A screening methodology for building multiple energy retrofit measures package considering economic and risk aspects

Abstract In recent years, more and more existing building has an ever increasing requirement of retrofit, which has been developed from individual measure to combination package of multiple measures. It has become the challenging task for screening building multiple energy retrofit measures package (BMERMP). In this paper, a stepwise screening methodology for BMERMP is proposed considering economic and risk aspects, which integrates Life-Cycle Cost (LCC) and Monte Carlo (MC) analysis. First of all, this paper puts forward a calculation reference table for energy saving retrofit potential. In addition, this paper also uses unit energy saving incremental cost (UESIC) to screen technology and suggests economic internal rate of return(EIRR)as cost-benefit analysis index of BMERMP. Furthermore, the authors propose the concept of Value-at-Risk (VaR) as an effective measure to control the risk. The VaR for EIRR is determined by risk variation and can be obtained through MC simulation. Meanwhile, the study takes a commercial building for example, this building comprises 19 measures in initial BMERMP by energy diagnosis and the VaR of initial BMERMP is 97.6%, which has a high risk. According to the ranking of UESIC value, when 4 high cost technologies are reduced, the VaR can be reduced to 0.07%, then the economy of BMERMP is feasible and the risk is controllable. This paper provides a new perspective for the decision maker to select BMERMP.

[1]  Diane J. Graziano,et al.  Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis , 2015 .

[2]  Lynn Price,et al.  The CO2 abatement cost curve for the Thailand cement industry , 2010 .

[3]  Edward Morofsky,et al.  A screening methodology for implementing cost effective energy retrofit measures in Canadian office , 2011 .

[4]  Ulrich Filippi Oberegger,et al.  Energy retrofit and conservation of a historic building using multi-objective optimization and an analytic hierarchy process , 2017 .

[5]  Meredydd Evans,et al.  An international survey of building energy codes and their implementation , 2017 .

[6]  Arthouros Zervos,et al.  Profitability of wind energy investments in China using a Monte Carlo approach for the treatment of uncertainties , 2014 .

[7]  Simon Roberts,et al.  Altering existing buildings in the UK , 2008 .

[8]  S Guatelli,et al.  Monte Carlo simulations for medical physics: From fundamental physics to cancer treatment. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[9]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit strategies: A model and an application , 2012 .

[10]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[11]  Ruchi Choudhary,et al.  Optimum building energy retrofits under technical and economic uncertainty , 2013 .

[12]  Edward Morofsky,et al.  Effectiveness of single and multiple energy retrofit measures on the energy consumption of office bu , 2011 .

[13]  J. J. McArthur,et al.  Portfolio retrofit evaluation: A methodology for optimizing a large number of building retrofits to achieve triple-bottom-line objectives , 2016 .

[14]  Anna Magrini,et al.  Energy Audit of Public Buildings: The Energy Consumption of a University with Modern and Historical Buildings. Some Results , 2016 .

[15]  Rahman Saidur,et al.  Energy consumption, energy savings, and emission analysis in Malaysian office buildings , 2009 .

[16]  Paul Cooper,et al.  Existing building retrofits: Methodology and state-of-the-art , 2012 .

[17]  Jung-Ho Huh,et al.  Operation and control strategies for multi-storey double skin facades during the heating season , 2012 .

[18]  Edwin H.W. Chan,et al.  ANP model for sustainable Building Energy Efficiency Retrofit (BEER) using Energy Performance Contracting (EPC) for hotel buildings in China , 2013 .

[20]  Eleonora Annunziata,et al.  Enhancing energy efficiency in public buildings: The role of local energy audit programmes , 2014 .

[21]  Giuliano Dall'O',et al.  A methodology for evaluating the potential energy savings of retrofitting residential building stocks , 2012 .

[22]  Snehamay Khasnabis,et al.  A simulation approach for estimating value at risk in transportation infrastructure investment decisions , 2013 .

[23]  Zahra Sadat Zomorodian,et al.  Energy retrofit techniques: An experimental study of two typical school buildings in Tehran , 2015 .

[24]  Laura Gabrielli,et al.  Evaluation of energy retrofit in buildings under conditions of uncertainty: The prominence of the discount rate , 2017 .

[25]  Douglas Probert,et al.  Monte-Carlo simulation of investment integrity and value for power-plants with carbon-capture , 2012 .

[26]  Radu Zmeureanu Assessment of the energy savings due to the building retrofit , 1990 .

[27]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[28]  Yeonsook Heo,et al.  Quantitative risk management for energy retrofit projects , 2013 .

[29]  Rehan Sadiq,et al.  Economic evaluation of building energy retrofits: A fuzzy based approach , 2017 .

[30]  Tarek Zayed,et al.  Fuzzy-Based Life-Cycle Cost Model for Decision Making under Subjectivity , 2013 .

[31]  Jin Si,et al.  Assessment of building-integrated green technologies: A review and case study on applications of Multi-Criteria Decision Making (MCDM) method , 2016 .

[32]  U. Berardi A cross-country comparison of the building energy consumptions and their trends , 2017 .

[33]  John Psarras,et al.  Assessing energy-saving measures in buildings through an intelligent decision support model , 2009 .

[34]  Kullapa Soratana,et al.  Increasing innovation in home energy efficiency: Monte Carlo simulation of potential improvements , 2010 .

[35]  Jin-Hua Xu,et al.  Integrated assessment of energy efficiency technologies and CO2 abatement cost curves in China’s road passenger car sector , 2016 .

[36]  Mary Ann Piette,et al.  Energy retrofit analysis toolkits for commercial buildings: A review , 2015 .

[37]  George Mavrotas,et al.  Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters , 2010 .

[38]  Tingting Liu,et al.  Cost-benefit analysis for Energy Efficiency Retrofit of existing buildings: A case study in China , 2018 .

[39]  Carsten Nathani,et al.  Economic potential of energy-efficient retrofitting in the Swiss residential building sector: The effects of policy instruments and energy price expectations , 2007 .

[40]  E. Pyrgioti,et al.  Optimal placement of wind turbines in a wind park using Monte Carlo simulation , 2008 .

[41]  Edward Henry Mathews,et al.  A Monte Carlo method for thermal building simulation , 2006 .

[42]  Chris Marnay,et al.  Optimizing Distributed Energy Resources and Building Retrofits with the Strategic DER-CAModel , 2014 .

[43]  Maurizio Cellura,et al.  Energy and environmental benefits in public buildings as a result of retrofit actions , 2011 .

[44]  Sang Hoon Lee,et al.  Commercial Building Energy Saver: An energy retrofit analysis toolkit , 2015 .

[45]  Xiaohua Xia,et al.  A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance , 2017 .

[46]  Deo Prasad,et al.  The role of post occupation evaluation in achieving high performance buildings through diagnostics , 2017 .

[47]  Philipp Geyer,et al.  Integrating requirement analysis and multi-objective optimization for office building energy retrofit strategies , 2014 .