The purpose of this paper is to demonstrate the feasibility of reducing electricity cost for a manufacturing factory through scheduling in a smart grid scenario while maintaining production throughput. Different from traditional rate structure, electricity price of smart grid is time varying and dependent on the total demand. The most common strategy for a factory to reduce electricity cost is to shift electricity usage from on-peak hours to off-peak hours. However, changes in manufacturing schedules affect power demand and electricity price. Moreover, a manufacturing process cannot be interrupted after being started. This dynamic coupling brings additional challenges to scheduling problem that is already NP-hard. In this paper, a time-indexed integer programming scheme is developed and implemented in General Algebraic Modeling System to solve the scheduling problem. To demonstrate the approach, a hypothetical region including power distribution/transmission system, residential/commercial buildings and a flow shop operating 8/16 working hours/day is considered. The operation of residential/commercial buildings is subject to time-of-use tariff and described in GridLAB-D. Simulation results show that the factory electricity cost is reduced by 2%–4% without any production loss. The results also suggest that in addition to residential/commercial buildings, it is possible to involve manufacturing facilities in demand-side management.
- Manufacturing Engineering Division
Manufacturing Scheduling for Energy Cost Reduction in a Smart Grid Scenario
- Views Icon Views
- Share Icon Share
- Search Site
Zhang, H, Zhao, F, & Sutherland, JW. "Manufacturing Scheduling for Energy Cost Reduction in a Smart Grid Scenario." Proceedings of the ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing. Detroit, Michigan, USA. June 9–13, 2014. V001T05A001. ASME. https://doi.org/10.1115/MSEC2014-3926
Download citation file: