2012Unpublished venueRequires access

Intelligent demand response scheme for energy management of industrial systems

Salman Mohagheghi, Neda Raji

Open publisher page 26 citations

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. While non-criticality of loads at the residential and commercial levels allows for demand reduction with relative ease, demand reduction 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. An intelligent system is designed in this paper for implementation of DR at an industrial site. Based on the various operational constraints of the industrial process, it determines the loads that could be potentially curtailed. Fuzzy/expert systems are used to derive a priority factor for different candidate loads. This information can then be used by the plant operator/DR client to make a comply/opt-out decision during a utility-initiated DR event.

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What this paper is about

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. While non-criticality of loads at the residential and commercial levels allows for demand reduction with relative ease, demand reduction 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. An intelligent system is designed in this paper for implementation of DR at an industrial site. Based on the various operational constraints of the industrial process, it determines the loads that could be potentially curtailed. Fuzzy/expert systems are used to derive a priority factor for different candidate loads. This information can then be used by the plant operator/DR client to make a comply/opt-out decision during a utility-initiated DR event.

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Available 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. While non-criticality of loads at the residential and commercial levels allows for demand reduction with relative ease, demand reduction 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. An intelligent system is designed in this paper for implementation of DR at an industrial site. Based on the various operational constraints of the industrial process, it determines the loads that could be potentially curtailed. Fuzzy/expert systems are used to derive a priority factor for different candidate loads. This information can then be used by the plant operator/DR client to make a comply/opt-out decision during a utility-initiated DR event.

Key concepts: Demand response, Load management, Computer science, Fuzzy logic, Operations research, Electricity, Reliability engineering, Process (computing)

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