(133a) Multi-Period Heat Integration for Chemical Plant Electrification with Energy Storage

To align with the net zero emissions goal, decarbonizing the chemical industry becomes crucial and inevitable. Industrial greenhouse gas (GHG) emission is the second largest source, taking 28% of the 2020 annual carbon emissions in the United States.[1] Electrification of the chemical industry enables the integration of cleaner energy sources such as renewables. However, the variation in renewable energy supply is a key challenge in electrifying industrial processes. Thermal energy storage (TES) utilizes low-cost storage mediums such as molten salt, rock, and sand that are non-toxic and more accessible, making it easier to scale up for industrial integration [2]. With the support from TES, renewable power electrical heaters can replace fossil fuel in chemical plants reducing at least one-third of the GHG emission in the manufacturing process.[2] Methods of mathematical optimization can be used to synthesize and design of utility systems. Papoulias and Grossmann (1983) proposed a MILP approach based on the LP transshipment model for the synthesis of flexible utility for changing process demand.[3] Multiperiod heat integration (MPHI) was developed to take into account process parameters varying over time. TES was introduced to MPHI problems to reduce energy demand and waste. Möhren et al. presented a simultaneous MILP model for TES integrating waste heat recovery [4]. Another TES-integrated MPHI was formulated into a non-convex MINLP model to optimize power cycle heat exchanger networks with different operation modes for renewables. [5]

So far, MPHI only considers several operating models for utility systems and energy storage. No research has time discretization involved in the multi-period model. There is a need for systematic methods that simultaneously consider the time-varying utility supply from renewables, the integration of TES with heat exchanger networks, and the scheduling of optimal charging/discharging operations. In this work, we first extend the LP transshipment model to minimize the TES size while maximizing the heat integration between hot and cold process streams under a time-varying renewable supply. Including year-wide energy scheduling with hourly resolution results in a large-scale optimization model. We propose a solution strategy to combine daily model with hourly resolution with yearly model with daily resolution to reduce the size of model.

The general problem for the multi-period heat integration with renewable utility and TES is constructed as the following. We are given a set of hot and cold stream inlet and outlet temperatures, flow rates, and heat capacity. Wind or solar power profile (Texas 2020 from ERCOT) is assigned to the plant in proportion to total generation with a constant price. Besides TES, the plant is equipped with a green hydrogen burner to encounter extreme situations. The objective is to minimize the levelized cost of the electrical heating (LCOEH) to obtain the optimal TES capacity, the amount of green hydrogen for backup heating, and hourly scheduling of TES charging and discharging. By varying the share proportion, we found that the variation in electricity supply dramatically influences the size of TES capacity and the use of backup green hydrogen. The LCOEH increased by 30% when the share of wind was reduced from 0.4% to 0.3%. The LCOEH is tripled in the solar case, further illustrating this conclusion. When the variation is too significant, burning green hydrogen during peak hours is more profitable than sizing up energy storage. Furthermore, we found that the increasing cost of green hydrogen affects the LCOEH to some extent depending on the variation in the electricity profile. The case study is extended to varying electricity prices by including real-time and day-ahead electricity price profiles in 8 locations in Texas. The flexibility in changing electricity demand allows the plant to take advantage of price variations, leading to lower energy costs. Future studies could consider participating in electricity market demand bidding to gain profit for flexible operations and alleviate increasing energy costs from electrification.

References

[1] EIA. (2020). Annual Energy Outlook 2020 with Projections to 2050.

[2] Zantye, M. S., Gandhi, A., Li, M., Arora, A., & Hasan, M. M. F. (2022). A systematic framework for the integration of carbon capture, renewables and energy storage systems for Sustainable Energy. Computer Aided Chemical Engineering, 2089–2094.

[3] EIA. (2021). Manufacturing Energy Consumption Survey 2018.

[4] Papoulias, S. A., & Grossmann, I. E. (1983). A structural optimization approach in process synthesis—I. Computers & Chemical Engineering, 7(6), 695–706.

[5] Möhren, S., Schäfer, C., Meyer, J., & Krause, H. (2022). A simultaneous approach for integration of thermal energy storages in industrial processes using multiperiod heat integration. Energy Storage and Saving, 1(2), 117–128.

[6] Elsido, C., Martelli, E., & Grossmann, I. E. (2021). Multiperiod optimization of heat exchanger networks with integrated thermodynamic cycles and thermal storages. Computers & Chemical Engineering, 149, 107293.