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

这项研究致力于优化智能电网的性能，在智能电网中，异质和分布式电源(DG)彼此之间以及与电网之间断续地进行本地通信。随着可再生能源变得更具成本效益，从而更容易进入电网，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通常是间歇性的和异步的，因此需要实施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电源既能竞争又能协同地向本地负荷和主电网供电。这些概念的实施通过确保具有高分布式电源渗透率的电网是高效和可靠的，从而具有变革性。智能优点：拟议的研究解决了智能电网运营商在处理地理上分散的不可预测和可变的分布式电源发电时所面临的基本挑战，即优化电网性能和可靠性。提出了一种新的分布式控制和优化设计框架，实现了配电网分布式发电总量的自主调度和自动电压控制。这一框架包括综合研究三种相互依赖的分析和设计方法，以改善电网的运行。首先，由于个别可再生能源分布式发电的有功功率输出通常是可变和不可预测的，我们将利用共享的本地通信网络设计分布式控制，使它们能够形成协作和自我进化的微电网，从而可以对微电网的综合发电输出进行调度，而不是单一的分布式发电输出。其次，我们将制定分布式优化和控制算法，以确保配电网内无功调节和电压稳定的鲁棒收敛解，以及静态有载调压开关、静止无功补偿装置和分布式电源的多个时间尺度之间的有效协调。最后，通过Stackelberg(Leader-Follower)博弈算法，我们将探索允许电网运营商作为领导者与DG自主互动的策略，并开发定价控制以优化整个电网的运行，而不考虑微电网单独或共同采用什么策略来优化自身的经济效益。拟议的研究将集中于将这三个原则结合起来，以得出革命性的整体和多层次的方法，以应对在DG渗透率较高的情况下优化电网运行所涉及的挑战。该框架具有分布式控制算法和优化算法只需要局部信息，但使配电网整体自适应，算法性能可分析量化的显著特点。规划环境改善研究的初步结果显示，拟议的架构在IEEE 34母线配电网和IEEE 399-1997网络上是成功的。广泛影响：拟议研究对智能电网运作的技术影响将是变革性的和深远的。更具体地说，拟议的新框架不仅优化了电网的运行，降低了配电网的网损，还实现了公用事业公司和客户之间的博弈论关系，使更多的客户有经济动机安装即插即用的DG机组，并优化自己的利益。从更广泛的角度来看，拟议的研究将对大型复杂系统的性能优化产生重大影响，类似于智能电网，在制定成员参与规则方面，使意图追求自己目标的多个独立主体的行为能够得到引导和执行，以实现系统范围的利益。通过学生培训和课程开发，该项目还将促进劳动力发展，培训目前短缺的可再生能源和电力系统领域的学生。

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.