(133a) Multi-Period Heat Integration for Chemical Plant Electrification with Energy Storage
AIChE Annual Meeting
2023
2023 AIChE Annual Meeting
Topical Conference: Decarbonization of the Chemical Industry through Electrification
Decarbonization by Electrification
Tuesday, November 7, 2023 - 8:05am to 8:23am
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
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