Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration

Abstract The integration of renewable energy technologies and building renovation are the two main procedures for improving energy sustainability of buildings at neighborhood scale. It is difficult, however, to optimize these procedures simultaneously. This study focuses on improving energy sustainability of Hemberg, a Swiss village with a population of about 900, through optimizing these two procedures. For this purpose a computational platform was developed, combining software CitySim, HOMER Pro, QGIS and Rhinoceros. The energy demand on hourly basis for the buildings in the village was analyzed through comparing the current demand with that after retrofitting according to the Swiss energy labels (i) Minergie and (ii) Minergie-P. Swiss energy maps were used to identify the most promising renewable energy sources while three scenarios were considered for solar PV integration and energy system improvements. The first scenario presents the current condition in the village, while the second scenario explores improvements in electricity generation and the third in both electricity and heat generation. The results show that retrofitting of all buildings according to Minergie reduces the space heating demand by 70–85% and reduces the fluctuations in energy demand, thereby allowing the integration of more renewable energy. According to the simulations, building-integrated solar PV panels can cover the total annual energy demand of the village when considering the Minergie and Minergie-P scenarios. However, the energy system assessment shows that it is difficult to reach beyond 60% when integrating non-dispatchable renewable energy technologies. Finally, and more importantly, integration of wind energy at system level has an important impact in the hub.

[1]  Jan Carmeliet,et al.  Towards an energy sustainable community: An energy system analysis for a village in Switzerland , 2014 .

[2]  D. Assouline,et al.  Effects of urban compactness on solar energy potential , 2016 .

[3]  Jean-Louis Scartezzini,et al.  Outdoor human comfort and climate change. A case study in the EPFL campus in Lausanne , 2015 .

[4]  D. Assouline,et al.  Quantifying rooftop photovoltaic solar energy potential: A machine learning approach , 2017 .

[5]  Jérôme Henri Kämpf,et al.  On the modelling and optimisation of urban energy fluxes , 2009 .

[6]  Guoqiang Hu,et al.  Optimal coordination of air conditioning system and personal fans for building energy efficiency improvement , 2017 .

[7]  Himanshu Gupta,et al.  Developing a roadmap to overcome barriers to energy efficiency in buildings using best worst method , 2017 .

[8]  Kaamran Raahemifar,et al.  Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system , 2017 .

[9]  Darren Robinson,et al.  Computer Modelling for Sustainable Urban Design: Physical Principles, Methods and Applications , 2011 .

[10]  J. Kämpf,et al.  Optimisation of buildings' solar irradiation availability , 2010 .

[11]  Hamidreza Zareipour,et al.  A Probabilistic Energy Management Scheme for Renewable-Based Residential Energy Hubs , 2017, IEEE Transactions on Smart Grid.

[12]  Sergio Copiello Building energy efficiency: A research branch made of paradoxes , 2017 .

[13]  A. T. D. Perera,et al.  A novel simulation based evolutionary algorithm to optimize building envelope for energy efficient buildings , 2014, 7th International Conference on Information and Automation for Sustainability.

[14]  Sanyuan Niu,et al.  A BIM-GIS Integrated Web-based Visualization System for Low Energy Building Design , 2015 .

[15]  Ilaria Ballarini,et al.  Energy refurbishment of the Italian residential building stock: energy and cost analysis through the application of the building typology , 2017 .

[16]  Javier Tarrío-Saavedra,et al.  Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data , 2017 .

[17]  Shady Attia,et al.  Energy efficiency in the Romanian residential building stock: A literature review , 2017 .

[18]  Qiusheng Li,et al.  Performance assessment of tall building-integrated wind turbines for power generation , 2016 .

[19]  François Maréchal,et al.  Multi-objective, multi-period optimization of district energy systems: IV – A case study , 2015 .

[20]  Rahula A. Attalage,et al.  Converting existing Internal Combustion Generator (ICG) systems into HESs in standalone applications , 2013 .

[21]  Rehan Sadiq,et al.  Renewable energy integration into community energy systems: A case study of new urban residential development , 2018 .

[22]  Jean-Louis Scartezzini,et al.  Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid , 2017 .

[23]  V. P. C. Dassanayake,et al.  Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission , 2013 .

[24]  Ralph Evins,et al.  A review of computational optimisation methods applied to sustainable building design , 2013 .

[25]  Silvia Coccolo,et al.  Bioclimatic Design of Sustainable Campuses using Advanced Optimisation Methods , 2017 .

[26]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[27]  Jean-Louis Scartezzini,et al.  On the impact of local climatic conditions on urban energy use: A case study , 2016 .

[28]  Sean B. Walker,et al.  Modeling and optimization of a network of energy hubs to improve economic and emission considerations , 2015 .

[29]  Jean-Louis Scartezzini,et al.  Design In The Desert. A Bioclimatic Project With Urban Energy Modelling , 2013, Building Simulation Conference Proceedings.

[30]  Jean-Louis Scartezzini,et al.  An integrated approach to design site specific distributed electrical hubs combining optimization, multi-criterion assessment and decision making , 2017 .

[31]  Anibal T. de Almeida,et al.  Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings , 2017 .

[32]  V. K. Sethi,et al.  Critical analysis of methods for mathematical modelling of wind turbines , 2011 .

[33]  Michael Stadler,et al.  Improving energy efficiency via smart building energy management systems. A comparison with policy measures , 2015 .

[34]  M. P. G. Sirimanna,et al.  A techno-economic analysis for an integrated solar PV/T system with thermal and electrical storage — Case study , 2015, 2015 Moratuwa Engineering Research Conference (MERCon).

[35]  J. Carmeliet,et al.  Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential , 2017 .

[36]  Rahula A. Attalage,et al.  Sensitivity of internal combustion generator capacity in standalone hybrid energy systems , 2012 .

[37]  J. Carmeliet,et al.  Evaluation of photovoltaic integration potential in a village , 2015 .

[38]  Volker Coors,et al.  The influence of data quality on urban heating demand modeling using 3D city models , 2017, Comput. Environ. Urban Syst..

[39]  Ali Motamed,et al.  Potential advantages of a multifunctional complex fenestration system with embedded micro-mirrors in daylighting , 2016 .

[40]  Jérôme Henri Kämpf,et al.  A verification of CitySim results using the BESTEST and monitored consumption values , 2015 .

[41]  Diane Perez,et al.  A framework to model and simulate the disaggregated energy flows supplying buildings in urban areas , 2014 .

[42]  Jan Carmeliet,et al.  Multiobjective optimisation of energy systems and building envelope retrofit in a residential community , 2017 .

[43]  François Maréchal,et al.  Multi-objectives, multi-period optimization of district energy systems: III. Distribution networks , 2014, Comput. Chem. Eng..