The Role of Power Electronic Converters in Microgrid Technology: A Review of Challenges, Solutions, and Research Directions

The paper is on the role of power electronic converters in microgrid technology: A review of challenges, solutions and research directions. The objective of the paper is to perform a comprehensive overview of the role of power electronic converters in microgrid technology, focusing on challenges, solutions, and research directions. Findings revealed that major challenges of power electronic converters integration in microgrid technology are voltage and frequency regulation issues, power quality issues, creative management and coordination challenges, and Integration of renewable energy sources. The solutions to these problems are advanced control algorithms such as Model Predictive Control (MPC); deployment of active power filters or harmonic compensators to reduce harmonic distortion and improve power quality; iimplement a centralized control system with centralized monitoring controllers to coordinate the operation of several converters and ensure consistent operation; and combining multiple renewable energy sources in a hybrid energy system to diversify generation sources and reduce the gap. The future research directions include, among others, advanced control strategies, grid-forming converters, wideband semiconductor, and cyber-security and Resilience. The paper concludes that the integration of power electronic converters into microgrid technology presents both opportunities and challenges. Although these converters play an important role in the efficient conversion, distribution and utilization of energy in microgrids, they face various technical and practical challenges. To mitigate these challenges, the implementation of advanced control strategies, grid-forming converters, etc., is inevitable.&nbsp

Analysis of DC microgrids as stochastic hybrid systems

A modeling framework for dc microgrids and distribution systems based on the dual active bridge (DAB) topology is presented. The purpose of this framework is to accurately characterize dynamic behavior of multi-converter systems as a function of exogenous load and source inputs. The base model is derived for deterministic inputs and then extended for the case of stochastic load behavior. At the core of the modeling framework is a large-signal DAB model that accurately describes the dynamics of both ac and dc state variables. This model addresses limitations of existing DAB converter models, which are not suitable for system-level analysis due to inaccuracy and poor upward scalability. The converter model acts as a fundamental building block in a general procedure for constructing models of multi-converter systems. System-level model construction is only possible due to structural properties of the converter model that mitigate prohibitive increases in size and complexity. To characterize the impact of randomness in practical loads, stochastic load descriptions are included in the deterministic dynamic model. The combined behavior of distributed loads is represented by a continuous-time stochastic process. Models that govern this load process are generated using a new modeling procedure, which builds incrementally from individual device-level representations. To merge the stochastic load process and deterministic dynamic models, the microgrid is modeled as a stochastic hybrid system. The stochastic hybrid model predicts the evolution of moments of dynamic state variables as a function of load model parameters. Moments of dynamic states provide useful approximations of typical system operating conditions over time. Applications of the deterministic models include system stability analysis and computationally efficient time-domain simulation. The stochastic hybrid models provide a framework for performance assessment and optimization --Abstract, page iv

Data-Driven Modeling Through Power Hardware in the Loop Experiments: A PV Micro-Inverter Example

The increasing penetration of wind turbines, photovoltaics (PV), fuel cells, microturbines, cogeneration, energy storage systems, and other Distributed Energy Resources (DER) presents both challenges and opportunities for distribution systems. A deep understanding of the characteristics of those devices, as well as accurate modeling, are essential to plan, design, and control modern distribution grids. The objective of this research is to define data-driven modeling techniques that allow capitalizing on the results of Hardware In the Loop (HIL) and Power Hardware In the Loop (PHIL) testing by creating models of the devices under test (DUT) –also for closed-source, proprietary systems– using the collected data. For the development of data-driven models, we used Artificial Neural Networks (ANN) and Fast Fourier Transform (FFT) to parameterize pre-defined model structures. We demonstrated the proposed approach using three PV micro-inverters, the proposed approach handles the nonlinearity of a full range grid voltage (0.88-1.10 p.u), not just under the normal grid voltage, including burst mode. No prior knowledge of internal components, structure, and control algorithm is assumed in developing the model. Results show the effectiveness of the approach, which is particularly suitable to model DERs. As a part of this research, we also would develop an approach to model DERs during abnormal grid conditions. The model will be parameterized by a set of automated PHIL tests. We verified the viability of our approach characterizing micro-inverters from three different manufacturers. It was found that it is possible to develop an approach to mimic the behavior of the internal protection system of microinverter during abnormal grid voltage, without prior knowledge of intimate control algorithms or hardware configuration

Modeling Electronic Power Converters in Smart DC Microgrids - An Overview

EPCs (Electronic Power Converters) 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

A Control Strategy for Self-Sustained and Flexible DC Nanogrids

Microgrids are becoming a potential solution for combining distributed generation units, such as photovoltaic panels, wind turbines and energy storage systems. As a simple and small version of a microgrid, a nanogrid is a power distribution system that is suitable for a single node, such as a small building or a private house. The nanogrid can be flexibly connected to or disconnected from other power entities through a gateway. In most cases, the nanogrid is connected to the utility grid to avoid the power outage and to increase the operational efficiency. However, the current standalone nanogrid model is not suitable because an imbalance between the generated and consumed electrical power might occur. The main objective of this research work is to develop a self-sustained and flexible control strategy for autonomous direct current (DC) nanogrids in remote and rural areas without the need for a communication system. The proposed control strategy for the nanogrids is based upon a hierarchical control, in which the primary control manages the power balance inside the nanogrids and the secondary control is responsible for removing deviation of the DC bus voltage caused by droop operation. The state of charge (SoC) of the battery and the external DC bus voltage are taken into account in the proposed control strategy in order to avoid the overcharge/deep discharge of the battery as well as the collapse of the external DC bus. The control algorithm also ensures a flexible exchange of power inside a nanogrid as well as among multiple nanogrids without any extra digital communication link. Bidirectional power flow among multiple nanogrids is implemented through a dedicated interconnected bidirectional Dual Active Bridge (DAB) DC/DC converter installed inside each nanogrid to ensure a galvanic isolation among multiple, interconnected nanogrids. The proposed control strategy is validated through both simulations and experiments. Simulation and experimental results are used to validate the operation of the proposed control algorithm and prove the resemblance between theory and experiments. However, in order to implement the proposed control strategy, a model of the DC nanogrid has to be developed. For that reason, modeling of every single converter in the system should be conducted. The second important contribution of this research is modeling and control for converters independently, including a bidirectional buck converter and a dual active bridge converter. A small-signal model based on the state-space averaging technique for the bidirectional buck converter is developed, in which only the mean value (i.e. “zeroth” harmonic) of the state variables is taken into account. On the other hand, the generalized state-space averaging-based modeling method is used to obtain the state-space representation of the DAB converter, in which the direct current (DC) component and the fundamental harmonics in the Fourier series expansion of state variables are considered. Transfer functions from control-to-output are determined, which will be used to define two controllers for the current and voltage loops in a cascaded control structure. Simulations and experiments will be used to validate the operation of the proposed method. As aforementioned, modeling and control for each converter in the DC nanogrid is performed separately. Nevertheless, when these converters are connected to form a complete DC nanogrid, they will affect each other and the stability of the entire system is influenced as well. To overcome this problem, a model of the entire system has to be developed and the system stability has to be analyzed. For this purpose, the small-signal transfer function of a DC nanogrid is synthesized from the small-signal transfer functions of every single converter of the system. Using this transfer function, the system stability is analyzed and the secondary controller is designed. Simulation and experimental results are used to verify a stable operation of the DC nanogrid system

A fast remotely operable digital twin of a generic electric powertrain for geographically distributed hardware-in-the-loop simulation testbed

The automotive industry today is seeing far-reaching and portentous changes that will change the face of it in the foreseeable future. Digitalisation and Electrification are two of the key megatrends that is changing the way vehicles are developed and produced. A recent development in R&D process is the Hardware-in-the-Loop (HIL) method that uses a hybrid approach of testing a physical prototype immersed in a virtual environment, which is nowadays being creatively re-applied towards geographically separated multi-centre testing strategies, that suits the horizontally integrated and supply-chain driven industry very well. Geographical separation entails the deployment of a “Digital Twin” in remote centre(s) participating in multi-centre testing. This PhD aims to produce a highly robust, efficient, and rapidly computable Digital Twin of a generic electric powertrain using the multi-frequency averaging (MFA) technique that has been extended for variable frequency operation. This PhD also aims to commission a local HIL simulation testbed for a generic electric power inverter testing. The greater goal is to co-simulate the local HIL centre testing a prototype inverter, and its Digital Twin in a different location “twinning” the prototype inverter as best as possible. A novel approach for the Digital Twin has been proposed that employs Dynamic Phasors to solve the system in the frequency domain. An original method of multiplication of two signals in the frequency domain has been proposed. The resultant model has been verified against an equivalent time domain switching model and shown to outperform appreciably. A distinctive advantage the MFA Digital Twin offers is the “fidelity customisability”; based on application, the Twin can be set to compute a low (or high)-fi model at different computational cost. Finally, a novel method of communicating high-speed motor shaft position information using a low-speed processing system has been developed and validated. This has been applied to run real-life HIL simulation cycles on a test inverter and effects studied. The two ends of a multi-HIL testbed, i.e., local HIL environment for an inverter, and its Digital Twin, has been developed and validated. The last piece of the puzzle, i.e., employing a State Convergence algorithm to ensure the Digital Twin is accurate duplicating the performance of its “master”, is required to close the loop. Several ideas and process plans have been proposed to do the same