Proposed DC Microgrid Research Articles

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.

Performance Enhancement of Droop Controlled DC Microgrid Using Solar Fed Hybrid BIFRED Converter with RBFNN

Electric power system’s structure is shifting from a generator to a converter-based, posing the risk of reduced system inertia. This transition is due to the increased penetration of power electronics-based renewable energy sources (RESs) and Energy Storage Systems (ESS). The microgrid concept supports their accommodation into electric networks; however, it requires appropriate control to solve the voltage stability issues created by reduced inertia. The proposed DC microgrid aims to maintain voltage stability in DC-link comprising wind, solar, and ESS, irrespective of changes in operating conditions. An improved Z source- Boost Integrated Fly Back Rectifier/Energy Storage DC–DC (BIFRED) converter is proposed to increase the output voltage of photovoltaic (PV) system to achieve better DC-link voltage stability. Radial basis function neural network (RBFNN) approach is used to harvest the maximum power possible from the PV panel and to improve the conversion efficiency. The proposed DC microgrid also includes Wind Energy Conversion System (WECS) powered by a Doubly Fed Induction Generator (DFIG). It integrates droop control with virtual inertia and damping control and effectively handles the voltage stability issues brought out by RESs and loads. Artificial neural network (ANN) optimized droop-controlled bidirectional converter (BDC) is implemented to integrate ESS and resolve the imbalance created by ESS. The work is validated by MATLAB simulation model and a laboratory prototype.

Multi-level coordinated control of islanded DC microgrid integrated with electric vehicle charging stations with fault ride-through capability

This paper presents an integrated control framework for islanded DC microgrid (MG) with electric vehicle (EV) charging stations, energy storage unit, and AC/DC loads. The proposed DC MG consists of two DC buses with different voltages, photovoltaic (PV) arrays as intermittent power generation sources, energy storage, three EV charging stations, and a three-phase inverter to power time-varying three-phase loads. Control strategies are proposed to select the charge-discharge mode of EVs that does not negatively affect the MG, as well as coordinate the production and demand management. A central control system enables the operation of MG under different working conditions by managing generation, consumption, and energy storage and also monitors the performance of the charging/discharging unit and the issues relevant to commands of different charging/discharging programs. A specific control strategy has also been implemented to provide fault ride-through (FRT). As a result, the MG feeds its loads in any operating condition. Nonlinear simulations are carried out in MATLAB/SIMULINK software to show that the proposed control methods easily and effectively achieve all objectives, including supplying DC/AC loads, managing battery/resources/charging stations, and detecting/dealing with AC faults.

Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System

Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO.

Detection and classification of DC and feeder faults in DC microgrid using new morphological operators with multi class AdaBoost algorithm

DC microgrids with energy storage systems based on photovoltaic (PV) and wind energy are gaining popularity as a means to offer users with reliable supply in either a stand-alone or grid connected mode. However, because DC and AC side faults have similar current–voltage profiles, developing a viable safety strategy for the proposed integrated DC microgrid is difficult. Traditional protection techniques based on pre-defined thresholds are unable to discriminate between DC and AC side faults, and so fail to offer independent control actions in both circumstances. In this context, new morphological operators with improved AdaBoost algorithm is proposed for detecting and classifying the AC and DC side faults in the proposed DC microgrid. To explore this, current signals are captured at the DC bus of the proposed integrated DC microgrid. The captured signals comprise background noise which is eliminated by dilation erosion difference operator (DEDO) and opening closing difference (OCDO) operators. The two operators work together to meet the accurate fault detection to avoid nuisance tripping by multiscale operation of structuring element (SE). For effective outcomes the multiple scales are optimized by sparse kurtosis (SK) index. The optimized scales are passes through target features to retrieve the data. The acquired data is sent into the multi-class AdaBoost approach, which recognizes faults by modifying the distribution of data and iteratively adjusting the weight of each instance. The proposed system's efficacy is tested using the MATLAB/Simulink platform under various operating situations such as load variation, irradiation and fault resistance changes. The proposed algorithm's superiority is demonstrated by comparing it to existing approaches using confusion matrix (CM) parameters.

Modeling and control of islanded DC microgrid fed by intermittent generating resources

In this paper an islanded microgrid fed through the wind and solar energy resources is presented. The power flow within the microgrid is controlled using a Neutral Point Clamped Dual Active Bridge (NPC-DAB) converter. In the proposed dc microgrid, the solar energy source is associated at the low voltage (LV) bus and the wind energy source is connected at the high voltage (HV) bus. A permanent magnet synchronous generator (PMSG) machine is used in wind energy conversion system. The real time solar radiation and wind speed data of Rupangarh, Rajasthan, India is used as an input for renewable energy resource. The NPC-DAB will work as a power electronics juncture for expediting the energy exchange in the islanded DC Microgrid. The proposed closed loop controller based on the capacitor voltage and load voltage will expedite a complete automatic operation of the islanded DC-microgrid considering various load changes. The system is studied without storage element as the automatic control of energy generation and load feeding is carried out by the NPC-DAB, also this makes the scheme cost effective. The optimum duty ratios for NPC-DAB operation are obtained and thus the increased load demand is met. The modeling of PMSG, NPC-DAB and wind energy system is discussed in details in this work. The proposed system is studied in MATLAB/Simulink environment and results are obtained for different load variations. All the wind control parameters, NPC-DAB waveforms, load waveforms are also plotted using MATLAB.

A cost‐effective DC microgrid system for resilient power supply to grid‐connected societies

AbstractLocal renewable energy sources have the potential to provide power security to societies. The main objective of this research is to improve electricity reliability in grid‐connected societies in case of the grid failure or during blackouts. Here, a cost‐effective method to utilize local irregular renewable energy sources for community applications has been proposed. Solar PV, pumping units, domestic loads, and pico hydro are the main parts of the proposed system. The storage water tank also performs the function of pump storage to deliver power during off‐sun periods. The pump storage generates electricity by using the unused potential of the water stored in the storage tank. In islanded mode, the system delivers power to the village to essential loads and fluctuating power to the dump load. The paper provides a case study in which the proposed DC microgrid has been demonstrated for a society to validate the system's performance for different practical scenarios. It has been validated for financial and technical feasibility through economic analysis and MATLAB Simulation performance respectively.

A new adaptive integral back stepping fractional order sliding mode control approach for PV and wind with battery system based DC microgrid

DC microgrids are very much focused on the present scenario due to the increase of power applications. In this paper, a new adaptive integral back stepping fractional-order sliding mode control (AIBFOSMC) is proposed for the DC microgrid in which renewable distributed energy sources with energy storage systems are presented. Initially, mathematical equations are developed for the proposed DC microgrid and then control equations for PV with wind and battery are derived. To extract the maximum power from the PV and wind systems, an improved incremental conductance (IIC) based maximum power tracking method (MPPT) is introduced in this paper. The main aim of this control is to stabilize the DC bus voltage under different operating conditions such as generation change, DC load change, temperature change and DC faults. The stability of the proposed controller is estimated by the Lyapunov theory. The performance of the proposed controller is tested in terms of chattering elimination in MATLAB/Simulink environment during different uncertainty conditions. The proposed controller superiority is verified by comparing the proposed controller results with the different existing controllers like proportional integral (PI) control and sliding mode control (SMC).

On the modelling, analysis, and design of a suboptimal controller for a class of wind/PV/battery based DC microgrid

This study deals with the comprehensive modelling, analysis, and control of a DC microgrid (MG) in islanded mode. The proposed DC MG comprises a wind turbine, a photovoltaic (PV) source, battery storage, DC/DC source, and load side converters with DC loads. To this aim, a circuit-oriented modelling of the whole system is developed. The PV source is modelled with a single-diode electrical circuit. Afterward, a mathematical model of the system with state-space representation was derived. A detailed analysis of PV system design is performed because the parameters of PV are appearing in the dynamic model of DC MG. For the purpose of controller development, the dynamic model of the DC MG, which is modelled by a non-linear-non-affine eight-order system, is linearized around an equilibrium point using the Jacobian matrix framework, while stability using eigenvalue is carried out showing that the stability is guaranteed under operating condition. Finally, for the first time, the state-dependent Riccati equation (SDRE) technique is proposed to find the optimal regulation problem for the DC MG with non-linear-non-affine dynamics. The numerical simulation studies first confirm the validity of the performed mathematical, then the effectiveness of the proposed non-linear controller is evaluated under illumination and load change.

An efficient dynamic power management model for a stand-alone DC Microgrid using CPIHC technique

The power generation using solar photovoltaic (PV) system in microgrid requires energy storage system due to their dilute and intermittent nature. The system requires efficient control techniques to ensure the reliable operation of the microgrid. This work presents dynamic power management using a decentralized approach. The control techniques in microgrid including droop controllers in cascade with proportional-integral (PI) controllers for voltage stability and power balance have few limitations. PI controllers alone will not ensure microgrid’s stability. Their parameters cannot be optimized for varying demand and have a slow transient response which increases the settling time. The droop controllers have lower efficiency. The load power variation and steady-state voltage error make the droop control ineffective. This paper presents a control scheme for dynamic power management by incorporating the combined PI and hysteresis controller (CPIHC) technique. The system becomes robust, performs well under varying demand conditions, and shows a faster dynamic response. The proposed DC microgrid has solar PV as an energy source, a lead-acid battery as the energy storage system, constant and dynamic loads. The simulation results show the proposed CPIHC technique efficiently manages the dynamic power, regulates DC link voltage and battery’s state of charge (SoC) compared to conventional combined PI and droop controller (CPIDC).

Optimal sizing of wind–PV‐based DC microgrid for telecom power supply in remote areas

New telecommunication towers are installed in remote/rural areas to facilitate the increasing connectivity worldwide. With concerns over environmental issues, such towers are to be environmentally friendly. Conventionally, diesel generator supply power to towers in remote/rural areas, which leads to carbon emission. Also, the operation of diesel generator entails considerable operating cost (fuel and maintenance costs). Thus, a wind-photovoltaic (PV) based DC microgrid is proposed for supplying power to telecommunication towers in remote/rural areas ensuring reliable, economical, and green power supply. Therefore, techno-economic analysis is carried out here to determine the feasibility and cost of electricity (COE) per unit of the proposed DC microgrid. A non-dominated sorting genetic algorithm II is implemented to solve the multi-objective optimal sizing problem to achieve a trade-off between the cost and the reliability. Hourly solar irradiation and wind speed data is used for long-term analysis equivalent to the lifespan of the battery. Further, de-rating factor and maximum power point tracking factor are considered while modelling the renewable resources. The loss of power supply probability, excess energy, and COE are calculated and different scenarios are studied to examine the techno-economic feasibility of the proposed DC microgrid.

Energy Management Strategy for Rural Communities’ DC Micro Grid Power System Structure with Maximum Penetration of Renewable Energy Sources

The AC and DC power system structures need to be modernized to meet consumer demands. DC microgrids are suitably admired due to their high efficiency, consistency, reliability, and load sharing performance, when interconnected to DC renewable and storage sources. The main control objective for any DC microgrid is providing proper load–power balancing based on the Distributed Generator (DG) sources. Due to the intermittent nature of renewable energy sources, batteries play an important role in load–power balancing in a DC microgrid. The existing energy management strategy may be able to meet the load demand. However, that technique is not suitable forrural communities’ power system structure. This research offers an energy management strategy (EMS) for a DC microgrid to supply power to rural communities with solar, wind, fuel cell, and batteries as input sources. The proposed EMS performs the load–power balancing between each source (renewable and storage) in a DC microgrid for dynamic load variation. Here, the EMS handles two battery sources (one is used to deliver power to the priority load, and the other is utilized in the common DC bus) to meet the required demand. The proposed EMS is capable of handling load–power balancing using renewable energy sources with less consumption of non- conventional energy sources (such as a diesel generator). The performance of the system is analyzed based on different operating conditions of the input sources. The MATLAB/Simulink simulation model for the proposed DC microgrid with their EMS control system is developed and investigated, and their results are tabulated under different input and load conditions. The proposed EMS is verified through a laboratory real-time DC microgrid experimental setup, and the results are discussed.

Coordinated Energy Management in Resilient Microgrids for Wireless Communication Networks

This paper proposes a novel approach to power wireless communication network base stations (BSs) in order to achieve higher resilience. In the proposed dc microgrid, resilience is improved with the coordinated operation of renewable energy sources, energy storage devices, and BS traffic using an integrated controller that adjusts BS traffic and distributes stored energy among cell sites based on weather forecast and other operational conditions. The essential role played by power electronics active power distribution nodes in this resilient microgrid is also explained. Resilience improvements are evaluated quantitatively with metrics analogous to that of availability, which considers energy storage levels, power generation, and load. The analysis shows that the proposed system allows reducing service restoration times by a factor of 3 or improve resilience by double-digit percentage points, while the battery life is extended by about 10%. Additionally, battery bank size needs and PV array footprints can be reduced without compromising resilience. The proposed microgrid is implemented for wireless communication networks because both energy and communications are identified in the U.S. Presidential Policy Directive 21 as the two infrastructures that are especially critical for community resilience. Nevertheless, this same technology can also be used in other applications, such as residential neighborhoods or industrial campuses.

Scalable DC Microgrids for Rural Electrification in Emerging Regions

We present the design and experimental validation of a scalable dc microgrid for rural electrification in emerging regions. A salient property of the dc microgrid architecture is the distributed control of the grid voltage, which enables both instantaneous power sharing and a metric for determining the available grid power. A droop-voltage power-sharing scheme is implemented wherein the bus voltage droops in response to low supply/high demand. In addition, the architecture of the dc microgrid aims to minimize the losses associated with stored energy by distributing storage to individual households. In this way, the number of conversion steps and line losses are reduced. We calculate that the levelized cost of electricity of the proposed dc microgrid over a 15-year time horizon is $0.35/kWh. We also present the experimental results from a scaled-down experimental prototype that demonstrates the steady-state behavior, the perturbation response, and the overall efficiency of the system. Moreover, we present fault mitigation strategies for various faults that can be expected to occur in a microgrid distribution system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost-effective method for electrifying emerging regions.

Electric Vehicle Charging Stations Feeded by Renewable: PV and Train Regenerative Braking

At present, electric vehicles are charged at home at night when lower off-peak rates could apply. The purpose of this pilot project is to demonstrate the viability of providing renewable power charging services for electric vehicles at railway station parking lots for commuters during the day. The power needed to feed the charging station comes from a photovoltaic system as well as from the regenerative braking of a 25-kV AC railway traction system (RTS) and a 3-kV DC RTS. The design of the proposed DC microgrid for the charging station also involves an energy storage system (ESS) (battery of variable size and supercapacitor) and the connection to a secondary distribution network (SDR). The paper analyses the two most challenging technical issues: (1) the strategy of power management and converter control; (2) the impact of the size of the ESS. Although an increase in the size of ESS benefits the charge from renewable power, this increases the total investment inevitably. The smart strategy developed from power management, based on DC bus sensing, aims at maximizing the usage of available renewable power by mitigating the impact of charging station on the SDR.

Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid

In the context of sustainable buildings, this paper investigates power flow management for an isolated DC microgrid and focuses on efficiency and energy cost reduction by optimal scheduling. Aiming at high efficiency, the local produced power has to be used where, when, and how it is generated. Thus, based on photovoltaic sources, storage, and a biofuel generator, the proposed DC microgrid is coupled with the DC distribution network of the building. The DC bus distribution maximizes the efficiency of the overall production-consumption system by avoiding some energy conversion losses and absence of reactive power. The isolated DC microgrid aims to minimize the total energy cost and thus, based on forecasting data, a cost function is formulated. Using a mixed integer linear programming optimization, the optimal power flow scheduling is obtained which leads to an optimization-based strategy for real-time power balancing. Three experimental tests, operated under different meteorological conditions, validate the feasibility of the proposed control and demonstrate the problem formulation of minimizing total energy cost.

Integration of Wind Power and Wave Power Generation Systems Using a DC Microgrid

In order to study the uncertainty and intermittent characteristics of wind power and wave power, this paper proposes an integrated wind and wave power generation system fed to an ac power grid or connected with an isolated load using a dc microgrid. The proposed dc microgrid connects with a wind power generator through a voltage-source converter (VSC), a wave power generator through a VSC, an energy storage battery through a bidirectional dc/dc converter, a resistive dc load through a load dc/dc converter, and an ac power grid through a bidirectional grid-tied inverter. The studied integrated wind and wave system joined with the dc microgrid is modeled and simulated using the written program based on MATLAB/Simulink. Root-loci plots of the studied system under various speeds of the wave generator are analyzed. To examine the fundamental operating characteristics of the studied integrated system joined with the dc microgrid, a laboratory-scale platform is also established. Comparative simulation and experimental results reveal that the studied integrated system can maintain stable operation to supply power under different operating conditions using the proposed dc microgrid.

Modeling and Simulation of DC Microgrids for Electric Vehicle Charging Stations

This paper focuses on the evaluation of theoretical and numerical aspects related to an original DC microgrid power architecture for efficient charging of plug-in electric vehicles (PEVs). The proposed DC microgrid is based on photovoltaic array (PVA) generation, electrochemical storage, and grid connection; it is assumed that PEVs have a direct access to their DC charger input. As opposed to conventional power architecture designs, the PVA is coupled directly on the DC link without a static converter, which implies no DC voltage stabilization, increasing energy efficiency, and reducing control complexity. Based on a real-time rule-based algorithm, the proposed power management allows self-consumption according to PVA power production and storage constraints, and the public grid is seen only as back-up. The first phase of modeling aims to evaluate the main energy flows within the proposed DC microgrid architecture and to identify the control structure and the power management strategies. For this, an original model is obtained by applying the Energetic Macroscopic Representation formalism, which allows deducing the control design using Maximum Control Structure. The second phase of simulation is based on the numerical characterization of the DC microgrid components and the energy management strategies, which consider the power source requirements, charging times of different PEVs, electrochemical storage ageing, and grid power limitations for injection mode. The simulation results show the validity of the model and the feasibility of the proposed DC microgrid power architecture which presents good performance in terms of total efficiency and simplified control.

Adaptive Energy Management in Redundant Hybrid DC Microgrid for Pulse Load Mitigation

This paper investigates the real-time control and energy management of a dc microgrid incorporating hybrid energy sources with various loading schemes. In the proposed dc microgrid, a supercapacitor bank, a power buffer, and a pulse load are directly connected to a dc bus. The proposed system configuration highly improves the grid redundancy and reduces the total power losses. Moreover, the supercapacitor bank not only supplies the pulse load, but also supports the grid during transient periods when it is highly loaded. However, the energy management and control of such a system is more complex, since the heavy pulse load can cause high power pulsation and voltage drop. To reduce the adverse effects of the pulse load, a new real-time energy management system (EMS) with an adaptive energy calculator (AEC) based on the moving average measurement technique is developed. The proposed microgrid was implemented in hardware and experimentally tested. The results were compared with other methods, such as direct voltage control and continuous current averaging approaches. The results show that the developed EMS with AEC technique properly share the power and control the bus voltage under different loading conditions, while the adverse effects of the pulse load are highly reduced.

An Adaptive Control System for a DC Microgrid for Data Centers

In this paper, an adaptive control system for a dc microgrid for data centers is proposed. Data centers call for electric power with high availability, and a possibility to reduce the electric losses and, consequently, the need for cooling. High reliability can be achieved by using local energy sources, and by using a dc power system, the number of conversion steps, and therefore also the losses, can be reduced. The dc microgrid can also supply closely located sensitive ac loads during outages in the ac grid. The proposed dc microgrid can be operated in eight different operation modes described here, resulting in 23 transitions. The control system coordinates the operation of converters, sources, and switches used in the dc microgrid. The control system is tested in the simulation software package PSCAD/EMTDC, and the results of the most interesting transitions are presented. The results show that it is possible to use the proposed dc microgrid to supply sensitive electronic loads and also, during ac-grid outages, supply closely located sensitive ac loads. To reduce the current transients experienced by grid-connected ac/dc converters, fast grid-outage detection and fast switches are required.