Smart grid, distributed generators, cooperative control, cooperative distributed optimization, microgrid


Smart grid is more than just the smart meters. The future smart grids are expected to include a high penetration of distributed generations (DGs), most of which will consist of renewable energy sources, such as solar or wind energy. It is believed that the high penetration of DGs will result in the reduction of power losses, voltage profile improvement, meeting future load demand, and optimizing the use of non-conventional energy sources. However, more serious problems will arise if a decent control mechanism is not exploited. An improperly managed high PV penetration may cause voltage profile disturbance, conflict with conventional network protection devices, interfere with transformer tap changers, and as a result, cause network instability. Indeed, it is feasible to organize DGs in a microgrid structure which will be connected to the main grid through a point of common coupling (PCC). Microgrids are natural innovation zones for the smart grid because of their scalability and flexibility. A proper organization and control of the interaction between the microgrid and the smartgrid is a challenge. Cooperative control makes it possible to organize different agents in a networked system to act as a group and realize the designated objectives. Cooperative control has been already applied to the autonomous vehicles and this work investigates its application in controlling the DGs in a micro grid. The microgrid power objectives are set by a higher level control and the application of the cooperative control makes it possible for the DGs to utilize a low bandwidth communication network and realize the objectives. Initially, the basics of the application of the DGs cooperative control are formulated. This includes organizing all the DGs of a microgrid to satisfy an active and a reactive power objective. Then, the cooperative control is further developed by the introduction of clustering DGs into several groups to satisfy multiple power objectives. Then, the cooperative distribution optimization is introduced iii to optimally dispatch the reactive power of the DGs to realize a unified microgrid voltage profile and minimize the losses. This distributed optimization is a gradient based technique and it is shown that when the communication is down, it reduces to a form of droop. However, this gradient based droop exhibits a superior performance in the transient response, by eliminating the overshoots caused by the conventional droop. Meanwhile, the interaction between each microgrid and the main grid can be formulated as a Stackelberg game. The main grid as the leader, by offering proper energy price to the micro grid, minimizes its cost and secures the power. This not only optimizes the economical interests of both sides, the microgrids and the main grid, but also yields an improved power flow and shaves the peak power. As such, a smartgrid may treat microgrids as individually dispatchable loads or generators.


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Graduation Date





Qu, Zhihua


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Electrical Engineering and Computer Science

Degree Program

Electrical Engineering








Release Date

May 2013

Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)


Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic