National Grid s Use of Big Data and Advanced Analytics to Enhance EE Program Success

Transcription

1 National Grid s Use of Big Data and Advanced Analytics to Enhance EE Program Success Dale Kruchten Director Analytics, Modeling & Forecasting National Grid

2 Presentation Introduction to National Grid s Analytics, Modeling & Forecasting Team Current state of National Grid s Energy Efficiency Programs, Challenges We Face and the Approaches We Take National Grid s use of Big Data & Advanced Analytics in Staged Approach to EE Program Delivery Optimization Advantages of National Grid s Approach Question and Answers

3 Introduction to National Grid s Analytics, Modeling & Forecast Team Part of the Customer Group, supporting Business Strategy, Marketing, Sales and Energy Procurement, but also support almost every other part of National Grid, including the UK. 18 team members, 12 with PhDs in various engineering disciplines, advanced applied math, computer modeling, operations research and economics. Extensive experience and expertise in big data analytics, highperformance computing, forecasting, time series modeling, agent-based simulation, optimization, data mining, GIS-based analyses and many other advanced quantitative disciplines. Established an extensive database of both internal and external data and knowledge pertaining to our customers, potential customers, the markets we serve and the energy technology with which we deal. Established a cloud-based, high-performance computing Virtual Private Cloud on Amazon to support our work. Established links with 3 DoE National Laboratories and a number of leading US universities.

4 Current State of Energy Efficiency Programs Changing regulatory requirements Increasing energy efficiency goals Continual advancements in technology Consideration of social benefits Evolving building industry landscape Huge portfolio of existing buildings Ever increasing customer awareness Extremely diverse customer sectors Changes in distinctive competency and approach of internal stakeholders

5 Energy Efficiency (EE) Program Challenges EE Program Delivery Goal Achieve the greatest possible EE impact at the least possible cost Key EE Program Delivery Challenges Incentive Optimization & Customer Targeting Opportunity Discovery & Sales Assistance Implementation & Tracking New Program Development

6 National Grid s Approaches to Addressing Energy Efficiency (EE) Program Challenges Key EE Program Delivery Challenges Incentive Optimization & Customer Targeting Opportunity Discovery & Sales Assistance Rational Program Goal Setting New Program Development Customer Analyses Propensity Analysis Relative Analysis Specific Analysis

7 Customer-Specific Staged Approach to EE Program Delivery Optimization Copyright 2014 National Grid USA Service Company, Inc.

8 Stage 2: COFEE Customer Optimization For Energy Efficiency Copyright 2014 National Grid USA Service Company, Inc.

9 Big Data Used in COFEE Internal Data (Utility) Customer data Consumption data Previous Energy Efficiency measures, retrofits & rebates data External Data (Public Domain/Purchased) Firmographic Envirographic Psychographic Demographic Tax Records Building Permits Parcel/Structure aerial image & LIDAR data to support determination of structure geometry Weather Data GIS Data

10 Modeling Expert System to Create Baseline Models Copyright 2014 National Grid USA Service Company, Inc.

11 Baseline Model Example

12 Model Tuning System to Calibrate Baseline Models Copyright 2014 National Grid USA Service Company, Inc.

13 Calibrated Baseline Model Example Model Actual Electricity Consumption (kwh) M 0 Gas Consumption (Therms) Electricity Consumption (kwh) M 1

14 Customer EE Optimization System Copyright 2014 National Grid USA Service Company, Inc.

15 Customer EE Optimization Example

16 Customer EE Assessment System Main Objective Investment Energy Costs Other O&M Costs Knock-On effects Non-reoccurring Costs Relevant Indicators Financial Analysis (Individual Customer s Point of View) Energy cost reduction (yearly costs and annuity of own/net investment) Net investment (gross investment minus subsidies/fundings) Macro-economic Analysis (Portfolio & Regulator s Point of View) Saving of primary energy consumption Gross investment (effectiveness of investment in achieving stated energy usage and emissions reductions goals) Energy costs including expected rise of energy costs Utilities, maintenance, labor, legal compliance etc. e.g. positive impact on revenue, increase in market share, etc. Savings in repair costs that would occur without changing the energy usage systems; costs for authorization (construction permits) IRR / MIRR (Minimum required IRR as INPUT) Pay-back Period Net Present Value (NPV) Benefit Cost Ratio (BCR) e.g. positive impact on economy, etc. Additional yearly energy system cost per unit of primary energy saved Impact of Financial Constraints on Optimization Criteria Maximum absolute savings vs. Maximum IRR/MIRR Maximum absolute primary energy savings vs. Minimum additional cost per unit of primary energy saved

17 COFEE Driven EE Program Opportunity Discovery & Sales Assistance Copyright 2014 National Grid USA Service Company, Inc.

18 Advantages of National Grid s Approach Lower program administration costs Reduction in modeling time Can help find deeper energy savings Buy-in from key internal stakeholders Easier to standardize system and train trade allies In-depth accessibility: No issues with proprietary software Continually updating database of building energy models Symbiotic uses: EE goal analysis, Load growth forecasting, DG & DR growth potential, etc. DoE's involvement: Increases credibility Technology Leader: Enhances National Grid s brand

19 Speaker: Dale Kruchten Director Analytics, Modeling & Forecasting National Grid 100 Old Country Road Hicksville, NY (516)