DC Load Research Articles

The Improved Coordinated Control Method of Islanding and Grid-Connected Operations for DC Microgrids

The DC microgrid plays an essential role in realizing smart power supply systems for the DC loads. It combines various distributed generations and transmits the power to the loads or the utility grid through the DC bus. It attaches great importance to energy management because of the load and photovoltaic power fluctuations. This paper proposes an improved coordinated control based on the characteristics of various distributed generations and utility grids. The proposed method can realize the stabilization of bus voltage when the system switches between multiple operating modes in grid-connected and island mode due to the change of energy. Compared with the present ways, the proposed method provides a further optimization on the design of charging for energy storage units in the grid-connected mode and the current sharing with the backup storage units in the islanded mode which is conducted by SoC balancing droop control. Finally, the DC microgrid simulation platform is built to verify the correctness and feasibility of the proposed method.

Modeling and simulation of electrical generation systems based on PEM fuel cell-boost converter using a closed loop PI controller

This paper presents dynamic modeling and control of a proton exchange membrane fuel cell (PEMFC). The system consists of a DC–DC Boost converter, DC–DC regulator, and DC load. The goal of the research is the construction of an emulator of the PEM Fuel Cell stack, that of a device having the same behavior as the real system, and evaluates performances of a stand-alone power system. Since the fuel cell produces DC output voltage that is unregulated. PWM (pulse width modulated) DC–DC converters are assumed to operate in continuous-conduction mode and the PI compensator in the feedback path is designed with voltage-mode control to convert the unregulated voltage into a desired voltage level, during different load variations. The results show the output voltages of the studied PEMFC connected with PWM DC–DC converters during a DC load change are stable. A PEMFC system is modeled using Matlab/Simulink and is interfaced with the mathematical model of fuel cell and various waveforms are presented in the paper.

Temperature Estimation in Lithium-Ion Cells Assembled in Series-Parallel Circuits Using an Artificial Neural Network Based on Impedance Data

Lithium-ion cells are widely used in various applications. For optimal performance and safety, it is crucial to have accurate knowledge of the temperature of each cell. However, determining the temperature for individual cells is challenging as the core temperature may significantly differ from the surface temperature, leading to the need for further research in this field. This study presents the first sensorless temperature estimation method for determining the core temperature of each cell within a battery module. The accuracy of temperature estimation is in the range of ΔT≈1 K. The cell temperature is determined using an artificial neural network (ANN) based on electrochemical impedance spectroscopy (EIS) data. Additionally, by optimizing the frequency range, the number of measurement points, input neurons, measurement time, and computational effort are significantly reduced, while maintaining or even improving the accuracy of temperature estimation. The required time for the EIS measurement can be reduced to 0.5 s, and the temperature calculation takes place within a few milliseconds. The setup consists of cylindrical 18,650 lithium-ion cells assembled into modules with a 3s2p configuration. The core temperature of the cells was measured using sensors placed inside each cell. For the EIS measurement, alternating current excitation was applied across the entire module, and voltage was measured individually for each cell. Various State of Charge (SoC), ambient temperatures, and DC loads were investigated. Compared to other methods for temperature determination, the advantages of the presented study lie in the simplicity of the approach. Only one impedance chip per module is required as additional hardware to apply the AC current. The ANN consists of a simple feedforward network with only one layer in the hidden layer, resulting in minimal computational effort, making this approach attractive for real-world applications.

Multi-objective control and optimization of a stand-alone photovoltaic power conversion system with battery storage energy management

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Energy Management Strategy for PV PSO MPPT / Fuel Cell/Battery Hybrid System with Hydrogen Production and Storage

Due to the high uncertainty of renewable energy sources, and the diversity of energy sources and storage systems, it is mandatory to seek a controller that manages all these renewable energy sources and this hybrid energy storage. This paper proposed a renewable energy management system using flatness control and PID and PSO technologies that track the maximum power point from the PV array and manages the energy storage elements. Two energy storage are adopted: battery storage and hydrogen tank. The proposed (Nero-fuzzy) controller also works to fill the hydrogen storage tank wisely and safely by controlling the alkaline electrolyzer and the tank's pressure. The main aim of this combined system is to attain power stability. Since the PV is the primary production source, a PSO MPPT is a proposed system for optimum power delivered by the PV under different radiation and temperature conditions. The fuel cell has been used to compensate for the energy lost when there is a lack of control due to weather conditions or high-power demand by the DC load. A battery was coupled to the DC bus to respond quickly to the power requirement. When the radiation intensity is 1000 W/m2, the PV will generate enough 18 kW to supply the load, run the electrolyzer 7 kW, and charge the batteries. While in the radiation change, when it is 240 W/m2, the solar panels produce (4.3 kW) and the load (4.7 kW). The battery works first because of its quick response, and then the fuel cell works to generate instead of it, which is sufficient to operate the load only. The results show a stable and fast response grid under different weather conditions and load scenarios.

Multi-Objective Optimization for DC Microgrid Using Combination of NSGA-II Algorithm and Linear Search Method

With the penetration of renewable sources, the DC microgrid is much more efficient and flexible to link renewable power generators and DC loads. Due to the uncertainties in both sources and loads, it is hard to maintain the economic and optimal operation simultaneously in DC microgrids. To solve it, this paper builds a multi-objective optimization model including the operation cost, power loss, and load expectation ratio to satisfy the overall optimal management and system economy requirement. To fasten the optimization, a novel hybrid algorithm that combines the non-dominated sorting genetic algorithm-II (NSGA-II) and linear search method (LSM) is proposed as NSGA-LSM, which uses the fast global searching capacity of the LSM to accelerate the iteration process of NSGA-II. Therefore, it is superior to optimize more than two objectives, and then suitable for the microgrid with different kinds of renewable sources. Finally, the simulation in a DC microgrid with distributed photovoltaics (PVs) as an example verifies the above analysis well.

A Modular Multilevel Converter Station With Multiple MVac Ports

Medium voltage (MV) ac/dc hybrid distribution network attracts much interest due to its high reliability, high power quality, and high compatibility with distributed generation and dc loads. As the key enablers to interconnect ac and dc grids, the converter stations usually adopt the modular multilevel converter (MMC) topology due to its superior modularity and scalability. Unlike the conventional stations that are implemented with multiple MMCs to accommodate multiple ac feeders, a novel multi-ac-port MMC station (MACP-MMC), which evolves from the MMC topology, is proposed in this paper to interconnect different ac feeders and facilitate flexible power exchange in the MV hybrid distribution network. In addition to the benefits inherited from the MMC, the proposed MACP-MMC also features easy multiport extension, bidirectional power flow adjustment, high power density and low cost. The operation principle, energy balance mechanism, and control strategies are elaborated. Finally, its effectiveness is verified by both simulation results in a 3-MVA case and experimental results obtained from a 3-kVA scaled-down lab prototype.

A New Hybrid Modular Multilevel Rectifier as MVac–LVdc Active Front-End Converter for Fast Charging Stations and Data Centers

As the power demands from DC energy-storage and loads continue to grow in electric vehicle (EV) fast charging stations and data centers, the power delivery infrastructure faces challenges with the installation of bulky, heavy, and slow responsive line-frequency power transformer (LFT). These transformers are required to step-down the feeder voltage from medium-voltage (MV) ac grid-service to low-voltage (LV) ac, followed by LVac-LVdc rectifiers. This approach results in a large equipment footprint, heavy conductor copper usage, and lower efficiency. Consequently, there is increasing interest in exploring direct interface from MVac to LVdc without the need for LFT. This paper proposed a new solution called MVac-LVdc hybrid modular multilevel rectifier (HMMR). The HMMR serves as a centralized step-down active front-end converter, enabling power delivery to LVdc with a reduced number of dc/dc back-end isolated converters. Compared to the modular multilevel converter (MMC) used as the MVac interface solution, the proposed HMMR could save the submodule number by 40%, reduce losses by 22%, and significantly reduce the footprint area by 37%, effectively increasing the power density and reducing the construction cost. Moreover, the proposed HMMR has the potential to operate in both unity and non-unity power factor modes, allowing it to provide the grid-support functionality. The performance of HMMR is evaluated and compared with full-bridge MMC in the case of 13.8 kV ac to 6 kV dc. The feasibility of the proposed converter is verified by the simulation results with the same specifications. Finally, a scale-down 1.4 kV HMMR prototype is developed to validate the effectiveness of the proposed converter.

Modeling and Simulation of Marine PV Inverter System Based on MPPT Algorithm

Under the influence of the policy of low carbon development, the development of new energy ships is receiving wide attention. Aiming at the fluctuation of the ship power grid caused by the intermittent and random nature of the PV power generation system, this paper builds a set of ship PV power generation microgrid system models based on the Matlab/Simulink simulation platform. Under the conditions of ambient temperature, light intensity, and other factors, the front-stage Boost circuit uses the MPPT conductance increment method to obtain the maximum power point of the PV module. The rear-stage inverter uses SPWM (sinusoidal pulse width modulation) inverter control in the rear stage to observe the output effect under different solar radiance, and the system is also designed to automatically switch the DC load when the light intensity is too low, which reduces the system energy loss and improves the operation efficiency of the marine PV system.

Topology comparative assessment for hybrid medium-voltage AC/DC networks

Recently, there has been significant growth in integrating renewable energy generation and electric vehicles and deploying new technologies into the electrical networks, leading to new challenges for planning, design, operation, control and protection of the distribution grid. In particular, an increasing amount of large-scale DC load and generation is expected to be connected to the distribution system, especially at the MV level. Thus, adoption of new grid topologies is required and among others, hybrid AC/DC networks have emerged as an attractive pathway to meet new grid operational requirements. This paper evaluates future hybrid AC/DC distribution systems at the MV level, considering potential network topologies and new technologies. Steady-state analysis is performed to compare five topologies of hybrid MV AC/DC networks. Key technologies for such hybrid grids such as voltage source converters and fast charging stations are considered. The results provide quantitative comparisons between topologies regarding large-scale DC load and generation integration capability, contingency operation, sensitivity analysis. Several challenges presented to the protection and fault location that are caused by the adoption of new topologies are also identified. The topologies are compared by network losses, voltage and power factor constraints, analysing the different benefits and drawbacks of the hybrid AC/DC topologies.

Nonlinear control design and stability analysis of hybrid grid-connected photovoltaic-Battery energy storage system with ANN-MPPT method

The problem of controlling a grid-connected solar energy conversion system with battery energy storage is addressed in this work. The study’s target consists of a series and parallel combination of solar panel, DC/DC converter boost, DC/AC inverter, DC/DC converter buck-boost, Li-ion battery, and DC load. The main objectives of this work are: (i) PV voltage regulation: the PV panel voltage must track a given reference voltage corresponding to the maximum power point. The optimal reference voltage is generated by an optimizer based on the artificial neural network (ANN); (ii) DC link voltage regulation: The DC link voltage must track a reference voltage as closely as possible. (iii) PFC requirement: The grid currents must be sinusoidal with the same frequency and in phase with the voltage grid. The periodic nature of solar energy and the frequent fluctuations in load demand reduce battery life and charging performance. To deal with these limitations and ensure the battery’s safety, a battery energy management algorithm is developed with the following objectives: (iv) CC mode: When the battery’s state of charge is less than 100% (SOC<100%), it indicates that the battery is not fully charged, this aim has been adopted. The battery current should follow a constant reference as closely as possible to the permissible limits battery current; (v) CV mode: When the battery is completely charged, this mode is switched on. The voltage at the battery terminals then achieves its reference signal, which corresponds to the charge state’s of the battery SOC=100%. In addition, the energy management system which generates several energy flow scenarios, in this work, the focus is to balance the energy flows between the load and the different energy sources to minimize the system costs, to ensure the stability of the grid and to improve the power quality. To do this, a mathematical modeling of the overall system was performed. Subsequently, backstepping controllers are then synthesized in order to ensure the control objectives. The closed loop control convergence is formally analyzed using Lyapunov’s stability theory and its performances are illustrated by simulation. As a result, the simulation results indicate that the proposed controllers perform admirably in achieving their objectives.

Operation of The Hybrid Energy Resources with Storage System Participation

This paper focused on the optimal operation of the hybrid energy resources in the off-grid state considering energy storage participation. The hybrid energy resources consist of wind turbine (WT), photovoltaic (PV), diesel generator (DG), and energy storage system for supplying energy to DC and AC load demand with maximum reliability. The operation of the proposed energy system based on energy control and energy optimization is modeled. The energy optimization and energy control are implemented by heuristic and nonlinear quadratic programming approaches via optimal power flow on the resources side. The energy control is done based on the weight sum method in different operation states of the system. Also, the impact of the energy storage system on the hybrid energy resources is considered as backup resources. The energy control modeling is implemented via mathematical simulation and numerical analysis in the two operation states in the summer and winter seasons for verifying the proposed approaches. Finally, the results of the energy control show optimal states of the energy system in supplying demand with considering the energy storage system.

Solar powered grid integrated charging station with hybrid energy storage system

To overcome the deficiency in fossil fuels and their environmental effects, the popularity of the integration of renewable energy sources and the adoption of electric vehicles is growing day by day. But high pricing and a lack of available charging stations are impeding EV adoption. Also, the weather-dependent RES power generation creates demand and generation disparity in a microgrid system. Hence, energy storage technology integration is crucial to increase the possibility of flexible energy demand with the charging of EVs and ensure that extra generated power can be stored for later use. In this proposed EV charging architecture, high-power density-based supercapacitor units (500−5000W/L) for handling system transients and high-energy density-based battery units (50−80Wh/L) for handling average power are combined for a hybrid energy storage system. In this paper, a power management technique is proposed for the solar-powered grid-integrated charging station with hybrid energy storage systems for charging electric vehicles along both AC and DC loads. For the charging of electric vehicle batteries, the stepwise constant current control charging method is proposed in which the charging current will decrease with an increase in the state of charge of vehicle batteries. The performance efficacy of the proposed system is confirmed through both MATLAB/Simulink and OPAL-RT simulation.

Energy Balance in a Standalone PV Battery Hybrid Generation System on Solar-Powered Aircraft Using the Model Predictive Control Method

This paper proposes a battery state of charge (SOC)-based energy management strategy using hierarchical distributed model predictive control (HDMPC) for a standalone microgrid on solar-powered long-endurance aircraft. The microgrid was innovatively designed as a two-layer structure in which the first layer consists of a photovoltaic generation and battery storage system named the PV battery module (PBM). The second layer, named the microgrid subsystem (MGSS), consists of several PBMs, each of which supplies power to a specific DC load on the aircraft. The control system is divided into two levels: the grid-level model predictive control (MPC) and the converter-level MPC. The grid-level MPC adopts a distributed model predictive control strategy to obtain the reference power of each module. The converter-level MPC calculates the control variables of converters using a supervisory model predictive control (SMPC) strategy. The new microgrid structure and the proposed control strategy have improved the reliability of the energy system and increased its energy utilization rate.

A Survey on Multi-Active Bridge DC-DC Converters: Power Flow Decoupling Techniques, Applications, and Challenges

Multi-port DC-DC converters are a promising solution for a wide range of applications involving multiple DC sources, storage elements, and loads. Multi-active bridge (MAB) converters have attracted the interest of researchers over the past two decades due to their potential advantages such as high power density, high transfer ratio, and galvanic isolation, for example, compared to other solutions. However, the coupled power flow nature of MAB converters makes their control implementation difficult, and due to the multi-input, multi-output (MIMO) structure of their control systems, a decoupling control strategy must be designed. Various control and topology-level strategies are proposed to mitigate the coupling effect. This paper discusses the operating principles, applications, methods for analyzing power flow, advanced modulation techniques, and small signal modelling of the MAB converter. Having explained the origin of cross-coupling, the existing power flow decoupling methods are reviewed, categorized, and compared in terms of effectiveness and implementation complexity.

Optimal nanogrid planning at building level

This paper presents a planning framework for active buildings as an Energy Nano-Grid (ENG), determining the optimal size and generation mix of distributed energy resources (DERs) and battery energy storage (BES) system, the type of ENG that can be either AC or DC, and the optimal energy management (EM). Due to the increasing penetration of battery energy storage devices, electric vehicles (EVs) and even DC loads on the utility side, DC ENGs would potentially be more useful than AC ENGs by reducing the number of converters, facilitating the connection of various types of distributed energy resources and loads to the common bus with simplified interfaces, and mitigating the losses associated with AC/DC energy conversion. Therefore, the selection of the type of ENG is an economic issue where the planning objective includes the investment, operation and maintenance costs of energy resources, the investment costs of battery energy storage (BES) and converters, and the costs/revenues for buying/selling energy from/to the upstream grid or neighbor ENGs. In this way, the proposed program achieves an optimal load sharing. Optimal results might be affected in terms of some system specifications such as the ratio of DC load (from 0.4 to 1) at ENG, the maximum permissible installation capacity of BESs (from 200 to 800 kWh), and maximum discharge power that EVs can deliver to the ENG or upstream network (from 50 to 200 kW). Using some numerical case studies associated with three residential ENGs, result show that increase in the rate of DC load has the highest effect on the type of ENG (DC feeder is adopted for DC load rate 0.6 at ENG 1 and ENG 2, and 0.8 at the third one) through decrease in investment and operation costs, meanwhile, the capacity of BES directly affect the size of generation units, and the maximum discharging power of EVs just support peak load supply due to being out of the park lot during the day. The proposed planning model is analyzed in detail to demonstrate its applicability, effectiveness and control.

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.

Energy harvesting through piezoelectric technology

Small-scale energy harvesting using non-conventional methods are becoming very popular because they are eco-friendly. When the vehicles move on the road, the energy from vehicle movement can be harnessed using piezoelectric transducers. This paper proposes a scheme for harvesting the energy using piezoelectric technology to power a DC lamp of 12 V and 12 W. Synthetic-type Lead Zirconate Titanate (PZT) piezo transducers are used in the current work. These transducers are highly sensitive to applied pressure but the voltage generated will be less than 1.5 V from each cell. Therefore, 16 cells are connected in series to obtain a voltage of 24 V. The voltage generated from piezo transducers will be alternating in nature. Therefore, this voltage is rectified using a 1ϕ full bridge rectifier. The rectifier output is fed to a DC-DC buck-boost converter to get the required voltage and currents for a DC load of 12 W. Modeling of the piezo transducer, simulation and experimental results of the proposed method are presented in this paper. The proposed configuration can be deployed to harvest energy on the roads due to vehicle movements which can be used for lighting the lamps on the roads.

Protection of radial MVDC electric network based on DC circuit breaker and DC fuses

The medium voltage direct current (MVDC) electric networks are emerging in power distribution and collection. The MVDC is an attractive solution in the applications involving DC loads or sources where high energy efficiency and high power flow controllability are required. The emergence of MVDC networks strongly relies on the availability of the appropriate protection strategies and equipment. This article proposes a protection strategy for a multi-terminal radial MVDC network based on the principle commonly used in the existing AC distribution networks. The protection strategy involves a single DC circuit breaker (DCCB) and multiple DC fuses. The proposed strategy limits the quantity of sophisticated and costly DCCB used. The cost-effective DC fuses are proposed to protect the parts of the network where the expected short-circuit occurrence is limited and where the automatic reclosing is not mandatory. The article concludes that low grounding resistance is beneficial allowing to easily isolate the faulty circuit. The simulation models are developed allowing to confirm the theoretical analysis.