Electrical load tracking scheduling of steel plants under time-of-use tariffs

Abstract

Electrical load tracking is a critical strategy for energy intensive industries to provide demand response (DR) in today’s electricity markets. It can take advantage of time-of-use (TOU) tariffs in steelmaking-refining-continuous casting (SRCC) scheduling. In this paper, we develop a continuous-time mixed integer nonlinear programming (MINLP) model subject to energy-awareness and TOU tariffs to manage the electrical load tracking scheduling of SRCC. Due to the complex cases among load intervals of electrical load tracking, time-slots of TOU tariffs and processing cycles of jobs result from different time granularities of the electrical load tracking and TOU tariffs, we formulate the objective functions with derived general formulations that can apply to all cases. An improved strength Pareto evolutionary algorithm 2, AHSPEA2, is developed to solve this proposed model, whose search ability and population diversity are enhanced greatly by two strategies, the arithmetic crossover operator and the improved hybrid self-adaptive mutation operators. The computational results demonstrate that AHSPEA2 is far superior and prove its effectiveness in providing high-quality scheduling plans which follow the pre-contracted load curve carefully to decrease deviations and reduce electricity costs simultaneously.