Manufacturing Scheduling for Energy Cost Reduction in a Smart Grid Scenario

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.

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An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

Energies ◽

10.3390/en13164266 ◽

2020 ◽

Vol 13 (16) ◽

pp. 4266

Author(s):

Amit Shewale ◽

Anil Mokhade ◽

Nitesh Funde ◽

Neeraj Dhanraj Bokde

Keyword(s):

Smart Grid ◽

User Satisfaction ◽

Dynamic Pricing ◽

New Technologies ◽

Heuristic Algorithms ◽

Optimization Techniques ◽

Future Research ◽

Scheduling Problem ◽

Electricity Cost ◽

Pricing Schemes

Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

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