DataRen: A Territorial Energy Demand Modelling Tool

1. Energy System Group, Institute for Environmental Sciences (ISE), Department F.-A. Forel for Environmental and Aquatic Sciences (DEFSE), University of Geneva, stefan.schneider@unige.ch F de Oliveira, S Schneider, P Hollmuller DataRen, a territorial energy demand modelling tool

2. Background: energy demand of Geneva • The Canton of Geneva is located in the western part of Switzerland • 490’578 inhabitants (2015)[1] • Total final energy demand: 9.81 TWh (2015)[1] ~50 % is for domestic heat production Final energy for heating (kWh/m2) 0 - 83 83 - 138 138 - 166 166 - 222 222 - 361 361 - 666 >666 [1]: OCStat www.ge.ch/statistique/ 1282

3. Background: heat demand • Heat demand for space heating (SH) and domestic hot water (DHW) in Geneva: 5.5 TWh during 2015 Buildings before 1980: 67% of demand DH = 50% Natural Gas + 50% waste heat  94% of fossil resources 1282

4. Goal of this work: Current situation: The estimated energy demand for territorial energy planners is done using heterogeneous approaches, depending on the hired engineer office that conducts the study • Goals: • Offer to the public authorities an unified approach to characterize the energy demand of Geneva at a spatio-temporal level • Collaboration with the local utility “Service Industriels de Genève” to integrate the model into their renewable heat production strategy 1282

5. Table of content • Spatio-temporal heat demand (domestic space heating and hot water) • Spatio-temporal cooling demand • Spatio-temporal electricity demand • Evolution of demand until 2035 • Case study: a district of Geneva • Conclusions 1282

6. Spatio-temporal heat demand Annual heat demand measurements (~ 13’000 buildings) [2] SH Decomposition model : SH & DHW [3] and extrapolation DHW SH & DWH dynamics for a given neighbourhood energy upscaling Thermal load [MW] Hourly dynamics measurements (6 sub-stations) SH & DWH [4] [2] : SITG IDC data base (https://ge.ch/sitg/) [3] : Schneider S, Khoury J, Lachal B and Hollmuller P 2016 Geo-dependent heat demand model of the Swiss building stock In: Proc. Sustainable Built Environment Conference (SBE16) [4] : Faessler J, Fraga C, Hollmuller P, Pahud D and Quiquerez L 2016 Implementation challenges of geothermal heat pump with gas boiler in existing district heating In: Proc. European geothermal congress (EGC2016) 1282

7. Spatio-temporal cooling demand Hourly dynamics measurements (6 sub-stations) [5] Decomposition model : comfort & process [7] Cooling load [MW] power upscaling comfort & process dynamics for a given neighbourhood Cooling power requestcomfort & process [6] (929 registered AC units, 274 MW nominal capacity ) • [5] Faessler J, Hollmuller P, Lachal B and Viquerat PA 2012 Valorisation thermique des eaux profondes lacustres: Le réseau genevois GLN et quelques considérations générales sur ces systèmes Archives Des Sciences65215–28 • [6] : Hollmuller P, Faessler J and Lachal B 2014 Enjeux de la climatisation au niveau territorial: Le cas de Genève In: Proc. 18. Status-Seminar «Forschenfür den BauimKontext von Energie und Umwelt» • [7]: de Oliveira F, Schneider S,Quiquerez L, Lachal B and Hollmuller P 2017 Spatial and temporal characterization of energy demand and resources for an existing and dense urban district in Geneva Energy Procedia 112 259–64 1282

8. Spatio-temporal cooling demand Normalized load curves for process and comfort cooling Ext. Temperature [oC] Solar irradiation [MJ/m2] Load [%] Process Total 1282

9. Spatio-temporal electricity demand Annual electric demand measurements (building level, SIG billing database) Decomposition model : residential & non-residential [8] energy upscaling Hourly dynamics for a given neighbourhood Electrical load [MW] Hourly dynamics measurements (cantonal level) [8] :Schneider et al. 2017 Spatial–Temporal Analysis of the Heat and Electricity Demand of the Swiss Building Stock Frontiers in Built Environment 3(53) 1282

10. Evolution of heat demand until 2035 • Addition of 3.5% of heated surface with respect to 2015 • Reduction of 17.7 % of HDD (-215 K.days) • 0%, 1% or 2.5% of retrofit of MF buildings built between 1961 and 1980 (~1 TWh, 18% of demand) 1282

11. Consolidation: DataRen database and web-service DataRen DB Web-service HTTP R, Php, Java, Phyton … Building stock Results • Existing buildings stock: • FBI list • New and/or retrofit buildings: • Heated surface and building categories • (SH demand based on existing buildings after 2010) • Additional cooling and electric power • Annual demand: • SH • DHW • Comfort cooling • Datacentre cooling • Electricity • Hourly loads Reports 1282

12. Case study: a district of Geneva Cooling • The “Jonction” district: • 107 hectares • 857 buildings • 9’654 dwellings • 11’133 inhabitants • Representative for a district of the centre of Geneva • Will partially be connected to the DH network “CAD eco” Households Industry & services Building common parts Electricity Comfort Process 1282

13. Jonction district: overview Thermal load [MW] Solar (pv + therm) Heat Cooling load [MW] Hydrothermal Cooling Demand Resources Electric load [MW] Geothermal boreholes Electricity 1282

14. Conclusions • Geo-dependent characterisation of energy demand at the level of the canton of Geneva • Consolidation of various data into a centralized database sharing data by way of a web-service to offer an unified approach to all stakeholder • For districts having fractions of electricity demand per activity sector far from Geneva’s average the simple approach of downscaling the total load curve could be enhanced • The dynamic of comfort cooling could be enriched with data issued of more case studies • Matching the energy demand with local available resources permits simulating alternative energy systems with high share of renewables 1282

15. Stefan Schneider stefan.schneider@unige.ch 1282 Work funded by the local utility «Services Industriels de Genève» (SIG)