Electronic power converters (EPCs) are the key elements of the smart dc microgrid architectures. In order to enhance the controllability of the system, most of the elements are envisioned to be connected to the different buses through EPCs. Therefore, power flow, stability, and dynamic response in the microgrid are function of the behavior of the EPCs and their control loops. Besides, dc microgrids constitute a new paradigm in power distribution systems due to the high variability of their operating conditions, owing to the intermittent behavior of the renewable sources and customer energy consumption. Furthermore, in order to deal with this variability, the power converters can modify their operation mode, adding complexity to the dynamic and stability analysis of the system. This paper gives an overview of the various analytical and blackbox modeling strategies applied to smart dc micro/nanogrids. Different linear and nonlinear modeling techniques are reviewed describing their capabilities, but also their limitations. Finally, differences among blackbox models will be highlighted by means of illustrative examples.

Maritime applications have found in the integration of the electric power system a way to further improve efficiency and reduce the weight of new electric ships. This movement has led scientists to integrate smart management systems to optimize the overall behavior of the grid. In this context, power electronics play a key role in linking the different elements of the power architecture. Moreover, the transition toward a dc distribution, which has already been established in other applications, is being regarded as a promising alternative to ease the integration of renewable sources, batteries, and the ever increasing number of dc loads. In this paper, blackbox models are proposed as a tool to foresee the effect of these complex interactions, overcoming the lack of detailed information about the power converters. Large-signal strategies are proposed in order to consider nonlinearities in the static and dynamic behavior of the converters. An accurate model of the physical layer is essential to allow intelligent systems to take the most out of the system performance. This approach offers the opportunity to study the dynamic response of complex interconnected systems, tune the system-level controllers, design protections, or assess the compliance of the system dynamics with the standards. Experimental results are included in order to validate the proposed method.

DC electric power distribution is becoming popular due to the proliferation of renewable sources and storage elements in applications such as electric vehicles, ships, aircrafts, microgrids, etc. These systems are characterized by a high integration of power electronic converters. From a system-level perspective, it would be desirable to design this kind of systems using commercial-off-the shelf converters. However, in general, the manufacturers do not provide a behavioral model of the devices in order to analyze the dynamic behavior of the interconnected system before the actual implementation. In the literature, several blackbox modeling techniques have been proposed to overcome this lack of information. This paper proposes the integration of dynamic weighting functions to the polytopic model in order to improve the accuracy of the behavioral models when the input variables change sharply. A boost converter is used as case study and the performance of the proposed model is compared with the most relevant techniques that can be found in the literature.

Modern electric power distribution systems are progressively integrating electronic power converters. However, the design of electronic-power-converter-based systems is not a straightforward task, as the interactions among the different converters can lead to dynamic degradation or instabilities. In addition, electric power distribution systems are expected to consist of commercial-off-the-shelf converters, which implies limited information about the dynamic behavior of the devices. Large-signal blackbox modeling approaches have been proposed in order to obtain accurate dynamic models of commercial converters that can be used for system-level analyses. However, most of the works are focused on DC-DC converters. In this work, a large-signal blackbox model is proposed to model grid-connected three-phase DC-AC converters. An experimental setup has been used to demonstrate the limitations of small-signal models and the capability of the proposed modeling approach to capture the dynamic behavior of the converter when large perturbations are applied. Finally, the automation of the model identification process is discussed.

Microgrids are a growing trend toward a new power distribution system that allows for a high penetration of renewable distributed generation, electrical energy storage systems and controllable loads. This modern concept exploits the advances in electronic technology to improve efficiency, reliability, and flexibility, while reducing cost and size. Furthermore, microgrids can operate in a grid-connected mode or in an islanded operation mode. Electronic power converters are essential in this kind of architecture in order to interface every element to the system. They introduce controllability into the system and dynamic decoupling. However, the control of these elements from a system-level perspective remains a challenge. The dynamic interaction among the power converters, their variable operation modes, and the lack of information about commercial converters, are some of the factors that complicate an integrated analysis. This work proposes a large-signal black-box modeling approach able to characterize the nonlinear behavior dc/ac converters. The study is particularized to a converter interfacing a renewable source with the grid. Nevertheless, this methodology can be extended to different operating modes. The performance of the model is compared with the detailed switching model of the converter.