A Demand Response Schema in Industry: Smart Scheduling Approach for Industrial Processes

The manufacturing sector is recognized as the largest energy consumer within the smart grid. Excessive energy usage in production lines poses significant challenges, such as increased peak demand, high energy costs, strained grid resources, and power outages. Implementing demand response programs can address these issues and provide reliable and stable power to customers. Additionally, the integration of renewable energy sources can notably reduce carbon emissions and support sustainability objectives. To enhance efficiency, scheduling and intelligent manufacturing techniques can shift the execution time of production processes to off-peak periods and adjust consumption patterns on the production line. In this study, we propose a method called Scheduling for Industrial Processes to modify Energy consumption behavior (SIPE) under specified deadlines. SIPE offers economic benefits through an energy storage system for industrial customers participating in demand response programs. Moreover, it modulates energy consumption based on a maximum negotiated energy cost, which is determined as the highest allowable energy consumption cost within a defined scheduling period between the power provider and industrial customers. The proposed approach coordinates processes based on their durations and defined constraints. To evaluate the effectiveness of this approach, we selected Additive Manufacturing, as it is one of the most energy-intensive industries and is used across various manufacturing fields. We conducted numerous experiments by varying production parameters in the manufacturing line and compared the results with state-of-the-art approaches. The performance evaluation results demonstrate a significant reduction in both energy costs and power demand specially during peak periods.

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Published
2024-12-24
DOI
https://doi.org/10.1109/ikt65497.2024.10892614
Citations
1
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.35
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A Demand Response Schema in Industry: Smart Scheduling Approach for Industrial Processes

Maryam Tayefeh Mahmoudi, Hamid Abrishami, Negin Shafinezhad
1 citations
Scheduling and Optimization Algorithms
0.35
article

A Demand Response Schema in Industry: Smart Scheduling Approach for Industrial Processes

Maryam Tayefeh Mahmoudi, Hamid Abrishami, Negin Shafinezhad
article en
1 citations

Abstract

The manufacturing sector is recognized as the largest energy consumer within the smart grid. Excessive energy usage in production lines poses significant challenges, such as increased peak demand, high energy costs, strained grid resources, and power outages. Implementing demand response programs can address these issues and provide reliable and stable power to customers. Additionally, the integration of renewable energy sources can notably reduce carbon emissions and support sustainability objectives. To enhance efficiency, scheduling and intelligent manufacturing techniques can shift the execution time of production processes to off-peak periods and adjust consumption patterns on the production line. In this study, we propose a method called Scheduling for Industrial Processes to modify Energy consumption behavior (SIPE) under specified deadlines. SIPE offers economic benefits through an energy storage system for industrial customers participating in demand response programs. Moreover, it modulates energy consumption based on a maximum negotiated energy cost, which is determined as the highest allowable energy consumption cost within a defined scheduling period between the power provider and industrial customers. The proposed approach coordinates processes based on their durations and defined constraints. To evaluate the effectiveness of this approach, we selected Additive Manufacturing, as it is one of the most energy-intensive industries and is used across various manufacturing fields. We conducted numerous experiments by varying production parameters in the manufacturing line and compared the results with state-of-the-art approaches. The performance evaluation results demonstrate a significant reduction in both energy costs and power demand specially during peak periods.

Ferdowsi University of Mashhad (IR)
Industry, innovation and infrastructure
Openalex Percentile: Top 31%
Scheduling and Optimization Algorithms
0.35
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A Demand Response Schema in Industry: Smart Scheduling Approach for Industrial Processes — Maryam Tayefeh Mahmoudi, Hamid Abrishami, et al. · (2024) | TGRS Research Map | TGRS