Abstract
Droop control has been well adopted for multi-source multi-load DC microgrids (MGs) due to its inherent modularity and reliability by only using local measurements, especially with the most commonly-used voltage-current droop ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V-I</i> ) mode. While another implementation named current-voltage droop ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I-V</i> ) mode also can be adopted. With the same value of droop gain in these two modes, a DC MG will have the same steady-state feature, but its dynamic performance of DC voltage control may be different. Our primary motivation is to investigate this phenomenon from the perspective of equivalent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLC</i> circuits. This paper proposes a generic reduced-order modeling method suitable for exploring the dynamic stability of DC voltage control with these two modes. By ignoring fast inner current control dynamic, each droop based DC voltage control unit can be modeled as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLC</i> parallel circuit in these two modes, which is convenient for modular modeling and extension. With these models, the essential cause of system dynamic stability difference and the physical meaning of key control parameters in these two modes can be revealed in an intuitive way. In addition, if the inner current control with slow dynamic cannot be ignored in some specific scenarios, a modified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLC</i> model can be still obtained to analyze its influence. Moreover, based on reduced-order models, analytical solutions of dynamic performance indexes have been obtained, through which the impact of control parameters on dynamic performance of DC bus voltage can be characterized. Finally, the effectiveness of the proposed reduced-order modeling method has been verified by detailed simulation and experiment results.
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