Abstract
Due to deregulations of the energy sector and the setting of targets such as the 20/20/20 in the EU, operators of public buildings are now more exposed to instantaneous (short-term) market conditions. On the other hand, they have gained the opportunity to play a more active role in securing long-term supply, managing demand, and hedging against risk while improving existing buildings’ infrastructures. Therefore, there are incentives for the operators to develop and use a Decision Support System to manage their energy sub-systems in a more robust energy-efficient and cost-effective manner. In this paper, a two-stage stochastic model is proposed, where some decisions (so-called first-stage decisions) regarding investments in new energy technologies have to be taken before uncertainties are resolved, and some others (so-called second-stage decisions) on how to use the installed technologies will be taken once values for uncertain parameters become known, thereby providing a trade-off between long- and short-term decisions.
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Notes
Data are based on the EnRiMa project deliverable D1.1 “Requirement Assessment”, for the test site FASAD in Asturias (Spain). Starting from an annual demand of 213.50 MWh, projections on the demand level have been simulated for all the periods.
A Sunmodule SW 245 by Solarworld has been considered (http://www.solarworld.de/en/home/). The availability factor has been computed using the on-line PGIS tool (Photovoltaic Geographical Information System) by the European Commission Joint Research Center—Institute for Energy and Transport: http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php.
The price for the PV panels has been taken from the PREOC price database (http://www.preoc.es/ retrieved 2013-02-12), whilst the price for the CHP has been gathered from the on-line seller myTub (http://www.mytub.co.uk/product_information.php?product=465447, retrieved 2013-02-12).
http://www.faen.es/nueva/Intranet/documentos/3577_Bases.pdf, retrieved 2013-02-13.
From EnRiMa project deliverable D1.1 “Requirement Assessment”.
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Acknowledgments
This work is partially supported by the European Commission’s Seventh Framework Programme via the “Energy Efficiency and Risk Management in Public Buildings” (EnRiMa) project (Number 260041). We acknowledge the rest of the partners of the project, whose contributions to the project have somehow influenced the authors: Stockholm University (Sweden), University College London (UK), SINTEF Group (Norway), International Institute for Applied Systems Analysis-IIASA (Austria), Center for Energy and Innovative Technologies-CET (Austria), Tecnalia Research and Innovation (Spain), HC Energia (Spain), and Minerva Consulting and Communication (Belgium). We also acknowledge national projects OPTIMOS3 (MTM2012-36163-C06-06), RIESGOS-CM (code S2009/ESP-1685), AGORANET (IPT- 430000-2010-32) CONTENT & INTELIGENCE (IPT-2012-0912-430000), HAUS (IPT-2011-1049-430000), EDUCALAB (IPT-2011-1071-430000), DEMOCRACY4ALL (IPT-2011-0869-430000) and CORPORATE COMMUNITY (IPT-2011-0871-430000).
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Cano, E.L., Moguerza, J.M., Ermolieva, T. et al. Energy efficiency and risk management in public buildings: strategic model for robust planning. Comput Manag Sci 11, 25–44 (2014). https://doi.org/10.1007/s10287-013-0177-3
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DOI: https://doi.org/10.1007/s10287-013-0177-3