Collaborative Research: Improving Energy Reliability by Co-Optimization Planning for Interdependent Electricity and Natural Gas Infrastructure Systems

合作研究：通过相互依赖的电力和天然气基础设施系统的协同优化规划提高能源可靠性

• 批准号: 1906780
• 负责人: Lei Wu
• 金额: $ 19.98万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1906780&HistoricalAwards=false
• 关键词: Collaborative; Research; Improving; Energy; Reliability

The electricity grid and the natural gas network are two essential infrastructure systems in the U.S. energy industry. They are designed and managed independently. However, because of the planned retirement of many coal-fired generators, the deeper penetration of renewable energy sources, and the commercially sustainable gas price, their interactions have intensified over the last five years. Hence, in order to ensure environmentally friendly, reliable, and cost-effective electricity and gas production and delivery, it is important to jointly optimize these two systems. However, due to their scales, complexities, and requirements/regulations, such a co-optimization planning problem is very challenging in both modeling and computation aspects. To address this critical challenge, this project will build analytical decision support models and design efficient solution methods to aid the energy industry in formulating and computing practical-scale co-optimization problems. The effectiveness and benefit of co-optimization planning will be demonstrated and evaluated through an actual microgrid project and industrial collaborations. In addition to including doctoral students in research and creating educational materials for the next generation energy system planners and operations researchers, concrete projects will be designed to involve underrepresented students on utilizing analytical/computational tools to address real energy problems. Previous research on co-optimization planning of electricity and gas systems is limited, while also often neglecting critical reliability considerations, key random/uncertain factors, or the long-term multi-stage nature of planning problems. This research project will address these shortcomings by (1) investigating key interactions between electricity and gas systems with different spatial-temporal granularities as well as multiple planning and operation levels; (2) building a set of co-optimization planning models that simultaneously consider the multi-stage planning horizon, the hourly chronological operation details with critical random/uncertain factors, and the requirements of long-term reliability and short-term flexibility; (3) designing and implementing high-performance computational methods and tools through advanced decomposition strategies, strong approximation approaches, and effective hybrid methods; and (4) validating, demonstrating, and promoting the developed models and computational tools through an on-going microgrid project and established industry connections.

电网和天然气网络是美国能源行业的两个基本基础设施系统。 它们是独立设计和管理的。但是，由于许多燃煤发电机的计划退休，可再生能源的更深层次的渗透以及商业上可持续的天然气价格，他们的相互作用在过去五年中加剧了。 因此，为了确保环境友好，可靠和成本效益的电力以及天然气的生产和交付，共同优化这两个系统很重要。 但是，由于它们的量表，复杂性和要求/法规，因此在建模和计算方面，这种合作的计划问题都非常具有挑战性。 为了应对这一关键挑战，该项目将建立分析决策支持模型和设计有效的解决方案方法，以帮助能源行业制定和计算实践规模的合作问题。 合作计划的有效性和好处将通过实际的微电网项目和工业合作来证明和评估。 除了在研究中包括博士生和为下一代能源系统计划者和运营研究人员创建教育材料外，具体项目还将旨在使代表性不足的学生使用分析/计算工具来解决实际的能源问题。 先前关于电力和天然气系统合作计划计划的研究是有限的，同时也经常忽略关键可靠性考虑因素，关键的随机/不确定因素或计划问题的长期多阶段性质。 该研究项目将通过（1）调查具有不同时空粒度以及多重计划和操作水平的电力和气体系统之间的关键相互作用来解决这些缺点； （2）建立一组合作的计划模型，这些模型同时考虑多阶段计划范围，每小时的时间顺序操作细节，具有关键的随机/不确定因素以及长期可靠性和短期灵活性的要求； （3）通过高级分解策略，强近似方法和有效的混合方法设计和实施高性能计算方法和工具； （4）通过正在进行的微电网项目和建立的行业联系来验证，演示和促进开发的模型和计算工具。

项目成果

Energy Flow Optimization for Integrated Power-Gas Generation and Transmission Systems

综合发电和输电系统的能量流优化

• DOI: 10.1109/tii.2019.2924927
• 发表时间: 2020
• 期刊: IEEE Transactions on Industrial Informatics
• 影响因子: 12.3
• 作者: Ding Tao;Xu Yiting;Wei Wei;Wu Lei
• 通讯作者: Wu Lei

Vulnerability Identification and Evaluation of Interdependent Natural Gas-Electricity Systems

• DOI: 10.1109/tsg.2020.2968178
• 发表时间: 2020-07
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Lu Nan;Lei Wu;Tianqi Liu;Yikui Liu;Chuan He

Distributionally Robust Scheduling of Integrated Gas-Electricity Systems With Demand Response

• DOI: 10.1109/tpwrs.2019.2907170
• 发表时间: 2019-03
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Chuan He;Xiaping Zhang;Tianqi Liu;Lei Wu