Decentralized Operations of Decarbonized Chemical Plants with Renewable-driven Transmission Systems
2026-06-22 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors explore a way to help power grids and ethane cracking plants work together more efficiently while keeping the plants' data private. They created a method that lets each side plan their energy use independently but still coordinate through minimal shared signals, improving speed and privacy. Using simulations based on Texas' power network, the authors found that keeping data isolated only slightly impacts optimal decisions and that emissions effects vary depending on energy load. This approach may help integrate clean electricity into chemical manufacturing without exposing sensitive information.
ElectrificationEthane crackingUnit CommitmentMicrogrid schedulingAlternating Direction Method of MultipliersData privacyDecentralized optimizationPower gridIndustrial decarbonizationSynthetic test case
Authors
Richard Reed, kazi Arman Ahmed, Saba Ghasemi, Zheyu Jiang, Paritosh Ramanan
Abstract
Electrification of ethane cracking offers a promising pathway to industrial decarbonization, provided that the electricity is sourced from renewable energy. However, integrating electrified chemical plant microgrids with a decarbonized power grid requires joint operations planning between Independent System Operators and chemical plants, which is hindered by the highly confidential nature of plant operational data. In this paper, we propose a privacy-friendly decentralized framework based on data isolation that jointly optimizes the Unit Commitment problem in the power system and microgrid scheduling in electrified ethane cracker plants. The framework employs the Alternating Direction Method of Multipliers, augmented with an auxiliary system-level penalty that accelerates convergence, allowing each subsystem to solve its local subproblem and share only minimal coordination signals. To reflect real-world conditions, numerical experiments are conducted on the ACTIVSg2000 test case, a synthetic model of the Texas transmission network, with 26 chemical plants identified from Texas mapped to their nearest grid connection points. In doing so, we characterize the cost of privacy-friendly decomposition on joint power and chemical system decisions, showing that data isolation results in consistently small optimality gaps, and that its emissions consequences are load-dependent and non-monotone.