Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology.

This paper presents a multi-objective problem, whose resolution is carried out using a task management methodology and an evolutionary algorithm. The multi-objective problem includes demand response to real-time prices (RTP). Two objectives have been considered: the daily cost of electricity and consumer dissatisfaction, minimizing both. Hourly prices corresponding to a tariff currently existing in Spain have been used to evaluate the daily cost of the consumed electricity. The degree of compliance with the daily task programming required by the user has been used to evaluate the dissatisfaction of the consumer. Using a multi-objective evolutionary algorithm (NSGA-II), it has been possible to use the concept of Pareto optimality in order to determine the best solutions for the problem. The results show that the applied methodology can lead to cost savings between 6% and 12% without substantially changing the consumption habits of the consumer. If the consumer is willing to change his/her consumption habits, then the cost savings can reach 50%. In addition to cost savings, this methodology can reduce energy losses in the electric grid because the shift of consumption from peak to off-peak hours. [ABSTRACT FROM AUTHOR]