A multi-objective residential load management based on self-adapting differential evolution

Abstract

• An adaptive multi-objective optimization method for demand response is proposed. • Epsilon constrains method is used to get a single point lies within Pareto front. • A tiered incentive can encourage customers to participate in demand response program. • The results indicate significant cost reduction (53.34%) and peak load (43.04%). The current grid system is unable to meet the ever-increasing demands for energy due to annual population growth and the increasing number of energy-consuming devices that are being used in the modern era. One solution that helps to balance available generation capacity with required demand, and increases the grid reliability is the demand response (DR). For this purpose, this paper presents a residential load scheduling algorithm to manage the operation time of household appliances depending on the time of use (ToU) tariff. A self-adapting multi-objective differential evolution (SaMODE) algorithm was developed to find the optimal operation points of home energy appliances, with the aim of minimizing the customer electricity bill and peak load while maintaining the customer's convenience. The proposed tri-objective problem is solved by using the Epsilon-constraint optimization method. In addition, a prior approach is adopted to manage the tradeoff among objectives according to the customer's preferences. The desirable customer preferences are ensured through the prior approach by restricting two objectives to specific values, then finding an optimal corresponding solution for them. The findings and discussion demonstrate that the proposed SaMODE strategy benefits the customers by reducing their energy bill for up to 53.34% with an acceptable level of inconvenience. On the other hand, the power suppliers have also received benefits by decreasing the peak energy demand by up to 43.04%, which in turn increases the stability of the power system and lessen the burden on the utility company.