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Remotely Controllable Consumer Perspective Demand Response using Genetic Algorithm

With advancement in technology, there is a huge demand for electrical energy. This paper focuses on consumer's perspective of Demand Response using load shifting technique to minimize the energy consumption during peak hours and to reduce the operating cost of the consumer. This is done by scheduling an hourly energy consumption value for a load, such that the energy consumed during peak hours is low. This optimization problem is solved by using Genetic algorithm. It is considered as a consumer's perspective because the Demand Response process takes place by the consumer's preference to accept or decline the demand signal sent from the utility to control the load. This paper also focuses on automating Demand Response by implementing a remotely controllable system to schedule the load and to control the load on the consumer's side. The scheduled values from Genetic algorithm are stored in ThingSpeak, a cloud platform, so that the controller controlling the load can read the value from ThingSpeak and control the load accordingly. Simulations of Genetic Algorithm were done using MATLAB.