CAREER: Maximizing Renewable Energy Integration: Optimal Management of Uncertainty with Responsive Demand

职业：最大化可再生能源整合：通过响应需求优化不确定性管理

• 批准号: 1453615
• 负责人: Catherine Anderson
• 金额: $ 50万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453615&HistoricalAwards=false
• 关键词: CAREER; Maximizing; Renewable; Energy; Integration

The electric power system is a highly complex, interacting system that manages to balance demand and supply on very short time-scales, despite inherent variability and uncertainty on both sides of this balance. While use of a significant level of renewable energy is crucial to addressing issues of climate change, addition of these unreliable sources exacerbates the complexity of system operations. Effective use of consumer-side resources in power system operations and markets will allow the maximum use of variable renewable resources, leading to beneficial economic and environmental outcomes for society as a whole. In order to address the inclusion of demand-side resources effectively in the future grid, the response reliability of this resource must be both understood and included in power system operations and decision-making. The goal of this research is the development of an efficient and effective framework for integrated use of demand-side and renewable resources in the evolving power system. The project will also develop interactive activities to facilitate introduction of systems thinking into k-12 classrooms, in support of the Next Generation Science Standards. A primary challenge of adding additional uncertain resources to the power systems lies in the increase in dimensionality and complexity of the existing optimization models used for system planning and dispatch. The outcomes of this project are transformative because they provide a robust and truly representative framework integrating uncertain resources on both the demand and supply side, at the fundamental operational time frames of the power system and markets. These goals will be achieved through three primary aims 1) Build the tools to accurately describe intermittent renewable resources, such as wind and solar, to better characterize the uncertainties and interactions for tractable representation in stochastic optimization methods, 2) Characterize responsive demand resources across multiple time scales including characteristics, constraints and uncertainties of various classes, and 3) Develop the multi-period stochastic optimization framework that includes DR into a scalable security constrained unit commitment model by including dispatchable DR resources as stochastic constraints, with additional constraints on frequency and duration of response, and to ensure that the solutions generated are available in reasonable time frames to benefit power system operations. The proposed project will support the evolution to the Smart Grid through provision of analytical tools to manage increasing complexity, and to understand the synergies of new technologies. The educational contribution of this project has components designed to enhance participation of women and under-represented minorities in the field, and build excitement about computational applications in energy systems among graduate students. In addition, this project will reach nearly 100 science classrooms in the Northeast US through collaborative development and delivery of teacher professional development and classroom materials supporting systems thinking and modeling, a new cross-cutting theme in the Next Generation Science Standards.

电力系统是一个高度复杂、相互作用的系统，它设法在很短的时间尺度上平衡需求和供应，尽管这种平衡的双方都存在固有的可变性和不确定性。虽然大量使用可再生能源对解决气候变化问题至关重要，但增加这些不可靠的能源会加剧系统运行的复杂性。在电力系统运行和市场中有效利用消费者侧资源将允许最大限度地利用可变的可再生资源，从而为整个社会带来有益的经济和环境成果。为了有效地解决未来电网中需求侧资源的纳入问题，必须了解需求侧资源的响应可靠性，并将其纳入电力系统运行和决策。本研究的目标是在不断发展的电力系统中，为综合利用需求侧和可再生资源开发一个高效和有效的框架。该项目还将开发互动活动，以促进将系统思维引入k-12教室，以支持下一代科学标准。为电力系统增加额外不确定资源的主要挑战在于用于系统规划和调度的现有优化模型的维数和复杂性的增加。该项目的成果具有变革性，因为它们提供了一个强大的、真正具有代表性的框架，在电力系统和市场的基本运行时间框架内，整合了需求侧和供应侧的不确定资源。这些目标将通过三个主要目标实现：1)建立准确描述间歇性可再生资源（如风能和太阳能）的工具，以更好地表征随机优化方法中的不确定性和相互作用，2)表征响应性需求资源跨多个时间尺度，包括各种类别的特征、约束和不确定性；3)通过将可调度的DR资源作为随机约束，以及对响应频率和持续时间的附加约束，将包含DR的多周期随机优化框架发展为可扩展的安全约束单元承诺模型，并确保生成的解决方案在合理的时间框架内可用，从而使电力系统运行受益。拟议的项目将通过提供分析工具来管理日益增加的复杂性，并了解新技术的协同作用，从而支持向智能电网的发展。这个项目的教育贡献部分旨在提高妇女和代表性不足的少数群体在该领域的参与，并在研究生中建立对能源系统计算应用的兴奋。此外，该项目将通过合作开发和提供教师专业发展和支持系统思维和建模的课堂材料，覆盖美国东北部近100个科学教室，这是下一代科学标准中的一个新的跨领域主题。

项目成果

Statistical Bus Ranking for Flexible Robust Unit Commitment

• DOI: 10.1109/tpwrs.2018.2864131
• 发表时间: 2019-01-01
• 期刊: IEEE TRANSACTIONS ON POWER SYSTEMS
• 影响因子: 6.6
• 作者: Gupta, Amandeep;Anderson, C. Lindsay
• 通讯作者: Anderson, C. Lindsay

Stochastic dynamic programming approach to managing power system uncertainty with distributed storage

• DOI: 10.1007/s10287-017-0297-2
• 发表时间: 2017-12
• 期刊: Computational Management Science
• 影响因子: 0.9
• 作者: Luckny Zéphyr;C. L. Anderson