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How Measurement and Verification Activities Supports Energy - - PowerPoint PPT Presentation

How Measurement and Verification Activities Supports Energy Management: Case Studies Genevive Gauthier, National Director - Consulting Energy Summit 2018, Vaughan, Ontario May 30, 2018 1 AGENDA Todays Challenge: M&V at a Glance


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How Measurement and Verification Activities Supports Energy Management: Case Studies

Geneviève Gauthier, National Director - Consulting

Energy Summit 2018, Vaughan, Ontario May 30, 2018

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AGENDA

M&V at a Glance Case Studies Questions

Today’s Challenge: Demonstrating that only data and humans can maximize the benefits

  • f energy management

practices.

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M&V AT A GLANCE

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WHAT IS M&V?

Monitoring: › Observe or track progress

  • ver a certain period.

Measurement and Verification: › Process of quantifying through measurements energy and cost savings and verifying that expected performance is met.

Monitoring is not M&V

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M&V AND ENERGY MANAGEMENT

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CASE STUDY COMBINING THE EXPERTISE OF M&V EXPERTS WITH OPERATIONAL STAFF

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INITIAL SITUATION

› Steam is produced by fuel oil and biomass › Project:

  • Implement an Energy Management System (ISO 50001)
  • Save $750,000 per year

› Carried a pilot project (workshop) to evaluate the potential of implementing an EMS: focus on biomass boiler

Fuel Oil Consumption Annual Daily Total production facility 5,000,000 litres 13,700 litres Biomass boiler 500,000 litres 1,370 litres

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KPIs - BIOMASS STEAM BOILER

Fuel Oil Consumption Annual Daily Including scheduled maintenance shutdown 500,000 litres 1,370 litres Excluding scheduled maintenance shutdown 219,000 litres 600 litres

Wood chips Oil Steam

KPI = 600 litres / day KPI = 1,370 litres / day

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WORKSHOP WITH STAFF

Wood chips Oil Steam

24 Hours

KPI = 600 litres / day

February 28th Daily Consumption = 542 litres

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WORKSHOP RESULTS

› Reduce the cleaning time › Expected savings of $52,000 per year › Investment (pilot workshop): $15,000 › Payback is less than 4 months

Fuel Oil Consumption Annual Daily Days in operation only 219,000 litres 600 litres Optimal operation 100,500 litres 275 litres Savings Potential 118,500 litres 325 litres

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CASE STUDY IDENTIFY O&M OPPORTUNITIES WITH SIMPLE M&V TECHNIQUES

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INITIAL SITUATION

Engineers CEO M&V Expert

Hired to:

  • Estimate what could

be the expected energy cost increase if the energy management program stops

O&M measures, resulting from the deployment of an energy management contract, has led to substantial energy savings.

The program has to be sustained Why pay to maintain (and not decrease) the energy cost ?

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INITIAL ANALYSIS BY THE CEO

  • 2 000

4 000 6 000 8 000 10 000 April May June July August September October November December January February Mars x 1000 lb steam 2014 2015

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M&V EXPERT - ENERGY MODEL

› Energy consumption was modelled for the last year of the energy management initiative › 95% confidence interval was desired. › During a 5-month period, only

  • ne month was statistically

significantly higher than the base year

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CUSUM – SEE THE LIGHT!

› Disruptive event in April. › In 4 months, 2,750,000 lbs of steam has been wasted = $55,000.

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CONCLUSION

› M&V does not have to be expensive or rely on high-tech measuring equipment and software › Simple techniques can sometimes lead to surprising results.

M&V experts can shed lights on energy performance, even without fully understanding the process

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QUESTIONS ?

Econoler Key Expertise Some Canadian clients

› 35-year old Canadian consulting firm, specialized in the design, evaluation, financing and facilitation of energy efficiency projects and programs. › Team of 60 experts, including engineers, economists, as well as financial and marketing specialists.

Geneviève Gauthier National Director, Econoler ggauthier@econoler.com P (514) 764-9695 x4310

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APPENDIX

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M&V SAVINGS = AVOIDED COST

International Performance Measurement and Verification Protocol (IPMVP)

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OPPORTUNITIES IN TIMES OF CHANGE

A B

› Energy fluctuates because of controllable and uncontrollable factors. › Identify what you can control and extract lessons learned from the data.