COLLABORATIVE RESEARCH: Commitment, Expansion, and Pricing in Uncertain Power Markets: Discrete Hierarchical Models and Scalable Algorithms

合作研究：不确定电力市场中的承诺、扩展和定价：离散层次模型和可扩展算法

• 批准号: 1408366
• 负责人: Uday Shanbhag
• 金额: $ 21万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408366&HistoricalAwards=false
• 关键词: COLLABORATIVE; RESEARCH; Commitment; Expansion; Pricing

Commitment, Expansion, and Pricing in Uncertain Power Markets: Discrete Hierarchical Models and Scalable AlgorithmsOur study is motivated by the impact of uncertainty, nonlinearity, and hierarchy on critical long-term planning (such as transmission expansion) and operational problems (such as unit commitment problems (UCPs) and transmission switching) in power systems and markets. The optimal resolution of such problems is of significant relevance. For instance, algorithms for UCPs have saved billions of dollars annually while analogous schemes for transmission switching may have similar potential. Yet, much of the available technology can only cope with deterministic linear problems. But nonlinear generalizations are assuming increasing relevance and emerge from incorporating reactive power and voltage management through AC load flows, while uncertainty in problem data, such as demand and availability, leads to a massive growth in problem complexity. Equally challenging are the hierarchical problems, arising from optimizing the expansion of transmission assets subject to subsequent energy market behavior, recognizing long run regulatory, economic, and technology uncertainties. Both the planning and operational problems lead to inordinately challenging optimization problems and no general purpose algorithms exist for the scalable resolution of such problems, motivating the proposed research. Through relationships with independent system operators for two US markets, our research will help inform stakeholder discussions concerning the design of markets for electric energy and capacity. More efficient short-run and long-run markets lower the cost and enhance the environmental sustainability of the power sector. In particular, our collaboration with PJM Interconnection will examine commitment, switching, and expansion problems under uncertainty, reliability pricing models for ensuring generation adequacy, and the design of reliability premiums in the context of integrating renewables. The proposal is equipped with an educational plan that includes the organization of research workshops and professional course development.Our goals are twofold: (a) Stochastic optimization models: We consider development of: (i) Operating models for stochastic unit commitment problems emphasizing reactive power management, transmission switching, and stochasticity; and (ii) Planning models for transmission expansion in uncertain settings as well as the pricing of reliability, in particular the adequacy of generation and transmission resources. (b) Scalable algorithms: These models lead to mixed-binary stochastic optimization problems possibly complicated by nonlinearity. Unfortunately, existing decomposition schemes are ill-equipped to address such problems and we consider two broad directions. (i) A stochastic barrier-cut scheme that utilizes a combination of interior point methods, cutting plane techniques, and Schur-complement methods to develop scalable interior-point methods for contending with mixed-binary stochastic nonlinear programs; and (ii) Stochastic mixed-integer quadratic programs, including potential use of semidefinite programming relaxations, both in terms of developing cutting-plane schemes as well as approximate solutions. If successful, this research will lead to scalable computational tools for resolving a range of stochastic optimization problems, complicated by hierarchy, discreteness, and nonlinearity.

不确定电力市场的承诺，扩展和定价：离散的层次结构模型和可扩展算法的研究是出于不确定性，非线性和层次结构对关键长期计划（例如传输扩展）和运营问题（例如单位承诺等关键长期计划（例如单位承诺）的影响而激发电力系统和市场中的问题（UCP）和传输切换）。此类问题的最佳解决方案是显着相关性的。例如，UCP的算法每年节省数十亿美元，而用于传输切换的类似方案可能具有相似的潜力。但是，许多可用的技术只能应对确定性的线性问题。但是，非线性概括正在假设相关性越来越大，并且通过通过交流负载流纳入反应能力和电压管理而出现，而问题数据（例如需求和可用性）的不确定性会导致问题复杂性的大量增长。同样具有挑战性的是层次问题，这是由于优化以随后的能源市场行为的优化扩展的传输资产的扩展，并认识到长期监管，经济和技术不确定性。计划和操作问题都导致了极具挑战性的优化问题，并且对于可扩展的此类问题的可扩展解决，不存在通用算法，从而激发了拟议的研究。通过与两个美国市场的独立系统运营商的关系，我们的研究将有助于告知利益相关者有关电能和容量市场设计的讨论。更有效的短期和长期市场降低了成本并增强了电力部门的环境可持续性。特别是，我们与PJM互连的合作将检查不确定性，可靠性定价模型，以确保发电充足性以及在整合可再生能源的背景下的可靠性保费设计。该提案配备了一个教育计划，其中包括研究研讨会和专业课程开发。我们的目标是双重的：（a）随机优化模型：我们考虑开发：（i）用于强调反应性力量的随机单位承诺问题的操作模型管理，传输切换和随机性； （ii）规划不确定设置中传输扩展的计划模型以及可靠性的定价，尤其是发电和传输资源的充分性。 （b）可扩展算法：这些模型导致混合二进制随机优化问题可能因非线性而复杂。不幸的是，现有的分解方案缺乏解决此类问题的能力，我们考虑了两个广泛的方向。 （i）一种随机屏障切割方案，利用内部点方法，切割平面技术和Schur-complement方法的组合来开发可扩展的内点方法，以与混合二进制的随机非线性程序竞争； （ii）随机混合二级二次程序，包括在开发切皮平面方案和近似解决方案方面的潜在使用半决赛编程松弛。如果成功的话，这项研究将导致可扩展的计算工具，以解决一系列随机优化问题，这些问题因层次结构，离散性和非线性而复杂。