This document summarizes an event on energy system optimization trends hosted by the National Cleaner Production Centre in South Africa. It discusses the SA Industrial Energy Efficiency project which aims to reduce GHG emissions and enhance industry competitiveness through improved energy efficiency. The presentation covers elements of system optimization like evaluating work requirements and matching supply to demand. It also discusses the benefits of a structured long-term approach to system optimization over an ad-hoc approach and how to measure energy use through metrics and key performance indicators to track improvements.

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NATIONAL CLEANER PRODUCTION CENTRE
SOUTH AFRICA
Energy System Optimisation: Latest
Developments and Trends
Sashay Ramdharee
Regional Project Manager
14 September 2017

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• SA Industrial Energy Efficiency (IEE) Project
• Why energy systems optimisation?
• Elements of system optimisation
• Maintaining efficiency in complex environments
• Counter measures
• SA’s energy efficiency business case
o The purpose of energy metrics
o Definition of energy performance indicators
o Regression basics
o Understanding and interpreting regression result
Content

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To reduce GHG emissions and enhance competitiveness of
industry through improved energy efficiency and the
transformation of the market for industrial energy efficiency
products and services.
IEE Project Objectives

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Phase I (2010 – 2015)
Project Stakeholders
Phase II (2016 – 2019)

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IEE – A Global Programme
Planned
activities
Operational
South Africa
Moldova
Russia
Turkey
Ecuador
Malaysia
Thailand
Vietnam
India
Philippines
Egypt
Indonesia
Iran
Ukraine
Colombia
Macedonia
Myanmar
Other donors
 Swiss State Secretariat for Economic Affairs
 UK Department for International Development
 Government of South Africa
Operational in 17 countries.
Planned activities in 10 countries.

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IEE Project in numbers
2015 – Phase I Conclusion
384
Industrial
plants
engaged
2 153
GWh energy
saved
R1.74 b
Energy cost
avoided
1.9 m
tonnes CO2e
mitigated
55
case
studies

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Why Energy Systems Optimisation?

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Energy Systems Optimisation (ESO)
Component Approach Systems Approach
• Segregate components.
• Analyse performance of each in isolation.
• Look at how the entire group functions
together.
• How will changing one impact the other?
• Sales consultant selling a specific
technology (e.g. VSD, steam trap).
• More knowledge of system and its
interactions between components.
• 2-5% efficiency gains on the total energy
consumption.
• 10-50% average efficiency gains.
Shifting FOCUS from Component optimisation to Systems optimisation!!!

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Industrial Energy Systems
Typical Achievable Savings
• Motors: Motor-driven equipment
accounts for 64 percent of electricity
consumed by South African industries.
?
• Steam systems 20%
• Compressed air 20-50%
• Pump systems ?
• Fan systems ?

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• Evaluating work requirements, what do we actually
require?
• Matching system supply to these requirements.
• Eliminating or reconfiguring inefficient uses and practices
(throttling, open blowing, leaks, insulation etc.).
• Changing out or supplementing existing equipment
(motors, fans, pumps, compressors) to better match work
requirements.
• Applying sophisticated control strategies that allow
greater flexibility to match supply with demand.
• Identifying, correcting and upgrading maintenance and
operating practices.
• Performance Review.
Elements of System Optimisation

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ESO Benefits
 It saves industrial firms money.
 It increases reliability of operations.
 It has a positive effect on productivity and competitiveness.
 It can offer attractive financial and economic returns.
 Improved security of supply.
 ….
Why it is not happening?
Then

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• Senior management not realising the scale of opportunity.
• Management focus is on production and not on energy efficiency.
• Lack of information and understanding of financial and qualitative benefits.
• Lack of adequate technical skills to assess performance, developing and implementing
EE measures and projects.
• Processes change over time and inefficiencies can re-occur.
• ……
Barriers to ESOs

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How can system energy efficiency be maintained in this
complex, changing environment?

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Ad-hoc Approach to System Optimisation
0 5Years
-25%
-20%
-15%
-10%
-5%
0
+5%
Costs
Costs high
= Audit
Waste cutting, some
investment
Under control.
Costs high again:
Where’s that last audit?
Here we
go again!

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Structured Approach to System Optimisation
0 3Years
-20%
-25%
-15%
-10%
-5%
0
+5%
Costs
Investment
Initial savings
sustained
Housekeeping first – then investment
Becomes company
culture
Senior management commit to
programme

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• Phase 1: “Low hanging fruit” –
operational and maintenance
 Fix leaks
 Fix insulation
 Switch-off
 Run optimum equipment
 Optimum settings
 Check controls
 Training
 Maintain improvements
What Sort of Actions to Take?
• Phase 2: Investment required
 Variable speed drives
 Piping or ducting modifications
 New technologies
 EE Motors

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HOW DO YOU MEASURE ENERGY CURRENTLY AND WHAT
IS THE BEST APPROACH?
• kWh last month compared with the same month last
year.
• Actual cost compared with budgets.
• kWh compared with kWh of another similar factory.
• kWh/unit of production.
• Moving total of 12 months.
• More complex methods.
Energy Metrics for System Optimisation

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Is this Good Information?
0
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kWhpermonth

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Is this Good Information?
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
2010
2011
2012
2013

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Is it Easy to Show Improvement?
Yes, Introduction of ISO50006!

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• Determine assessment level (system, process, facility).
• Determine energy use of interest (dependent variable).
• Identify consumption drivers (independent variable).
• Collect historical consumption and driver data.
• Establish a baseline year (Year 0).
• Analyse link between consumption and drivers.
• Assess changes in EnPI relative to Year 0.
EnPI Development

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• Represent consumption VS
relevant variable.
• Observe the dispersion.
• Obtain the formula.
• Remember: Y= mX + c.
• c and m are constants.
• X is a measured “relevant
variable”.

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• What does the R2 mean?:
• High R2:
a) Strong correlation. Not necessarily good performance.
b) High R2 does not mean a lack of low cost saving potential.
• Low R2:
a) There are other variables.
b) Saving opportunities in operational control.
• P-value: Probability of being significant. P< 0.1
• Significance F: If F< 0.1 model is significant.
Understand and Interpret Results

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What can be Achieved
20,000
40,000
60,000
80,000
100,000
120,000
140,000
EnergyConsumption
10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000
Production
After (with
EnMS)
Before (without
EnMS)
© UNIDO, 2013

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The aim is to:
• Develop a model for expected performance.
• Compare actual with expected.
• Quantify performance, +ve or -ve
• React to deviations.
• Communicate to build commitment.
Building Confidence in Models

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NATIONAL CLEANER PRODUCTION CENTRE
SOUTH AFRICA
THANK YOU
Any Questions?
PHONE: 074 254 3488
EMAIL: sramdharee@csir.co.za
WEBSITE: www.ncpc.co.za