EMW Russell 2014
- 2. American Council for an Energy-E鍖cient Economy www.aceee.org ≒ ACEEE: a nonpro鍖t 501(c)(3). Catalyst to advance energy e鍖ciency policies, programs, technologies, investments & behaviors. ≒ 50 sta鍖 in DC, MD, DE, MI, WA & WI ≒ Focus on end-use e鍖ciency in industry, buildings, u%li%es & transporta%on ≒ Other research in economic analysis; behavior; na%onal, state & local policy. ≒ Funding: Founda%on Grants (52%) Contract Work & Gov. Grants (20%) Conferences and Publica%ons (20%) Contribu%ons and Other (8%)
- 3. Outline for Today ≒ Aspira%on: de鍖ne and recognize the full range of non-energy bene鍖ts (NEBs) resul%ng from energy e鍖ciency improvements ≒ Intermediate 鍖ndings ≒ Issues and opportuni%es for developing and using such informa%on 3
- 4. WHY NEBs? PLANT LEVEL ≒ Real dollars, full bene鍖ts of energy improvements remain hidden ≒ NEBs values accrue beyond u%lity bills Stronger accoun%ng of cash 鍖ows and investment returns Broader organiza%onal and business impacts Broader range of interested stakeholders Increased credibility, defense of energy management func%ons
- 5. WHY NEBs? REGIONAL ENERGY MARKETS ≒ Real dollars 竪 enhanced jus%鍖ca%on for capital investment ≒ Greater business returns 竪 greater compe%%veness and viability of u%lity ratepayers ≒ Clear accoun%ng of NEBs values: Stronger transparency of avoided costs for u%li%es Closer alignment of u%lity tari鍖s with actual value and bene鍖t of energy use Improved structuring of demand-side rebates and incen%ves
- 7. Next Best Outcomes If the ideal is not available ≒ Es%mate the e鍖ort needed to ahain the ideal: Sources? De鍖ni%onal consistency? Data standardiza%on? Time and e鍖ort? ≒ Census of what currently IS available. 7
- 8. CRITICAL ASSUMPTIONS ≒ INTEGRITY OF OBSERVATION. Change in energy consump%on can be wholly ahributable to speci鍖c projects. Change in energy consump%on and NEBs for any one project can be isolated from other simultaneous projects in the same facility. ≒ INTEGRITY OF MEASUREMENT. Accurate consump%on baseline measures. Measurement of savings are standardized across all observa%ons. ≒ NORMALIZATION OF VARIANCE. Before/Aner consump%ons are normalized for produc%on, weather, etc. When comparing two or more facili%es, adjustments can be made for di鍖erences in u%liza%on factor (annual hours of opera%on) and overall system design. ≒ INTEGRITY OF DATA COLLECTION. All facili%es have the ability and mo%va%on to measure and consistently document project-speci鍖c energy use before and aner EACH improvement. (NOTE: U%lity programs rou%nely rely on customer self-measurement and repor%ng.) There are no proprietary issues forbidding the facility to share its energy data. 8
- 9. 2.3 ELECTRICAL POWER 2.31 DEMAND MANAGEMENT 2.311 Thermal Energy Storage 2.313 Scheduling 2.319 Miscellaneous 2.32 POWER FACTOR 2.33 GENERATION OF POWER 2.331 DC 2.332 AC 2.34 COGENERATION 2.35 TRANSMISSION 2.351 Transformers 2.352 Conductor Size 2.4 MOTOR SYSTEMS 2.41 MOTORS 2.411 Opera%on 2.413 Hardware 2.414 Motor System Drives 2.415 Motor Maintenance and Repair 2.42 AIR COMPRESSORS 2.422 Hardware 2.423 Opera%ons 2.43 OTHER EQUIPMENT 2.431 Opera%ons 2.432 Hardware 2.7 BUILDINGS AND GROUNDS 2.71 LIGHTING 2.711 Level 2.712 Opera%on 2.713 Controls 2.714 Hardware 2.72 SPACE CONDITIONING 2.721 Maintenance 2.722 Opera%on 2.723 Hardware - Hea%ng & Cooling 2.724 Hardware - Air Circula%on 2.725 Evapora%on 2.726 Controls 2.727 Humidity control 2.729 Miscellaneous 2.73 VENTILATION 2.74 BUILDING ENVELOPE 2.742 Solar Loading 2.744 In鍖ltra%on 2.749 Miscellaneous NAICS INDUSTRY CLASSIFICATIONS 311, 312 Food, Beverages, Tobacco 313, 314 Tex%les & Allied Mill Products 315, 316 Apparel, Leather, & Allied Products 321 Wood Products 322 Paper 323 Prin%ng & Related 324 Petroleum & Coal 325 Chemicals 326 Plas%cs & Rubber Products 327 Nonmetallic Mineral Products 331 Primary Metals 332 Fabricated Metal Products 333 Machinery 334 Computer & Electronic Products 335 Electrical Equipment, Appliances, and Components 336 Transporta%on Equipment 337 Furniture & Related Products 339 Miscellaneous 33 Electricity improvement measures 18 Manufacturing industries -- 594 dis%nct measure+industry matches 30 replica%ons of each measure+industry -- 17,820 observaFons required to achieve staFsFcal validity. This is the number BEFORE accounFng for regional variance. U.S. DOE INDUSTRIAL ASSESSMENT CENTER DATABASE ENERGY CONSERVATION MEASURE CLASSIFICATION SYSTEM 9 HOW MUCH DATA?
- 11. OUR APPROACH ≒ Survey of North American experience ≒ ACEEE covered U.S. contacts, Navius covered Canada ≒ Obtained 51 U.S. items, 35 Canadian. Sources included u%li%es and allied regulatory groups, governments, academics, consultants, vendors, and literature. ≒ Items include reports, databases, project summaries 11
- 12. RESPONSE TYPES ≒ REAL, PROJECT SPECIFIC DATA (mostly CADDET) Based on post-implementa%on measurement ≒ EXPERT ESTIMATES BASED ON REAL PROJECT ANALYSES (IAC Database) Central tendencies derived from large numbers of ex ante project analyses ≒ INDIRECT HARD DATA Sta%s%cs describing frequency of NEBs observa%on, paybacks with/without NEBs, etc. ≒ MEASUREMENT PROTOCOLS Formulaic models for evalua%ng NEBs based on expert consensus ≒ PRACTICAL INTERPRETATIONS Project-level rules of thumb for NEB es%ma%on ≒ DIOGNOSTIC & METHODOLOGICAL THEORY Approaches for es%ma%ng NEBs (program administrator audiences) ≒ ANECDOTAL OBSERVATIONS Reference to speci鍖c projects impacts, minimal quan%鍖ca%on ≒ CONCEPTUAL JUSTIFICATION Theore%cal discussion impacts, no quan%鍖ca%on ≒ DEAD-ENDs No response, or no informa%on to report 12
- 13. 86 RESPONSES This summary DOES NOT include the IAC Database 鍖ndings (2,378 U.S. project savings es%mates) * Includes many global examples Not enough for sta%s%cally signi鍖cant measure of of central tendency, con鍖dence intervals, etc. as originally envisioned. * 13 0 5 10 15 20 25 30 DEAD ENDS CONCEPTUAL JUSTIFICATION ANECDOTAL OBSERVATIONS DIOGNOSTIC & METHODOLOGICAL THEORY PRACTICAL INTERPRETATIONS MEASUREMENT PROTOCOLS INDIRECT HARD DATA REAL PROJECT-SPECIFIC NEI DATA CANADA U.S.
- 14. 14 ABOUT EXISTING NEBs DATA ≒ Majority of documenta%on used to support evalua%ons of energy program cost e鍖ec%veness (total resource planning, societal impact costs, etc.) Data are not built up from individual projects (bohom up), but derived from aggregate program results (top down). ≒ Virtually all top-down NEI/NEB metrics/mul%pliers are derived from expert consensus, NOT from measurement of actual installa%ons. ≒ Single largest data source o鍖ers es%mates, not actuals (IAC Database). 16,300 assessments, 123,000 projects
- 15. EXAMPLE: CADDET* CASE STUDIES *Centre for the Analysis and Dissemina%on of Demonstrated Energy Technologies ≒ Data closest to mee%ng the criteria sought by this study ≒ 26 individual industrial, electrotechnology projects. Of these: 77% quan%fy kWh savings 4% quan%fy natural gas savings 42% describe gross savings (all forms) in dollars 58% describe electricity savings in dollars Dollar savings some%mes provided for various NEBs (from 4% to 31%) 35% provide simple payback The balance simply note that various NEBs exist, without quan%鍖ca%on Even this best data falls FAR short of the quanFty and quality needed 15
- 16. EXAMPLE: INDIRECT HARD DATA: (Woodru鍖, et al) hhp://www.leonardo-energy.org/sites/leonardo-energy/鍖les/documents-and-links/EECORE%20Bene鍖ts.pdf PERCENT OF RESPONDENTS CLAIMING NON-ENERGY BENEFITS NON-ENERGY BENEFIT Woodru鍖, et al Study N=63 Reduced maintenance material cost 92% Reduced maintenance labor 71% Permanent CAPEX avoidance 33% Avoided procurement cost 63% Avoided purchases of carbon o鍖sets 10% Enhanced PR, Image 44% 16
- 17. EXAMPLE: Massachusehs Technical Reference Manual hhp://www.ma-eeac.org/Docs/8.3_TRMs/1MATRM_2013-15%20PLAN_FINAL.pdf O&M ($) SAVINGS PER UNIT INSTALLED OVER ECONOMIC LIFE MEASURE LOW HIGH VARIOUS LIGHTING & LIGHTING SENSOR FIXTURES (p. 418) $0.41 $33.65 ANNUAL NEI VALUE PER kWh SAVED BY C&I LARGE RETROFIT PROJECTS CHP Systems Administra%ve costs, O&M $0.015 Prescrip%ve Ligh%ng Administra%ve costs, material handling, material movement, other labor costs, O&M, sales revenue, waste disposal $0.027 Prescrip%ve HVAC Administra%ve costs, other costs, other labor costs, O&M, rent revenue $0.097 Custom HVAC Administra%ve costs, material handling, material movement, other costs, other labor costs, O&M, product spoilage, rent revenue, sales revenue, waste disposal $0.024 Custom Ligh%ng $0.059 Refrigera%on $0.047 Other $0.056 Factors based on results of various studies conducted by KEMA, TetraTech and Op%mal Energy for Massachusehs program administrators 17
- 18. IAC DATABASE: Highlights MEASURE FREQ ANNUAL kWh SAVED NATGAS mmBtu SAVED PER MWh OTHER FUEL mmBtu SAVED PER MWh NON- ENERGY CENTS SAVED PER kWh RATIO: $NON-ELEC SAVINGS PER $ELEC SAVED AIR COMPRESSORS - Hardware 496 105,663,196 0 0 1.43 0.24 AIR COMPRESSORS - Opera%ons 25 1,450,649 0 0 0.34 0.06 MOTORS - Hardware 188 37,641,217 0.47 0 0.43 0.12 MOTORS Maintenance, Repair 23 3,889,539 0 0 0.84 0.14 MOTORS System Drives 1,123 331,467,976 0.34 0.17 0.06 0.06 OTHER MOTORS SYSTEMS - Hardware 511 82,807,274 1.54 0.11 12.25 2.21 OTHER MOTOR SYSTEMS - Opera%ons 12 1,255,260 0 0 16.07 2.68 Elec @ $0.06/kWh, natural gas @ $6.00/mmBtu Savings values are es%mates provided by experts across many projects Ra%os are based on mul%ple project values in summa%on Data NOT adjusted for region, annual u%liza%on factors, overall system design, opera%ng strategy, etc. 18
- 19. 19 AVAILABLE NEBs FINDINGS IN 2014 ≒ Overwhelmingly anecdotal data. Sta%s%cal reliability remains elusive. ≒ Indirect measurement of NEBs: rules of thumb or prescribed percent adders sanc%oned by u%lity regulators (7.5% to 30% of saved energy value) ≒ Small samples of describe frequency of observed NEBs by type, no valua%on
- 20. MOVING FORWARD: Opportuni%es to Generate Solid NEBs Data ≒ En%%es* interested in improving NEBs data ≒ Recent North American energy program rebates & incen%ves have generated lots of custom project feasibility data ≒ Consider a syndicated e鍖ort across par%cipa%ng u%li%es to generate NEB data from project reports: Pull from past reports, or Develop a collabora%ve data protocol to ensure consistent data collec%on across u%li%es, generate data from future custom projects 20 * BC Hydro, Ontario Power Authority, Manitoba Hydro, AMEC Environmental Infrastructure, SaskEnergy, NR Canada, Michaels Energy (support contractor to 22+ u%li%es), Na%onal Grid, PGE, WSU Clearinghouse
- 21. CONCLUSIONS ≒ Su鍖cient data does not yet exist to reliably predict NEBs ≒ Second Best data includes: Expert es%mates across many proposals (IAC database, n=2,378 since 2000) and Massachusehs Technical Reference Manual. S<ll very limited number of technologies, industries Actual data for a very small number (n=26) of projects (CADDET) Surveys describing percent of facili%es detec%ng NEBs ≒ Reconsider: Seek NEBs to improve simple payback? Or seek beher investment metrics for energy savings? 21