1. page 1 Characterization of heat demand using an emergy-based indicator for sustainability optimization  Stefano Coss 1,2,3  Clément Rebillard 3  Vittorio Verda 2  Olivier Le Corre 1 1 Ecole des Mines de Nantes, Energy Systems and Environment, France 2 Politecnico di Torino, Department of Energetics, Italy 3 Veolia Research and Innovation, Centre de recherche de Limay, France

2. page 2 Outline I.State of the art II.Problem setting and objectives III.Methodology/Model design i.Energy service system and emergy system diagram ii.Plant design and operating model iii.Indicator development: “load concavity index (lci)” iv.Sustainability metric definition - optimization IV.Case study – data and results V.Conclusion and outlook

3. page 3 State of the art  Heating plants supply low-exergy heat into DHN  Different consumer demand characteristics for hot-water- and heating demand →Heat load is determined by the consumer‘s demand  Desired: A smooth heat load profile without peak-loads  Implementation of DSM measures contribute through f.e.:  Thermal insulation (peak-load reduction)  Heat storage integration („smoothing“)  Integration of absorption cooling (base-load increase) →DSM measures manipulate the heat load towards better „supplyability“

4. page 4 Problem setting and objectives  No characterization for the heat load curve available!  No emergy analysis was performed before to quantify the impact of DSM measures to energy systems!  Effect of capital expenses substituting resource consumption through DSM? → Thus an indicator is proposed which →characterizes the heat load profile in general →is able to model the impact of DSM measures to the load profile →is directly related with the sustainability of the energy supply →Emergy assessment of different DSM scenarios

5. page 5 energetic energy service non-energetic energy service  3 consumer classes:  Residential  Commercial  Industrial →Define the heat load  DHN is a black-box model  Heating plant:  Biomass unit (base-load)  Gas boiler 1 (medium-load)  Gas boiler 2 (peak-load)  3 DSM techniques:  Peak-load decrease  “Smoothing”  Base-load increase Emergy system diagram Introduction to the energy service systemThe emergy system diagram Unit emergy values ItemInput typeUnitUnit emergy value [seJ/unit]Reference R[J]5.62 E4 a (Romitelli 1999) N[J]9.47 E4 a (Odum 1996) F[Euro] b 1.20 E12 a (Andrić et.al. 2014)

6. page 6 Plant design and operating model Heat load model Operating model

7. page 7 Indicator development: “load concavity index (lci)”  3 DSM techniques:  Peak-load decrease (DSMpeak)  “Smoothing” (DSMq)  Base-load increase (DSMbase) Indicator defintion : Resulting heat load curves

8. page 8 Sustainability metric definition - optimization Sustainability metric definition: Total emergy consumption System energy efficiency Optimization problem:

9. page 9 Case study – data  District heating network data:  Location: Nantes/France  Total heat demand: 5.31 E7 MJ/year  20% industrial, 80% residential+commercial consumers  Transmission losses of the grid: 19 %  Resulting initial heat load curve (HCinit):  Peak load: 4.09 MW  Base-load: 0.48 MW Description Biomass boiler (B) Gas boiler 1 (G1) Gas boiler 2 (G2)Unit 0.800.850.90[-] (0.8,0.5)(0.6,0.5)(0.3,0.5)([-],[-]) (0.5,0.0)(0.3,0.0) ([-],[-]) Plant and operational model data

10. page 10 Case study –results Optimization results Optimization procedure

11. page 11 9.06 E12-3.18 E140.98-0.19 0.720.45-4.25 E14-2.90 E14 Optimization results f(lci) Fitting coefficients Optimum design variables: f(lci) Case study –results/optimization Biomass unit design Gas unit 1 design

12. page 12 Case study –results/emergy assessment  Implementation of DSM measures – scenario comparison Scenario comparison-available investment costs ItemUnit lci[1]1.361.071.091.05 Renewable inputs (R)[seJ/y]4.45E+181.88E+184.07E+186.79E+18 Non-renewable inputs (N)[seJ/y]4.20E+178.81E+162.15E+172.63E+17 Purchased inputs (F)[seJ/y]02.99E+175.85E+171.25E+18 Yield (Y)[1]4.87E+182.27E+184.87E+188.31E+18 Emergy yield ratio (EYR)[1]-7.588.316.64 Emergy investment ratio (EIR)[1]0.000.150.140.18 Emergy loading ratio (ELR)[1]0.090.210.200.22 Emergy assessment results

13. page 13 ■The load concavity index (lci) is able to →characterizes the heat load profile →to deduce gains from DSM measures ■Integration of DSM, thus decreasing lci shows →higher sustainability through improvements in energy efficiency and emergy consumption →reduced total emergy flow →the possibility for external investment cost for service integration ■Emergy assessment of DSM integration results in →improved emergy investment ratio (EIR) →but a worse result in emergy loading ratio (ELR) →Emergy analysis: Industrial systems design based on ecological considerations! Conclusion and outlook

14. page 14 Thank you for your attention!