SCCPD – Load Curve and Demand Forecasting System

Between 2001 and 2002, UniSoma developed for Companhia Paulista de Força e Luz (CPFL), in partnership with Serviços Especializados (SEST) and RAM Computer Systems, SCCPD – Load Curve and Demand Forecasting System. This system collects and stores information regarding the active and reactive load curves at substations, transformers and large CPFL consumers. Besides this, the system supports several types of analysis, such as, demand forecast, according to the ONS (National Electric System Operator) requirements.

Problem Description

A load curve is a graph that shows the amount of energy being consumed over time at a determined point of the electric network. It’s a fundamental piece of information for the correct sizing of equipments. In case of industrial plants, the load curve is specially monitored during peak hours, when the energy cost is highest – in some situations, the production is reduced at this moment, or temporarily ceased.

At companies that market energy, such as CPFL, load curves are used to measure distribution losses. The load curves are also used in elaboration of demand forecast. Such studies are conducted to calculate future tension levels, which provide the optimum resource planning allocation for operations and expansion of the distribution network. Besides this, the demand forecast of various national electric companies are consolidated by ONS to analyze the current energy market supply conditions of SIN (National Interconnected System), as well as the generation of the Annual Electrical Operations Plan. In the “Load Forecast Consolidation” document, ONS attributes responsibilities and establishes the systematic of the consolidation process.

The Challenge

In July 2001, in partnership with SEST (Specialized Services) and RAM Computer Systems, UniSoma began development of SCCPD – Load Curve and Demand Forecasting System. The main objectives set for the project, at the time, were:

From a transactional point of view, the System should consolidate into a single data the base daily load curves for equipments and CPFL consumers (a) stored in mainframe – 5 million daily load curves, collected from 1992 to 1997 and (b) measured by registers and converted into load curve data base according formatting processes.

In terms of demand forecasting, the greatest challenge identified from the beginning was the size of the problem. The ONS specifications for load forecasting at the time were as follows:

A simple exercise can illustrate the magnitude of the problem. Considering a population of 250 substations, a load curve with 96 points (every 15 minutes), 2 kinds of load curves (active and reactive), 3 types of days (Saturday, Sunday and a typical weekday) for short term forecasting and 2 types (Saturday and a typical weekday) for medium and long term forecasting, 4,6 million points are necessary for the forecasting.

The Solution

SCCPD was delivered in mid 2008. The system is based on three layers. The user layer is composed of user interface components. From the business layer, logical drives control the sequencing and execution of business rules, as well as the transactional integrity of conducted operations. Finally, there is a data layer whose methods guarantee the integrity and availability of corporate information.

In practical terms, the Load Curve System (developed in Borland Delphi© on top of an Oracle© database) is basically composed of the following functionalities:

For ONS demand forecasting an orthodox formulation of Holt Winter’s adaptive time series with additional trends and seasonality was opted. The result of the data treatment is a load curve, which represents well the daily behavior of substations for each month considering the calibration data. For calibration, a set of interest point were chosen (instead of the 96 points that compose the whole curve), afterwards applying a “mask” representing the hourly evolution profile. The methodology was implemented in PL/SQL.

Besides forecasting for ONS a “Ten Year Diverse Study” was conceived, regarding the forecasting for substations and for a 15 year horizon, the maximum demand stratified by demand class. This study guides CPFL planning giving information regarding the load forecast for substations for the next years. The methodology adopted for the “Diverse Study” was based on a series of hypothesis and procedures internally developed at the company and that constitute, broadly, in the (a) breakdown of a reference curve according to known types of consumer classes and (b) application of growth rates to disaggregated load curves. The disaggregation procedure could be represented in the form of a Linear Programming Model. Due to this, the application was developed using the AIMMS tool.



 AIMMS Service Partner UniSoma