DC Microgrid Research Articles

Cost-effective soft-switching ultra-high step-up DC–DC converter with high power density for DC microgrid application

DC microgrids are integral to smart grids, enhancing grid reliability, power quality, and energy efficiency while enabling individual grid independence. They combine distributed and renewable energy sources, reducing overall energy consumption. High-gain DC–DC converters are crucial for elevating voltages from low-voltage DC sources like solar panels and wind turbines in DC microgrids. This paper introduces a non-isolated DC–DC converter designed to achieve ultra-high step-up (UHSU) voltage conversion utilizing a two-winding coupled inductor (CI). The propounded UHSU configuration achieves a substantial voltage increase by employing low duty cycles and a decreased turn ratio for the CI, resulting in a smaller core size. Moreover, this UHSU circuit incorporates soft-switching capabilities for both power switches and diodes, enhancing its efficiency. By keeping the voltage stress on the switches low, the design minimizes losses and improves overall efficiency. The operational modes are thoroughly analyzed, and comparisons with other topologies are presented to demonstrate the effectiveness of the proffered UHSU circuit. Finally, the performance of the UHSU circuit is validated through the construction and testing of a 150-W laboratory prototype operating at a switching frequency of 50 kHz, with Vin = 20 V and Vout = 300 V.

Optimal planning of wind and solar complementary AC/DC microgrids under distributed power capacity constraints

Abstract The conventional AC/DC microgrid wind-solar complementary optimization planning method mainly uses the CvaR (conditional value at risk) risk value stochastic model to calculate the randomness of output electricity price, which is vulnerable to changes in load expectations, resulting in the per unit value of photovoltaic load output not meeting the actual demand of the microgrid. Therefore, under the constraints of distributed generation capacity, an optimal planning method of wind-solar complementation for AC/DC microgrids is designed. That is to say, considering the distributed generation capacity constraints, the optimal planning model of wind-solar complementation of AC and DC microgrids is constructed, and the bidirectional adjustment equation of the optimal planning of wind-solar complementation of AC and DC microgrids is generated, thus realizing the optimal planning of wind-solar complementation of AC and DC microgrids. The experimental results show that the PV output per unit value is higher and the load output per unit value is lower in the design of the wind-solar complementary optimization planning method of AC/DC microgrid considering distributed generation capacity constraints, which meets the requirements of efficient operation of the microgrid and has certain economic value.

Data-Driven Distributed Predictive Control for Voltage Regulation and Current Sharing in DC Microgrids With Communication Constraints.

The use of nonideal communication networks makes communication constraints become a topical issue in the research of dc microgrids. How to design a distributed secondary control scheme for voltage recovery and accurate current sharing in islanded dc microgrids subject to communication constraints is of interest in this article. In order to restore the bus voltage to the rated value, a nonlinear element is first introduced into the primary control layer. Then, the closed-loop system of primary control is modeled as a data-driven time-varying linear system. Based on the established model, considering communication constraints, a distributed secondary predictive control strategy is developed to achieve accurate current sharing. While actively compensating for network delays and packet losses, the proposed method renders mathematical physical models unnecessary for the traditional predictive control, and simultaneously completes the multitask in dc microgrids. Finally, several case studies are conducted on a hardware microgrid experimental platform, which not only verifies the effectiveness of the designed data-driven predictive control strategy but also tests microgrid properties such as the plug-and-play ability.

A study on the macro-micro two-stage site selection of electric-hydrogen hybrid refueling stations based on GIS and Fuzzy-TODIM

With the increasing global demand for clean energy and sustainable development, hydrogen energy has gained significant attention as an environmentally friendly energy carrier. Hydrogen refueling stations, serving as crucial intermediaries in hydrogen production, storage, transport, and refueling, play a pivotal role in the hydrogen energy industry's layout. A novel model for hydrogen refueling stations based on DC microgrids utilizing renewable energy sources for hydrogen production and refueling has become a focal point of interest. However, there is currently limited research on the site selection of electric-hydrogen hybrid refueling stations. Addressing current needs and research gaps, this study proposes a macro-micro two-stage site selection model based on DC microgrids. In the macro-level site selection stage, factors such as regional photovoltaic and wind resources, hydrogen energy policies, and the layout of the hydrogen energy industry are comprehensively considered, with Guangdong Province, China selected as the study area. ArcGIS is utilized to create thematic layers and establish constraints. The DEMATEL method is employed to determine the weights of macro-level site selection criteria. The results indicate that the most suitable areas are located in coastal regions. This study's site selection model offers a scientific and systematic approach to evaluating electric-hydrogen hybrid refueling stations in DC microgrids, providing a scientific basis for decision-making for governments and investors.

A Family of Bidirectional Solid-State Circuit Breakers With Increased Safety in DC Microgrids

A Family of Bidirectional Solid-State Circuit Breakers With Increased Safety in DC Microgrids

A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids

A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids

A Novel Scalable and Reliable Control for DC Microgrids With Varying Number of Agents.

Existing scalable control methods mainly rely on a fixed block-diagonal structure for the Lyapunov matrix, potentially resulting in numerical infeasibility issues. To overcome this limitation, this article proposes a novel scalable and reliable control scheme for dc microgrids. Initially, a general model for dc microgrids is established to enhance reliability, considering scenarios involving loss of control effectiveness (LoCE) and offset faults. Subsequently, a structured free-weight matrix technique is introduced to mitigate negative coupling effects of power lines, and to address numerical infeasibility by avoiding the assumption about the Lyapunov matrix. Furthermore, the stability of the entire dc microgrid is guaranteed by checking local agent conditions, independently of power line couplings. Therefore, the proposed control scheme ensures plug-and-play scalability with varying number of agents. Finally, theoretical results are validated through numerical simulations using the MATLAB/SimPowerSystems toolbox.

Linear Extended State Observer-Based Distributed Secondary Control of DC Microgrids Under False Data Injection Attacks

Linear Extended State Observer-Based Distributed Secondary Control of DC Microgrids Under False Data Injection Attacks

Region of Attraction Estimation for Large-Scale DC Microgrids With Low Computations

Region of Attraction Estimation for Large-Scale DC Microgrids With Low Computations

A Dual LC Interleaved Four-Port Converter for Bipolar DC Microgrids

A Dual LC Interleaved Four-Port Converter for Bipolar DC Microgrids

A Low-Complexity Artificial Neural Network-Based Optimal Droop Gain Design Strategy for DC Microgrids Onboard the More Electric Aircraft

A Low-Complexity Artificial Neural Network-Based Optimal Droop Gain Design Strategy for DC Microgrids Onboard the More Electric Aircraft

On the Resilience Analysis of DC Microgrids With Power Buffer Control

On the Resilience Analysis of DC Microgrids With Power Buffer Control

Series DC Arc Fault Detection in DC Microgrids Based on Distributed Unknown Input Observers

Series DC Arc Fault Detection in DC Microgrids Based on Distributed Unknown Input Observers

Enhancing stability and voltage quality in remote DC microgrid systems through adaptive droop control approach

Enhancing stability and voltage quality in remote DC microgrid systems through adaptive droop control approach

Distributed MPC-Based Constant Voltage Control for DC Microgrids Under DoS Attacks

Distributed MPC-Based Constant Voltage Control for DC Microgrids Under DoS Attacks

Advancing Electric Vehicle Charging Ecosystems With Intelligent Control of DC Microgrid Stability

Advancing Electric Vehicle Charging Ecosystems With Intelligent Control of DC Microgrid Stability

Dynamic Nonlinear Droop Control (DNDC): A Novel Primary Control Method for DC Microgrids

Dynamic Nonlinear Droop Control (DNDC): A Novel Primary Control Method for DC Microgrids

Effective dynamic energy management algorithm for grid-interactive microgrid with hybrid energy storage system

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink.

A novel development of advanced control approach for battery-fed electric vehicle systems

In today’s context, there is a clear preference for DC microgrids over AC microgrids due to their better compatibility with generating sources, loads, and battery energy storage systems (BESS). However, the intermittent nature of renewable resources disrupts the balance between power generation and load demand. It raises concerns regarding power management and quality in the power system. Control strategies are essential to address these challenges. This article focuses on developing a novel control strategy to ensure stability in microgrid systems. The proposed control structure utilizes a second-order multi-agent system (MAS) to enhance the power-sharing and coordination in the microgrid network. For effective control of battery energy storage units, a Voltage–Power (V-P) reference-based droop control and leader–follower consensus method is employed. The control approach consists of primary and secondary control layers. The primary layer uses a V-P reference-based droop control strategy to allocate load components to storage units. The secondary control layer aims to restore DC bus voltage using a MAS-based consensus protocol. The MAS approach offers greater flexibility and requires less computational power than other strategies such as Model Predictive Control (MPC). The enhanced control structure incorporates a current ratio modification loop to adjust the current ratio between the converters, thereby modifying gain and improving the voltage profile. This novel control optimizes the reliability and stability of the proposed DC microgrid system. The effectiveness of the enhanced consensus-based secondary control strategy is demonstrated using the MATLAB/Simulink platform.

A model predictive control based MPPT technique for novel DC-DC converter and voltage regulation in DC microgrid

A model predictive control based MPPT technique for novel DC-DC converter and voltage regulation in DC microgrid