(554d) Assessment of the Demand Response Particibility Potential of Industrial Processes Based on Cost-Effectiveness of Process Designs | AIChE

(554d) Assessment of the Demand Response Particibility Potential of Industrial Processes Based on Cost-Effectiveness of Process Designs


Liu, Y. - Presenter, University of California, Davis
Campos, G. - Presenter, University of California, Davis
Palazoglu, A., University of California, Davis
El-Farra, N., University of California, Davis
Balancing power supply and demand has become increasingly important and challenging under an ambitious Renewable Portfolio Standard Energy-intensive industries could potentially participate in different Demand Response (DR) mechanisms to provide grid stability and lower their operating cost at the same time. In general, to be able to participate in the DR-market an energy-intensive process needs a large enough capacity to provide high operational flexibility, while its dynamics should be suitable for engaging in the slow-varying electricity market. For large electricity end-users, the consideration of DR participation aspects in the design stage is essential, having the potential to greatly affect the final operational cost.

Currently, research in this area emphasizes mostly the operational aspects (scheduling and control) of energy-intensive processes under time-varying electricity programs. Participation in the short-term market has gained particular attention among researchers recently1,2.In addition, model-based bidding strategies for industrial processes have also been investigated3. However, incorporating DR objectives directly into the design and operation of chemical processes requires accounting for the plant capacity as well as the intrinsic process dynamics. Furthermore, the design decision usually requires longer timescales compared to the DR operational decisions, resulting in a multiscale model that is often intractable. These issues highlight the difficulty in quantifying the impact of design decisions on the DR participation potential of a process.

In this work, we propose a novel model-based analytical framework utilizing the DR supply curves to systematically evaluate the “cost-effectiveness” for the design of the process when considering the participation into different types of DR services (such as load shifting under Day-Ahead market or Proxy Demand Response). The DR supply curves, with the information on the available DR capability and the levelized investment cost of the process design, are constructed based on different DR services and different market scenarios. Using the California Independent System Operator (CAISO) wholesale electricity market as the reference, we implement the time-series aggregation-based methodology to develop the multi-scale electricity price profiles. We then implement the Mixed-Integer Dynamic Optimization framework to demonstrate the cost-benefit of incorporating DR aspects into the design. As a motivating example, the developed framework is illustrated using a CSTR-storage based model4,5. The following questions are addressed in this study: (1) To what degree can the expansion of the equipment and/or storage capacity of the energy-intensive process at the design stage facilitate the DR-participation? (2) How can the physical capacity and the process flexibility be balanced in order to participate in the DR market? (3) Whether there would be economic opportunities offered by incorporating the DR objectives into the process design.

  1. Dowling, A. W., Kumar, R. & Zavala, V. M. A multi-scale optimization framework for electricity market participation. Appl Energ 190, 147–164 (2017).
  2. Otashu, J. I. & Baldea, M. Scheduling chemical processes for frequency regulation. Appl Energ 260, 114125 (2020).
  3. Schäfer, P., Westerholt, H. G., Schweidtmann, A. M., Ilieva, S. & Mitsos, A. Model-Based Bidding Strategies on the Primary Balancing Market for Energy-Intense Processes. Comput Chem Eng 120, 4–14 (2018).
  4. Tong, C., Palazoglu, A., El‐Farra, N. H. & Yan, X. Energy demand management for process systems through production scheduling and control. Aiche J 61, 3756–3769 (2015).
  5. Beal, L. D. R., Petersen, D., Grimsman, D., Warnick, S. & Hedengren, J. D. Integrated Scheduling and Control in Discrete-time with Dynamic Parameters and Constraints. Comput Chem Eng 115, 361–376 (2018).