Load Research in the Smart Grid Era. Analytics for Harvesting Customer Insights

Transcription

1 1 Load Research n the Smart Grd Era Analytcs for Harvestng Customer Insghts

2 2 Agenda A lttle hstory Load research 101 What about the future? Conclusons

3 3 By Defnton: What s Load Research? An actvty embracng the measurement and study of the characterstcs of electrc loads to provde a thorough & relable knowledge of trends, and general behavour of the load characterstcs of the customers servced by the electrcal ndustry. Smply put: Load Research allows utltes to study the ways ther customers use electrcty, ether n total or by ndvdual end uses. Msson: Bulds the foundaton allowng the corporaton to leverage knowledge of electrc customer energy use patterns to enhance or protect shareholder value

4 Hstory of Load Research Load research began n the 1930s after WWII, there was sgnfcant electrc load growth n the U.S.1970s ol embargo slowed the expanson Utltes had over-forecasted need for capacty, and started to use load research for a better way to forecast components of ther system load In 1978, Publc Utltes Regulatory Polces Act (PURPA) requred the utlty ndustry to develop load research programs as a bass for cost-of-servce flngs In the 1980s, load research progressed from ts prmary role n class cost allocaton and rate desgn to other purposes such as Cost of Servce end use nsghts supportng EE/DR Customer Today, Load Research contnues to evolve, supportng multple busness functons whle helpng utltes embrace the challenges of Bg data End-Use Research System Plannng Segment Load Research Prcng Forecastng Dstrbuton Plannng Demand Response EE Evaluaton EE Program Desgn Slde 4

5 Load Research Supports Many Busness Functons Generaton Plannng Net System Output analyss Model Development Capacty Plannng Load Duraton Curves Rates/Prcng Class Demand Studes Bllng Determnants Allocaton Schedules Sample Desgn & Management Class & System Peak Analyss Major Account Demand Analyss Electrc Choce Load Proflng Forecastng Settlement Evaluaton LR Dstrbuton Plannng Substaton Load Analyss Transformer Szng Crcut Load Studes Load Management Loss Studes Customer Sde Servces & Analyss Demand Response Performance Contrbuton/Impacts On Peak Demographc Studes End-Use Load Studes Market Segmentaton & Targetng Major Account Analyss Indvdual Customer Analyss 1 Most Publc Servces Commssons requre that rate case Cost-of-Servce studes are based on Load Research demand allocatons - $165B of nvestment allocated usng Load Research 5

6 6 Load Research Overvew The Load Research Lfe Cycle Understandng Your Customers Resdental Load kw December kw Peak Week Need for Informaton Model Results Prelm. Plan Expand Sample Desgn Sample Data Populaton Data Sun 14 Mon 15 Tue 16 Wed 17 Thu 18 Fr 19 Sat 20 Sun 21 Local Tme Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Local Tme The MBSS Rato Model The MBSS rato model s a heteroscedastc, zero-ntercept regresson model relatng y to a sngle varable x. Prmary Equaton Secondary Equaton Defnton: error rato er Sample Desgn & Analyss N 1 N 1 1 N 1 N N 1 N 1 y x sd Here x 0 x The three parameters of the model are beta, the error rato, and gamma, denoted, er, 0 er N 1 N x 1 beta 0.8 er 0.6 gamma 0.8 n 0 97 N 116 n 53 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10, ,000-20,000-30, ,000 40,000 60,000 Error Rato Descrpton of assocaton Example > 1 Extremely weak but possble DSM end use meterng wth very poor trackng data 1 Very weak, conservatve Market research wth poor supportng data assumpton 0.8 Rather weak Resdental load study 0.6 Weak General servce load study 0.5 Typcal DSM logger study wth good trackng data 0.4 Strong Large C&I load study 0.2 Very strong End use meterng vs. DOE-2 smulatons 0.1 Extremely strong 1st year persstence study for motors 0 Least possble value, perfect assocaton Not expected Breakng Down the System Load kw System Pumpng Very Large Power Large Power Small Power Resdental Jurs 2 Resdental Jurs 1 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Local Tme 6

7 7 Load Research Processng Intalze System Hstorcally: Plan Sample An Month Analyss Cycle Sample Desgn/ Draw Sample Import Populaton Data Import System Data Import Sample Data (Samplng s Kng!) Prelmnary Plan Populaton Bllng Data Post- Stratfy Analyss Load Research Lfe Cycle Sample Desgn Hourly Analyss Specal Analyss Report Results Surveys & Audts Data Collecton The Promse of Today: An Analyss Cycle n Weeks/Days/Hours (Sample may be unlmted!) Meter Installatons

8 Ganng Customer Insghts Data Vsualzaton (Vsualze-IT) A pcture s worth a thousand words one of the man goals of data vsualzaton s to make t possble to absorb large amounts of data quckly 8 Commercal Bg Box Retal School Thermal Storage Athletc Feld Grocery Cat Fsh Pond

9 9 Ganng Customer Insghts Data Fuson Today s challenge s no longer the avalablty of prmary data but the ntegraton of that prmary data wth secondary sources Utltes need effectve strateges for extractng value and nsghts from nterval load nformaton data fuson can help

10 10 Smart Energy Program Analyss Buy/Use Applance Buy Tme- Based Rate Plan Buy Prepay Rate Plan Buy/Use HVAC Buy/Use Water Heatng Buy/Use Lghtng Acton Consumer Choce Drect load control Control Thermostat Moble/ Internet Swtches/ Plugs HAN Gateway/ Tablet Smart Energy Offers Tme-based prcng Drect load control Energy nfo dsplays Smart thermostats Prepad electrcty Applcatons Smart applances EV plans Self generaton Others Awareness EID Portal Alerts Peer comparson Advce & Behavoral Source: KEMA, adapted from Relant Energy

11 Ganng Customer Insghts Gettng Under the Load Profle 11 Gettng underneath the total faclty load curve contnues to be an ndustry goal Work contnues on nnovatve, costeffectve measurement technques for collectng load data as an alternatve to more expensve sub-meterng Non-Intrusve Load Montorng (NILM) Statstcal Methods lke Condtonal Demand Analyss (CDA) Subtracton Algorthms Low-Cost Sensors EPRI supported NILM research along wth recent nnovatons and new market entrants have revtalzed ths topcal area

12 Gettng Under the Curve Condtonal Demand Analyss (CDA) CDA produces an estmate of the allocaton of total consumpton for a customer among the end uses wthn the customer ste An old statstcal dea that s new agan Low cost, analytcal heavy opton Performs well wth large sgnature applances wth dfferentaton Typcally ft to annual or monthly whole premse consumpton, but, can also be ft on daly or hourly load data Lnear and/or Non-lnear models can be used Mult-collnearty can be an ssue 12

13 Gettng Under the Curve Subtracton Algorthms 13 Some load shapes are so dscernble that smple subtracton algorthms work qute effectvely for frst order approxmatons Used extensvely n prcng and demand response program evaluatons Set up test/control (comparson) envronment for study Demand Electrc Vehcles Average Hourly Weekday Comparson: Phase 2 vs Phase 3 Applance: Total Applance Bundle Analyss Type: Demand Hour Tme of Use Phase 2 Phase 3

14 14 Gettng Under the Curve Low Cost Sensors Relatvely modest nvestment snce ELCAP Increasng utlty nterest n end-use nformaton & performance Recent regonal Intatves (NEEP, NWPPC) Cost a concern Some lmted deployments

15 15 Where to Start Assessng the Four Stages of Analytc Readness

16 16 A comprehensve roadmap for predctve analytcs starts wth segmentng each opportunty nto functonal components (14 lsted) that a utlty can evaluate on ther own mert, takng nto account the mpacts to current busness processes and IT systems Data Analytcs Check-Up

17 17 Road Map Strategy Group the opportuntes nto feasble sets based on ROI, ease of mplementaton, mpact on busness processes, and nvestments n IT systems Assemble these sets and assocated nvestments nto a comprehensve roadmap Lay out the opportuntes along a short-, md- and longterm horzons for fundng and mplementaton

18 18 Conclusons Load research and load research technques make as much sense today as they dd n the past More data doesn t necessary mean more nformaton Plannng for the future paramount to enterprse analytcs

19 19 Thank You! DNV GL SAFER, SMARTER, GREENER