To put their generation capacity to good use and generate maximum profits, Gas Natural Fenosa needed to understand the factors behind electricity consumption and production. These factors include relative costs and capacity of generation assets; peak selling times for power; energy usage patterns; forecasts of temperature, wind, and rain; value of carbon credits; and transmission capacity on the grid. Determining how best to allocate demand involved solving a complex optimization problem that was beyond the capabilities of commercially available software. Furthermore, it was difficult to adapt commercially available models to changes in the regulatory and market environment of the Spanish power market.

“We must be able to respond quickly to changes in regulations, production capacity, and demand patterns,” says Caballero. “We tried using a commercial software package, but it could not provide the many answers we needed. In our situation, closed systems don’t work well. We needed an open development platform that we could use to develop our own algorithms and calculations.”

Gas Natural Fenosa engineers used MathWorks tools to develop models that predict demand, minimize production costs, and verify infrastructure capacity. The team used MATLAB^® to develop a set of core models that analyze available data, forecast results, and optimize generation plans. Each MATLAB model accesses a central database for historical power consumption and price data, weather forecasts, and parameters for each power plant, including maximum power out, efficiency, costs, and all the operation constraints that influence the plant dispatch. The model processes the data using algorithms developed and fine-tuned by the market team. The results are written back to the database, where they are accessed by market traders or by another model for further processing.

To develop a wind farm model, Caballero used MATLAB to correlate historical wind-strength measurements with actual electrical wind production. Starting with a simple linear correlation, he improved the model using knowledge of the underlying technology.

The team used Optimization Toolbox™ to solve linear programming problems—for example, to minimize production costs among available production plants given a set of constraints, including carbon caps and maximum capacity. They used Statistics and Machine Learning Toolbox™ to develop and evaluate price simulation scenarios and to produce value at risk (VaR) reports for management position. Using MATLAB Compiler™, the team created standalone programs for each model. These programs, which run automatically day and night, enable the development team to better manage updates and to control access to the models.

The team used Simulink^® to model the behavior of generators in the company’s infrastructure.