Title :
Dynamic demand response solution for industrial customers
Author :
Mohagheghi, Salman ; Raji, Neda
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Colorado Sch. of Mines, Golden, CO, USA
Abstract :
Electric demand side management (DSM) focuses on changing the electricity consumption patterns of end-use customers through improving energy efficiency and optimizing allocation of power. Demand response (DR) is a DSM solution that targets residential, commercial and industrial customers, and is developed for demand reduction or demand shifting at a specific time for a specific duration. In the absence of on-site generation or possibility of demand shifting, consumption level needs to be lowered. Whereas non-criticality of loads at the residential and commercial levels allows for demand reduction with relative ease, reducing the demand of industrial processes requires a more sophisticated solution. Production constraints, inventory constraints, maintenance schedules and crew management are some of the many factors that have to be taken into account before one or more processes can be temporarily shut down. Some of these constraints can be viewed along the overall performance of the system, while others need to be analyzed and evaluated in real-time. A system is proposed in this paper that dynamically ranks loads and workstations within an industrial site as candidates for demand reduction. Constraints on the daily operation of the industrial system are taken into account in conjunction with real-time assessment of inventory buildup. A fuzzy/expert-based system combined with an optimization module verifies whether and, if applicable, by how much the plant can participate in a utility-initiated DR event, while satisfying its local operational constraints. A case study is proposed for a sample production line to further explain the design concepts.
Keywords :
demand side management; energy conservation; fuzzy set theory; power consumption; DR event; DSM; Electric demand side management; crew management; demand reduction; dynamic demand response solution; electricity consumption pattern; energy efficiency; fuzzy-expert-based system; industrial customer; industrial process; inventory constraint; maintenance schedule; optimization module; production constraint; real-time assessment; Job shop scheduling; Load management; Maintenance engineering; Optimization; Real-time systems; Workstations; Demand side management; demand response; energy management; industrial automation system; inventory management;
Conference_Titel :
Industry Applications Society Annual Meeting, 2013 IEEE
Conference_Location :
Lake Buena Vista, FL
DOI :
10.1109/IAS.2013.6682583