Hierarchical Control With Voltage Balancing and Energy Management for Bipolar DC Microgrid

Kim, Sunghun; Byun, Hyung-Jun; Jeong, Won-Sang; Yi, Junsin; Won, Chung-Yuen

doi:10.1109/tie.2022.3210517

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…Simultaneous voltage regulation and powersharing can be achieved with hierarchical control [130]. The reference [131] presents a hierarchical control strategy for bipolar DC microgrids, with a specific emphasis on voltage balancing and power management. This study examines the functionality of droop-based primary controls and their The method is based on the assumption that the costs vary randomly in every period by each microsource and utility network.…”

Section: Combined Voltage Control and Power Managementmentioning

confidence: 99%

A Critical Review on DC Microgrids Voltage Control and Power Management

Al-Ismail

2024

IEEE Access

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Direct current (DC) microgrids are becoming increasingly important due to a number of causes, including the widespread use of DC loads, the integration of solar photovoltaic (PV) and energy storage devices (ESDs), and the absence of frequency and reactive power control issues. The control of DC bus voltage, power management, effective power split among the ESDs, and state of charge (SoC) restorations are important in a DC microgrid. However, DC bus voltage control and power management are difficult since the microgrids connect several distributed generators (DGs), loads, utility grids, and ESDs to the DC bus using power electronic converters. It is imperative to properly control the DC bus voltage and manage power among the sources and loads in order to maintain the stability and reliability of DC microgrids. DC microgrids can be controlled by employing centralized, decentralized, distributed, multi-level, and hierarchical control systems to ensure safe and secure operation. Besides, advanced control techniques, such as nonlinear, robust, model predictive, artificial intelligence, and many others, are employed. This article critically reviews two main aspects of DC microgrids: voltage control and power management. The challenges and opportunities for voltage control and power management in DC

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Section: Combined Voltage Control and Power Managementmentioning

confidence: 99%

A Critical Review on DC Microgrids Voltage Control and Power Management

Al-Ismail

2024

IEEE Access

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“…However, the application of a non-linear function to an output function may lead to data loss. In this study, we applied a sigmoid function as the activation function, which is reflected in the simulation results presented in the following section [28]. The MATLAB R2020b toolbox used to train the ANN applied in the paper was validated through three steps.…”

Section: Designing the Ann Structure Using Matlabmentioning

confidence: 99%

Current Compensation Method in a Distribution System Based on a Four-Leg Inverter under Unbalanced Load Conditions Using an Artificial Neural Network

Kim,

An,

et al. 2024

Energies

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This study proposes an unbalanced current compensation method based on a four-leg inverter using an artificial neural network (ANN) under unbalanced load conditions. Distribution systems exhibit rapid load variations, and conventional filter-based control methods suffer from the drawback of requiring an extended time period to reach a steady state. To address this problem, an ANN is applied to calculate the unbalanced current reference and enhance dynamic performance. Additionally, because of the periodic incorrect output inherent in the ANN, applying it to a proportional–integral controller would result in an error being directly reflected in the current reference. In the aforementioned problem, an ANN is applied to the dq0 coordinate system current controller to compensate for the periodic incorrect output in the current reference calculation. The proposed ANN-based unbalanced current compensation method is validated through PSIM simulations and experiments.

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Section: Problem Statementmentioning

confidence: 99%

“…Based on the current operating situations of RDGs and frequency deviation, the fuzzy controller provides base weights w ess and w ren for the energy storage system and renewable distributed generations. With the scaling factors optimized by the SCA algorithm and the proposed modification formula (31), the weights for the energy storage system and each renewable resource are modified. The new w dg,i and w ′ ess are sent to the MPC strategy to solve the optimization problem (24).…”

Section: Adaptive Fuzzy-mpc Controllermentioning

confidence: 99%

“… 1)The load restoration process will also lead to reactive power imbalance and voltage oscillation. Considering that the frequency control and voltage control could be decoupled and designed separately, we assume that specific voltage control strategies [31, 32] can effectively suppress voltage oscillation, which will not be discussed in detail in this paper. 2)The restoration plan for generation integration and load integration is obtained by solving specific optimization problems, which usually aim at reducing restoration costs or maximizing recovery loads. Our strategy will not involve the design of the recovery plan of generations and loads; however, our proposal can effectively improve frequency control in each stage, control the pace of the recovery process by reducing the restoration time required for each stage, and accelerate the overall recovery process. 3)We assume that there is no line congestion. …”

Section: Problem Statementmentioning

confidence: 99%

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Fuzzy model predictive control for frequency regulation of temporary microgrids during load restoration

Wang,

et al. 2023

IET Renewable Power Gen

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Temporary microgrids containing a high proportion of distributed renewable generations can offer great benefits during the recovery stage after large‐scale power system blackout, by picking up critical load and speeding up the entire grid recovery process. To solve the frequency security problem of temporary microgrids faced in the load restoration process, this paper proposes an adaptive fuzzy model predictive control (adaptive fuzzy‐MPC) strategy to obtain effective frequency regulation to suit the special characteristics of this process. First, the load restoration plan within a short time frame is regarded as the feedforward information and assists in the establishment of the MPC model. Then, considering the uncertainty of renewable distributed generations, the regulation resources are cooperated by an adaptive fuzzy controller which provides the weights of different regulation resources adopted in MPC. Specifically, the base weights of the energy storage system and renewable distributed generations are obtained from fuzzy rules, and then the weight for each regulation resource is modified by the designed adjustment rules. Simulation results illustrate that the proposed strategy can efficiently improve frequency performance while making various frequency regulation resources with different regulation capacities cooperate properly, which can further accelerate the load restoration progress and enhance system resilience.

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Hierarchical Control With Voltage Balancing and Energy Management for Bipolar DC Microgrid

Cited by 12 publications

References 29 publications

A Critical Review on DC Microgrids Voltage Control and Power Management

A Critical Review on DC Microgrids Voltage Control and Power Management

Current Compensation Method in a Distribution System Based on a Four-Leg Inverter under Unbalanced Load Conditions Using an Artificial Neural Network

Fuzzy model predictive control for frequency regulation of temporary microgrids during load restoration

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