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

1159443 Grossmann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1990年代电力市场放松管制以来，电价受到每小时和季节变化的影响。随着风能和太阳能等可再生能源用于发电，这些问题也可能变得更加严重。这些趋势导致电力密集型行业的日常运营费用存在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，可用作优化设计的决策支持工具，从而在工厂运营中引入灵活性，以有效应对电价的不确定性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，我们认为作为第一步的确定性的情况下，当电价和需求被假定为已知的（例如，在预测方面）的优化方法的发展。我们提出了一个短期的混合整数操作优化模型，该模型是基于离线计算或测量的工厂数据，并与设计和长期的容量规划问题，其中涉及安装或升级设备，或增加存储容量的决定。为了解决单个工厂的多尺度混合整数线性规划（MILP）模型，我们计划开发一个定制的双层分解算法。此外，考虑的情况下，由几个工厂的过程网络，我们打算调查的解决方案的大规模模型的拉格朗日分解方案的基础上，一种新的混合切割平面和次梯度法，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，我们打算调查一种新的混合随机规划/鲁棒优化方法的治疗。其基本思想是用多阶段随机规划对长期设计决策和不确定需求进行建模，用鲁棒优化对短期运营决策和不确定价格进行建模。所提出的模型将与普莱克斯提供的空气分离设备的过程模型和真实的世界数据进行测试。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规划问题的多尺度集成，产品需求和电价的不确定性的处理，以及开发有效的计算算法来解决大规模优化模型。为了应对这些挑战，我们提出了一种多尺度集成的战略，有效地将运营模式与设计和容量规划模型相结合。此外，我们提出的分解方案，有可能有效地解决大规模的确定性模型的电力密集型工厂，特别是空气分离厂的现实过程网络。最后，我们提出了一个潜在的有前途的混合随机规划/鲁棒优化模型和求解方法，以预期的产品需求和电力price.Broader Impact的不确定性的影响：拟议的GOALI项目有可能产生显着的经济节约在企业范围内优化的电力密集型行业，使他们更具竞争力。拟议的GOALI项目将包括博士的暑期实习。学生和PI访问普莱克斯。该项目还具有合作的潜力，并向卡内基梅隆大学先进工艺决策中心的几家石油、化学和工程/软件公司传播基本方法。我们还打算与卡内基梅隆大学的电能系统小组合作。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MINLP的网络网站上提供。最后，我们还计划参加卡内基梅隆大学的大学外展计划，我们打算通过简单的日常例子，让高中生了解空气分离技术以及在电价波动下运行的主要问题，例如如果公用事业公司收取的电价按小时变化，则决定在家中打开和关闭哪些电器。

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.