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Knowing Your Customers Better Through Load Research. Presented By: Lawrence M. Strawn Senior Retail Pricing Coordinator Orlando Utilities Commission September 21, 2004. Agenda. Background Define load research Getting started Meter sample selection Uses of data.
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Knowing Your Customers Better Through Load Research Presented By: Lawrence M. Strawn Senior Retail Pricing Coordinator Orlando Utilities Commission September 21, 2004
Agenda • Background • Define load research • Getting started • Meter sample selection • Uses of data
Orlando Utilities Commission • Located in Orlando, Florida • OUC provides electric, water and chilled water to Orlando, St Cloud and parts of unincorporated Orange and Osceola Counties. • OUC net generating capability is 1,285 MW. • The peak demand for FY2003 was 1,019 MW Winter and 969 MW Summer
Orlando Utilities Commission • The State of Florida is regulated • OUC provides fully bundled service (generation, transmission and distribution) • OUC electric customers: • Residential 158,000 (86%) • Commercial 25,000 (14%) • Total 183,000 • OUC does not have a large industrial load
Load Research • The process of gathering, verifying, and aggregating interval meter data to determine the behavior and timing of consumer demand.
Getting Started • Determine your objective • Metering equipment • Hardware required • Software required • Required resources • Determine meter sample selection • Capital and ongoing costs
OUC’s Load Research Objective • Gain a better understanding of customer and customer class usage characteristics. • Understand the costs associated with servicing varying usage characteristics. • Develop rate strategies that send the appropriate rate signals to customers. • Have necessary information available to develop innovative rates. • Seasonal time of use rates. • Curtailable rates. • Photovoltaic rates.
Metering Equipment Alpha solid state meter with load profile capability on circuit board. An internal modem is preferred. OUC’s meter alliance provider is Elster Electricity, LLC.
Hardware Required Handheld Itron Laptop Computer Phone Line to Meter And / or And / or Server
OUC is using: Premier Plus 4 MV90 Pervasive for MV90 Other products available Stark Datamatic Software is required to: Download Verify Store Organize Display Software Required
Required Resources Information Technology Forecasting Billing
Required Resources Meter Readers collect the interval load data using handheld Itrons. It takes approximately 3 - 5 minutes per meter to download the data Information Technology Forecasting Billing
Required Resources Meter Operations collects the raw data, verifies it, and ensures it is stored correctly in MV90. Information Technology Forecasting Billing
Required Resources Information Technology provides hardware and software support Information Technology Forecasting Billing
Required Resources Commercial Account Reps use the data when working with customers to 1) ensure they are on the correct rate or 2) help them better understand their operations. Information Technology Forecasting Billing
Required Resources Retail Pricing Coordinators use the data for the Cost of Service and Rate Design. Information Technology Forecasting Billing
Required Resources Commercial Energy Auditors use the data to help customers understand how their usage affects their monthly bill Information Technology Forecasting Billing
Required Resources Forecasting uses the data to prepare the sales forecast Information Technology Forecasting Billing
Meter Sample Selection • Review your load research objective. • Assess what data you already have. • Are there small customer classes or groups of meters you can gather 100% (census)? • Which customer classes are too large to census and must be sampled?
Number of Meters by Rate Class Census Sample
Determining Sample Size n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error
Determining Sample Size n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error
Population Standard Deviation • Standard deviation (σ) of what? • Kilowatt hours. • Load factors. • Average market value per MWh. • Market Price Vector • Florida Municipal Power Pool (FMPP) clearing house price vector • Calculated the σ of the average market value per MWh for 100 GSD2 secondary meters ($3.77).
Determining Sample Size n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error
Determining Sample Size n = Sample Size Z = Level of Significance (1.960 for 95% confidence, 1.645 for 90%) σ = Population Standard Deviation E = Acceptable Amount of Sampling Error
Determining Sample Size By randomly sampling 55 meters within a customer class, OUC can be 95 percent certain that the sample’s market value per MWh will represent the population’s market value within plus or minus $1.00/MWh.
Uses of Data Calculating Allocation Factors to Use in the Cost of Service and Rate Design
Step 3: Derive Residential/GSND Class Shape Res/GSND Line loss calculations must be included
Step 4: Verify Shape of Res/GSND Compare average market value per MWh of calculated shape to that of sample shape. The two market values should be within $1.00 per MWh.
Step 5: Compute Allocation Factors • Monthly coincident peak (12CP) • Non-coincident peak (NCP) • Average and excess demands • Average market value per MWh • By class • By time period within class
Sample Load Shapes * Florida Municipal Power Pool (FMPP) Clearing House Price Vector
Sample Load Shapes $39.85/MWh $39.00/MWh * Florida Municipal Power Pool (FMPP) Generation Clearing House Price Vector
Time of Use Rates(Summer) * Florida Municipal Power Pool (FMPP) Clearing House Price Vector
Other Uses of Load Research Data • OUConsumption Online • Enables customers to understand their cost causation • Totalize kW on contiguous site • Look at cost for an individual customer • Energy conservation programs