The power grid is designed to reliably match electricity supply and demand. This task has become increasingly challenging due to high fluctuations in electricity demand and increasing penetration of intermittent renewable energy into the electricity supply mix. In recent years, the notion of a smart grid has been evolving, which represents the concept of a power grid in which the major operations—electricity generation, transmission, distribution, and consumption—are executed in a coordinated and efficient manner (see figure below).
A major component of the smart grid concept is demand response (or demand side management), which refers to the active management of electricity consumption. Demand response leverages the flexibility on the consumers' side to respond to varying grid conditions, and it has proven to be a very effective means to improve grid efficiency and reliability. The industrial sector is particularly well suited for demand response, mainly due to the large sizes of individual industrial loads and the high level of flexibility in many manufacturing processes.
In our research, we take the perspective of power-intensive chemical processes, such as cryogenic air separation and steel manufacturing, and investigate the benefits from demand response. If properly executed, demand response will help power-intensive industries achieve significant cost savings as well as improve grid reliability without adding more power generation capacities. However, industrial demand response is a challenging systems problem and requires (1) accurate modeling of operational flexibility, (2) integration of production and energy management, (3) decision-making across multiple time and space scales, and (4) optimization under uncertainty.
Zhang, Q., Bremen, A. M., Grossmann, I. E., & Pinto, J. M. (2018). Long-term electricity procurement for large industrial consumers under uncertainty. Industrial & Engineering Chemistry Research, 57, 3333-3347.
Zhang, Q. & Grossmann, I. E. (2016). Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives. Chemical Engineering Research & Design, 116, 114-131. Invited article for Roger Sargent Special Issue.
Zhang, Q., Morari, M. F., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load. Computers & Chemical Engineering, 86, 106-119.
Zhang, Q., Cremer, J. L., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). Risk-based integrated production scheduling and electricity procurement for power-intensive continuous processes. Computers & Chemical Engineering, 86, 90-105.
Zhang, Q., Sundaramoorthy, A., Grossmann, I. E., & Pinto, J. M. (2016). A discrete-time scheduling model for continuous power-intensive process networks with various power contracts. Computers & Chemical Engineering, 84, 382-393.
Zhang, Q., Grossmann, I. E., Heuberger, C. F., Sundaramoorthy, A., & Pinto, J. M. (2015). Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets. AIChE Journal, 61(5), 1547-1558.