GOALI: Multi-scale Optimization for the Design, Capacity Planning and Operation of Power Intensive Process Networks under Uncertain Electricity Prices and Market Demands

GOALI：电价和市场需求不确定下电力密集型过程网络的设计、容量规划和运营的多尺度优化

• 批准号: 1159443
• 负责人: Ignacio Grossmann
• 金额: $ 30.2万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1159443&HistoricalAwards=false
• 关键词: GOALI; Multi; scale; Optimization; Design

1159443GrossmannSummary: Power intensive industries such as air separation, cement and chlor-alkali manufacturing will face increased electricity prices in the future due to environmental pressures to reduce CO2 emissions. Furthermore, another challenge is that since electricity markets became deregulated in the 1990s, electricity prices have been subject to hourly as well as seasonal variations. These are also likely to become more acute as renewable sources of energy like wind and solar are introduced for power generation. These trends have led to a considerable amount of uncertainty and variability in the daily operating expenses of power intensive industries, which in turn affect their competitiveness and long term planning. The aim of this GOALI proposal, which will be performed in collaboration with researchers from Praxair, is to develop a multi-scale modeling framework that can be used as a decision-making support tool to optimize designs so as to introduce flexibility in plant operations to effectively address uncertain hourly variations in electricity prices and uncertain product demands. In order to tackle the problem of uncertain electricity prices and product demands, we consider as a first step the development of an optimization methodology for the deterministic case when the electricity prices and demands are assumed to be known (e.g. in terms of forecasts). We propose a short-term mixed-integer operational optimization model that is based on offline computations or measured plant data, and that is integrated with the design and long-term capacity planning problem, which involves decisions on installing or upgrading equipment, or increasing storage capacity. To solve the resulting multi-scale mixed-integer linear program (MILP) model for a single plant, we plan to develop a tailored bi-level decomposition algorithm. Also, to consider the case of process networks consisting of several plants, we intend to investigate the solution of the large-scale model with a Lagrangean decomposition scheme based on a novel hybrid cutting plane and subgradient method to accelerate convergence. As a second major step, we will address the treatment of uncertainties of product demands and electricity prices for which we intend to investigate a novel hybrid stochastic programming/robust optimization approach. The basic idea is to model the long-term design decisions and uncertain demands with multistage stochastic programming, and the short term operating decisions and uncertain prices through robust optimization. The proposed models will be tested with process models and real world data of air separation plants supplied by Praxair.Intellectual Merit: The major intellectual challenges in this project lie in the multi-scale integration of the short-term operational model with the design and long-term capacity planning problem, the treatment of uncertainties in product demands and electricity prices, and the development of effective computational algorithms for solving large-scale optimization models. In order to address these challenges, we propose a strategy for multi-scale integration that effectively incorporates the operational model with the design and capacity planning model. Furthermore, we propose decomposition schemes that have the potential of effectively tackling large-scale deterministic models for realistic process networks of power intensive plants, particularly for air separation plants. Finally, we propose a potentially promising hybrid stochastic programming/ robust optimization model and solution method in order to anticipate the effect of uncertainties in the product demands and electricity prices.Broader Impact: The proposed GOALI project has the potential of yielding significant economic savings in the enterprise wide optimization of power intensive industries to make them more competitive. The proposed GOALI project will involve summer internships for the Ph.D. student and visits by the PI to Praxair. The project also has the potential of collaboration and dissemination of the basic methodologies to several petroleum, chemical and engineering/software companies of the Center for Advanced Process Decision-making (CAPD) at Carnegie Mellon. We also intend to collaborate with the Electric Energy Systems Group at Carnegie Mellon. We plan to involve undergraduates for documenting case studies that will be made available through the internet in our cybersite on MINLP. Finally, we also plan to participate in the University outreach program at Carnegie Mellon where we intend to expose high school students to technology on air separation and major issues in operation under fluctuating electricity prices by using simple day-to-day examples like deciding what appliances to turn on and off at their houses if the utilities charged electricity prices that changed on an hourly basis.

1159443Grossmannsummary：由于环境压力减少二氧化碳排放，将来将面临空气分离，水泥和氯 - 阿尔卡利制造等电力密集型行业。 此外，另一个挑战是，由于电力市场在1990年代被放松管制，因此电力价格受到小时和季节性变化的影响。由于引入了诸如风能和太阳能之类的可再生能源，因此它们也可能变得更加敏锐。这些趋势导致了电力密集型行业的日常运营费用的大量不确定性和可变性，这反过来又影响了他们的竞争力和长期计划。该目标提案的目的将与Praxair的研究人员合作执行，是要开发一个多规模建模框架，该框架可以用作决策支持工具，以优化设计，以便在工厂运营中引入灵活性，以有效地解决电力价格价格不确定的小时变量，并且产品需求不确定。为了解决不确定的电价和产品需求的问题，我们认为是为确定性案例的优化方法开发的第一步，当时将电价和需求假定为已知（例如，根据预测）。我们提出了一个基于离线计算或测量工厂数据的短期混合工作优化模型，该模型与设计和长期容量计划问题集成在一起，其中涉及关于安装或升级设备或增加存储容量的决策。为了解决所得的多尺度混合刻机线性程序（MILP）模型，我们计划开发量身定制的双层分解算法。同样，考虑到由几种工厂组成的过程网络的情况，我们打算根据基于新型混合切割平面和亚级别方法来研究大规模模型的解决方案，以加速收敛。作为第二个主要一步，我们将解决对产品需求和电价不确定性的处理，我们打算研究一种新型混合随机编程/强大的优化方法。基本思想是通过多阶段随机编程以及短期运营决策和不确定的价格来对长期设计决策和不确定的需求进行建模。提出的模型将通过Praxair提供的空气分离厂的过程模型和现实世界数据进行测试。智能优点：该项目的主要智力挑战在于短期运营模型与设计和长期容量计划问题的多尺度整合，对不确定性需求和电力价格的不确定性处理的计算算法以及有效的Algorith的开发在不确定性的情况下处理。为了应对这些挑战，我们提出了一种多尺度集成的策略，该策略将操作模型与设计和容量计划模型有效地结合在一起。此外，我们提出的分解方案具有有效地解决电力密集型植物的现实过程网络的大规模确定性模型，特别是对于空气分离植物而言。最后，我们提出了一种潜在有希望的混合随机编程/强大的优化模型和解决方案方法，以预测不确定性在产品需求和电价中的影响。BROADER的影响：拟议的Goali项目具有在企业的广泛优化的电力密集型工业中产生大量经济节省的潜力，以使其更具竞争力。拟议的守门员项目将涉及博士学位的暑期实习。学生和PI访问Praxair。该项目还具有将基本方法的合作和传播给卡内基·梅隆（Carnegie Mellon）高级过程决策中心（CAPD）的几家石油，化学和工程/软件公司。我们还打算与卡内基·梅隆（Carnegie Mellon）的电能系统集团合作。我们计划让本科生记录案例研究，这些案例研究将通过我们在MINLP上的网络网络提供。最后，我们还计划参加卡内基·梅隆（Carnegie Mellon）的大学外展计划，我们打算通过使用简单的日常示例（例如，如果公司在一个小时基础上发生了充电的电力价格，在房屋中开启和关闭的电器，则将高中生的空气分离和运营中的重大问题技术访问。