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.

化工部门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者。随着对化学品需求的持续增长，全球化学品生产迫切需要脱碳。使用电力化学品和电力加热技术的电气化被认为是化学工业最有前途的减排战略之一，前提是使用可再生能源电力。随着未来几年电气化工作的加强，化学工业将看到消耗大量电力的化学过程的数量急剧增加。这些进程将面临电力供应和定价越来越具有时间敏感性的挑战，因此需要有很大的业务灵活性，以确保安全和具有成本效益的业务。灵活性的一个主要来源在于多个相互关联的工厂（以及各自的多个自利利益相关者）的协调。重要的是，由于有大量的电力密集型过程，必须在真实的时间内进行有效的协调，这与现代电网的运行方式非常相似。然而，这对于化学行业来说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双边合同来协调其运营。在这项研究中，研究小组的目标是开发有效的计算方法，用于协调多利益相关者的过程网络，提高整个系统的性能，同时确保所有利益相关者从合作中受益。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，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ENG/CBET的电化学系统和过程系统，反应工程和分子热力学计划共同资助。拟议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指的是调整电力消费者的负荷分布以响应价格变化。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，最大限度地提高DR的整体效益，同时考虑本地目标，数据隐私要求和公平的利润分配。(ii)风险意识的可中断负荷的合作提供，其目的是进一步纳入不确定性和风险的措施，考虑通过提供可中断负荷参与备用市场。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。(iii)基于市场的电气化化学工业的协调，其中将探索用于实时协调的分配优化的替代方法。具体而言，将基于交易操作灵活性的理念，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进行密切的产学合作，这是至关重要的，因为它需要在学术研究的前沿开发新的计算方法，并将其应用于工业用例，以获得实际见解并展示所提出方法的价值。这项研究将推进多智能体决策的广泛领域的艺术状态。虽然这项工作将集中在一个特定的制造环境中，方法将是足够的一般性，使他们可以适应许多其他应用。一般来说，它们可以应用于条件和资源可用性对时间高度敏感和不确定的多利益相关者系统。该奖项反映了NSF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