Importance of Data Driven Decision Making in Enterprise Energy Management | Dr. Satish Kumar

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Global HSE Conference | Sept 26 - 27 2013 | New Delhi, India

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  • 289 million as of WEO 2011, 400 million as of WEO 2007. Access to electricity definition? 1 fan, 1 light bulb?A village having an electricity line = electrified. Far more villages “electrified” than “households with access”
  • We’re all connected – in a real sense – more than we might realize. Disparity will catch up with us some time – or at least from time to time.
  • Explain the rising electricity use due to increase in commercial buildings and due to the architectural form that is not appropriate for Indian climate
  • Ankur to update using latest data from ECO-III project – Aalok’s email Also round-off all the nos to avoid “false accuracy”
  • India, #2, at extreme risk
  • Four climate sensitive health consequences: malaria, malnutrition, diarrhea and inland flood fatalitiesAggregated predictions from 21 supercomputer models of global climate, by UK Met Office Hadley Center:- Flooding risk goes up 3 times- 69% agricultural land worse off15 million people affectedhttp://www.independent.co.uk/environment/climate-change/climate-change-do-you-want-the-good-news-or-the-bad-6272822.html
  • Importance of Data Driven Decision Making in Enterprise Energy Management | Dr. Satish Kumar

    1. 1. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Topic: Importance of Data Driven Decision Making in Enterprise Energy Management By: Dr. Satish Kumar
    2. 2. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Outline 1 Indian Context 2 Building Sector – Energy Benchmarking 4 Conclusions 3 ISO 50001
    3. 3. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Sustainable Growth Conundrum - I Total Floor Space (Billion m2) Includes Commercial and Residential 8 41 Vehicle Fleet (Millions) Includes 2 and 3 wheelers, Passenger Vehicles, Buses and Trucks 51 377 Total Power Demand (Terawatt hours) Includes both Utilities and Captive 700 3870 Cement Demand (Million tonnes) 127 860 X 5 X 7 X 5 X 7 Source: McKinsey’s India Urban Awakening, 2010
    4. 4. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management 147; 0.5% 368; 1.3% 804; 2.7% 889; 3% 1,068; 3.6% 1,151; 3.9% 1,427; 4.9% 1,593; 5.4% 5,595; 19% 6,550; 22% 29,381 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 UAE France Germany Africa Latin America Japan India Russian… USA China World Million Tonnes of CO2 India could become the SECOND largest emitter of GHG emissions in the world at a per capita emission of 5 tonnes of CO2 Source: CO2 Emissions from Fuel Combustion - IEA (2010) Sustainable Growth Conundrum - II
    5. 5. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management No. of People Without Access to Power and Relying on Biomass (million) Countries/Region # of People Lacking Access to Electricity # of People Using Biomass for Cooking Africa 587 657 Sub- Saharan Africa 585 653 Developing Asia 799 1,937 China 8 423 India 404 855 Other Asia 387 659 Latin America 31 85 Developing Countries* 1,438 2,679 World** 1,441 2,679 Note: *Includes Middle East Countries, ** Includes OECD and Transition Economies Source: Energy Poverty, International Energy Agency (2010)
    6. 6. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Access to Electricity: A Social Imperative Images: www.aiche.org
    7. 7. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management July 2012 Blackouts ● Largest power outage in world history ● Affected 620 million people ● Half of our population ● 9% of world population ● 22 states ● 32 GW (a sixth of nationwide generation capacity) taken offline Sources: mapsofworld.com, Wikipedia
    8. 8. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Electricity Consumption (in Million kWh) 21.7% 21.4% 19.1% 100% Others Industrial Agriculture Commercial Domestic 2020-21E 1,493,457 8.1% 35.3% 11.4% 26.1% 2010-11 648,802 9.2% 34.7% 10.0% 24.8% 2006-07 455,749 7.5% 37.6% 8.8% 24.4% 9.2 % 8.7% x % Electricity consumption CAGR Note: Others include Railways, Public water pumping & lighting and bulk supply Source: Central Statistics Organization (for 2007 fig) 18th Electric Power Survey draft report, CEA, July 2011
    9. 9. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management End Use Sector Energy Use (IEA) Source: IEA 2009
    10. 10. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Planned vs. Achieved Generation Source: Power Sector in India (KPMG 2011)
    11. 11. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management The Energy Dilemma Energy demand in India by 2030 The requirement The availability Energy is scarce, expensive, unclean State Electricity Tariff Increase Rate Effective From Punjab ~ 13 % 1 April 2013 Kerala 7% 1 May 2013 AP ~ 23% 1 April 2013 Haryana 13% 1 April 2013 Karnataka 25 Paise 1 May 2013 Peak Shortage Energy Shortage
    12. 12. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Energy Efficiency is a No Brainer Primary Fuel 100 units 33 units 24 units 1 unit saved at end user 4.2 units saved at the power plant Power plant Efficiency = 33% T&D loss = 27% Source: Central Electricity Authority (2009) T & D Losses also include electricity losses unaccounted for
    13. 13. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Outline 1 Indian Context 2 Building Sector – Energy Benchmarking 4 Conclusions 3 ISO 50001
    14. 14. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Commercial Buildings Growth Forecast • Currently, ~ 659 million m2 (USAID ECO-III Internal Estimate Using MOSPI, CEA and Benchmarked Energy Use data) • In 2030,~ 1,900 million m2 (estimated)* – 66% building stock is yet to be constructed Year: 2010 659 M m2 Year: 2030 * Assuming 5-6% Annual Growth Current 34% Yet to be built 66% SOURCE: USAID ECO- III Project
    15. 15. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Commercial Electricity Use Growth Growth of Electricity Consumption in Commercial Sector in India (2003-08) SOURCE: Central Electricity Authority. 2009. General Review 2009 28201 31381 35965 40220 46685 11.3 14.6 11.8 16.1 0 10000 20000 30000 40000 50000 2003-04 2004-05 2005-06 2006-07 2007-08 GWh Growth in % over the previous year
    16. 16. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Rate of Growth of Energy Use 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2015 2020 2025 2030 New Buildings Existing Buildings
    17. 17. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Scope for Massive improvement BE LEAN - Halve the demand Review standards, reduce losses, avoid waste. times BE MEAN - Double the efficiency Buy efficient equipment, use it efficiently, avoid system losses, tune it all up. times BE GREEN - Halve the carbon in the supplies With on-and off-site measures equals You’re down to one-eighth of the CO2 BUT YOU NEED TO TAKE ALL THE STEPS!
    18. 18. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Reporting and Benchmarking at Two Levels ENERGY IMPORTED TO THE SITE (and associated emissions) • The fuel and energy commodities the building has to buy in. • Complies with national policy drivers. • Gives the headline CO2 indicator in EPCs and DECs. BUILDING ENERGY USE (BEU), with onsite renewables added • To gauge the building’s efficiency, whatever the supply mix. • To maintain comparability with existing benchmarks. • To charge on to occupiers. • So poor buildings can’t hide under low-carbon supplies. The two are identical where there are no onsite renewables
    19. 19. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Reporting and Benchmarking it used to be relatively simple … 1. Define the boundary of the premises. 2. Collect annual energy use data by fuel. 3. Identify the building type and floor area (confirm area units). 4. Multiply each fuel use by the appropriate CO2 factor. 5. Calculate performance indicators: • Electricity - kWh/m2 per annum. • Fossil fuels - kWh/m2 per annum. • Carbon dioxide - kg CO2/m2 per annum. 6. Adjust if necessary, e.g. for weather and occupancy. 7. Review against appropriate reference data, e.g. • Published benchmarks, e.g. consumption guides. • Performance in previous years. • Peer review versus comparable buildings. • Savings targets.
    20. 20. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Top-Down Entry Level 1. DEFINE THE PREMISES AND ENERGY-RELATED BOUNDARIES • Ideally combining metering availability with management responsibility. • Confirm if for landlord’s services, tenant’s direct supplies only, or the lot? 2. COLLECT BASIC DATA • Building type, e.g. office. Start with CLG classification for DECs? • Measure of extent, usually the floor area. Gross, nett and treated … • Annual electricity imported across the boundary, kWh. • Annual imports of other fuels, reported in kWh gross calorific value by fuel. 3. CALCULATE PERFORMANCE INDICATORS (and not just for carbon) • kWh/m2 of electricity. • kWh/m2 of combustion fuel and heat (ideally with heat weighted). • kg/m2 of CO2 at published factors (but other factors may also be needed) Also recommended – kWh/m2 of weighted energy (an indication of overall energy performance) Proposed weightings 1 for fuel, 1.25 for heat, 4.2 for electricity.
    21. 21. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Reporting and Benchmarking Can we interpret the results fairly?
    22. 22. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Benchmarking Data for Buildings Mean for different commercial buildings (Source: Building Energy Benchmarking study undertaken by the USAID ECO-III Project) Offices Area (m2) # Annual Hours kWh kWh/m2/year kWh/m2/hr Office (All) 17,100 4,570 3,457,000 242 0060 Public sector 12,800 2,420 1,380,000 109 0048 Private sector 18,600 5,350 4,202,000 290 0064 One shift 21,600 2,120 2,389,000 158 0075 Two shift 8,800 4,290 2,064,000 243 0058 Three shift 23,900 8,120 6,929,000 348 0044 Conditioned >=50% 14,600 4,820 3,615,000 269 0065 Conditioned <50% 28,600 3,420 2,727,000 83 0037 Hospitals Area (m2) # Beds kWh kWh/m2/year kWh/bed/year Multi specialty hospitals 8,200 170 2,398,000 362 13,998 Hotels Area (m2) # Rooms kWh kWh/m2/year kWh/room/year 1-3 star Hotels 9,300 100 2,347,000 271 19,396 4-5 star Hotels 14,300 150 3,513,000 274 20,381 Shopping Malls Area (m2) kWh kWh/m2/year kWh/m2/hr Shopping Malls 10,700 2,370,000 252 0056 Source: USAID ECO III Project
    23. 23. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management India Whole Building Data Whole Building Energy Use Metrics Whole Building Metric Units Standard Better Best Annual Energy Use kWh/m2.a 250 150 60 Peak Energy Use W/m2 90 40 20 Annual Energy Use/Occupant kWh/a/person 2250 1350 585 Source: LBNL Best Practices Guide for High Performance Indian Office Buildings 2012
    24. 24. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Performance Rating Tool for Hotels User Input Relative Ranking Based on Database of Indian Hotels Relative Percentage
    25. 25. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Outline 1 Context 2 Present Status 4 Conclusions 3 ISO 50001
    26. 26. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management ISO 50001 in Perspective International Management Standards Quality ISO 9001 Environment ISO 14001 Energy Management ISO 50001 New Health & Safety OHSAS 18001
    27. 27. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management ISO 50001 in a Nutshell • Helps establish management systems and processes to improve energy performance, in particular energy efficiency • Applies to all types and sizes of organizations • Defines how to develop and implement an energy policy – Establish objectives, targets and action plans Introduction 27
    28. 28. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management ISO 50001 in a nutshell • Can be used for certification/registration and/or for self- declaration of an organization's Energy Management System • Doesn't determine absolute requirements for energy performance. Commitments will be specified in the organization’s energy policy • Easy integration with other ISO management systems (Quality, Environment, Occupational health and safety) Introduction 28
    29. 29. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Energy Management must – be initiated by General Management – have an identified person in charge – be communicated at all levels – comprise a detailed Energy policy – supported by solid measurement – include a continuous Improvement process ISO 50001 – Framework
    30. 30. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management EnMS- Management Review • Inputs to the management review shall include: – Follow-up actions from previous management reviews; – Review: Policy and energy performance; – Status of corrective and preventive actions and recommendations for improvement – Projected energy performance for the following period • Outputs from the management review shall include: – Improvements in the energy performance since the last review; – Changes to the energy policy, objectives, targets, etc.; – Clear allocation of resources Introduct30
    31. 31. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management • ISO 50001 is an international standard that ISO 50001: A Business Catalyst • Governments can promote • Companies can adopt • Citizens can advocate for • Influences 60% Energy Use
    32. 32. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management ISO 50001: A Business Catalyst ISO 50001 brings multiple benefits to organizations CO2 reduction Energy Savings FrameworkCompliance Sustainability Image
    33. 33. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Adoption of ISO 50001 Globally (Top 20 countries by number of sites) • Europe leads the uptake in ISO 50001 certifications with more than 80% of the total certified sites • Germany: the market leader for ISO 50001 • Industrial firms have been the earliest adopters of the standard 7 % of the ISO 50001 certified sites in India are Schneider Electric sites
    34. 34. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management ISO 50001: Future Outlook Expected level of investment in ISO 50001 by Industry Group in 2013-2014 • Non Industrial firms starting to investigate ISO 50001 • ISO 50001 appeals to firms with existing centralized energy governance structures • Larger firms (revenues greater than $1 billion) are more likely to invest in ISO 50001.
    35. 35. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Making Energy Use and Savings Visible • Establish an Energy Baseline = energy use and energy consumption over a significant period of activity (e.g. 12 months) • Energy performance measured against the Energy Baseline • Identify Energy Performance Indicators (EnPI's) to monitor and measure Energy performance – EnPI’s refer to quantitative targets (e.g. energy use per unit of output) – EnPI’s customized for each organization or company – EnPI’s tracking should demonstrate continuous improvement of energy performance across the organization • Define and Implement Energy Measurement Plan, appropriate to the size and complexity of the organization Introduction 35
    36. 36. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management The Toyota Story! ● Toyota Motor Manufacturing Kentucky Inc. (TMMK), manufactures 500,000 vehicles per year—roughly 2,000 vehicles per day in two production shifts per day, five days a week. ● Energy Conservation Measures ● Condensed start-up time in Paint Dept. from 6 hours to 1 hour ● Eliminated compressed air blowoff ● Used meters for command and control ● Changed out process equipment ● Changed out facility HVAC, lighting ● Troubleshooting ● Assigning energy as raw material input From 1996 until now, the plant reduced energy significantly (in MMBTU/vehicle) 1996 11.32 2001 8.89 2008 5.81 2012 6.28 “It’s truly an enterprise system with a series of controllers and distributed servers to make that efficient, because we’re monitoring 30,000 points every few seconds, and storing 4,000 of those points in a database”, Mark Rucker, Manufacturing, Toyota.
    37. 37. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management World’s First building to get ISO 50001 ! A smart building ● Equipped with Schneider Electric solutions, including Remote Energy Monitoring ● Electric Vehicles charging station with PV solar panel roof ● Connected to the building vs. the grid ÷4 Final energy consumption vs. previous sites in the area 80 kwh/m²/annum Final energy consumption ROI in 5 to 7 years Certified •ISO14001 •HQE Exploitation •NF EN16001 •ISO 50001
    38. 38. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management ISO 50001 – Summary • The ISO 50001 international standard on Energy Management will cover organization processes to improve Energy performance, esp. Energy Efficiency (EE) • ISO 50001 includes quantitative items to make energy use visible and controllable. – It is based on a detailed Energy policy, including energy baseline, performance targets & action plans, KPIs • ISO 50001 could be the business catalyst that EE needs – a standard that governments can promote – companies can adopt – citizens can advocate for • ISO 50001 means benefits for businesses/organizations interested in cutting energy costs, improved productivity, and better energy management
    39. 39. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Outline 1 Context 2 Present Status of the Building Energy Efficiency Sector 4 Conclusions 3 ISO 50001 Framework
    40. 40. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Right Steps in the Right Order 1. Start with the need/service in mind, not the amount of “stuff” required to provide it. Check your assumptions. 2. Reduce the loads that cause the need for the service first – using passive means and interactive measures. 3. Select appropriate system types and design for elegance – question rules of thumb. 4. Use efficient equipment (most people start here). Look for the most efficient technology options available. 5. Switch off when not needed (controls) (most of the rest start here). 6. Examine waste streams: for reuse – by other systems/functions. Can waste be reduced? 7. Count all benefits and costs – upstream and downstream, capital and life-cycle. Use the right metrics. Source: Rocky Mountain Institute
    41. 41. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Potential of Building Energy Efficiency? • Business as Usual Existing Commercial Buildings: – Energy use intensity – ~250-300 kWh/sq. m. • Based on benchmarked data for over 1,000 commercial buildings all over India • Best Practice (Cost-Effective) New Building: – Energy use intensity – ~70-80 kWh/sq. m. • Actual numbers from a best practice ITES building
    42. 42. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Tragedy of a Pilot Project • Top management will pay the same attention to all projects • Integrated Building Design is a wonderful concept that will work swimmingly well in all projects • Companies will invest (in terms of people and time) the same level of effort in all projects • The A-team of designers, consultants, engineers, and site people will also work on typical projects • Lessons learned are portable and replicable • No extra cost is incurred in ensuring the success of the pilot project • Pilot projects set the benchmark for performance which can easily be matched by typical projects
    43. 43. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Importance of Plumbing and Philosophy The society which scorns excellence in plumbing as a humble activity and tolerates shoddiness in philosophy because it is an exalted activity will have neither good plumbing nor good philosophy: neither its pipes nor its theories will hold water - John W. Gardner
    44. 44. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Impacts of Climate Change Source: http://www.guardian.co.uk/environment/2010/oct/21/climate-change-superpowers, accessed 2012-09-06
    45. 45. Technical Session # 3B Topic : Importance of Data Driven Decision Making in Enterprise Energy Management Impacts of Climate Change Source: UCL Lancet Climate Change Health Impacts Study 2009 Disclaimer: Territorial boundaries are indicative, not precise

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