A genetic algorithm approach to energy consumption scheduling under demand response

The drive towards a modern, efficient and information-driven grid - the "Smart Grid" - necessitates the incorporation of computationally intelligent infrastructure. For instance, residential load management strategies may require the scheduling of appliances in order to achieve certain objectives such as load factor maximization/peak-to-average (PAR) ratio minimization or minimization of energy cost. This paper presents an approach to one of such load scheduling problem, which involves the use of the metaheuristic optimization technique that is Genetic Algorithms (GA). We consider a scenario in which dynamic pricing is adopted and the objective is to minimize the overall cost of electricity payment while satisfying a set of constraints. MATLAB was used as the simulation platform and results confirm that Genetic Algorithm can optimize energy consumption over a set of constraints we have defined, thus minimizing overall electricity cost for the Nigerian consumer in a smart pricing environment.