analysis of different heat pump systems for space heating," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 7(2), pages 104-112, February.
    35. Garmabdari, R. & Moghimi, M. & Yang, F. & Lu, J., 2020. "Multi-objective optimisation and planning of grid-connected cogeneration systems in presence of grid power fluctuations and energy storage dynamics," Energy, Elsevier, vol. 212(C).
    36. Manfren, Massimiliano & Aste, Niccolò & Moshksar, Reza, 2013. "Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation," Applied Energy, Elsevier, vol. 103(C), pages 627-641.
    37. Dominković, Dominik Franjo & Junker, Rune Grønborg & Lindberg, Karen Byskov & Madsen, Henrik, 2020. "Implementing flexibility into energy planning models: Soft-linking of a high-level energy planning model and a short-term operational model," Applied Energy, Elsevier, vol. 260(C).
    38. Johansson, Petter & Vendel, Martin & Nuur, Cali, 2020. "Integrating distributed energy resources in electricity distribution systems: An explorative study of challenges facing DSOs in Sweden," Utilities Policy, Elsevier, vol. 67(C).
    39. Kohler, M. & Blond, N. & Clappier, A., 2016. "A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France)," Applied Energy, Elsevier, vol. 184(C), pages 40-54.
    40. Andoni, Merlinda & Robu, Valentin & Flynn, David & Abram, Simone & Geach, Dale & Jenkins, David & McCallum, Peter & Peacock, Andrew, 2019. "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 143-174.
    41. Bollinger, L.A. & Davis, C.B. & Evins, R. & Chappin, E.J.L. & Nikolic, I., 2018. "Multi-model ecologies for shaping future energy systems: Design patterns and development paths," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3441-3451.
    42. Pfenninger, Stefan & DeCarolis, Joseph & Hirth, Lion & Quoilin, Sylvain & Staffell, Iain, 2017. "The importance of open data and software: Is energy research lagging behind?," Energy Policy, Elsevier, vol. 101(C), pages 211-215.
    43. Wang, Yang & Kuckelkorn, Jens & Zhao, Fu-Yun & Spliethoff, Hartmut & Lang, Werner, 2017. "A state of art of review on interactions between energy performance and indoor environment quality in Passive House buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1303-1319.
    44. Markard, Jochen & Raven, Rob & Truffer, Bernhard, 2012. "Sustainability transitions: An emerging field of research and its prospects," Research Policy, Elsevier, vol. 41(6), pages 955-967.
    45. Tronchin, Lamberto & Manfren, Massimiliano & Nastasi, Benedetto, 2018. "Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 341-353.
    46. Maria Ferrara & Valentina Monetti & Enrico Fabrizio, 2018. "Cost-Optimal Analysis for Nearly Zero Energy Buildings Design and Optimization: A Critical Review," Energies, MDPI, vol. 11(6), pages 1-32, June.
    47. Markard, Jochen & Hoffmann, Volker H., 2016. "Analysis of complementarities: Framework and examples from the energy transition," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 63-75.
    48. Liu, Xuezhi & Mancarella, Pierluigi, 2016. "Modelling, assessment and Sankey diagrams of integrated electricity-heat-gas networks in multi-vector district energy systems," Applied Energy, Elsevier, vol. 167(C), pages 336-352.
    49. Westermann, Paul & Deb, Chirag & Schlueter, Arno & Evins, Ralph, 2020. "Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data," Applied Energy, Elsevier, vol. 264(C).
    50. Deng, S. & Wang, R.Z. & Dai, Y.J., 2014. "How to evaluate performance of net zero energy building – A literature research," Energy, Elsevier, vol. 71(C), pages 1-16.
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