Grid-connected Microgrid Research Articles

An overview of the current Advanced Techniques for Frequency Regulation in grid-connected and off-grid Microgrids.

The integration of renewable energy sources into the power system is an important step towards a sustainable energy transition. This transition could subsequently introduce substantial variability that critically impacts key operational parameters, such as frequency and voltage. This variability poses significant challenges, especially within microgrid configuration, both in grid-connected and isolated modes. Therefore, ensuring the stability of these parameters I paramount of their operational efficiency, reliability, and longevity. Despite these challenges, recent advancements in the field have led to the development of numerous advanced methodologies and control strategies designed to mitigate the impact of renewable sources on microgrid frequency stability. This paper provides a comprehensive overview of these state-of-the-art technologies and methodologies, including cutting-edge technologies such as adaptive load frequency control and Time-series prediction-based approaches. It offers insights into their application, effectiveness, and advantages in frequency control during microgrid operation, contributing to the ongoing discourse on integrating renewable energy sources with enhanced grid stability. Moreover, the discussion section provides insights and barriers impacting the implementation of these technologies in the current microgrid system and the power grid in general.

A Rule-Based Bionic Energy Management System for Grid-Connected Community Microgrid Using Peer-to-Peer Trading With Rapid Settlement

A Rule-Based Bionic Energy Management System for Grid-Connected Community Microgrid Using Peer-to-Peer Trading With Rapid Settlement

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

Resilience analysis and improvement strategy of microgrid system considering new energy connection.

With the increasing demand for electricity, microgrid systems are facing issues such as insufficient backup capacity, frequent load switching, and frequent malfunctions, making research on microgrid resilience crucial, especially to improve system power supply reliability. This paper proposes a method for analyzing the resilience metric of new energy grid-connected microgrid system, and proposes optimization strategies to improve resilience. Firstly, a measurement method for the resilience of the microgrid system is established based on the operating characteristics of the system components. Secondly, the sensitivity relationship between system resilience and parameters is established, and an optimization model for resilience improvement strategies of microgrid systems based on parameter sensitivity is constructed. Finally, simulation verification is conducted based on the IEEE 37-node microgrid system. The results show that the proposed measurement method can scientifically and reasonably measure the resilience of the microgrid system, and the resilience improvement strategy significantly improves the operational resilience, verifying the effectiveness and robustness of the proposed analysis method.

Power Quality Improvement and Seamless Transfer in a Grid-connected Microgrid System using H∞ Controller

Power Quality Improvement and Seamless Transfer in a Grid-connected Microgrid System using H∞ Controller

Adaptive Neuro-Fuzzy Damping Controller of Grid-Connected Microgrid Hybrid System Integrating Wind Farms and Batteries

Adaptive Neuro-Fuzzy Damping Controller of Grid-Connected Microgrid Hybrid System Integrating Wind Farms and Batteries

Enhancing Grid-Connected Microgrid Power Dispatch Efficiency Through Bio-Inspired Optimization Algorithms

Enhancing Grid-Connected Microgrid Power Dispatch Efficiency Through Bio-Inspired Optimization Algorithms

Energy Management of Microgrid Considering Demand Response Using Honey Badger Optimizer

Recently, Microgrids (MGs) have received great attention for solving power system problems, due to their low environmental effects and their economic benefits. This paper proposes a new application of an effective metaheuristic optimization method, namely, Honey Badger Algorithm (HBA), to solve energy management for optimal dispatch of the grid connected MG incorporating Demand Response programs (DRP). Honey badger algorithm is used to solve an incentive DRP, with the aim of minimizing the total cost, which includes conventional generators fuel cost and the cost of power transaction with the main grid considering the load demand. In this paper, two case studies are conducted using HBA and simulation results are compared with those obtained by other algorithms (particle swarm optimization and JAYA algorithm). First case consists of three diesel generators, a PV generator and a wind generator. To prove the scalability of the HBA, the second case, which is much larger, is tested. Simulation results for both case studies obtained by PSO, JAYA, and HBA are deeply discussed. The results show the HBA's effectiveness in solving the energy management with DR problem for MG compared with other well-known optimization techniques.

Optimal Configuration of Hybrid Energy Storage Capacity in a Grid-Connected Microgrid Considering Laddering Carbon Trading and Demand Response

In order to enhance the carbon emission reduction capability and economy of the microgrid, a capacity optimization configuration method considering laddered carbon trading and demand response is proposed for a grid-connected microgrid consisting of photovoltaic, battery and hydrogen storage devices. Combined with the mathematical model and system structure of each unit in the microgrid, the integrated operation control strategy is determined in this paper. A demand response model containing three load types is developed to reduce the stress on the storage and generation side. A carbon-trading mechanism is introduced into the operating costs to establish a configuration model with annual overall profit and power supply reliability as the optimization objectives. The non-dominated sorting genetic algorithm II is used to optimize the capacity of each unit, and the effectiveness of this model is verified by taking a microgrid in a region in Northwest China as an example to analyze the impacts of ladder carbon trading and demand response on the configuration results and system operation.

Multiple Grid-Connected Microgrids with Distributed Generators Energy Sources Voltage Control in Radial Distribution Network Using ANFIS to Enhance Energy Management

Voltage conditions and power quality for customers and utility equipment are significantly impacted by the addition of microgrid-generating sources within distribution networks. Designing the right control for distributed generators for the various generating units of a Microgrid is important in enabling the synchronization of renewable energy generation sources, energy storage unity, and integration of Microgrids into a radial distribution network. This research provides control mechanisms based on an adaptive technique employing ANFIS, to reduce fluctuation of voltage and current difficulties faced when multiple renewable energy sources and storage systems are incorporated into a distribution network. A step-by-step Voltage Source Converter (VSC) Controller was designed for controlling the DC voltage power sources used. The ANFIS training, test system modeling, and the distributed energy source were modeled in MATLAB/SIMULINK 2021a Software. Four microgrids were developed each consisting of a Photovoltaic plant, Wind Turbine, and Battery Storage System. Non-critical and critical loads were considered during the system testing. The simulated result reveals that the proposed control system works effectively in maintaining a constant system voltage of 340VAC which significantly mitigates system voltage and current fluctuation without using any static synchronous compensator (STATCOM) and power system stabilizers.

Model predictive control–based robust-control strategy of distribution control for a grid-connected AC microgrid

The challenge of ensuring the reliable running of power systems has gotten more difficult in recent years due to the rising complexity of power system networks. The decreasing accessibility of fossil fuels has necessitated a greater dependence on renewable energy sources, such as solar systems, wind power, and hydroelectric power, by the international community. As a result, there is an increasing demand for AC microgrids to offer an effective approach for distributing power. The power system networks that consist of microgrids frequently have a significant number of failures, surpassing 80%. These failures occur because microgrids are susceptible to unexpected changes in different distributed generating sources. The variations greatly impair the operating efficiency of the microgrid and have negative consequences for the distribution system. The microgrid consists of numerous dispersed generation units and local loads. The load in a microgrid exhibits parametric uncertainty, which adds to the fluctuation observed in its performance. The formulated control strategy is Model Predictive Control, which aims to achieve robust performance even in the presence of unaccounted-for loads, dynamic loads, harmonic loads, and both balanced and unbalanced loads. The authors of this paper have developed a control approach that utilizes model predictive control (MPC) and is characterized by its robustness and optimality. MPC has the ability to predict the future behavior of a certain system. The controller successfully mitigates and reduces any disruptions that may occur within the power distribution system by taking into account its healthy characteristics. The model is implemented in the MATLAB Simulink environment, where it produces an accurate and appropriate total harmonic distortion value. The model was compared to previous efforts and significantly improved by increasing some crucial parameters by up to 90%. The value functions as a measure of the controller's performance quality and the improved efficiency of the microgrid system.

Congestion management ancillary service at the distribution level through grid-connected microgrid based on DLMP and HFPSO-TOPSIS approach

Congestion management ancillary service at the distribution level through grid-connected microgrid based on DLMP and HFPSO-TOPSIS approach

Filter Capacitor Current Dynamics-Based Frequency-Domain Fault Detection Approach for Grid-Connected Low-Voltage DC Microgrid

This paper proposes a cost-effective short circuit fault detection method for a distribution network in the ac grid-connected low-voltage dc (LVDC) microgrid. In this method, a typical dc system is considered where a number of dc/dc, ac/dc power converters and loads are interfaced to the dc link. Further, these loads are dedicated as local loads to the converter based on the locality of the same. The discharge of a filter capacitor (FC) in power converters in the dc system is the foremost indicator of a fault. Such dynamic behavior of FC can be utilized in the identification of a fault. This method is a time-frequency domain-based wavelet transform (WT), which utilize the current dynamics of an FC for quantitative analysis of a fault current. Additionally, a detailed thresholds selection process is also explained. MATLAB/Simulink model is used to carry out simulation studies. The experimental validation of WT based fault detection method is executed on the ac grid-connected LVDC microgrid testbed interfaced to the dSPACE 1104 controller.

Fault Location of Grid-connected Microgrid Lines Based on AVMD and Double-ended Traveling Wave Ranging

To enhance the accuracy of fault location in microgrid lines, a fault location method based on adaptive variational mode decomposition (AVMD) and double-ended traveling wave ranging is proposed. Firstly, a microgrid radial structure model, incorporating wind, light, and water systems, is established. The parameters of variational mode decomposition are optimized by using the Aquila optimizer (AO) algorithm, allowing the original fault signal to be decomposed into multiple modal components using AVMD. Only a limited number of modal components retain the fault signal information. The effective weighted peak relevance index (EWPR) is employed to select the sensitive modal components, identified as the four modes that best preserve the fault information, for signal reconstruction. Finally, the reconstructed signal is applied for fault localization. Simulation results indicate that the proposed method is scarcely influenced by the fault type, exhibiting superior accuracy compared to the single-ended traveling wave method.

Event-Triggered Model Predictive Control for the Inverter of a Grid-Connected Microgrid With a Battery-Supercapacitor HESS

Event-Triggered Model Predictive Control for the Inverter of a Grid-Connected Microgrid With a Battery-Supercapacitor HESS

Optimal battery scheduling in solar-plus-storage grid-connected microgrid for profit and cost efficiency: A use case on an Israeli microgrid

This paper presents an optimal energy management algorithm for solar-plus-storage grid-connected microgrid simulated on a real full-scale small town microgrid test-case, taking into account the daily solar energy generation as well as the electricity demand to ensure that the battery is charged and discharged at the optimal times to balance energy supply and demand. The optimization is performed using dynamic programming and finding the minimum-cost-path, to minimize the total cash-flow. The formulation is utilized with two variations such that two business models are considered, optimizing cost efficiency in both aspects of minimizing cost and maximizing profits, according to the scenario. The first model maximizes self-consumption, and the second model is the case of retail electricity providers. The results are compared to real data from a solar-plus-storage grid-connected microgrid located in Israel, that is currently managed by a rule-based scheduling procedure. The results show that by using the optimal control schedule, the revenue increases up to 8% during the spring and autumn when the weather conditions are typically moderate. In summer and winter, where the climate conditions are more extreme and the demand for energy is at its peak, the full revenue potential can be realized by using the suggested optimal control algorithm, offering an increased profit of up to 30%. Unlike prior studies which are restricted to small use-cases, this paper leverages a unique and private large-scale data-set from a small town, enabling a thorough exploration of the optimization’s real-world viability and scalability. Through rigorous simulations, the findings underscore the method’s exceptional potential in effectively integrating PV-plus-storage assets and optimizing energy use in power grids.

Optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

ABSTRACT The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.

Optimal scheduling of a microgrid with power quality constraints based on demand side management under grid-connected and islanding operations

Power quality (PQ) issues are direct consequences of integrating power electronics components and non-linear loads into microgrids. These issues can also be triggered by an unbalanced loading in the microgrid. Certainly, they affect the daily operation scheduling of the microgrid. This paper proposes an optimal harmonic power flow (OHPF) framework for the daily optimal scheduling of a grid-connected microgrid, which is constructed by combining the optimization formulation and harmonic power flow (HPF). Within the framework, PQ is evaluated by observing three indices including voltage magnitude, voltage total harmonic distortion (THDV), and voltage unbalance factor (VUF). A non-iterative mitigation scheme based on demand side management (DSM) is proposed to avoid PQ indices violations and is integrated into the optimization formulation part of the OHPF as a set of load constraints. The proposed constraints allow a flexible combination of different DSM actions including load shedding and interphase load transfer (ILT). Furthermore, the constraints to limit voltage magnitude, voltage THD, and VUF are integrated into the formulation in the form of penalty functions. The software implementation of the proposed OHPF involves Julia-based JuMP.jl and Gurobi solver for optimization part and OpenDSS for harmonic load flow part. The effectiveness of the proposed framework is tested using a modified IEEE 37-bus feeder for different load fluctuations, which allow different combinations of PQ indices violations. The assessment involves both normal and intentional islanding conditions. The results demonstrate that, under different random events, the proposed framework can avoid the violation of PQ indices limits in most cases without adding an excessive computational burden to the original optimization and harmonic load flow algorithms.

Techno-economic potential of multi-energy community microgrid: The perspective of Australia

This study aims to assess the techno-economic viability of a grid-connected microgrid for providing low-cost, reliable energy supply in regional parts of Australia. In this research, a regional community with solar and wind energy potential is examined as a case study. The study evaluates five different system configurations with and without energy storage based on the available resources. The results indicate that the most reliable and cost-effective system is the grid-connected microgrid with solar, wind, diesel generator, and battery. The energy cost for this system is calculated as $0.152/kWh, which is 7.11% less expensive than the base scenario that solely utilizes grid power. The results also demonstrate that the proposed system can achieve a reduction in operating costs by 7.37%. Additionally, this microgrid configuration may potentially save 174.647 metric tonnes of CO2 annually. Finally, sensitivity analysis is performed to identify the critical sensitive parameters. The results reveal that variations in solar radiation, wind speed, real interest rate, and battery storage lifetime have a significant impact on the design of microgrids. The study findings would help microgrid planners, analysts, and policymakers to identify the latest design constraints and formulate appropriate strategies for renewable-based community power systems.

ILA Optimisation Based Control for Enhancing DC Link Voltage with Seamless and Adaptive VSC Control in a PV-BES Based AC Microgrid

The paper presents a grid-connected microgrid with a photovoltaic system and a battery as a storage element. The optimal design and control of storage elements and power quality improvement are enhanced using sigmoid-function-based variable step size (SFB-VSS) adaptive LMS control. The DC-link voltage and battery current are enhanced using an ILA-optimization-based PI controller. Comparative analysis shows that an ILA-optimized PI controller improves battery stress and DC-link voltage fluctuations, enhancing overall system stability. The relative percentage error of Vdc is only 0.5714% for ILA-optimized values as compared to GA, PSO, and manually tuned PI gains which are 0.857%, 1.14285%, and 0.86%, respectively. ILA-optimized parameters also enhance battery current, reducing stress on the battery. The system was studied under various dynamic conditions, achieving power balance in all conditions. The system has the capability of seamless transfer of control from GC mode to SA mode when the grid is disconnected. The proposed VSC control shows better performance in steady-state and dynamic conditions, maintaining a THD under 5%, which follows IEEE standard 519, and providing better DC offset rejection, fewer oscillations in the weight component of the load, and better convergence. The proposed control also enhances the frequency of the grid, ensuring a smooth transition between modes. The system is simulated in the MATLAB Simulink environment, and all the optimization techniques were carried out offline.