(579a) Energy Demand Response of Process Systems through Production Scheduling and Control

Authors: 
Tong, C., University of California, Davis
Wang, X., University of California, Davis
El-Farra, N. H., University of California, Davis
Palazoglu, A., University of California, Davis

In recent years, demand-side activities have been the focus of significant attention in energy policy decisions due to the significant benefits they can bring both at the economic and operational levels. In this context, the market price of electricity is the dominant incentive and influences heavily the electricity consumption of industrial customers. Demand Response (DR) is achieved through mechanisms that discourage the energy load when the real-time electricity price is high and vice versa. Although the economic potential of DR for industrial processes has been recognized in a number of recent studies [2-4], it should be noted that, since DR requires, by definition, varying production levels, the consideration of transition behavior between different operating modes is an issue that remains open.

An examination of the existing research on DR shows that the transition behavior problem has not been adequately addressed in the literature. For example, an optimal production planning model for continuous power-intensive processes under time-sensitive electricity prices was presented in [1] where transitions were considered. However, the focus of that study was only on minimum stay constraints for describing ramp-up transition and rate of change constraints for restricting transitions between operating points. The dynamic profile of the transition behavior was not taken into account in the optimization formulation. The potential opportunities of DR for a chemical manufacturing facility were illustrated recently in [5] under the assumption that the manufacturing facility can be operated at continuous production levels with the transitional behavior ignored.

From the standpoint of DR, the interest lies primarily in determining the optimal production levels at each time instant. The control problem is thus closely related to responsive demand given that the major task of controllers is to determine the optimal values of manipulated and controlled variables in order to achieve different production levels. Generally, production scheduling and control problems should be addressed simultaneously rather than sequentially [6, 7]. Some early attempts to tackle simultaneous scheduling and control problems can be found in the literature [8-10]. However, energy consumption was not considered in these studies. In addition, one of the most challenging aspects of plant scheduling that needs to be addressed is the incorporation of energy constraints related to electricity pricing and availability.

Motivated by these considerations, the objective of this work is to present a new optimization-based approach for energy demand management in process systems that explicitly considers dynamic transition behavior and effectively handles time-sensitive electricity prices at the same time. The presented study is explorative, focusing on demand response which takes into account the dynamic profile of the transition process, simultaneous scheduling and control, and time-sensitive electricity prices. The proposed formulation is illustrated using a chemical process example where the energy required is assumed to be proportional to the material flow and the process has to satisfy an hourly demand for the products. A mass storage unit is added to provide the necessary flexibility, and the electricity prices are assumed to vary on an hourly basis. A lower bound for the product hourly demand expressed as a constant rate is specified. Steady-state operating modes for different production levels are also specified a priori, as well as the price of the inventory and raw material costs. The problem then consists of the simultaneous determination of the operating mode for the plant (i.e., production level) and the control profile for the production changes. The major objective is to minimize the production cost, which includes a transition cost (i.e., raw material waste and electricity waste during the transitions), inventory cost, and electricity cost. The scheduling and control problem is formulated and solved within a MINLP framework and solved. The proposed formulation is shown to be capable of realizing demand responsiveness in terms of operating mode switches and transitional behavior.

References:

[1] S. Mitra, I. E. Grossmann, J. M. Pinto, and N. Arora, “Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes,” Comp. Chem. Eng., 38, 171–184, 2012.

[2] Y. Wang and L. Li, “Time-of use based electricity demand response for sustainable manufacturing systems,” Energy, 63, 233–244, 2013.

[3] H.G. Kwag and J.O. Kim, “Optimal combined scheduling of generation and demand response with demand resource constraints,” Appl. Energy, 96, 161–170, 2012.

[4] M. Paulus and F. Borggrefe, “The potential of demand-side management in energy-intensive industries for electricity markets in Germany. Appl. Energy, 88, 432–441, 2011.

[5] D.I. Mendoza-Serrano and D. J. Chmielewski, “Demand response for chemical manufacturing using economic MPC. In Proc. 2013 American Control Conf, Washington DC, pp. 6655–6660.

[6] R. Mahadevan, F. J. Doyle, and A. C. Allcock, “Control-Relevant Scheduling of Polymer Grade Transitions,” AIChE J., 48, 1754–1764, 2002.

[7] R. H. Nystrom, R. Franke, I, Harjunkoski, and A. Kroll, “Production campaign planning including grade transition sequencing and dynamic optimization,” Comp. Chem. Eng., 29, 2163–2179, 2005.

[8] A. Flores-Tlacuahuac and I. E. Grossmann, “Simultaneous cyclic scheduling and control of a multiproduct CSTR,” Ind. Eng. Chem. Res., 45, 6698–6712, 2006.

[9] A. Flores-Tlacuahuac and I. E. Grossmann, “Simultaneous scheduling and control of multiproduct continuous parallel lines,” Ind. Eng. Chem. Res., 49, 7909–7921, 2010.

[10] J. Zhuge and M. G. Ierapetritou, “Integration of scheduling and control with closed loop implementation,” Ind. Eng. Chem. Res., 51, 8550–8565, 2012.

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