Stochastic Genetic Algorithm and its Application as a Demand Control Tool for Houses with Thermal Energy Storage Systems

This paper presents a load control tool for houses with Thermal Energy Storage systems (TES) within the framework of direct load control. A control vector’s creation method is presented: this vector – that works as a time switch to the electric heating system of the house – is daily given to the customer, offering the most economic 24-hour loading pattern that satisfies his heating demand and allows to estimate the actual storage level of the house. The loading pattern is generated by using the Stochastic Genetic Algorithm (SGA), an application of the standard Genetic Algorithm. As a complement of the SGA, it is as well presented a storage level updating tool that allows tracking the customer’s behaviour regarding to his adjustment to the given control.