EAGER: Fundamentals of Modeling and Control for the Evolving Electric Power System Architectures

该项目的主要目标是将开创性的一般原则正式化，以支持不断变化的电力系统的投资规划和运营协议。拥有这一点对于评估候选硬件和软件技术的影响以及创造一种通过相互补充性能和共同满足社会需求来利用灵活技术的环境至关重要。由于许多重要技术的性能在很大程度上取决于它们随着时间的推移和利益攸关方(系统模块)的整合程度，因此有效的框架需要支持随时间和不同系统决策者之间的数据启用和交互的决策。在这个项目中，采用了复杂动态系统的观点，它认识到决策的这种分布式性质，并引入了新的建模、控制和协议原理，以支持系统在时间和空间上的集成。与不断发展的电力系统体系结构中缺乏理论上可证明的性能形成鲜明对比的是，该项目探索了基本原理，并采取了重大步骤来解决这一拖延已久的问题。最重要的影响将是电力行业架构的演变方式。特别是，该项目将促进采用数据支持的决策和自动化。值得注意的是，这种方法将有助于教育学生将新兴的电力能源系统视为需要和可能进行大量创新的复杂动力系统。这项研究的结果将通过一个关于复杂能源系统设计和控制的讲习班加以传播。下一代监控和数据采集(SCADA)可以基于根据本项目设想的动态监控和决策系统(DyMonDS)框架定义的协议而发展。这种框架对于评估候选硬件和软件技术的影响以及创造一种通过相互补充性能和共同满足社会需求来整合和利用技术的环境至关重要。在这个项目中，采用了复杂动力系统的观点，它认识到决策的这种分布式性质，并引入了支持时间和空间系统集成的新的建模、控制和协议原理。主要问题是如何确保这些系统的稳定性和效率，而不增加信息管理的复杂性和对网络安全的影响。一旦回答这一复杂问题的原则形成，就有可能支持选择“正确的”技术，并根据通过传感、通信和控制获得的数据来利用这些技术。为了确立这样的原则并不从根本上改变行业，该项目引入了平衡机构(BAS)对当今自动发电控制的概括，本质上是合作的。BA之间的交互由每个区域补偿其自己的区域控制误差(ACE)来管理，ACE是由内部功率偏离计划和进入BA的联络线流量偏差造成的净功率不平衡。这是一个很好的例子，通过基于简单的平衡ACE协议的协作进行分布式控制。在本项目中，ACE被概括为与每个智能平衡机构(IBA)相关联的交互变量(IntVar)的概念。值得注意的是，intVar在功率和功率变化率方面有物理解释，但它不限于当今行业所做的假设，它允许不同的非传统体系结构，例如嵌套在现有BA中的小型IBA。IBA可以是不同空间和时间粒度的组件或子系统，并且与技术无关；它们的规范、建模和交互式多层控制是所有人通用的intVar。该项目专门针对协议的原则和通用DyMonDS框架的增强标准，以便遵循该框架可以基于PI最近的发现催化性能增强，即intVar动态可以用火用(执行有用的工作的潜力)和无能(浪费的工作)的比率来解释。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The main objective of this project is to formalize ground-breaking general principles in support of investment planning and operating protocols for the changing electric energy systems. Having this is crucial for assessing effects of candidate hardware and software technologies and for creating an environment in which flexible technologies are utilized by complementing each other’s performance and by jointly meeting the societal needs. Since the performance of many technologies of interest greatly depends on how well they are integrated over time and stakeholders (system modules), an effective framework needs to support decisions that are data-enabled and interactive over time and among different system decision makers. In this project a complex dynamical systems point of view is taken which recognizes such distributed nature of decisions and introduces novel modeling, control and protocol principles in support of system integration in time and space. In sharp contrast with the lack of theoretically-provable performance in the evolving electric power system architectures, this project explores fundamentals and takes major steps toward solving this long-overdue problem. The most important impact will be on the way electric power industry architectures evolve. In particular, the project will catalyze the adoption of data-enabled decision making and automation. Notably, this approach will help educate students to think about emerging electric energy systems as complex dynamical systems for which much innovation is needed and possible. Results of this research will be disseminated through a workshop on design and control of complex energy systems. The next generation Supervisory Control and Data Acquisition (SCADA) can evolve based on protocols defined according to a dynamic monitoring, and decision system (DyMonDS) framework envisioned in this project. Such framework is crucial for assessing effects of candidate hardware and software technologies and for creating an environment in which technologies are integrated and utilized by complementing each other’s performance and by jointly meeting the societal needs. In this project a complex dynamical systems point of view is taken which recognizes such distributed nature of decisions and introduces novel modeling, control and protocol principles in support of temporal and spatial system integration. The main question is how to ensure stability and efficiency of these systems without increasing complexity of information management and consequent implications on cyber-security. Once principles for answering this complex question are formalized, it becomes possible to support selection of “right” technologies and for utilizing them based on data obtained through sensing, communications and control. To establish such principles and not radically change the industry, this project introduces a generalization of today’s Automatic Generation Control by the Balancing Authorities (BAs) which is fundamentally cooperative in nature. The interactions between BAs are managed by each area compensating its own Area Control Error (ACE) which is the net power imbalance contributed by both internal power deviations from schedules and by the tie-line flow deviations into the BA. This is a great example of distributed control through cooperation based on simple protocols of balancing ACE. In this project the ACE is generalized into a notion of an interaction variable (intVar) associated with each intelligent Balancing Authority (iBA). Notably, an intVar has a physical interpretation in terms of power and rate of change of power, but it is not restricted to the assumptions made by the industry today, and it allows for different unconventional architectures, such as small iBAs nested within the existing BAs. iBAs can be components or subsystems of diverse spatial and temporal granularity, and are technology agnostic; their specifications, modeling and interactive multi-layered control are in terms of an intVar common to all. This project targets specifically principles for protocols and enhanced standards for a general DyMonDS framework so that following this framework one can catalyze performance enhancements based on the PI’s recent finding that the intVar dynamics can be interpreted in terms of rates of exergy (potential to perform useful work) and anergy (wasted work).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.