SEDS Review Chris Marnay Michael Stadler Inês Lima Azevedo Judy Lai Ryoichi Komiyama Sam Borgeson Brian Coffey May 7, 2009 SEDS Building Sector Module LAWRENCE BERKELEY NATIONAL LABORATORY is a U.S. Department of Energy National Laboratory Operated by the University of California

Building Sector in the SEDS Context Macroeconomics Biomass Coal Natural Gas Oil Biofuels Hydrogen Liquid Fuels Heavy Transportation Industry Light Vehicles Macroeconomics Converted Energy Primary Energy End-Use Buildings Electricity

3 Importance of the Building Sector  Large share of energy demand Building sector accounts for: 73% of electricity demand (2007), 33% of gas demand (2007), 38% of CO2 emissions (2006)  Has been fastest growing Electricity demand growth from 2000 to 2007 Building: +383 TWh, Industry: -58 TWh, Transport: +2 TWh (+2.2%) (-0.8%) (+5.3%)  Slow stock turnover (about 80 years)  Active, passive technologies & on-site generation (ZNEB)

Building Sector Data Flow Macroeconomics Natural Gas Building Liquid Fuels Liquids Fuel Electricity Natural Gas Light Fuel Oil Price Natural Gas Price Electricity Price GDP Macroeconomics Capital Investments Natural Gas Demand Fuel Oil Demand CO 2 Emissions Incoming Data Outgoing Data Population Disposal Income Peak Electricity Demand Base Electricity Demand Electricity Electricity Demand

5 Preliminary Assumptions  One national region Uniform energy price, uniform solar isolation… Space heating & cooling are considered by Census region  Two building types (commercial & residential)  Annual decision making  Fixed market share allocation: α constant over the forecast period (except for PV)

6 Buildings Lite Module  Covers both residential and commercial  Tracks building stock  Meets building services  Treats buildings systemically  Enables analysis of major technologies R&D programs  Uses expert elicitation of potential advances  Runs stand-alone or integrated

7 Buildings Module Logic Flow inputs (GDP, population, fuel prices etc.) floorspace forecast service demand forecast (not energy!) passive high, medium and low efficiency buildings on-site generation (PV) outputs (electricity, gas, light fuel demand,CO2 emissions, PV generation) heating cooling lighting, DHW, refrigeration, ventilation, etc active - Heating insulation - Solar gains - Daylighting - Natural Ventilation - HDD, CDD - Lighting - Hot water - Refrigeration - Ventilation - Plug loads R&D is considered in lighting Policy instruments R&D is considered in PV

8 Early R&D Effect Examples first cut photovoltaic example:  Takes stochastic inputs for GDP, energy prices, & population  Applies PV performance forecast  Implements expert elicitation of potential advances  Employs the systemic approach (other early example is solid state lighting)

9 Data Sources  Historic floorspace input data are based on commercial and residential energy intensity indicators based on Pacific Northwest National Laboratory(PNNL), Energy End-Use Flow Maps for the Buildings Sector, D.B. Belzer, September 2006  The final energy demand data, obtained from PNNL, CBECS, & RECS, as well as the Annual Energy Review (AER) for 2005, for each fuel were divided by the floorspace estimates for 2005 and used for service demand forecasting.  Equipment types were considered for refrigeration, space cooling, space heating, lighting, water heating, and ventilation. All other end- uses were categorized as plug loads. The installed equipment stock information is based on Berkeley Lab’s own calculations derived from appliance manufacturers’ shipments data, CBECS, RECS, McGraw-Hill Corporation’s Analytics and AEO-07 (Annual Energy Outlook 2007)

10 PV Module Modeling Framework  Regression approach : PV generation is predicted by regression - including PV cost as independent variable - not including PV cost as independent variable  Logit function approach - two kinds of α are assumed. with time lag : α is regressed with time lag of alpha and electricity price without time lag : α is regressed with electricity price - initial α determined by maximum likelihood

11 Expert Forecasts: PV in Commercial Sector

Effect on Demand (Commercial) Total commercial sector electricity demand Commercial PV generation (No DOE Funding) *Logit function approach (with time lag of alpha) *Results are simulated by building module, stand-alone

PV Generation of Commercial Sector in 2050 *Above results are simulated by integrated module (version R170)

Net Energy Demand of Building Sector Total Net Energy Demand (Light fuel, Gas and Electricity) Carbon Cap - Base Case in 2050 *Above results are simulated by integrated module (version R170) 14

CO2 Emissions of Building Sector CO2 Emissions Carbon Cap - Base Case in 2050 *Above results are simulated by integrated module (version R170) 15

16 Future Work  *Regionality  Other passive & active technologies * Windows, heat-pump water heaters, geothermal heat pump, & ……..  Building types  Other on-site generation technologies; CHP …  Logit sophistication

ON TO THE NEXT MODULE

Comparison of PV in Building Sector* with AEO209 * Commercial + Residential, using integrated version R.170 PV Capacity PV Capacity & Generation **AEO2009: Annual Energy Outlook 2009 with Projections to 2030, Updated Annual Energy Outlook 2009 Reference Case with ARRA

Sensitivity Analysis Base case: No carbon regulation, no forcing of prices, no DOE funding, R&D improvements High oil price scenario: $100/bbl in 2005, ramps linearly to $500/bbl by 2030, and stays at $500/bbl for rest of simulation High natural gas price: $8/MMBtu in 2005, ramps linearly to $50/MMBtu by 2030, and stays at $50/MMBtu for rest of simulation Carbon cap: Starts at 5902 million metric tCO2/yr in 2010, ramps linearly down to 4000 million metric tCO2/yr by 2035 (roughly 80% of 1990 levels), and stays at 4000 million metric tCO2/yr for rest of simulation. DOE R&D program: Implement improvements associated with target-level funding for technologies that have program goals. Subsidy 50%: 50% of PV cost is subsidized.

20 MS = Market share LCOE = levelized cost of energy (>0) υ = utility α = scaling factor i = technology types i ∈ {utility electricity, PV gen.} t = time Role of Logit Alpha

21 PV Generation in Building Sector (Without DOE funding) *Results are simulated by building module, stand-alone version 04/09/09

22 Maximum PV Generation based on the Peak Load Constraint and PV generation in the Building Sector (Without DOE Funding) *Results are simulated by building module, stand-alone version 04/09/09

23 PV Generation in the Building Sector under DOE Funding Scenario (Alpha is Subject to “With Time Lag”) *Results are simulated by building module, stand-alone version 04/09/09

24 From DOE projections! no DOE 2015 no DOE 2020 no DOE 2020 with DOE funding DOE projection for LED device cost in 2015 (with funding) DOE projection for device cost in 2020 (with funding) $/klumen 2010 with DOE funding 2015 with DOE funding percentile S.S. Lighting Example

25 SSL Efficacy 2020 with DOE funding DOE’s 2007 estimate for LED luminaire efficacy DOE’s 2012 projection for luminaire efficacy (with funding) 2020 no DOE funding DOE’s 2010 projection for LED luminaire efficacy DOE’s 2015 projection for luminaire efficacy (with funding)

26 Lighting Consumption (Commercial) *Results are simulated by building module, stand-alone version

PV Generation of Building Sector in 2050 *Above results are simulated by integrated module (version R170)

28 Buildings Module Logic Flow inputs (GDP, population, fuel prices etc.) floorspace forecast service demand forecast (not energy!) passive high, medium and low efficiency buildings on-site generation (PV) outputs (electricity, gas, light fuel demand,CO2 emissions, PV generation) heating cooling lighting, DHW, refrigeration, ventilation, etc active - Heating insulation - Solar gains - Daylighting - Natural Ventilation - HDD, CDD - Lighting - Hot water - Refrigeration - Ventilation - Plug loads R&D is considered in lighting Policy instruments R&D is considered in PV