Building Technologies Office Program Peer Review Alexis Abramson Building Technologies Office Chief Scientist [email protected] Prioritization Tool April 2, 2013 1 | Program Name or Ancillary Text eere.energy.gov The prioritization tool provides quantitative insight to decision-making Vision: develop an analytical tool that considers building efficiency measures and technologies, and assesses and compares their potential value into the future Uses: • Inform programmatic decision-making • Examine “what if” scenarios • Create targets for FOAs • Set programmatic goals • Provide public with a comprehensive analytical tool FY13 peer review completed March 2013 BTO review of outputs ongoing in April 2013 – no output review today 2 | Building Technologies Office eere.energy.gov The analytical framework involves defining inputs, calculating, and generating outputs • Performance improvement • Cost U = S X E -1 X SD = S X C • Market Inputs • Lifetime Consumption • Stock and flow dynamics Service Demand per • Technology diffusion unit stock • Cost of Conserved Energy (CCE) Analysis • Staging framework Efficiency • CCE and annual energy savings • Technical potential • Full-Adoption potential Equipment Stock Outputs • BTO-Adoption adjusted potential • Staged potential Total energy use 3 | Building Technologies Office eere.energy.gov Outputs are viewed through different ‘lenses’ LENS 1: Imagine replacing all existing stock with the new measure overnight = Technical Baseline Potential s U energy T consumed Technical B LENS 2: A ‘stock and flow’ model accounts for potential today in 2030 unit replacement, elimination or addition = Maximum Adoption Potential (unstaged) Energy technology/measure LENS 3: To avoid ‘double counting,’ measures with lowest CCE “stage” first to capture their B t ) D share of the market = Maximum Adoption s s o U c T Potential (staged) e B c / C $ u ( E d E LENS 4: Market penetration and BTO influence e C A R C on acceleration can be examined using the Bass BTUs saved Diffusion Model to represent more “realistic” Improve market diffusion = Adjusted Adoption Potential performance 4 | Building Technologies Office eere.energy.gov Reviewing… #1: How much energy is currently used for lighting Business As Usual and is expected to be used in the future? (BAU) #2: Imagine that every lamp was replaced with an Technical LED lamp overnight. What would be the reduction Potential in energy use? #3: What if we replaced every lamp at the end of its Maximum life with an LED lamp (or at an accelerated rate Adoption when it was cost effective to do so)? How much Potential energy would be saved then? #4: Now we want as accurate a depiction as Adjusted possible of the rate our LED lamps are adopted by Adoption the market, taking into consideration the impact of Potential DOE’s spending on R&D, deployment, or standards activity. 5 | Building Technologies Office eere.energy.gov Imagine replacing existing stock with the new measure overnight = Technical Potential Accounts for market size and relative performance between measure and baseline, but does not account for cost effectiveness Residential R-10 Windows BAU 2000 s 1800 U T B 1600 T y 1400 g r e 1200 n e y 1000 Energy saved! r a m 800 i r P 600 400 200 Technical potential 0 2010 2030 2050 Year Fit BAU: Extrapolation of BAU based on NEMS Preliminary 6 | Building Technologies Office eere.energy.gov ‘Stock and flow’ accounts for replacement, elimination or addition = Maximum Adoption Potential (unstaged) Stock turnover slowly brings energy use closer to technical potential Residential R-10 Windows 2000 BAU 1800 s U T 1600 B Maximum Energy saved! T y adoption g 1400 r e potential n e y r 1200 a m i r P 1000 Technical potential 0 2010 2020 2030 2040 2050 Year Preliminary Staging is then used on ‘Maximum Adoption Potential’ to avoid double counting 7 | Building Technologies Office eere.energy.gov FY13 output example: water heating maximum adoption potential, 2030 (unstaged) $50 R: Gas water heater electronic ignition > 500 TBTUs TP $45 100–500 TBTUs TP C: Solar WH gas back < 100 TBTUs TP up $40 ) y r a m $35 R: More insulation for i r p water heaters U, $30 T B M $25 R: Solar WH gas back M R: condensing gas WH / up $ ( $20 y C: HPWH (COP 2.0) g er $15 R+C: gas absorption n e HPWH d C: condensing gas WH R: solar WH electric e $10 v r R: HPWH COP 2.2 back up e s R: WH insulation n $5 o blankets c R+C: Integrated heat f o C: HPWH t $- pump s o C 0 200 400 600 800 1000 1200 1400 1600 Primary energy savings: 2030 (TBTUs) Preliminary 8 | Building Technologies Office eere.energy.gov FY13 output example: water heating maximum adoption potential, 2030 (staged) R+C: gas HPWH, tech $50 limit R+C: ASHPWH, tech ) limit > 500 TBTUs TP y $45 r 100–500 TBTUs TP a R: Solar WH gas m < 100 TBTUs TP i backup r $40 p , U R: solar WH electric T B $35 M backup M R: More insulation for / $30 $ water heaters ( y g R: condensing gas r $25 e WH n e d $20 e v C: Solar WH gas back r e s up n $15 R+C: Gas absorption o c HPWH f t o $10 R: WH insulation R+C: Integrated heat s o blankets C pump $5 C: HPWH R: HPWH COP 2.2 $- 0 200 400 600 800 1000 1200 1400 1600 Primary energy savings: 2030 (TBTUs) Preliminary 9 | Building Technologies Office eere.energy.gov Market penetration scenarios can also be explored = Adjusted Adoption Potential • Bass Diffusion Model (p’s and q’s) accounts for market penetration • Market acceleration can be further enhanced through BTO investment • Standards/codes action initiates path towards full adoption Residential R-10 Windows BAU 2000 1800 s Energy U Adjusted T B 1600 saved! adoption T y potential g r 1400 e n e Maximum y r adoption a 1200 m potential i r P 1000 Technical potential 0 2010 2020 2030 2040 2050 Preliminary Year 10 | Building Technologies Office eere.energy.gov