Slide 1. Modelling of Energy Systems. Rangan Banerjee. Department of Energy Science & Engineering, IIT Bombay. Web:

Transcription

1 Slide 1 Department of Energy Science & Engineering, IIT Bombay Web:

2 Slide 2 What is an Energy System? Why model Energy Systems?

3 Steps in Model Development Slide 3 Context Nature of Model Validation Usefulness

4 Slide 4 Energy Systems Modelling Equipment Design LFR #1 Systems Design / Analysis Potential Estimation System Integration Policy Modelling Energy Efficiency Glass Furnace #2 Mining,Water Pumping Renewable Energy Solar Thermal Power Buildings #4 Solar Water Heater Microgrids #3 Wind Solar PV #5a Bio Hydrogen PV #5b Building Energy Analysis PV-Battery #6 Energy Economics Model #7 Solar Thermal Jatropha

5 #1 Equipment Design Slide 5 Context Heat Losses Trapezoidal Cavity Linear Fresnel Reflector (LFR) Nature of Model Two Dimensional- Mass, momentum, energy balances Fluent Validation Experimental setup, electric Heating, KG design Usefulness Improved understanding of heat losses from LFR cavities Effect of variation of design parameters

6 Equipment Design- LFR Slide 6 Sahoo et al, Solar Energy, 2012 Validation of heat loss Effect of different height H* = H/d H* = a)2.25, b)3.0,c)3.75

7 #2 Energy Efficiency -Glass Furnace Slide 7 Context Industrial Glass furnace Model based benchmarking Nature of Model Mass and Energy Balances zones Heat loss correlations Validation Field Measurements, nine industrial glass furnaces Industry workshop Usefulness Target SEC considering furnace characteristics SEC (kj/kg ) Furnace number Target SEC Actual SEC

8 Energy Efficiency -Glass Furnace Model Schematic Slide 8 Vishal S. et al. Energy Conversion and Management 48 (2007)

9 Experimental validation Slide 9 V. Sardeshpande et al. Applied Energy 88 (2011)

10 Model Usage Slide 10 Vishal S. et al. Energy Conversion and Management 48 (2007)

11 Sankey diagram - Glass Furnace Slide 11 Vishal S. et al. Energy Conversion and Management 48 (2007)

12 #3 Microgrids Slide 12 Context Microgrid isolated, grid support PV- battery- ultracapacitor- hydrogen storage Optimal sizing hybrid storage Supply, Demand variability Nature of Model Lumped parameter model for components Cumulative Energy supply and demand curves-pinch, Design space approach Different time horizons Polymonial equations, Optimisation Validation Data logging for load variation lift, welding loads Characteristics of components manufacturers catalog Comparison with detailed simulation Usefulness Identification of feasible regions design space Based on cost can obtain minimum cost hybrid solutions

13 Inputs for Microgrid model Slide 13 Insolation at New Delhi: (a) weekly average annual data and (b) hourly average daily data for a winter day Jacob et al., Applied Energy, 2018 For a remote village (a) Seasonal variation of load for the year, (b) hourly variation of load For a remote welding shop instantaneous variation in load for an hour

14 Microgrid model equations Slide 14 Rate of Energy stored in storage device, dq s dt = P t D(t) η stored P(t) Source Power, D(t) Load Power Or Q s t + Δt = Q s t + t t+δt P t D(t) ηstored For a very small time interval Δt Q s t + Δt = Q s t + P t D(t) η stored Constraints 1)For the entire time horizon, T Qs(t=0) = Qs (t=t) 2) Storage charge level Qs(t) 0 for all time values 3) Generation should not exceed maximum power Minimum Storage Capacity = max(q s) DoD Jacob et al., Applied Energy, 2018

15 1 (t=0) Short-term Data in second Energy 3 (t=15minutes) time (in log scale) Mid-term Data in hour Energy 5 (t=1day) Long-term Data in week Energy 7.5 (t=1year) Pinch Analysis and sizing curve Slide 15 Pinch for P1 PV size cumulative generation cumulative load mismatch Pinch Point Pinch Point Pinch Point Design Space for hybrid energy storage (kwh) Infeasible region Infeasible region Infeasible region Feasible region L1 M1 Feasible region M2 Feasible region S1 S Quadratic fitting for the boundary points of the design space of stand-alone welding shop (a) Hydrogen storage, (b) VRLA battery (c) Supercapacitor Jacob et al., Applied Energy, 2018 Time P1 PV size P

16 Microgrid Results Slide 16 Case Study Case 1 Rural Village Case 2 Telecom tower Case 3 Welding Shop Case 4 Backup for lift PV (kw p ) Fuel Cell (kw) Electrolyser (kw) Optimum Configuration Hydrogen tank (m 3 ) VRLA battery (kwh) Supercapacitor (Wh) COE ( /kwh) COE ( /kwh) for the different configurations of remote rural village case study (a) using present cost (b) using US DOE target cost for hydrogen storage Jacob et al., Applied Energy, 2018

17 #4 Buildings Slide 17 Context Direct coupling of Optimisation algorithm with simulation computationally intensive for Building design Three storey building in Delhi Validation Energy Plus used for validation Error less than 10% Used for 3-storeyed house in Delhi Team Shunya NNature of Model Surrogate Models to approximate simulation Experimental Design (Fractional Factorial Design, Response Surface Methodology) Annual Cooling, Lighting Energy Optimisation- Genetic Algorithms Usefulness Methodology- reduces computational time, enables optimisation

18 Building model methodology Slide 18 Dhariwal and Banerjee, Building Simulation, Springer Nature (2017)

19 Building Orientation and inputs Slide 19 Dhariwal and Banerjee, Building Simulation, Springer Nature (2017)

20 Building Correlations developed Slide 20 Dhariwal and Banerjee, Building Simulation, Springer Nature (2017)

21 Solution Procedure Slide 21 Dhariwal and Banerjee, Building Simulation, Springer Nature (2017)

22 #5a Solar PV Potential Slide 22 Context Roof top PV potential in city Solar Mission focus on land based large plants Nature of Model Area Estimation, Insolation estimation, PV Device and System(PV Syst) Results and Analysis Validation Samples for land use types Comparison of PVA with literature Usefulness Transparent Methodology Load profiles and PV generationtypical days different months Applied for other cities Bengaluru

23 PV Potential model framework Slide 23 Singh and Banerjee, Solar Energy, 115, 2015

24 Satellite imagery PV Model Slide 24 ELU map of Ward A of MCGM Corresponding Satellite Imagery for the area from Google Earth Singh and Banerjee, Solar Energy, 115, 2015

25 MUs PV generation results Slide 25 Capacity Factor for Mumbai Jan, 2014 Typical Load Profile vs PV Generation Axis Tracking Fixed 19 deg Axis Highest eff. 1-Axix Median eff Annual Average with 1- Axis Tracking Annual Average with Fised 19 deg deg. Fixed Highest eff deg. Fixed Median eff. Typical Hourly Demand, Jan Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

26 #5b Solar PV Slide 26 Context Conventionally Hydro Thermal scheduling problem Increased PV and renewable share Impact on grid? Nature of Model PV must run Two stage formulation using dynamic programming and linear programming Validation Proposed HTSS algorithm compared with stochastic algorithm in literature Savings of 8.2% Case study of Mumbai actual data Usefulness Impact on load profile for different PV penetration

27 #5b Solar PV integration -Algorithm Slide 27 Singh and Banerjee, Solar Energy 157 (2017)

28 #5b Solar PV integration Objective function Slide 28 Singh and Banerjee, Solar Energy 157 (2017)

29 #5b Input Data on hydro, thermal plants Mumbai Slide 29 Singh and Banerjee, Solar Energy 157 (2017)

30 #5b Sample Results -Mumbai Slide 30 Singh and Banerjee, Solar Energy 157 (2017)

31 #6 PV Battery Slide 31 Context Microgrid scenarios for India-Urban, rural, industrial and isolated Model based analysis of energy for a PV battery system Nature of Model Direct energy requirements for the PV battery system for the designed microgrid Validation Actual system components and their service life Usefulness Sustainability analysis Cradle to Gate Pay back time and net energy ratio Comparison of different options

32 Life Cycle Energy Analysis Flow diagram Slide 32 Material Production Energy Manufacturing Energy Transportation Energy Battery PV Module Balanc e of System Battery PV Module Balance of System Raw materials Steel,Al,Lead, Polypropylene Silicon Production Frame production PV Cell manufacturing Inverter and Fabrication of charge controller components Frameless PV modules PV Battery Microgrid system Material recycling Recycling Energy Waste disposal Emissions to air,water Das et al., CTEP, Springer, 2017

33 Energy flow diagram for PV battery system Slide 33 Fossil Energy Solar Energy E ind,1(pv) E ind,1(pv) /t PV Production and transport of PV array PV array η PV, t PV E ind,2(bos) E ind,3(ch) E ind,2(bos) /t BOS E ind,3(ch) /t CH Conversion to electricity, αpf,elec=0.38 Conversion to heat, αpf,heat=1 Production and transport of Frame and array support Production and transport of solar charge controller Frame and array support η BOS, t BOS Solar charge controller η CH, t ch E ind,4(b) E ind,4(b) /t B Production and transport of battery Battery η B, t B E ind,5(inv) E ind,5(inv) /t INV Production and transport of Inverter Inverter η I, t I Energy Output Das et al., CTEP, Springer, 2017

34 Energy Flow diagram of PV panel for India Slide 34 Growing of Silicon Quartz Mining 0.85 MJ/kg of Quartz Metallurgical Grade(MG) Silicon MJ/kg-MG Si Solar(Electronic) Grade Silicon Czochralski Single Crystal process production MJ/kg-EG Si MJ/kg CZ-sc-Si Panel Production Casting of multi crystalline Silicon MJ/kg mc-si MJ/m 2 of panel area Aluminium, Steel 49.9 MJ/m 2 of panel area Aluminium Frame MJ/m 2 of panel area BOS Manufacturing Energy Silicon Wafer Production Silicon Cell Production Panel and laminate assembly PV system MJ/m 2 of wafer area Glass, Copper MJ/m 2 of solar cell area MJ/m 2 of panel area Electrical Installation Inverter Charge controller MJ/kWp Zinc, Steel, Copper MJ/kWe Das et al., CTEP, Springer, 2017

35 EE in MJ/.kg EE in MJ/Wh Energy Requirement of microgrid components Slide 35 Component PV system (multicrystalline- Silicon) Frame Material Production Energy MJ pf /m 2 of sensing area MJ pf /m 2 of frame area Manufacturing Energy of PV panel, frame and BOS Transportation Energy Balance of System(BOS) Electrical Installation Charge Controller Inverter MJ pf /m 2 of panel area MJ pf /kwp MJ pf /kwe MJ pf /kwe 49.9 MJ pf /m 2 of panel area 0.34 MJ pf /kg of panel weight Battery Embodied Energy per unit mass for different batteries Battery Embodied Energy per unit storage capacity for different batteries Das et al., CTEP, Springer, 2017

36 Indicators for different PV+ battery configurations Slide 36 PV+battery configuration Energy Pay Back Time(years) Net Energy Ratio Energy Pay back ( % of cycle life) Emission Factor (kgco 2 /kwh of output) VRLA LFP-G NiMH(AB 2 ) NiMH(AB 5 ) NaS NiCd LiS EPBT = LifeTime Energy Input to the system Annual Energy Output NER = Annualised energy output Annualised energy input Das et al., CTEP, Springer, 2017

37 #7 Energy Economics Model Slide 37 Context Modelling framework to link Energy, Economy, Environment not adequate Impact of structure of economy, climate constraint Nature of Model Decomposition Analysis Input-Output Analysis Sectoral Optimisation model (Example for Power sector) Integrated Modelling Framework Validation Individual models compared with literature Comparison of sectoral shares globally Overall check of reasonableness Usefulness Impact of different development pathways high industry, high services Impact on Inequality Gini coefficient Impact on overall growth rate

38 Sectoral decomposition analysis Slide 38 Kanitkar et al. Energy for Sustainable Development 26 (2015)

39 Integrated modelling framework Slide 39 Decomposition Analysis Op Cost (C f ) Emissions (CEM) Resource Potential (RP f ) Operating Parameters Emissions Intensity Optimisation Energy Supply from BY New Capacity Addition Cap + Op Cost Δ Emissions Intensity of Energy (EM i /E i ) Δ Energy Intensity of GDP (E i /G i ) Δ Sectoral Contribution to GDP (G i /ΣG i ) Total Energy Supply in TY (IC h ) Energy sector investment (I i=energy ) ΔGDP (G) Energy Demand in TY (IC h ) Incomes and income distributions in TY (IC h ) Range for Each parameter I-O Analysis Government and private expenditure Total Demand for Goods and Services in TY BY Base Year TY Target Year Scenario Design for TY Economic growth and structure for TY (G i ) (G i /ΣG i ) BY economic structure (G i /ΣG i ), BY Inter-sectoral linkages Tejal Kanitkar (2016), Ph.D thesis, IIT Bombay

40 Scenario Drivers Slide 40

41 Sample scenario results Slide 41

42 Summing up Slide 42 Models representation of reality Improved decision making - component, system design,future sustainable routes, assess impacts, what if? Value judgements- trade-offs between criteria-optimising/ Satisficing Blend of models/ prototypes develop future sustainable energy systems Energy Systems in transition need new modelling approaches

43 Acknowledgment Slide 43 Vishal S. Faculty Tejal Kanitkar (Ph.D.-2016) Jay Dhariwal (Ph.D ) Ammu Susanna (Ph.D) Jani Das (Ph.D) Sudhansu Sahoo (Ph.D-2013) rangan@iitb.ac.in Rhythm Singh (Ph.D) Balkrishna Surve Sr. Project Assistant Thank you

44 References Slide Rhythm Singh,, Impact of large-scale rooftop solar PV integration: An algorithm for hydrothermal-solar scheduling (HTSS), Solar Energy, Vol. 157, , November A. S. Jacob, R. Banerjee, and P. C. Ghosh, Sizing of hybrid energy storage system for a PV based microgrid through design space approach, Applied Energy, (accepted) 3. Jani Das, Ajit Paul Abraham, P. C. Ghosh and, Life cycle energy and carbon footprint analysis of photovoltaic battery microgrid system in India, Clean Technologies and Environmental Policy, Springer, pp 1-6, November Dhariwal, J. and Banerjee, R., An approach for building design optimization using design of experiments, Building Simulation, Springer Nature, November 8, Sahoo, S.S., Singh, S., Banerjee, R., Thermal hydraulic simulation of absorber tubes in linear Fresnel reflector solar thermal system using RELAP, Renewable Energy, (86) , February Tejal Kanitkar (2016) An Integrated Modelling Framework for Energy, Economy, and Emissions in India, Ph.D Thesis, IIT Bombay, Mumbai. 7. Kanitkar, T., Banerjee, R., and Jayaraman, T., Impact of economic structure on mitigation targets for developing countries, Energy for Sustainable Development, (26) 56 61, June Singh, R., and Banerjee, R., Estimation of rooftop solar photovoltaic potential of a city, Solar Energy, Vol. 115, , May Vishal, S., Gaitonde, U.N., and Banerjee, R., Model based energy benchmarking for glass furnace, Energy Conversion and Management, 48, , October, Status of Higher Education in India