A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties

Net zero energy buildings (NZEBs) are promising to mitigate the increasing energy and environmental problems. For NZEBs, annual energy balance between renewable energy generation and building energy consumption is an essential and fundamental requirement. Conventional RES (renewable energy system) design methods for NZEBs have not systematically considered uncertainties associated with building energy generation and consumption. As a result, either the annual energy balance cannot be achieved or the initial investment of RES is unnecessarily large. Meanwhile, the uncertainties also have significant impacts on NZEB power mismatch which can cause severe grid stress. In order to overcome the above challenges, this study proposes a multi-criterion RES design optimization method for NZEBs under uncertainties. Under the uncertainties, Monte Carlo simulations have been employed to estimate the annual energy balance and the grid stress caused by power mismatch. Three criteria, namely the annual energy balance reliability, the grid stress and the initial investment, are used to evaluate the overall RES design performance based on user-defined weighted factors. A case study has demonstrated the effectiveness of the proposed method in optimizing the size of RES under uncertainties.

[1]  Dirk Saelens,et al.  Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation , 2012 .

[2]  Alessandra Scognamiglio,et al.  Photovoltaics in Net Zero Energy Buildings and Clusters: Enabling the Smart City Operation☆ , 2014 .

[3]  E.F. El-Saadany,et al.  Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization , 2010, IEEE Transactions on Power Systems.

[4]  Laurent Georges,et al.  Advanced control of heat pumps for improved flexibility of Net-ZEB towards the grid , 2014 .

[5]  Jaume Salom,et al.  Understanding net zero energy buildings: Evaluation of load matching and grid interaction indicators , 2011 .

[6]  P. Torcellini,et al.  Getting to Net Zero , 2009, The Energy Disruption Triangle.

[7]  Ali Naci Celik,et al.  Techno-economic analysis of autonomous PV-wind hybrid energy systems using different sizing methods , 2003 .

[8]  Elias B. Kosmatopoulos,et al.  A roadmap towards intelligent net zero- and positive-energy buildings , 2011 .

[9]  Thomas Lützkendorf,et al.  Performance analysis of commercial buildings—Results and experiences from the German demonstration program ‘Energy Optimized Building (EnOB)’ , 2012 .

[10]  Ken Nagasaka,et al.  Comparative analysis of support mechanisms for renewable energy technologies using probability distributions , 2010 .

[11]  P. Torcellini,et al.  Understanding Zero-Energy Buildings , 2006 .

[12]  Refrigerating ASHRAE handbook of fundamentals , 1967 .

[13]  Antonio Capone,et al.  Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey , 2014 .

[14]  Jlm Jan Hensen,et al.  Uncertainty analysis in building performance simulation for design support , 2011 .

[15]  Christina J. Hopfe,et al.  Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization , 2009 .

[16]  Ala Hasan,et al.  On-site energy matching indices for buildings with energy conversion, storage and hybrid grid connections , 2013 .

[17]  Yang Zhao,et al.  Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods , 2015 .

[18]  Brian Anderson,et al.  Uncertainty in the thermal conductivity of insulation materials , 2010 .

[19]  Jaume Salom,et al.  Analysis of load match and grid interaction indicators in net zero energy buildings with simulated and monitored data , 2014 .

[20]  Godfried Augenbroe,et al.  Exploring HVAC system sizing under uncertainty , 2014 .

[21]  Eike Musall,et al.  From Low-Energy to Net Zero-Energy Buildings: Status and Perspectives , 2011 .

[22]  Daryl R. Myers,et al.  Solar Radiation Modeling and Measurements for Renewable Energy Applications: Data and Model Quality , 2004 .

[23]  Enrico Zio,et al.  Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System , 2012, ArXiv.

[24]  Yu Wang,et al.  HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique , 2015 .

[25]  Min Xie,et al.  Effects of wind speed probabilistic and possibilistic uncertainties on generation system adequacy , 2015 .

[26]  Yongjun Sun,et al.  A multi-criteria system design optimization for net zero energy buildings under uncertainties , 2015 .

[27]  Kevin J. Lomas,et al.  Sensitivity analysis techniques for building thermal simulation programs , 1992 .

[28]  Roland Clift,et al.  Climate change and energy policy: The importance of sustainability arguments , 2007 .

[29]  Karsten Voss,et al.  Net zero energy buildings: A consistent definition framework , 2012 .

[30]  Ery Djunaedy,et al.  Oversizing of HVAC system: Signatures and penalties , 2011 .

[31]  P. Torcellini,et al.  Zero Energy Buildings: A Critical Look at the Definition; Preprint , 2006 .

[32]  Eike Musall,et al.  Zero Energy Building A review of definitions and calculation methodologies , 2011 .

[33]  Theodor Freiheit,et al.  Modified game theory approach to multiobjective optimization , 1988 .