SGCC Peer Connect: Beyond AMR/AMI

Transcription

1 SGCC Peer Connect: Beyond AMR/AMI Using Quantitative Analysis to Make the Business Case February 27, 2013

2 Today s Presenters Erik Ellis Manager, Smart Grid Planning Arizona Public Service Jerry Jackson Leader and Research Director Smart Grid Research Consortium

3 Housekeeping You will receive a copy of the slides To the you used to register You can ask questions as we go along Simply type into the question box, as we will explain or raise questions during the Q&A We will answer all the questions submitted If we are unable to get to all the questions, they will be answered individually after the presentation

4 Questions & Audio If this is what you see Click on the orange arrow to expand your dashboard. In order to ask questions over the phone, please log in with your Audio Pin. Click on the + sign to open up the questions box. Use the Questions box at any time to type questions. We will answer questions during a Q&A near the end of the call. Yes, you will receive the slides after the webinar.

5 Agenda 1. Arizona Public Service Company Leveraging Advanced Metering infrastructure (AMI) Results from pilot programs Selected AMI Data Analytics programs 2. Smart Grid Research Consortium Smart Grid Investment Model Modeling Analysis of Smart Grid Programs Developing the Most Cost-Effective Smart Grid Strategy 3. Questions and Answers Session

6 Speaker #1 Name Background Erik Ellis Manager of Smart Grid Planning Arizona Public Service Director of Utility Scale Solar at enxco (now EDF Renewable Energy) Vice President of Commercial Operations with Ausra (now Areva Solar) Director of Project Development at First Solar Bachelor of Science in Mechanical Engineering from the Worcester Polytechnic Institute

7 APS Agenda Leveraging AMI Data: what we ll cover: 1. Quick APS overview and status of AMI deployment 2. Leveraging AMI Data: Results from APS Integrated Volt-VAR Pilot Selected AMI Data Analytics programs & opportunities

8 APS Overview Investor-Owned Utility ~ 7000 MW Peak 1.1 M Customers (800,000 Residential) Began AMI deployment in 2006 AMI conversion to be completed in ,000,000 AMI meters installed 150,000 customers - P1D meters 700,000 customers - Elster

9 What is Integrated Volt-VAR Control?

10 APS IVVC Pilot

11 Selected 6 Feeders

12 AMI Voltage Monitoring

13 IVVC Assessment Plan

14 Power Factor/VAR Management Results

15 Voltage Management Results

16 Preliminary Results

17 Other AMI Data Uses Today Revenue Protection/Theft Ops Pinging/Point-in-Time Read Remote Connects/Disconnects Solar Assets Monitoring (APS) Pre-pay Pilot Loss Modeling Renewable Feeder Tool Transformer Load Evaluation Next 3-9 Months Solar Assets Monitoring (Customer) Voltage Exception Reporting Outage Management Home Area Network & Residential DR Pilot (via programmable thermostats) Ops Remote Disconnect Solar Forecasting by home/feeder/region Revenue Forecast/Unbilled Revenue

18 Speaker #2 Name Background Jerry Jackson Leader and Research Director Smart Grid Research Consortium President of Jackson Associates Professor at Texas A&M University A PhD in Economics from the University of Florida BS in Mathematics from the University of Tennessee- Knoxville

19 Beyond AMR/AMI: Using Quantitative Analysis to Make the Business Case Jerry Jackson, Ph.D. Leader and Research Director Smart Grid Research Consortium February 27, 2013 Smart Grid Research Consortium, LLC 37 N. Orange Avenue, Suite 500 Orlando, FL

20 The Smart Grid Research Consortium (SGRC) is an Independent Research and Consulting Firm Providing SG Financial Analysis and Software Formed at Texas A&M University to Develop Quantitative Business Case Tool Transitioned to an Independent Research & Consulting Firm Completed Initial Smart Grid Investment Model Development Implement SGIM for Wilson, NC Complete d DR/DA Detailed Modeling Initiated CoServ DR/DA Analysis Initiated Kerrville SG Analysis Jan 2010 Jan 2011 Dec 2011 Sept 2012 Dec 2012 Jan 2013 Feb 2013 Coops, Munis, Public Utilities Vetted with 15 utility applications Continued Software and Analysis Development and Support Smart Grid Research Consortium February 19,

21 APS Examples Show That Smart Grid Investment Planning Requires A Different Evaluation Approach Smart Grid Technology/ Program Interactions and Synergies Exist Across all Utility Functions (Enterprise Impacts) Current Investments Impact Future Investment Returns (Path Dependence) OMS,DM S,... AMI DA Waiting to Invest Can Reduce Risk (Real Options) Utility Resources are Limited DR CVR 21

22 The Smart Grid Investment Model TM (SGIM) Addresses These Challenges Scope: Back office Substations Feeders Meters In-Premise Programs Smart Grid Investment Model Focus Smart Grid Investment Model Impacts on Generation Functions 22

23 kw Model Applies Utility-Perspective Smart Grid Investment Framework Costs Technologies/Programs Benefits Utility Service Area/ Customer Characteristics Utility Service Area/ Customer Characteristics Residential Customer Utility Average August 5 Hourly Loads Hour of the Day Member Utility Energy Use Characteristics AC Water Heat Other Cost Estimates ($) Annual Smart Grid Discounted Cost/Benefit Analysis Benefits Estimates ($) 23

24 SG Technologies And Programs Determine System Load Impacts And Related Benefits Technology impacts Program impacts Monthly Customer Class End-Use Hourly Load Models Economic and utility impacts Cost/Benefit Calculations (As Appropriate for Each Utility) Avoided power purchase costs Avoided generation costs Avoided transmission costs Avoided G&T investment costs Avoided distribution investment costs Avoided equipment maintenance & capital costs 24

25 An SGIM Case Study Illustrates Benefits of a Comprehensive, Flexible Planning Process Composite utility Current AMR, no CVR or customer engagement Concern over future power cost increases Utility executives questions o Which SG investments are most attractive? o What is the business case? o How do uncertainties impact results? 25

26 SGIM Programs/Applications Can be Turned On to Evaluate Costs and Benefits 26

27 Summary Statistics Show General Investment Results 27

28 Cumulative Net Benefit Chart Provides Graphical Summary 28

29 Begin Analysis With AMI/Smart Meters Unattractive investment 29

30 Why? Benefits are Limited Drill Down 30

31 Add Cust Engagement: AMI + Direct Load Control (WHDLC)+ PCom Tstat (PCT) Better, but not a great investment (6.5% IRR, year PB (16.25 DPB), 4.4 $M NPV) 31

32 Drill-Down Dashboard Visualizations and C/B Tables Provide Investment Details 32

33 Consider Stand-Alone WHDLC + PCT Greater per unit cost but still an attractive investment option (14.2% IRR, 8.25 year PB (10 DPB), 12.6 $M NPV) 33

34 Also Evaluate Assumptions Behind Results: Stand- Alone PCTs Outcomes 34

35 Drill Down to See How DLC and PCT Impact System loads and Power Costs 35

36 DA Category: Consider Conservation Voltage Reduction (CVR) 20% of highest value substations (30.3% IRR, 5 year PB, 24.2 $M NPV) 36

37 CVR + Stand Alone WHDLC + PCT 20% Customers with elec Wh on DLC and PCT plus 20% of highest value substations on CVR (21.2% IRR, 6.25 year PB, 38.7 $M NPV) 37

38 What Effect Will Wholesale Price Increases Have? AMI + Cust Engagement AMI plus WH DLC and PCT along with 40% increase in wholesale prices still provide modest returns ( % IRR, year PB, $M NPV) 38

39 Increased Prices Also Improve CVR + Stand Alone WHDLC +PCT Results Stand-Alone WH DLC and PCT along with 40% increase in wholesale prices provides substantial returns ( % IRR, year PB, $M NPV) 39

40 Consider CVR Risk: 80%, 70% of Load Impact 80% 70% 40

41 Consider WHDLC, PCT Risk: 70%, 50% of Load Impacts 70% 50% 41

42 Conclusions at This Point Reject AMI/Smart meter investments at this time Identified CVR and stand-alone WH DLC and PCT as potentially attractive individual options o Evaluate more detailed program parameters Relatively little risk with CVR initiative Potential risk with WH DLC and PCT programs o Evaluate targeted programs to reduce risk 42

43 An Actual Application Would Include Additional Items and Analysis With Results Reflecting Utility- Specific Detail Current infrastructure (metering, communications, etc.) Water, natural gas, emergency, etc systems Distribution automation (auto reconfig, FDIR) Customer valuations of reliability Various levels of CVR, DA deployment Contributions to OMS, DMS other management systems Alternative program deployment and incentives C&I customers, program timing DER, storage, HEV, etc. Feeder-level investment strategy development Scenario analysis... 43

44 Conclusions: Developing the Most Cost-Effective Smart Grid Strategy Requires: Recognizing unique utility SG cost-related characteristics Applying quantitative financial investment analysis Applying different kinds of analysis o Forward-solve; backward-solve o Consider enterprise impacts o Consider path dependences and real options o Consider uncertainty and risks Developing an investment strategy Continuously monitoring and updating strategy Planning long-term, acting short-term 44

45 Questions/Comments? SGRC Provides: Full Turnkey SG Investment Analysis Optional in-house SGIM Software implementation SG investment decision support 45

46 Takeaways & Questions

47 Thank you! You will receive a copy of the slides to the address you used to register. Erik Ellis Manager, Smart Grid Planning Arizona Public Service Erik.Ellis@aps.com Jerry Jackson Leader and Research Director Smart Grid Research Consortium jjackson@smartgridresearch consortium.org Links to Resources: APS Automated Meter Information Center: SGRC Business Case Analysis Summary:

48 Smart Grid Investment Model TM Analysis Areas AMI/Smart meters Distribution automation o Volt/VAR Optimization o Conservation Voltage Regulation o Auto Reconfiguration o Fault Location Isolation and Restoration o Asset Sizing and Management o Customer Reliability Valuations Customer engagement (by customer class) o Direct load control o PC thermostats o In-facility monitors o Customer engagement o Pricing o Other programs Other technologies/programs 48

49 Utility Data Are Applied in SGIM Hourly Load Modeling kw Member Utility Data: Historical Billing Data Energy, Demand by Customer Class Historical 8760 System Loads, Weather Data, Utility Customer Hourly Load Databases (MAISY Customer Class/End-Use Customer Class End-Use Hourly Load Model Estimation Zip-detailed Service Area Customer Characteristics Utility Energy and Hourly Load Models Residential Customer Utility Average August 5 Hourly Loads AC Water Heat 3 Other Hour of the Day Hourly Load Forecasts Monthly kwh and hourly loads Customer class, end-use detail (space heat, central Ac, non-central AC, water heat, other) 20-year forecast horizon 49

50 Monthly Models Capture Seasonal and Other Time-Dependent Factors kwh/customer kwh/ cust 1000 Res- Actual Res- Estimate Com- Actual Com- Estimate Month

51 Customer Class Hourly Load Models Reflect Contributions To System Loads kw kw July-fcst July-actual Jan-fcst Jan-actual April-fcst April-actual Hour of Day 51

52 Users Can Modify Parameters That Are Supplied With The Software Package 52