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.
