Decentralized Operations of Decarbonized Chemical Plants with Renewable-driven Transmission Systems

2026-06-22

AI Summary

This study addresses data privacy challenges in the co-optimization of electrified cracking facilities and decarbonized power grids by proposing a privacy-preserving distributed optimization framework. Built upon a data-isolation architecture, the approach employs a modified alternating direction method of multipliers (ADMM) algorithm augmented with auxiliary system-level penalty terms to jointly solve the unit commitment problem for the power system and the microgrid scheduling problem for ethane crackers, requiring only minimal coordination signals to be exchanged. The method significantly accelerates convergence while preserving subsystem data privacy and, for the first time, quantifies the impact of privacy-aware decomposition on operational costs and carbon emissions in electricity-chemical integrated systems. Tested on the ACTIVSg2000 Texas grid, the solution consistently achieves a negligible optimality gap, with emission effects exhibiting load-dependent and non-monotonic behavior.

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.

Problem

• decentralized operations
• industrial decarbonization
• data privacy
• electrified chemical plants
• renewable-driven power systems

Innovation

• decentralized optimization
• privacy-preserving coordination
• electrified ethane cracking
• ADMM with penalty acceleration
• integrated power-chemical systems