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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


  1. How Measurement and Verification Activities Supports Energy Management: Case Studies Geneviève Gauthier, National Director - Consulting Energy Summit 2018, Vaughan, Ontario May 30, 2018 1

  2. AGENDA Today’s Challenge: M&V at a Glance Demonstrating that only data and humans Case Studies can maximize the benefits of energy management Questions practices. 2

  3. M&V AT A GLANCE 3

  4. WHAT IS M&V? Monitoring Monitoring: › Observe or track progress is not over a certain period. M&V Measurement and Verification: › Process of quantifying through measurements energy and cost savings and verifying that expected performance is met. 4

  5. M&V AND ENERGY MANAGEMENT 5

  6. CASE STUDY COMBINING THE EXPERTISE OF M&V EXPERTS WITH OPERATIONAL STAFF 6

  7. 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 7

  8. 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 = 1,370 litres / day KPI = 600 litres / day 8

  9. WORKSHOP WITH STAFF February 28th Daily Consumption = 542 litres Wood chips Oil Steam KPI = 600 litres / day 24 Hours 9

  10. 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 10

  11. CASE STUDY IDENTIFY O&M OPPORTUNITIES WITH SIMPLE M&V TECHNIQUES 11

  12. INITIAL SITUATION O&M measures, resulting from the M&V Expert deployment of an energy management contract, has led to substantial energy savings. Engineers Hired to: The program has to be - Estimate what could sustained be the expected energy cost increase if the CEO Why pay to maintain energy (and not decrease) the management program stops energy cost ? 12

  13. INITIAL ANALYSIS BY THE CEO 10 000 8 000 x 1000 lb steam 6 000 4 000 2014 2015 2 000 - April May June July August September October November December January February Mars 13

  14. 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 one month was statistically significantly higher than the base year 14

  15. CUSUM – SEE THE LIGHT! › Disruptive event in April. › In 4 months, 2,750,000 lbs of steam has been wasted = $55,000. 15

  16. 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 16

  17. Geneviève Gauthier QUESTIONS ? National Director, Econoler ggauthier@econoler.com P (514) 764-9695 x4310 Econoler Key Expertise › 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. Some Canadian clients 17

  18. APPENDIX 18

  19. M&V SAVINGS = AVOIDED COST 19 International Performance Measurement and Verification Protocol (IPMVP )

  20. OPPORTUNITIES IN TIMES OF CHANGE › Energy fluctuates because of controllable and uncontrollable factors. › Identify what you can control and extract lessons learned from the data. A B 20

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