Load Profiles

Introduction

The electric energy market is an hourly market with associated bids and hourly spot market prices. Each utility member of PJM is required to post hourly electric loads for every Load Serving Entity (LSE) serving retail load in the utility’s zone. To accomplish this, the utility must estimate hourly loads for customers who do not have hourly meters.

Some retail customers do not have meters capable of registering energy usage on an hourly basis. Load profiling is the process of allocating a customer’s accumulated kWh over a billing cycle to the individual hours in that cycle. Through load profiling, customers without hourly meters are able to participate in the electric retail market.

The allocation of total billed energy to specific hours may be based on historical load usage patterns (static load profiling) or real-time sample metering (dynamic load profiling). In the initial estimation for the settlement of energy among LSEs, BGE will use the static profiling method. In the final estimation and settlement, for which real-time metering data for the period to be settled have been collected, BGE will use the dynamic profiling method. Both of these methods are documented on this web site.

BGE’s load profiles are based on average Historical Hourly Load Data in kWh collected from a statistical sample of the segment to be profiled. From the sample data an average profile for each segment is created for each hour in the year. The sample data used to compute these averages are also utilized to calculate the hourly weather sensitive load profiles used for the day-after energy settlement with PJM.

Hourly Weather Sensitive Load Profile Methodology

Annually, a weather-adjusted, average hourly profiled load will be determined for each profiled segment on a daily basis in accordance with BGE’s load profiling methodology. This methodology is implemented in BGE’s settlement system, which computes profiled loads using the “Hourly Weather Sensitive”technique. This technique uses a defined season and day-type structure to run a linear regression of historical weather data on account load for each account segment. The profiles created consist of a series of regression equations expressing the relationship between weather and load for the pre-selected season and day-type combinations. The data for these regressions originate from the 1999 calendar year through the latest updated calendar year hourly weather and electric loads from the load research sample for each profiled segment.

Based on the season/day-type combination selected, the settlement system generates a weather response function for each hour represented by the season/day-type combination. The equation relates the historical loads to values in a weather range. The linear relationship is a piece-wise linear regression equation whose regression parameters are estimated using a search algorithm. The search algorithm identifies the optimal breakpoints for the regression lines such that the resulting regression model has the best possible statistical fit to the historical load data. The algorithm also ensures that boundary points between adjacent regression line segments of the weather response function coincide, thereby maintaining a continuous functional form.

These regression equations are used to compute estimated load values for the observed temperature points, such that each hour of the season/day-type combination will have a reported load value based on that hour’s weather. With such a specification, a representative load for a “typical” electric account within the profiled segment can be produced. The primary output of this profiling process is the load profile: a set of 24 hourly loads for a day-type and season combination.

BGE has also developed a tool called “BGE Profiler”. Users can input hourly weather data and a date, and BGE Profiler will calculate static weather sensitive hourly loads for that date. The tool has been set up in Microsoft Excel® and must be downloaded to use. It may be found under the BGE Profiler link on this web site.