Importance of Data Driven Decision Making in Enterprise Energy Management | Dr. Satish Kumar
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Transcript of Importance of Data Driven Decision Making in Enterprise Energy Management | Dr. Satish Kumar
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Outline
1 Indian Context
2 Building Sector – Energy Benchmarking
4 Conclusions
3 ISO 50001
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Sustainable Growth Conundrum - ITotal 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 7Source: McKinsey’s India Urban Awakening, 2010
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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)
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Access to Electricity: A Social Imperative
Images: www.aiche.org
Technical Session # 3BTopic : 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 offlineSources: mapsofworld.com, Wikipedia
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
End Use Sector Energy Use (IEA)
Source: IEA 2009
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Planned vs. Achieved Generation
Source: Power Sector in India (KPMG 2011)
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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 plantEfficiency = 33%
T&D loss = 27%
Source: Central Electricity Authority (2009)
T & D Losses also include electricity losses unaccounted for
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Outline
1 Indian Context
2 Building Sector – Energy Benchmarking
4 Conclusions
3 ISO 50001
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
2003-04 2004-05 2005-06 2006-07 2007-080
10000
20000
30000
40000
50000
2820131381
3596540220
46685
11.314.6
11.816.1
Growth in % over the previous year
GW
h
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Rate of Growth of Energy Use
2015 2020 2025 20300%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
New Buildings Existing Buildings
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Scope for Massive improvement
BE LEAN - Halve the demandReview standards, reduce losses, avoid waste.
timesBE MEAN - Double the efficiency
Buy efficient equipment, use it efficiently,avoid system losses, tune it all up.
timesBE GREEN - Halve the carbon in the supplies
With on-and off-site measuresequals
You’re down to one-eighth of the CO2
BUT YOU NEED TO TAKE ALL THE STEPS!
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Reporting and Benchmarkingit 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.
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Top-Down Entry Level1. 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.
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Reporting and Benchmarking
Can we interpret the results fairly?
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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 EnergyUse/Occupant kWh/a/person 2250 1350 585
Source: LBNL Best Practices Guide for High PerformanceIndian Office Buildings 2012
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Outline
1 Context
2 Present Status
4 Conclusions
3 ISO 50001
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
ISO 50001 in Perspective
International Management Standards
QualityISO 9001
EnvironmentISO 14001
Energy Management
ISO 50001
New
Health & SafetyOHSAS 18001
Technical Session # 3BTopic : 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 policyEstablish objectives, targets and action plans
Introduction 27
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
improvementProjected 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Framework
Compliance
SustainabilityImage
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
ISO 50001: Future OutlookExpected 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.
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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.
Technical Session # 3BTopic : 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
÷4Final energy consumption
vs. previous sites in thearea
80 kwh/m²/annumFinal energy consumptionROI in 5 to 7 years
Certified•ISO14001•HQE Exploitation•NF EN16001•ISO 50001
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : 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
Technical Session # 3BTopic : Importance of Data Driven Decision Making in Enterprise Energy Management
Impacts of Climate Change
Source: UCL Lancet Climate Change Health Impacts Study 2009Disclaimer: Territorial boundaries are indicative, not precise