Energy-efficient scheduling for an energy-intensive industry under punitive electricity price

Abstract

As global warming and climate change become more and more serious, governments and scholars are paying more and more attention to these problems. Considering that consumption in manufacturing accounts for about one-half of the world's total energy consumption, the industrial sector is extremely important for preventing global warming. Facing with a multi-objective scheduling problem in the energy-intensive industries under punitive electricity price, a hybrid flow shop scheduling problem with onsite PV power generation and battery system is proposed. An improved multi-objective evolutionary algorithm based on decomposition is adopted, which integrates the low-carbon method with existing algorithms in three stages. First, an enhanced NEH heuristic algorithm is used to improve the quantity of initial solution in the initialization. Then the right-shift and speed scaling method are adapted to reduce the total energy consumption without affecting the maximum completion time. In the third stage, the distribution of renewable energy is performed, and the renewable energy is rescheduled based on the electricity range. The result of random instances and critical peak pricing verify the effectiveness of the proposed study.