A multi-agent-based consensus algorithm for distributed coordinated control of distributed generators in the energy internet," IEEE Trans. Smart Grid, 2015.
This paper investigates the issue of accurate reactive, harmonic and imbalance power sharing in a microgrid. Harmonic and imbalance droop controllers are developed to proportionally share the harmonic power and the imbalance power among distributed generation (DG) units and improve the voltage quality at the point of common coupling (PCC). Further, a distributed consensus protocol is developed to adaptively regulate the virtual impedance at fundamental frequency and selected harmonic frequencies. Additionally, a dynamic consensus based method is adopted to restore the voltage to their average voltage. With the proposed methods, the microgrid system reliability and flexibility can be enhanced and the knowledge of the line impedance is not required. And the reactive, harmonic and imbalance power can be proportionally shared among the DG units. Moreover, the quality of the voltage at PCC can be greatly improved. Simulation and experimental results are presented to demonstrate the proposed method.Index Terms-microgrid, adaptive virtual impedance, reactive power sharing, harmonic power sharing, imbalance power sharing, distributed control, consensus protocol.
NOMENCLATURE ω DGReference angular frequency of the DG unit ω *The nominal angular frequency of the DG unit E DG The reference voltage magnitude of the DG unit E * The nominal voltage magnitude of the DG unit m n Droop coefficients P Q Measured active and reactive power after low-pass filtering X DGf,i The reactance of DG equivalent positive sequence impedances Q Rated,i The rated reactive powers of DG units E DGh,i Reference harmonic voltage magnitudes of the DG units E DGI,i Reference imbalance voltage magnitudes of the DG units Q Har,i Harmonic power of the i th DG unit Q Imb,i Imbalance power of the i th DG unit n h,i Coefficient of the harmonic droop controller m I,i Coefficient of the imbalance droop controller
This paper investigates the coordinated power sharing issues of interlinked ac/dc microgrids. An appropriate control strategy is developed to control the interlinking converter (IC) to realize proportional power sharing between ac and dc microgrids. The proposed strategy mainly includes two parts: the primary outer-loop dual-droop control method along with secondary control; the inner-loop data-driven model-free adaptive voltage control. Using the proposed scheme, the interlinking converter, just like the hierarchical controlled DG units, will have the ability to regulate and restore the dc terminal voltage and ac frequency. Moreover, the design of the controller is only based on input/output (I/O) measurement data but not the model any more, and the system stability can be guaranteed by the Lyapunov method. The detailed system architecture and proposed control strategies are presented in this paper. Simulation and experimental results are given to verify the proposed power sharing strategy. Index Terms-Interlinked microgrids, interlinking converter, power sharing, dual-droop control, data-driven model-free adaptive control.
In this paper, a novel discrete-time deterministic Q -learning algorithm is developed. In each iteration of the developed Q -learning algorithm, the iterative Q function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional Q -learning algorithm. A new convergence criterion is established to guarantee that the iterative Q function converges to the optimum, where the convergence criterion of the learning rates for traditional Q -learning algorithms is simplified. During the convergence analysis, the upper and lower bounds of the iterative Q function are analyzed to obtain the convergence criterion, instead of analyzing the iterative Q function itself. For convenience of analysis, the convergence properties for undiscounted case of the deterministic Q -learning algorithm are first developed. Then, considering the discounted factor, the convergence criterion for the discounted case is established. Neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of the deterministic Q -learning algorithm. Finally, simulation results and comparisons are given to illustrate the performance of the developed algorithm.
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