| 项目编号: | 1512379
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| 项目名称: | UNS: Sustainable Energy-Intensive Manufacturing via Demand Response Process Operations |
| 作者: | Michael Baldea
|
| 承担单位: | University of Texas at Austin
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| 批准年: | 2014
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| 开始日期: | 2015-09-01
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| 结束日期: | 2018-08-31
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| 资助金额: | USD268935
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | dr
; net peak power demand
; product demand
; peak demand
; demand-side resource
; energy-intensive chemical manufacturing process
; demand response
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| 英文摘要: | Baldea, 1512379
Energy 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 industrial
electricity 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/93602
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Michael Baldea. UNS: Sustainable Energy-Intensive Manufacturing via Demand Response Process Operations. 2014-01-01.
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