UNS: Sustainable Energy-Intensive Manufacturing via Demand Response Process Operations

Baldea，1512379美国和世界各地的能源生产正在发生变化，可再生能源的贡献在过去十年中增加了两倍。但是，可再生能源具有内在的可变性，其发电率会因风速、日照和云层覆盖等因素而随时间波动。电网能源消耗也各不相同，通常在下午晚些时候达到每日高峰，在清晨达到最低点。为了满足这一峰值需求，电网运营商必须开启额外的发电设施（称为“调峰电厂”），这些发电设施通常比基本负荷发电机效率更低，污染更大。平衡波动的能源资源和消费者是当前努力开发和部署智能电网的核心动力之一，智能电网的基础是发电和用电的密切整合和同步。联邦能源管理委员会（FERC）将需求响应（DR）战略定义为“在批发市场价格高或系统可靠性受到威胁时，需求侧资源从正常消费模式（响应经济激励和动态定价结构）改变用电量”，这将在协调智能电网的能源生产和消费方面发挥关键作用。DR预计将在未来十年内将美国的净峰值电力需求减少约5%（或约50GW），并在消除对新调峰电厂的需求以及减少现有电厂的使用和排放方面带来重大的可持续性效益。该项目旨在将灾难恢复战略应用于工业环境。知识优势：能源密集型化学制造过程（例如，空气分离、氨、氧化铝、氯碱）约占工业用电量的10%。由于它们的调节能力和存储能源密集型产品的能力，它们非常适合DR计划。DR要求工艺在原料质量、产品需求和环境条件波动的情况下，快速过渡并在广泛的操作范围内保持高效。虽然与DR操作相关的生产调度问题已经得到了广泛的解决，实施所产生的时间表的动态和控制方面得到的关注要少得多。因此，本项目的目的是i）建立一个新的框架，用于建模，分析和优化DR下运行的过程系统的动态和控制，ii）开发一个计算效率高的实现所提出的算法，并验证它在工业相关的过程（空气分离，热电联产）。这些结果将适用于分析和缓解现有设施中的动态瓶颈，以及新项目。DR中涉及的过程的动态和控制的优化将被制定为不确定性下的动态优化，并使用非线性系统识别理论启发的方法来解决。这种方法避免了传统的使用场景来捕捉不确定性。相反，不确定变量的波动将被描述为具有精确调谐的频率内容的伪随机多级信号，其将在动态优化的迭代期间施加在过程模型上。通过这种方式，系统的相关动态模式被选择性地激发（“识别”），并可以被最佳地调制，以最大限度地减少目标函数的期望值。更广泛的影响：通过与德克萨斯大学工程平等机会计划的持续合作，PI将招募来自代表性不足的群体，谁将获得宝贵的经验，对未来的研究生学习的本科研究人员。提案中提出的概念将纳入PI编写的工程系统数学建模课程。提出了一项新的外展倡议，为来自低收入少数民族家庭的中学生和家长团队提供现实生活中的工程经验，目的是支持学生？努力成为第一代大学毕业生。

Baldea, 1512379Energy generation in the United States and across the world is shifting, with the contribution of renewable sources tripling in the past decade. But, renewable sources are inherently variable and their generation rates fluctuate in time due to factors such as wind speed, insolation and cloud cover. Grid energy consumption varies as well, typically reaching a daily peak in the late hours of the afternoon and a minimum in the early morning. To meet this peak demand, grid operators must turn on additional generation facilities (referred to as "peaking plants"), which are typically less efficient and more polluting than base load generators. Balancing fluctuating energy resources and consumers is one of the central motivators behind current efforts to develop and deploy the smart grid, which is predicated on a close integration and synchronization of electricity generation and electricity use. Demand response (DR) strategies, defined by the Federal Energy Regulatory Commission (FERC) as "Changes in electric usage by demand-side resources from their normal consumption patterns [in response to economic incentives and dynamic pricing structures] at times of high wholesale market prices or when system reliability is jeopardized" will play a key role in coordinating energy generation and consumption in the smart grid. DR is expected to reduce net peak power demand in the US by about 5% (or about 50GW) over the next ten years, with significant sustainability benefits related to eliminating the need for new peaking plants and reducing the use of and emissions from existing ones. This project is aimed at applying DR strategies in an industrial context.Intellectual Merit: Energy-intensive chemical manufacturing processes (e.g., air separation, ammonia, alumina, chlor-alkali) account for about 10% of industrialelectricity consumption. They are ideally suited for DR initiatives due to their turndown capacity and ability to store energy intensive products. DR requires processes to transition rapidly and remain efficient across a broad operating envelope, in the presence of fluctuations in feedstock quality, product demand and ambient conditions. While the production scheduling problem associated with DR operation has been extensively addressed, the dynamic and control aspects of implementing the resulting schedules have received much less attention. Therefore, the present project aims to i) establish a novel framework for modeling, analyzing and optimizing the dynamics and control of process systems operating under DR and, ii) develop a computationally efficient implementation of the proposed algorithms and validate it on industrially relevant processes (air separation, combined heat and power). These results will be applicable to the analysis and mitigation of dynamic bottlenecks in existing facilities, as well as to new projects. The optimization of the dynamics and control of processes operating involved in DR will be formulated as a dynamic optimization under uncertainty, and solved using an approach inspired by nonlinear systems identification theory. THe approach eschews the traditional use of scenarios to capture uncertainty. Rather, the fluctuations of the uncertain variables will be described as pseudo-random multi-level signals with precisely tuned frequency content, which will be imposed on the process model during the iterations of a dynamic optimization. In this manner, the relevant dynamic modes of the system are selectively excited ("identified") and can be optimally modulated to minimize the expected value of the objective function.Broader Impact:Through an ongoing partnership with the University of Texas Equal Opportunity in Engineering program, the PI will recruit undergraduate researchers from underrepresented groups, who will acquire valuable experience towards future graduate studies. The concepts developed in the proposal will be incorporated in a course on Mathematical Modeling of Engineered Systems prepared by the PI. A new outreach initiative to provide real-life engineering experiences for middle-school student and parent teams from low-income, minority families is proposed, with the aim of supporting the students? efforts to become first-generation college graduates.