DC Microgrid Research Articles

Energy Management Strategy for a Net Zero Emission Islanded Photovoltaic Microgrid-Based Green Hydrogen System

Investing in green hydrogen systems has become a global objective to achieve the net-zero emission goal. Therefore, it is seen as the primary force behind efforts to restructure the world’s energy, lessen our reliance on gas, attain carbon neutrality, and combat climate change. This paper proposes a power management for a net zero emission PV microgrid-based decentralized green hydrogen system. The hybrid microgrid combines a fuel cell, battery, PV, electrolyzer, and compressed hydrogen storage (CHSU) unit aimed at power sharing between the total components of the islanded DC microgrid and minimizing the equivalent hydrogen consumption (EHC) by the fuel cell and the battery. In order to minimize the EHC and maintain the battery SOC, an optimization-based approach known as the Equivalent Consumption Minimization Strategy (ECMS) is used. A rule-based management is used to manage the power consumed by the electrolyzer and the CHSU by the PV system in case of excess power. The battery is controlled by an inverse droop control to regulate the dc bus voltage and the output power of the PV system is maximized by the fuzzy logic controller-based MPPT. As the hybrid microgrid works in the islanded mode, a two-level hierarchical control is applied in order to generate the voltage and the frequency references. The suggested energy management approach establishes the operating point for each system component in order to enhance the system’s efficiency. It allows the hybrid system to use less hydrogen while managing energy more efficiently.

Enhancing DC microgrid performance through machine learning‐optimized droop control

AbstractA machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.

Enhancing DC microgrid performance with fuzzy logic control for hybrid energy storage system

Enhancing DC microgrid performance with fuzzy logic control for hybrid energy storage system

Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).

A Fusion Adaptive Cubature Kalman Filter Approach for False Data Injection Attack Detection of DC Microgrids

With the widespread application of information technology in microgrids, microgrids are evolving into a class of power cyber–physical systems (CPSs) that are deeply integrated with physical and information systems. Due to the high dependence of microgrids’ distributed cooperative control on real-time communication and system state information, they are increasingly susceptible to false data injection attacks (FDIAs). To deal with this issue, in this paper, a novel false data injection attack detection method for direct-current microgrids (DC MGs) was proposed, based on fusion adaptive cubature Kalman filter (FACKF) approach. Firstly, a DC MG model with false data injection attack is established, and the system under attack is analyzed. Subsequently, an FACKF approach is proposed to detect attacks, capable of accurately identifying the attacks on the DC MG and determining the measurement units injected with false data. Finally, simulation validations were conducted under various DC MG model conditions. The extensive simulation results demonstrate that the proposed method surpasses traditional CKF detection methods in accuracy and effectiveness across different conditions.

Modeling and energy management strategy of hybrid energy storage in islanded DC micro-grid

Modeling and energy management strategy of hybrid energy storage in islanded DC micro-grid

Advancing voltage control in DC microgrids: a comparative study with IGWO-based H∞ control

Advancing voltage control in DC microgrids: a comparative study with IGWO-based H∞ control

Optimizing MPPT Control for Enhanced Efficiency in Sustainable Photovoltaic Microgrids: A DSO-Based Approach

The output of photovoltaic (PV) systems is significantly impacted by the vagaries of ambient temperature, solar irradiance, and environmental fluctuations. To achieve the utmost attainable power from PV systems, it is desired to be efficient at the maximum power point in diverse weather climates. Maximum power point tracking (MPPT) is used to schedule a designated location from where the highest power can be harvested. In the context of solar photovoltaic systems connected with DC microgrid platforms, this study introduces a recently developed drone squadron optimization (DSO) scheme that tracks the global maximum power point under PSCS difficulties. Furthermore, an exhaustive comparative analysis has been presented among particle swarm optimization (PSO), cuckoo search algorithm (CUSA), and grey wolf optimization (GWO) under different operating environments to endorse the supremacy of the nominated technique. The suggested method performs noticeably faster than many other methods currently in use, and in addition to offering the highest power, it can also use bidirectional power flow regulation in both constant and variable air conditions. Lastly, an MPPT system interfaced with the DC microgrid based on DSO ensures a sustainable and reliable architecture to provide at load in low power generating situations.

Optimizing power sharing and voltage control in DC microgrids using a novel adaptive droop control strategy based on current consensus algorithm

In DC microgrids, a contradiction between power equalization and bus voltage control exists under conventional droop control. To address this issue, this study proposes a current consensus algorithm-based adaptive droop control for hierarchical controlled DC microgrids. The strategy includes primary, secondary, and current-consensus algorithms. In the primary and secondary control layers, an improved droop control strategy is realized, where the secondary layer provides adaptive parameters for primary control. Also, a current consensus algorithm is suggested to ensure equal current sharing, where the iterative calculation results are sent as the input for two control layers. Based on the novel strategy, a small signal analysis is carried out to investigate the relationship between system stability and controller parameters. Finally, simulation and experiments are conducted to assess the effectiveness of the proposed control strategy in complex operating conditions such as resistive load step-change and plug-and-play situation. Experiment and simulation results indicate that the novel control strategy guarantees accurate bus voltage control with a maximum steady-state error of 1.58 % and simultaneously achieves power equalization regardless of line impedance differences and the abrupt change of system status. Moreover, the comparative experiment between the traditional droop control and the proposed strategy verifies the superiority of this novel method.

A current reference-enhanced strategy endows the GFC in DC microgrids better dynamic response

The constant voltage strategy (CVS) is more suitable for the small-capacity dc microgrid applications to form the dc bus voltage because it can eliminate the steady voltage deviation. However, its dynamic performance is slowed down by the integrator in the voltage loop and negatively influenced by the load steps. Some advanced methods have been conducted in the literature. However, their principle is to increase the system bandwidth, which is limited because the bandwidth is generally designed about one-tenth of the switching frequency and cannot be increased infinitely. This work pays efforts to increase the gain within the system bandwidth to accelerate the transient response and simultaneously eliminate the influence of the integrator. Therefore, the non-integrator disturbance observer-based (NIDOB) strategy is proposed in this work, and it can feed the load current into the current reference to replace the output of the integrator. Compared to the traditional and non-integrator directly-feedforward (NIDF) strategy, it has a better dynamic performance. Compared to the bandwidth-increased strategy, it does not introduce more noise. The theoretical analysis and experimental results prove its advantages.

PV Wind Battery Based DC Microgrid with Neural Network MPPT

This Research presents a novel approach to enhancing the performance and efficiency of a PV-wind-battery-based DC microgrid through the integration of a neural network maximum power point tracking (MPPT) system. The proposed system aims to optimize energy harvesting from photovoltaic (PV) panels and wind turbines while efficiently managing energy storage in batteries. By employing neural network algorithms, the MPPT system adapts to varying environmental conditions and load demands, thereby maximizing energy extraction and system stability. Two important RE (Renewable Energy) power sources—photovoltaic cells and wind energy systems—are examined in this technical study under a range of meteorological conditions. Initially, a state-of-the-art intelligent controller system was developed to help monitor the peak power point. For RES, an MPPT (maximum power point tracking) controller is necessary since weather patterns are unpredictable. The purpose of this work is to provide novel ways for power generation using solar and wind energy that are based on MPPT and IDNN (enhanced deep neural network). The integration of a hybrid PV/WES system into microgrids, or MGs, has the potential to lower THD values and enhance power quality. The simulation findings validated that the suggested IDNN system outperforms the current approach in various operating scenarios, as seen by decreased mean square error (MSE) rates, total harmonic distortion (THD), and computational complexity.

A large voltage gain and high output power boost converter for PV sources directly convertible to 400V DC microgrid

A large voltage gain and high output power boost converter for PV sources directly convertible to 400V DC microgrid

Enhanced energy management of DC microgrid: Artificial neural networks-driven hybrid energy storage system with integration of bidirectional DC-DC converter

Standalone microgrids using Photovoltaic (PV) systems might be a feasible alternative for powering off-grid populations. However, this form of application necessitates the use of energy storage systems (ESS) to control the intermittent nature of PV production. This paper proposes a novel energy management strategy (EMS) based on Artificial Neural Network (ANN) for controlling a DC microgrid using a hybrid energy storage system (HESS). The HESS connects to the DC Microgrid using a bidirectional converter (BC), that enables energy exchange between the battery and supercapacitor (SC). The effectiveness of the proposed approach in relation to the traditional control method is based on performance metrics such as overshoot and settling time. The proposed ANN control attains 18 % overshoot while step increases in PV generation and attains 21 % overshoot while decreasing PV generation. The results show that the proposed control method ensures continuous power supply from the PV system by optimising load balancing under various operating conditions, maintaining state-of-charge (SoC), and improving the voltage profile.

An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid

An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid

Unified control of high gain DC-DC converter for PV-battery hybrid system in a standalone and DC-microgrid applications

This paper presents power management and control of standalone PV and battery integrated hybrid system using high gain converter suitable for the DC Microgrid applications. The necessity of high gain DC-DC converter is raised due to generation of low output voltage of PV system. The attractive features of high gain converter are high voltage gain, continuous input current and suitability for integration of multiple input sources. The perturb and observe algorithm is used for tracing the maximum power point from solar and intermittency of solar is compensated by energy storage element battery. In the suggested hybrid system, the battery charging is carried out from DC bus voltage through buck converter and discharging to the load through high gain DC-DC converter. In the proposed work, Simulink model of proposed system rated for 1.2 kW PV module, 120 V, 42 Ah battery is developed using Simulink of MATLAB software and results are obtained under different operating conditions. Scaled experimental model of proposed hybrid system is implemented in laboratory environment using STM32F407VGT6 microcontroller and results are validated.

Improved solar photovoltaic performance in standalone low‐voltage direct current microgrids using sensor fault tolerant control

AbstractThe advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid.

A novel multi-port high-gain bidirectional DC–DC converter for energy storage system integration with DC microgrids

Bidirectional converters have often been used in numerous applications like DC microgrids, renewable energy, hybrid energy storage systems, electric vehicles, etc. The paper proposes a novel multi-port high-gain (NMPHG) bidirectional DC–DC converter that supports DC microgrid (DC-MG) applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, multiple input ports, lower switch voltage stress, and lower component count owing to the single converter with multiple input ports for DC microgrid applications. The detailed operational principle, analysis, and design considerations of proposed NMPHG bidirectional DC–DC converters are discussed. Furthermore, the loss analysis, detailed comparison with similar works, and efficiency analysis with non-modalities during forward power flow (LV to HV) and reverse power flow (HV to LV) modes are presented. The efficiency of the proposed converter is found to be 93.8% in forward power flow and 92.9% in reverse power flow modes at rated power. Finally, a hardware prototype of the proposed NMPHG bidirectional DC–DC converters is implemented with 100 W in FPF mode and 200 W in RPF mode with a TMS320F28335 processor and validated with theoretical counterparts.

Mitigating voltage deviation, SOCs difference, and currents disparity in DC microgrids using a novel piecewise SOC‐based control method

AbstractProper current sharing, DC bus voltage deviation reduction, and SOCs balancing, along with ensuring stability are the vital challenges of DC microgrids control algorithms. Addressing these challenges without communication links and a central controller is one of the priorities of control methods. Motivated by the above mentions, this paper presents a novel communication‐free control method. In this regard, a new parameter called “virtual current” is defined according to the unit current and its SOC. Then using a piecewise droop curve and the droop curve shift technique, the virtual current for each unit is determined. The units control coefficients and the relationship of the virtual current are allocated based on the location and power of the loads and RESs such that in the worst case; 1) SOCs are converged; 2) the DC bus voltage deviation is reduced; and 3) the current is appropriately distributed. The simulation and experimental results confirm that the proposed method can balance SOCs like SOC‐based methods and share power properly like piecewise droop methods while reducing DC bus voltage deviation.

Analysis and Development of a Bidirectional Three-Phase Hybrid Rectifier for DC Microgrids With Distributed Energy Resources

This work presents the analysis and development of a Bidirectional Three-phase Hybrid Rectifier (BTHR) for application in Direct Current (DC) Microgrids connected to the utility grid. The voltage regulation technique, called Series DC-bus Voltage Compensation (SDCVC), allows the design of an active front-end converter with high power density. The decentralized control strategy ensures low harmonic distortion of current in the utility grid in both operating modes (rectification and regeneration), in addition to providing DC-bus voltage compensation against power flow dynamics related to Renewable Energy Sources (RES). The operating principle, the control strategy, and the design criteria used for determining the transfer function of the proposed BTHR-SDCVC-DCMG, as well as to the design of the DC-bus voltage controller, are presented. To validate the theoretical analysis, a 1kW prototype was implemented and evaluated at the laboratory. Steady state analysis demonstrate that high efficiency, high power factor, and low harmonic distortion of current are achieved. Finally, transient analysis was performed and demonstrated that the stability of the DC-bus voltage with and without the insertion of RES into the DC microgrid can be assured deploying the proposed solution.

Decentralized Autonomous Energy Management Strategy for Multi-Paralleled Hybrid Energy Storage Systems in the DC Microgrid With Mismatched Line Impedance

Decentralized Autonomous Energy Management Strategy for Multi-Paralleled Hybrid Energy Storage Systems in the DC Microgrid With Mismatched Line Impedance