GOALI: Coordination of Multi-Stakeholder Process Networks in a Highly Electrified Chemical Industry

目标：在高度电气化的化工行业中协调多利益相关者流程网络

• 批准号: 2215526
• 负责人: Qi Zhang
• 金额: $ 36.18万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215526&HistoricalAwards=false
• 关键词: GOALI; Coordination; Multi; Stakeholder; Process

The chemical sector is currently the largest industrial consumer of oil and gas and a major emitter of greenhouse gases. As the demand for chemicals continues to grow, there is an urgent need to decarbonize global chemical production. Electrification using power-to-chemicals and power-to-heat technologies is considered one of the most promising emission reduction strategies for the chemical industry, provided that renewable sources of electricity are used. As electrification efforts intensify in the coming years, the chemical industries will see a dramatic increase in the number of chemical processes that consume large amounts of electricity. These processes will face the challenge of increasingly time-sensitive availability and pricing of electricity, such that significant operational flexibility will be required to ensure safe and cost-effective operation. A major source of flexibility lies in the coordination of multiple interconnected plants (and the respective multiple self-interested stakeholders). Importantly, with a large number of power-intensive processes, effective coordination must happen in real time, much akin to the way modern power grids operate. This would, however, be a major paradigm change for the chemical industry, which is used to highly steady operation and long response times, where different companies coordinate their operations mainly through long-term bilateral contracts. In this research, the research team aims to develop efficient computational methods for the coordination of multi-stakeholder process networks that improve the whole system's performance while ensuring that all stakeholders benefit from cooperation. To this end, new fair and privacy-preserving coordination mechanisms will be designed that can consider multiple products, plants, stakeholders, and spatiotemporal scales. Implemented at large scale, such coordination will not only enable the electrification of the chemical industry in the most cost-effective manner but also help improve grid reliability and facilitate further growth in renewable energy generation. This project is jointly funded by the Electrochemical Systems and the Process Systems, Reaction Engineering, and Molecular Thermodynamics programs of ENG/CBET.The proposed research plan is organized into three specific aims: (i) Distributed cooperative industrial demand response (DR), where DR refers to the adjustment of an electricity consumer's load profile in response to price changes. The researchers will develop capabilities for the distributed coordinated optimization of multiple connected plants (possibly across an entire supply chain), which maximizes the overall benefits from DR while accounting for local objectives, data privacy requirements, and fair profit allocation. (ii) Risk-aware cooperative provision of interruptible load, which aims to further incorporate uncertainty and risk measures to consider participating in the reserve market through the provision of interruptible load. Here, the major technical challenge is the computationally efficient modeling of endogenous uncertainty and multistage recourse. (iii) Market-based coordination of an electrified chemical industry, where an alternative approach to distribution optimization for real-time coordination will be explored. Specifically, a responsive and robust market mechanism will be developed based on the idea of trading operational flexibility. This project will be conducted in a close industry-university collaboration between the University of Minnesota and The Dow Chemical Company, which is crucial as it requires both the development of new computational methods at the cutting edge of academic research as well as their application to industrial use cases to gain practical insights and demonstrate the value of the proposed approaches. This research will advance the state of the art in the broad area of multi-agent decision making. Although this work will focus on a specific manufacturing context, the methods will be sufficiently general such that they can be adapted for many other applications. Generally, they can be applied to multi-stakeholder systems in which conditions and resource availability are highly time-sensitive and uncertain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

化工行业目前是石油和天然气的最大工业消费者，也是温室气体的主要排放源。随着对化学品的需求持续增长，迫切需要使全球化学品生产脱碳。如果使用可再生电力，电气化化学和电化热技术被认为是化学工业最有前途的减排战略之一。随着未来几年电气化努力的加强，化学工业将看到消耗大量电力的化学过程数量急剧增加。这些过程将面临电力供应和定价日益具有时效性的挑战，因此需要极大的业务灵活性，以确保安全和具有成本效益的操作。灵活性的一个主要来源在于多个相互连接的工厂（以及各自的多个自利利益相关者）之间的协调。重要的是，由于有大量的电力密集型过程，必须实时进行有效的协调，这与现代电网的运行方式非常相似。然而，对于化工行业来说，这将是一个重大的范式变化，该行业习惯于高度稳定的运营和较长的响应时间，不同的公司主要通过长期的双边合同来协调他们的业务。在本研究中，研究小组旨在开发多利益相关者过程网络协调的有效计算方法，以提高整个系统的性能，同时确保所有利益相关者都从合作中受益。为此，将设计新的公平和保护隐私的协调机制，可以考虑多种产品、植物、利益相关者和时空尺度。如果大规模实施，这种协调不仅能够以最具成本效益的方式实现化学工业的电气化，而且还有助于提高电网的可靠性，促进可再生能源发电的进一步增长。该项目由电化学系统、过程系统、反应工程和分子热力学项目共同资助。提出的研究计划分为三个具体目标：(i)分布式合作工业需求响应（DR），其中DR是指根据价格变化调整电力消费者的负荷分布。研究人员将开发多个连接工厂（可能跨越整个供应链）的分布式协调优化能力，在考虑本地目标、数据隐私要求和公平利润分配的同时，最大限度地提高DR的整体效益。（ii）风险意识型可中断负荷合作供给，旨在进一步纳入不确定性和风险措施，考虑通过可中断负荷的供给参与储备市场。在这里，主要的技术挑战是内生不确定性和多阶段追索权的计算效率建模。电气化化学工业的基于市场的协调，其中将探索实时协调的最佳分配的替代办法。具体而言，将在交易操作灵活性的基础上，建立一个反应灵敏、稳健的市场机制。该项目将在明尼苏达大学和陶氏化学公司之间的紧密产学研合作中进行，这是至关重要的，因为它既需要在学术研究的前沿开发新的计算方法，也需要将其应用于工业用例，以获得实际的见解，并展示所提出方法的价值。该研究将在多智能体决策的广泛领域中推进当前的技术水平。虽然这项工作将侧重于特定的制造环境，但这些方法将是足够通用的，因此它们可以适用于许多其他应用。一般来说，它们可以应用于条件和资源可用性具有高度时间敏感性和不确定性的多利益相关者系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Feasibility/Flexibility-based optimization for process design and operations

• DOI: 10.1016/j.compchemeng.2023.108461
• 发表时间: 2023-10
• 期刊: Comput. Chem. Eng.
• 作者: Huayu Tian;Jnana Sai Jagana;Qi Zhang;M. Ierapetritou

Mixed-integer recourse in industrial demand response scheduling with interruptible load

可中断负荷工业需求响应调度中的混合整数资源

• 发表时间: 2023
• 期刊: Computer Aided Chemical Engineering
• 作者: Jagana, Jnana Sai;Amaran, Satyajith;Zhang, Qi
• 通讯作者: Zhang, Qi