کنفرانس بین المللی فناوری اطلاعات و دانش

English

صفحه اصلی / پانزدهمین کنفرانس بین المللی فناوری اطلاعات و دانش

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

نویسندگان :

Negin Shafinezhad¹ Hamid Abrishami² Maryam Mahmoodi³

1- Ferdowsi University of Mashhad 2- Ferdowsi University of Mashhad 3- Ferdowsi University of Mashhad

کلمات کلیدی :

Smart grid،demand response،manufacturing processes،energy-aware scheduling،peak demand،energy cost

چکیده :

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.