(739f) Optimization-Based Process Design for Demand-Response Market Particibility

With the increasing penetration of intermittent energy resources and the ambitious policies on renewable portfolio standards, Demand Response (DR) has become an important strategy to satisfy the needs of the modern power grid system. The energy-intensive industries could participate in different DR mechanisms to lower their operating cost and provide grid stability as well. In general, it is believed that to be able to participate in the DR-market, an energy-intensive process needs to have enough capacity for the potential flexible operation and the dynamics of the process should be suitable for engaging in the slow-varying electricity market. The main focus of the literature related to participation of energy-intensive industries, particularly the cryogenic air separation processes, in the DR markets has been mainly on the optimal scheduling of the process subject to the time-varying electricity market, such as the day-ahead market. Recently, an an increasing number of researchers consider the participation of the energy-intensive process in the short-term electricity market, such as frequency control and real-time electricity markets with the inclusion of more rigorous process dynamics models. Dowling et al. [2] has pointed out the potential economic benefits for energy systems to participate in the short-term market, and Otashu and Baldea [3] studied the dynamic behavior of membrane-based chlor-alkali plants in the real-time electricity market.

For large-end electricity users, the cost of advanced metering to facilitate the DR-participation does not appear to be a significant barrier; rather, DR design is an extremely important consideration when decisions for investments are made [1]. However, the current body of literature emphasizes mostly the operational aspect of the participation in the DR market and is limited to addressing the capability for an energy intensive process to participate in the modern grid DR program, which we here define as particibility, in the process design stage. Incorporating DR objectives directly into the design and operations of chemical processes needs to account for the plant capacity design as well as the intrinsic process dynamics. Also, the design stage is usually at a different decision scale compared to the DR operational decision and thus, a multiscale model is usually required. However, the resulting model is usually intractable.

In this work, we present a general framework to optimize the design decisions (e.g., equipment size and quantity) and operational decisions (e.g., flow rates, temperatures, etc.) while incorporating the consideration of dynamic behavior using the chlor-alkali plant as an example. To reduce the complexity associated with direct incorporation of the first principle dynamic model into the design model, a simulation-based approach is first implemented to study the related energy-intensive equipment in the process and develop a surrogate model of the process. Then, a superstructure-based two-tier deterministic optimization problem utilizing the surrogate model is developed to co-optimize the design and operational variables. The following questions are addressed in this study: (1) To what degree can the expansion of the capacity of the energy-intensive process at the design can facilitate the DR-participation, namely, the particibility? (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.

References:

[1]Voytas R. Data collection for demand-side management for quantifying its influence on reliability Technical report December. North American Electric Reliability Corporation; 2007.

[2]Dowling, A. W., Kumar, R., & Zavala, V. M. (2017). A multi-scale optimization framework for electricity market participation. Applied Energy, 190, 147–164.

[3]Otashu, J. I., & Baldea, M. (2019). Demand response-oriented dynamic modeling and operational optimization of membrane-based chlor-alkali plants. Computers & Chemical Engineering, 121, 396–408.