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.

摘要：由于减少二氧化碳排放的环境压力，空分、水泥和氯碱制造等电力密集型行业未来将面临电价上涨。此外，另一个挑战是，自20世纪90年代电力市场放松管制以来，电价一直受到小时和季节变化的影响。随着风能和太阳能等可再生能源被引入发电，这些问题可能会变得更加严重。这些趋势导致电力密集型工业的日常运营费用具有相当大的不确定性和可变性，从而影响其竞争力和长期规划。该GOALI提案将与普莱克斯的研究人员合作执行，其目的是开发一个多尺度建模框架，作为优化设计的决策支持工具，从而在工厂运营中引入灵活性，有效地解决电价每小时不确定的变化和不确定的产品需求。为了解决不确定的电价和产品需求的问题，我们考虑作为第一步的优化方法的发展，为确定性的情况下，当电价和需求是已知的（例如，在预测方面）。我们提出了一个短期混合整数运行优化模型，该模型基于离线计算或测量工厂数据，并与设计和长期容量规划问题相结合，该问题涉及安装或升级设备或增加存储容量的决策。为了解决单一工厂的多尺度混合整数线性规划（MILP）模型，我们计划开发一种定制的双层分解算法。此外，考虑到过程网络由多个工厂组成的情况，我们打算研究基于新型混合切割平面和亚梯度方法的拉格朗日分解方案的大尺度模型解，以加速收敛。作为第二个主要步骤，我们将解决产品需求和电价的不确定性的处理，为此我们打算研究一种新的混合随机规划/鲁棒优化方法。其基本思想是用多阶段随机规划方法对长期设计决策和不确定需求进行建模，用鲁棒优化方法对短期经营决策和不确定价格进行建模。所提出的模型将用普莱克斯提供的工艺模型和空分工厂的实际数据进行测试。智力优势：本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规划问题的多尺度集成，产品需求和电价不确定性的处理，以及求解大规模优化模型的有效计算算法的开发。为了应对这些挑战，我们提出了一种多尺度集成策略，有效地将运营模型与设计和容量规划模型相结合。此外，我们提出了分解方案，这些方案具有有效解决电力密集型工厂，特别是空分工厂实际过程网络的大规模确定性模型的潜力。最后，我们提出了一个有潜力的混合随机规划/鲁棒优化模型和求解方法，以预测产品需求和电价不确定性的影响。更广泛的影响：拟议的GOALI项目有可能在电力密集型行业的企业范围优化中产生显著的经济节约，使其更具竞争力。拟议的GOALI项目将包括博士生的暑期实习和PI对普莱克斯的访问。该项目还具有合作和向卡内基梅隆大学高级过程决策中心（CAPD）的几个石油、化学和工程/软件公司传播基本方法的潜力。我们还打算与卡内基梅隆大学的电力能源系统集团合作。我们计划让本科生参与记录案例研究，这些案例研究将通过互联网在我们的网站上提供。最后，我们还计划参加卡内基梅隆大学的大学外展项目，在那里我们打算通过简单的日常例子，比如如果公用事业公司收取每小时变化的电价，就决定在家中打开和关闭哪些电器，让高中生了解空气分离技术和在波动的电价下运行的主要问题。