Energy cost minimization for unrelated parallel machine scheduling under real time and demand charge pricing

There is an increasing concern in manufacturing systems to reduce electricity costs and lower carbon footprints. There are usually two ways of reducing manufacturing electricity costs. One consists of reducing the total electricity demand by using more efficient equipment and processes, and the other focuses on scheduling algorithms to determine the best response to pricing policies put in place by energy companies. In parts of the United States these pricing policies consist of both a consumption charge, which is related to the total amount of energy consumed, and a demand charge for the highest average power consumed over a given time window. Consideration for both time-dependent consumption charges and demand charges when planning a production schedule (e.g. parallel machine scenario) can reduce the total electricity costs. In this paper, a mathematical optimization model is proposed to schedule jobs in which both demand charges and consumption charges are considered for a facility with parallel machines. Results show that total electricity costs can be reduced significantly when demand charges are included in scheduling decisions versus considering only consumption charges. Results further indicate a significant reduction in peak demands for the facility, which provides a benefit for both the facility and the utility. Results also indicate that considering both demand and consumption charges does not increase the total electricity consumed. This study further analyzes the implications of changing model parameters such as the number of machines, machine utilization, and the price of the demand charge. The results provide a better understanding of how demand charges can affect the schedule of a manufacturing facility if total energy costs are to be minimized. (C) 2018 Elsevier Ltd. All rights reserved.