Self-organizing Control and Scalable Game-theoretical Dispatch of Distributed Generations for High-Penetration Smart Grids

高渗透率智能电网分布式发电的自组织控制和可扩展博弈论调度

• 批准号: 1308928
• 负责人: Zhihua Qu
• 金额: $ 33.17万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1308928&HistoricalAwards=false
• 关键词: Self; organizing; Control; Scalable; Game

The proposed research is concerned with optimizing the performance of a smart power grid in which heterogeneous and distributed generation sources (DGs) intermittently and locally communicate with each other and with the grid. As renewable energy sources become more cost effective and hence more accessible to the power grid, the current centralized optimization and individual dispatch approach become unrealistic. Furthermore, because of the generally intermittent and asynchronous flow of information through local wireless communication networks, appropriate distributed control mechanisms and optimization methods need to be implemented. The application of distributed cooperative control, distributed optimization and distributed game strategies will enable the DGs to competitively and collaboratively provide power to both local loads and the main grid. The implementation of these concepts is transformative by ensuring that a power grid with a high penetration of DGs is efficient and reliable.Intellectual Merit: The proposed research addresses the fundamental challenges faced by smart grid operators in optimizing the grid performance and reliability while dealing with unpredictable and variable power generations of geographically dispersed DGs. We propose a new framework of distributed control and optimization designs that enable autonomous dispatch of the aggregate DG generation and automatic voltage control in distribution networks. This framework consists of investigating in an integrated manner three interdepended analysis and design methodologies to improve the operation of the power grid. First, since active power outputs of individual renewable energy DGs are generally variable and unpredictable, we will design distributed controls utilizing shared local communication networks in order to enable them to form collaborative and self-evolving microgrids so that aggregate generation outputs of the microgrids, instead of the single DG outputs, can be dispatched. Second, we will formulate distributed optimization and control algorithms so as to ensure robustly convergent solutions for: regulating reactive power and maintaining voltage stability within distribution networks, as well as effectively coordinating among multiple-time-scales of static on-load tap changers, static var compensators and distributed generation sources. Finally, through a Stackelberg (Leader-Follower) game algorithm, we will explore strategies that will allow the grid operator, acting as the leader, to autonomously interact with the DGs and develop pricing controls to optimize the operation of the entire grid regardless of the strategies adopted by the microgrids, either individually or collectively, to optimize their own economic benefits. The proposed research will focus on integrating these three principles to derive a revolutionary holistic and multi-level approach to the challenges involved in optimizing the operation of the grid with a high penetration level of DGs. The proposed framework has the prominent features that both the distributed control and optimization algorithms only require local information but make distribution networks adaptive as a whole and that performance of the proposed algorithms can be analytically quantified. Preliminary results obtained by the PIs have demonstrated success of the proposed framework on the IEEE 34-bus distribution network and the IEEE 399-1997 network.Broader Impact: The technological impact of the proposed research on the operation of smart power grids will be transformative and far reaching. More specifically, the proposed new framework not only optimizes power grid's operation and reduces loss in distribution networks but also enables a game-theoretic relationship between utility and customers so that more customers have economic incentives to install plugand- play-ready DG units and optimize their own benefits. From a broader perspective the proposed research will have a major impact on the performance optimization of large complex systems, similar to the smart grid, in formulating member participation rules so that the behavior of multiple independent agents whose intent is to pursue their own objectives can be guided and enforced to accomplish system-wide benefits. Through student training and course development, the project will also contribute to workforce development in training students in the areas of renewable energy and power systems where there is currently a shortage.

所提出的研究关注的是优化智能电网的性能，其中异构和分布式发电源（DG）间歇性和本地相互通信，并与电网。随着可再生能源变得更具成本效益，因此更容易进入电网，目前的集中优化和单独调度方法变得不现实。此外，由于通过本地无线通信网络的信息流通常是间歇和异步的，因此需要实现适当的分布式控制机制和优化方法。分布式协同控制、分布式优化和分布式博弈策略的应用将使分布式发电机能够竞争和协作地向本地负荷和主电网供电。这些概念的实施是变革性的，通过确保具有高渗透率的DG的电网是高效和可靠的。智力优点：拟议的研究解决了智能电网运营商在优化电网性能和可靠性，同时处理地理上分散的DG的不可预测和可变的发电所面临的根本挑战。我们提出了一个新的分布式控制和优化设计的框架，使自治调度的聚合DG发电和自动电压控制在配电网。该框架包括以综合的方式调查三个相互依赖的分析和设计方法，以改善电网的运行。首先，由于单个可再生能源DG的有功功率输出通常是可变的和不可预测的，我们将利用共享的本地通信网络设计分布式控制，以使它们能够形成协作和自我发展的微电网，使微电网的总发电输出，而不是单个DG输出，可以被调度。其次，我们将制定分布式优化和控制算法，以确保鲁棒收敛的解决方案：调节无功功率和维持配电网内的电压稳定，以及有效地协调多个时间尺度的静态有载分接开关，静态无功补偿器和分布式电源。最后，通过Stackelberg（领导者-追随者）博弈算法，我们将探索策略，使电网运营商，作为领导者，自主与DG互动，并制定定价控制，以优化整个电网的运行，无论微电网采用的策略，无论是单独还是集体，以优化自己的经济效益。拟议的研究将集中在整合这三个原则，以获得一个革命性的整体和多层次的方法，以优化具有高渗透水平的DG的电网运行所涉及的挑战。该框架的突出特点是分布式控制和优化算法都只需要局部信息，但使配电网作为一个整体的自适应性和所提出的算法的性能可以分析量化。PI获得的初步结果表明，成功的IEEE 34-总线配电网和IEEE 399-1997网络的建议框架。更广泛的影响：智能电网的操作拟议的研究的技术影响将是变革性的和深远的。更具体地说，所提出的新框架不仅优化了电网的运行，降低了配电网的损耗，而且还实现了公用事业和客户之间的博弈论关系，使更多的客户有经济动机来安装即插即用的DG单元，并优化自己的利益。从更广泛的角度来看，拟议的研究将产生重大影响的大型复杂系统的性能优化，类似于智能电网，在制定成员的参与规则，使多个独立的代理人的行为，其目的是追求自己的目标可以指导和强制执行，以实现系统范围内的利益。通过学生培训和课程开发，该项目还将促进劳动力发展，在目前短缺的可再生能源和电力系统领域培训学生。