1527-5268-1-LE.qxd ABSTRACT Energy systems are becoming increasingly complex, integrating across traditionally separate sectors such as transportation, heating, cooling and electricity. Integration through the use of district heating is the main topic of this editorial introducing volume 10 of the International Journal of Sustainable Energy Planning and Management. The editorial and the volume presents work on district heating system scenarios in Austria, grid optimisation using genetic algorithms and finally design of energy scenarios for the Italian Alpine town Bressanone- Brixen from a smart energy approach. 1. Introduction Smart energy systems [1–3] expand on the sector- specific approach of the smart grid approach by tackling the entire energy system more holistically and designing and optimising the entire system across traditional energy sectors with a view to harvesting synergies and flexibility at the lowest costs. Such an approach can pave the way for 100% renewable energy systems [3–5], and in this, district heating is a major enabler for cost- effective transitions to renewable energy [6–8]. This volume present work from the International Conference on Smart Energy Systems and 4th Generation District Heating held in Copenhagen, Denmark, August 2015 where the key-focus was on the integration of district heating systems into smart energy systems from the 4th generation district heating approach [9]. This approach includes low-temperature district heating (see e.g. how low-temperature district heating stands against individual solutions [10]); a production system characterised by integration with renewable energy supply and the organisation and design of specific public regulation measures including International Journal of Sustainable Energy Planning and Management Vol. 10 2016 1 ownership, tariffs, reforms to assist the implementation and integration of district heating (see e.g. [11] on the integration between wind power and heating systems from an organizational perspective). 2. District heating optimisation In this volume, Büchele et al. [12] investigate the potential for district heating and cooling in Austria using the bottom-up model Invert/EE-Lab. They determine significant potentials in Austria, with an economically feasible potential of 67% of the Austrian heat demand. The also determine that while e.g. heat from waste incineration and geothermal sources are cost competitive, cogeneration of heat and power cannot compete against natural gas boilers. Razani and Weidlich [13] investigate how genetic algorithms may be applied for analysing three scenarios for district heating networks – district heating with centralized heat storage, semi-decentralized heat storage and decentralized heat storage. They find that the central storage exhibits the best economy of the three – however also the largest energy losses. Decentralised heat * Corresponding author - email: poul@plan.aau.dk International Journal of Sustainable Energy Planning and Management Vol. 10 2016 1-2 Smart Energy Systems and 4th Generation District Heating �������� ��� � ��� ������� � �������� ��� ������������� �� �������� � ������� ���� � ��� ����� � ������������ � ��������������������������������������� ���� �������� � ������� ���� � ��� ����� � ������������ � ������������� ������!" ���#���$%������� &��� ��!���� ���� Keywords: Renewable energy; Smart energy systems; District heating and cooling; URL: dx.doi.org/10.5278/ijsepm.2016.10.1 Smart Energy Systems and 4th Generation District Heating 2 International Journal of Sustainable Energy Planning and Management Vol. 10 2016 storages, according to their findings, are the most expensive however also the most efficient in terms of minimising heat production ab works. 3. Smart energy systems at urban level Prina et al. [14] investigate smart energy systems with a case from the municipality of Bressanone-Brixen in Italy. Based on both a deterministic approach using the EnergyPLAN model [15] and an approach where EnergyPLAN simulations are combined with a metaheuristic approach, the authors design scenarios for the energy system in an approach similar to that presented by Mahbub et al. [16]. References [1] Lund H, Andersen AN, Østergaard PA, Mathiesen BV, Connolly D. From electricity smart grids to smart energy systems – A market operation based approach and understanding. Energy 2012;42:96–102. http://dx.doi.org/ 10.1016/j.energy.2012.04.003. [2] Lund H, Mathiesen BV, Connolly D, Østergaard PA. Renewable Energy Systems – A Smart Energy Systems Approach to the Choice and Modelling of 100 % Renewable Solutions. Chem Eng Trans 2014;39:1–6. http://dx.doi.org/ 10.3303/CET1439001. [3] Mathiesen BV, Lund H, Connolly D, Wenzel H, Østergaard PA, Möller B, et al. Smart Energy Systems for coherent 100% renewable energy and transport solutions. Appl Energy 2015;145:139–54. http://dx.doi.org/10.1016/j.apenergy. 2015. 01.075. [4] Mathiesen BV, Lund H, Karlsson K. 100% Renewable energy systems, climate mitigation and economic growth. Appl Energy 2011;88:488–501. http://dx.doi.org/10.1016/ j.apenergy.2010. 03.001. [5] Connolly D, Lund H, Mathiesen BV. Smart Energy Europe: The technical and economic impact of one potential 100% renewable energy scenario for the European Union. Renew Sustain Energy Rev 2016;60:1634–53. http://dx.doi.org/ 10.1016/j.rser.2016.02.025. [6] Xiong W, Wang Y, Mathiesen BV, Lund H, Zhang X. Heat roadmap China: New heat strategy to reduce energy consumption towards 2030. Energy 2015;81:274–85. http://dx.doi.org/10.1016/j.energy.2014.12.039. [7] Connolly D, Lund H, Mathiesen B V., Werner S, Möller B, Persson U, et al. Heat roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system. Energy Policy 2014;65:475–89. http://dx.doi.org/ 10.1016/j.enpol.2013.10.035. [8] Persson U, Möller B, Werner S. Heat Roadmap Europe: Identifying strategic heat synergy regions. Energy Policy 2014;74:663–81. http://dx.doi.org/10.1016/j.enpol. 2014. 07.015. [9] Lund H, Werner S, Wiltshire R, Svendsen S, Thorsen JE, Hvelplund F, et al. 4th Generation District Heating (4GDH). Integrating smart thermal grids into future sustainable energy systems. Energy 2014;68:1–11. http://dx.doi.org/10.1016/ j.energy.2014.02.089. [10] Østergaard PA, Andersen AN. Booster heat pumps and central heat pumps in district heating. Appl Energy 2016. http://dx.doi.org/10.1016/j.apenergy.2016.02.144. [11] Hvelplund F, Möller B, Sperling K. Local ownership, smart energy systems and better wind power economy. Energy Strateg Rev 2013;1:164–70. http://dx.doi.org/10.1016/ j.esr. 2013.02.001. [12] Büchele R, Kranzl L, Müller A, Hummel M, Hartner M, Deng Y, et al. Comprehensive Assessment of the Potential for Efficient District Heating and Cooling and for High-Efficient Cogeneration in Austria. Int J Sustain Energy Plan Manage 2016. http://dx.doi.org/10.5278/ijsepm. 2016.10.2. [13] Razani AR, Weidlich I. A genetic algorithm technique to optimize the configuration of heat storage in DH networks. Int J Sustain Energy Plan Manage 2016;10. http://dx.doi.org/ 10.5278/ijsepm.2016.10.3. [14] Prina MG, Cozzini M, Garegnani G, Moser D, Oberegger UF, Vaccaro R, et al. Smart energy systems applied at urban level: the case of the municipality of Bressanone-Brixen. Int J Sustain Energy Plan Manage 2016;10. http://dx.doi.org/ 10.5278/ijsepm.2016.10.4. [15] Østergaard PA. Reviewing EnergyPLAN simulations and performance indicator applications in EnergyPLAN simulations. Appl Energy 2015;154:921–33. http://dx.doi.org/ 10.1016/j.apenergy.2015.05.086. [16] Mahbub MS, Cozzini M, Østergaard PA, Alberti F. Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design. Appl Energy 2016;164:140–51. http://dx.doi.org/10.1016/ j.apenergy.2015.11.042. http://dx.doi.org/10.1016/j.energy.2012.04.003 http://dx.doi.org/10.3303/CET1439001 http://dx.doi.org/10.1016/j.rser.2016.02.025 http://dx.doi.org/10.1016/j.enpol.2013.10.035 http://dx.doi.org/10.1016/j.energy.2014.02.089 http://dx.doi.org/10.1016/j.esr. 2013.02.001 http://dx.doi.org/10.5278/ijsepm.2016.10.2 http://dx.doi.org/10.5278/ijsepm.2016.10.3 http://dx.doi.org/10.5278/ijsepm.2016.10.4 http://dx.doi.org/10.1016/j.apenergy.2015.05.086 http://dx.doi.org/10.1016/j.apenergy.2015.11.042 << /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /All /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Warning /CompatibilityLevel 1.4 /CompressObjects /Tags /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJDFFile false /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends true /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize true /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments false /PreserveOverprintSettings true /StartPage 1 /SubsetFonts true /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile () /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages true /ColorImageDownsampleType /Bicubic /ColorImageResolution 300 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages true /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile () /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /Description << /CHS /CHT /DAN /DEU /ESP /FRA /ITA /JPN /KOR /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken voor kwaliteitsafdrukken op desktopprinters en proofers. 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