SSD- Palazoglu Seminar

Location: AICES Seminar Room 115, 1st floor, Schinkelstr. 2, 52062 Aachen

Prof. Ahmet Palazoglu, Ph.D. - A new Design Paradigm to Address Demand Response Objectives in Process Systems

Department of Chemical Engineering, University of California, Davis, USA


With the continuing penetration of renewable sources into the power grid, the energy picture presented to the process industries has changed dramatically within the last 10 years. The most visible consequence is the ability to offer real-time electricity pricing by the grid operators as they manage a number of distributed power sources including renewables. If power is available directly from the renewables such as solar and wind, their intermittency challenges the operation of process systems as the available energy varies during the day. This leads to the use of hybrid systems where renewable sources are complemented with storage systems (batteries) and the process has the flexibility to draw energy from the grid or sell back to it when appropriate [1]. The variations on the supply side both in terms of price and availability result in a search for optimal allocation of loads (demand) during the day. Accordingly, demand response (DR) is defined as the ability of the operators to modify process conditions in real-time to take advantage of and respond to such variations and to formulate load shifting strategies. In this talk, I will summarize our ongoing work towards the goal of developing demand responsive process designs. Such designs are not only expected to accommodate variations in price and availability by modifying (scheduling) process steady-states [2] but also consider re-configuring the process flowsheet in real-time for a more holistic DR strategy [3]. The formulation of the design problem leads to a mixed integer nonlinear programming (MINLP) problem in which the objective function quantifies the capital and operating costs (CAPEX and OPEX) subject to recourse constraints that express scenario-dependent costs. Our recent studies include both deterministic and stochastic versions which present significant algorithmic challenges and these will be briefly discussed. The methodology will be illustrated by examples of process networks.


 [1] Wang, X., H. Teichgraber, A. Palazoglu N.H. El-Farra, “An Economic Receding Horizon Optimization Approach for Energy Management in the Chlor-Alkali Process with Hybrid Renewable Energy Generation,” J. Process Control, 24, 1318-1327 (2014).

[2] Tong, C., A. Palazoglu, N.H. El-Farra, X. Yan, “Energy Demand Management for Process Systems through Production Scheduling and Control,” AIChE J., 61(11), 3756–3769 (2015).

[3] Wang, X., N.H. El-Farra, A. Palazoglu, “Proactive Reconfiguration of Heat-Exchanger Super Networks,” Ind. & Eng. Chemistry Research, 54, 9178−9190 (2015).