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Modelling of Energy Systems-Renewables and Efficiency

Modelling of Energy Systems-Renewables and Efficiency

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Modelling of Energy Systems-Renewables and Efficiency

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  1. Modelling of Energy Systems-Renewables and Efficiency Rangan Banerjee Department of Energy Science and Engineering IIT Bombay Seminar at Strathclyde University, June 2, 2009

  2. Indian Examples • Analysis of wind integration into power system • PV- Battery storage – Design space • Solar Water Heater Diffusion • Optimal response to time of use tariff - process scheduling - cool storage -cogeneration • Benchmarking of glass furnace • Decision Support System for energy planning

  3. Issues in grid integration • Conventional power planning- hydro-thermal scheduling • How do we deal with renewables? • Capacity credit • New methodology based on Load Duration Curve • Illustrated for Wind in Tamil Nadu

  4. Tamil Nadu location

  5. Tamil Nadu – Grid Details

  6. TN – Installed wind power and wind energy generated

  7. Hourly variation of wind power Monthly variation of wind energy generated

  8. Capacity Credit Methodology

  9. Variation of CC

  10. Hourly wind speed data at sensor height Select major sites Wind turbine characteristics Hourly wind power at sensor height Hub height, power law index at each site Hourly wind power at hub height Installed capacity at each site Extrapolated hourly wind power generated in the state Continue to LDC Methodology Methodology – Wind (Micro level)

  11. Hourly wind speed data Select major sites Wind turbine characteristics Installed capacity of wind power Extrapolated hourly wind power generation Hourly load curve Effective load curve Divide load curve into 100 MW bins Record number of hours in each bin Frequency distribution of load over the year Sum up to obtain annual load duration curve Input n and n discrete wind capacities Evaluate for n discrete wind capacities Calculate effective base and peak load savings from different LDCs obtained

  12. Impacts on LDC

  13. Present LDC GDP growth rate Projected year Elasticity Projected LDC Projected LDC with renewables Projected renewables installed capacity Evaluate of discrete renewable energy capacities Compute base and peak capacity saved LDC Methodology for future scenarios

  14. TN – Wind energy scenarios for 2021

  15. Simulation for UK Site: Valley, Hollyhead, Anglessy (Wales)

  16. Wind power - load curve correlation in UK July – 0.88 Jan – (-)0.51 Mar – 0.48 Oct – 0.39 Average correlation factor over the year = 0.38

  17. No wind power 3200 MW wind (present installed capacity) 6000 MW wind (2011 estimate) Results for UK

  18. Installed solar power capacity Installed wind power capacity Installed biomass power capacity Hourly solar energy generated Hourly wind energy generated Hourly biomass energy generated Hourly total renewable energy generated Hourly load curve Effective load curve Divide load curve into equal-sized bins Record no. of hrs. in each bin Frequency distribution of load over the year Sum up to obtain annual LDC Input n and n discrete capacities of W, S and B Evaluate for n discrete renewable energy capacities Compute base and peak load savings from diff. LDCs

  19. Wind resource model Solar resource model WECS performance model Solar PV performance model Biomass power generation Micro level model Macro level model Utility generation model Load model Capacity expansion model Economic scenario Output: Capacity Savings

  20. Solar module characteristics (efficiency vs insolation) Hourly average insolation Select major sites Hourly solar power generation at each site Correction for panel inclination Cos () Correction for temperature effect Power coefficient (W/deg. C) Extrapolate based on installed capacity at each site to get hourly solar power generation for the state Continue to LDC Methodology TN Solar Methodology (Micro-level)

  21. Solar power – monthly variation

  22. Biomass power – monthly variation Installed capacity – 450 MW (340 MW from bagasse cogen)

  23. Wind and solar power required for 1000 MW avg. peak saving

  24. Wind and solar power installations to replace 1000 MW base power Slope = 0.8666 which is almost equal to ratio of capacity factors (0.18/0.21 = 0.857)

  25. Base capacity savings with wind + solar

  26. Peak capacity savings with wind + solar

  27. A B C Hybrid scenarios – Impacts on LDC

  28. Impacts on LDC – capacity savings

  29. Develop generic guidelines for design Case study of sample systems Decision making Analysis Integrated design method INPUT OUTPUT Compare with Existing design methods Sample design Design approach

  30. Load estimation Sizing Distribution network using ViPOR Load flow analysis Yes Is the current location of source gives minimum loss No End Relocate the source Integrated design of Isolated power system

  31. Integrated design-Summary

  32. Photovoltaic array Charge controller Inverter Load Battery bank DC bus AC bus Sizing of Photovoltaic-Battery Systems • Objective: • To arrive at the set of all feasible configurations (Array rating and Battery capacity) to meet a given demand following a time-series simulation of the system Schematic of the System

  33. Mathematical model • Energy balance Power from the photovoltaic array • For a small time step, battery energy: Hourly energy balance Repeatability of battery energy Non negativity of battery energy Battery storage requirement

  34. Inputs: Hourly solar insolation data, Hourly load data, Photovoltaic system efficiency, Power conversion efficiency Estimation of the solar insolation incident on the array System simulation to obtain the minimum array size and the corresponding battery capacity Calculation of the minimum storage capacity for different array sizes greater than the minimum Plot of sizing curve and the identification of the design space Photovoltaic-Battery System Sizing (Deterministic Approach)

  35. Graphical representation • Sizing curve for given solar insolation profile, load curve and system characteristics

  36. Photovoltaic-Battery System Sizing (Example) For a remote location in Sagar island, West Bengal • Demand profile For an average day • Solar insolation profile Averaged values for the month of December

  37. Sizing curve and Design-space for the example

  38. Mathematical Formulations • Chance constraint: • Incorporating energy conservation equation of the storage: • Deterministic equivalent: • Generation of design space incorporating other constraints

  39. PV-Battery System

  40. Solar Water Heaters • Estimate potential for solar water heaters in a given area • Develop generic framework ‘Diffusion of Renewable Energy Technologies’

  41. Factors Affecting Diffusion Of SWHS • Location- Insolation • Water Usage Pattern • Cost of electricity • Capital Cost • Reliability • Potential savings • Subsidies/ Financial Incentives

  42. INPUT DATA Water usage pattern Location (Monthly average hourly temperature and radiation data) Characteristics of SWHS TRNSYS Auxiliary heating requirement (Monthly average hourly data) Economic Analysis MS EXCEL Savings in Electricity Cost Payback Period Analysis Cost of electricity saved Selection and sizing of SWHS Micro Level Decision Model (Parametric Analysis) TRNSYS (Transient System Simulation Program developed at SEL, University of Wisconsin)

  43. Weather data Hourly Global Solar Radiation & Diffuse Solar Radiation SOLAR RADIATION PROCESSOR Hourly Solar Radiation on Collector Surface Hourly ambient Temperature COLLECTOR LOAD (Hourly hot water usage pattern) STORAGE TANK AUXILIARY HEATER Auxiliary heating requirement Information Flow Diagram of Micro –simulation for SWHS

  44. Single end usepoint Classification based on factors* (j) Sub-class (i, j) Single end use point Potential Micro simulation using TRNSYS Identification and Classification of different end uses by sector (i) Hot water usage pattern Weather data Residential (1) Nursing Homes (3) Hotels (4) Others (5) Hospital (2) POTENTIAL OF SWHS IN TARGET AREA Technical Potential (m2 of collector area) Economic Potential (m2 of collector area) Market Potential (m2 of collector area) Energy Savings Potential (kWh/year) Load Shaving Potential (kWh/ hour for a monthly average day) SIMULATION Auxiliary heating requirement * Factors affecting the adoption/sizing of solar water heating systems Capacity of SWHS (Collector area) Target Auxiliary heating No. of end use points Technical Potential Economic Potential Market Potential SWHS capacity Technical Potential Constraint: market acceptance Economic Constraint Base load for heating Constraint: roof area availability Potential for i = 3 Potential for i = 4 Potential for i = 5 Potential for i = 2 Potential for end use sector (i = 1) Electricity/ fuel savings Price of electricity Economic viability Investment for SWHS Model for Potential Estimation of Target Area Target area Weather data, area details

  45. Technical potential Pij for sub-class j in sector i is where j denotes sub-class of end use points in sector i. Psj is the simulation output for a single end use point fj denotes fraction of the end uses m is the total number of sub-classes. fajis fraction of roof area availability Niis the number of end uses points in sector i Technical Potential for sector i is where i denotes sector Technical Potential of SWHS P(T) in the target area is Potential Of SWHS

  46. Economic potential of SWHS P(E): subset of technical potential ve = 0, if payback period > maximum acceptable limit ve = 1, if payback period < maximum acceptable limit Economic Potential

  47. Payback Acceptance Schedule MARKET POTENTIAL fp,j is fraction of potential adopters meeting economic criteria.