Moving from Energy Efficiency Potential Studies to Load Forecasts: The Role of Customer Behavior and End-Use Energy Service Demand

Transcription

1 Moving from Energy Efficiency Potential Studies to Load Forecasts: The Role of Customer Behavior and End-Use Energy Service Demand Behavior, Energy, and Climate Change Conference November 15, 2010 Mike Ting Principal Energy Consultant, Itron, Inc. 1

2 Issue Statement Energy efficiency (EE) potential studies are experiencing a new wave of attention in the US and around the world Potential studies now taking center stage in policy-making to establish aggressive energy savings goals Most recently, potential studies are also being used for resource procurement planning purposes to modify baseline forecasts to be consistent with aggressive energy savings targets Some of the key uncertainties in these studies are related to predicting various forms of customer behavior 2

3 Presentation Overview Roles of different forms of customer behavior in potential studies and load forecasting > Methods and data sources used to simulate and predict those behaviors > Behavior-related uncertainties in these studies How these uncertainties affect program and resource planning activities and decisions Strategies to reduce behavior-related uncertainties going forward Growing need for scenario analysis and probabilistic modeling approaches 3

4 Customer Behavior in Potential Studies End-use energy service demand > Intensity of energy services (e.g. lumens of artificial lighting per ft 2 ) and hours of use (e.g. usage profiles, operation hours, equivalent full load hours) Sources include on-site surveys, end-use metering studies, and calibrated building simulation studies > Main objective in potential studies is to establish the current baseline values for end-use energy service demand in order to determine the savings potential (in terms of kwh, peak kw, or therms) associated with the adoption of particular energy efficiency measures 4

5 Customer Behavior in Potential Studies (2) Customer adoption of energy efficiency measures > How much of the energy efficiency resource is likely to be captured through utility programs Rebates Audits Marketing > Customer adoption usually modeled as a function of stock turnover, customer awareness, cost-effectiveness to customer, and market barriers Sources include revealed preference studies, conjoint surveys 5

6 Behavior-Related Uncertainties in Potential Studies Two principal classes of uncertainty in EE potential studies Current market uncertainty > Associated with estimates of end-use baselines and energy efficiency measure data Forecast uncertainty > Associated with future levels of: end-use energy service demand cost-effectiveness of measures customer adoption preferences and behavior interactions between utility portfolios, codes & standards, and other DSM programs and initiatives 6

7 Behavior-Related Uncertainties in Load Forecasting Load forecasts that use end-use models face similar levels of current market uncertainty as potential studies > This type of uncertainty minimized in load forecasts that use top-down econometric modeling approaches Essentially same set of forecast uncertainties as potential studies, with the addition of: > Weather > Macro economic conditions (population, demographics, economic output, mix of economic activities) 7

8 Treatment of End-Use Service Demand in Forecasting In both potential studies and load forecasting, end-use energy service demand is treated as constant or changing at long-run historical rates Valid assumption over short-term planning horizons (e.g. 5 years) Increasingly invalid assumption over longer-term horizons (e.g. 10+ years) for certain key end uses > Lighting > Plug loads > Datacenters and server farms > New end uses? Electric vehicles? 8

9 Effects on Planning Activities and Decisions EE program planning > Reduced ability to anticipate and strategically target sources of load growth > Sub-optimal allocation of R&D resources Resource planning > Reduced ability to plan for/quantify the full range of likely resource needs > Risk of under-procuring both renewable (in RPS states) and/or fossil supply 9

10 Strategies to Reduce Uncertainties Short-run strategies > Comprehensive revealed preference studies to improve precision of customer adoption forecasts conjoint surveys audit follow-up studies Long-run strategies > Systematic use of comprehensive scenario analysis in program and resource planning (particularly for DSM resources) > Development of modeling platforms AND DATA COLLECTION INFRASTRUCTURES to support probabilistic modeling approaches 10