Microgrid Power Research Articles

Power-sharing for dc microgrid with composite storage devices and voltage restoration without communication

In this paper, we propose a new decentralized control and power-sharing strategy to manage the power flow among energy sources (ESs), energy storage systems (ESSs) and the common dc-link. In the proposed technique, we eliminate all communication among the ESSs, to reduce the complexity and increase reliability, maintaining dc-link voltage restoration. In this context, batteries and ultracapacitors (UCs) are the ESSs, while ESs can be any power source such as photovoltaic, wind, fuel-cell and etc. This technique shares the microgrid power imbalance between batteries, proportionally to their state-of-charge (SoC) and energy capacity, achieving SoC equalization. The technique also promotes voltage restoration for the UCs, keeping their average voltage constant after supplying the power peaks during power transients. For all ESSs only local variables are measured, such as local current and dc-link voltage, with no shared data between ESSs. Small signal and stability analysis are performed, along with experimental results in a lab bench show the feasibility and performance of the technique.

Analysis of an Energy Management System of a Small Plant Connected to the Rural Power System

This paper presents the analysis of an energy management system (EMS) implemented to fulfill the requirements of a microgrid (MG) power supply owned by a small industrial company and connected to the rural power system. The main goal of this system is to ensure connection with the existing rural power system in terms of energy exchange, as well as to perform islanding mode operation of the microgrid based on the energy demand of the company. Power generated in the implemented microgrid is based on a hybrid system supported by renewable energy sources and an energy storage system. The aim of the developed energy management system implemented in this MG power system is to enable the microgrid to operate according to scheduled diagrams related to different load events. The presented investigation, analysis, and assessment of the implemented energy management system are based on the on-site measurements and observations of the long-term operation of the microgrid concerning the hybrid renewable energy-based generation system and the company energy demand, which is useful from perspective of the predicted deficiency of electrical energy in Poland, both for customers and transmission system operators. Furthermore, the observed drawbacks and failures of the implemented EMS and the MG are pointed out and discussed to overcome and minimize these disadvantages, and to improve the operational reliability of the whole system.

Microgrid Operations Planning Based on Improving the Flying Sparrow Search Algorithm

Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.

A Linear Regression Model for THD Sensitivity in PV Based Microgrid with Varying Insolation Levels

Solar PV systems can be used for powering small microgrids in rural area of developing countries. Generally, a solar power microgrid consists of a PV array, an MPPT, a dc-dc converter and an inverter, particularly as the general loads are A.C in nature. In a PV system, reactive current, unbalancing in currents, and harmonics are generated due to the power electronics-based converters as well as nonlinear loads (computers induction motors etc). Thus, estimation of the harmonics levels measured by the Total Harmonic Distortion (THD) is an essential aspect of performance assessment of a solar powered microgrid. A major issue that needs to be examined is the impact of PV system control parameters on the THD. In this paper, we take up this assessment for a small PV based rural microgrid with varying levels of solar irradiance. A Simulink model has been developed for the study from which the THD at equilibrium conditions is estimated. This data is in turn used to design a generalized Linear Regression Model, which can be used to observe the sensitivity of three control variables on the magnitude of the THD. These variables are: Solar Irradiance levels, Power Factor (PF) of connected load magnitude of the connected load (in kVA) The results obtained show that the greatest sensitivity is obtained for load kVA variation.

Distributed control for polygeneration microgrids: A Dynamic Market Mechanism approach

This paper develops a Dynamic Market Mechanism (μDMM) to optimally allocate electric and thermal power in a combined heat and power microgrid. The market mechanism is formulated as a receding horizon constrained optimization problem, from which an optimal automated transactive procedure is developed. Its operation is distributed in nature and incorporates the most up-to-date electric and thermal load estimates and renewable generation. The results of the market clearing of the proposed microgrid μDMM are the spot prices and schedules for electric and thermal power in addition to non-binding estimates of future prices and set-points. The performance of the μDMM is illustrated using simulation studies of a Combine Heat and Power (CHP) microgrid model based on the Smart Polygeneration Microgrid (SPM) located on the University of Genova Savona Campus. The results show that the μDMM has the ability to adapt to new information via updated forecasts and improve the performance of microgrid operation compared to a centralized method. This in turn makes the μDMM attractive for use in μEMS (microgrid Energy Management Systems) by allowing new energy resources to be added in a plug and play fashion.

Power coordination control strategy of microgrid based on photovoltaic generation

In order to solve the large-scale grid-connected photovoltaic cells caused by power fluctuations, power quality decline and other issues. This paper proposes and researches a power coordination control strategy for microgrid based on photovoltaic power generation. The principle of photovoltaic cells and the switching of maximum power point tracking and limited power mode are studied. The stability control methods of DC bus voltage, AC bus wire and frequency are studied. The model of microgrid is established and moreover, based on the power of microgrid and the charging state of storage battery, the operation of microgrid is divided into different working modes. The stable operation of microgrid is realized by adjusting the output power of each unit in different working modes. The calculation shows that the control strategy can effectively reduce the power fluctuation in the microgrid and improve the output power of renewable energy. Finally, the feasibility and effectiveness of the proposed methods are verified by experiments.

Distributed Secondary Controller to Ensure Proportional Sharing of Reactive Power in AC Microgrid

Distributed Secondary Controller to Ensure Proportional Sharing of Reactive Power in AC Microgrid

Short-Term Three-Phase Load Prediction With Advanced Metering Infrastructure Data in Smart Solar Microgrid Based Convolution Neural Network Bidirectional Gated Recurrent Unit

The collaboration development between the power supply, solar power, and storage batteries is a formidable commission in the smart solar microgrid. The efficient power management integrated into a smart solar microgrid is required to address these problems. The deep learning predictive tools are utilized to forecast the maximum building’s three-phase load power in the short term. In this project, historical data, from the solar module, battery module, grid data, and climate data, are collected by integrated advanced metering infrastructure in a smart solar microgrid in Taiwan. These historical data are utilized in predicting the building’s three-phase load power in the smart solar microgrid. This project proposed the hybrid model in the short-term three-phase loads forecasting based on convolution neural network bidirectional gated recurrent unit (CNN-Bi-GRU). The CNN-Bi-GRU utilizes the continuous-time sliding window, which is extracted features and reshaped into vectors by CNN layers. The hyperparameter optimization is utilized to construct the highest performance structure of the CNN-Bi-GRU model. The CNN-Bi-GRU performance is compared and evaluated with other state-of-arts models, which are the recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM), and Bi-GRU models. The experiment results prove that the forecasting accuracy could be effectively improved by a hybrid CNN-Bi-GRU model with an appropriate window size of sequential historical data. According to research knowledge, this is the first work to predict the building’s three-phase load power using multiple source data from advanced metering infrastructures (AMI) and deep learning CNN-Bi-GRU in the smart solar microgrid. The CNN-Bi-GRU model is successfully integrated into the efficient power management, which maintains the adequate battery storage level, solar power, and grid power balance under different weather conditions and working requirements of the building’s three-phase load power.

Decentralized Power Management of DC Microgrid Based on Adaptive Droop Control With Constant Voltage Regulation

An adaptive droop control with constant voltage regulation is proposed for the power and voltage management of a DC microgrid (DCMG) with multiple power sources, such as a utility grid (UG), a distributed generator (DG), an energy storage system (ESS), and an electric vehicle (EV). In the proposed scheme, the droop characteristics for the UG, ESS, and EV are adaptively changed according to electricity price conditions and state-of-charge (SOC) levels in order to optimize the DCMG operation flexibility as well as electricity cost. The proposed control method not only ensures the power-sharing of DCMG reliably without the use of a communication link, but also regulates the DC bus voltage stably at the nominal value. To achieve this, the proposed scheme consists of primary control and secondary control. The primary control is used to achieve power-sharing in the decentralized DCMG, while the secondary control is used to overcome the disadvantage of conventional droop control, i.e., to remove DC bus voltage deviations. Decentralized power management is also presented to enhance the DCMG system reliability in the presence of uncertainties such as DG generation power, the ESS and EV SOC levels, the grid and EV availabilities, the load demand, and electricity price conditions. The effectiveness of the proposed scheme is demonstrated in a comprehensive simulation and experiment under various conditions. The test results clearly confirm the control flexibility and overall performance of the proposed control scheme for decentralized DCMG system.

Analysis of the Application and Control of the Distributed Energy Storage System in Microgrid

Because of the intermittence and randomness of renewable energy and the continuous variability of load. By configuring a certain capacity of energy storage in microgrid, the active and reactive power throughput can be flexibly and quickly adjusted to smooth load fluctuations and reliability of power supply improvement. It can improve the power quality and keep the system stable. Therefore, the application potential of distributed energy storage system in microgrid and distribution system is huge. In terms of give full play to the performance advantages of energy storage through distribution system, it is necessary to study the core technologies of modular integration, topology, capacity design and control of energy storage through distribution system. The feasibility and value of energy storage through distribution system in power system engineering are demonstrated. Based on the analysis of relevant requirements, a energy storage through distribution system based on micro energy storage unit is proposed in this paper. This structure is very suitable for energy storage components to be connected to the micro-grid to achieve plug and play, high efficiency, high reliability and high flexibility of energy storage system applications.

Engaging Renewable Energy Education Using a Web-Based Interactive Microgrid Virtual Laboratory

Microgrids are the main building blocks of future smart grids. They provide a promising solution for integrating renewable energy sources and distributed power generation into the power grids. The high penetration of renewable energy sources and their intermittent nature have brought about significant challenges to the control and operation of microgrids. In recent years, an enormous amount of research has been conducted on microgrid dynamics, stability analysis, power management, and control. However, there has been limited effort to integrate cutting-edge technologies into classrooms and laboratories. Many academic institutions face special challenges in establishing microgrid laboratories because of the rapid development of technology, cost, and space requirements, as well as daily and seasonal variations in renewable energy sources. Virtual and remote laboratories have become widely accepted for conducting experiments in higher education. A virtual laboratory is particularly suitable for renewable energy-based microgrids to overcome cost, space, and time restrictions. In this study, a web-based virtual laboratory for microgrids with renewable energy sources was designed and used for renewable energy education. The virtual laboratory was developed using the LabVIEW, Microsoft® .Net Core, and MATLAB/Simulink®. Innovative courseware was developed around the virtual laboratory and used in several engineering technology courses to teach topics including solar and wind power, data acquisition, DC-DC converters, battery management, and microgrids. The evaluation showed that the virtual laboratory greatly increased students’ awareness, interest, and knowledge of renewable energy. It provides an interactive and convenient tool to engage students to experiment with renewable energy and microgrids.

Development of a Droop Control Strategy for Flexible Operation of Distributed Generators

Droop control is one of the control strategies utilized to establish a simple, effective, and a communication-less power sharing between several distributed generators (DGs) in power systems and microgrid networks. In this paper, the control of an inverter-based system operating with the proposed droop control is developed and analyzed. The proposed droop control with its adjustable nonlinearity level aims to flexibly control the DGs in order to fulfill multiple objectives that aim to stabilize the operation of the sources and optimize the power sharing. These objectives can be achieved, simultaneously, as the droop characteristics generated under the proposed control cover all the possible operating points in the frequency-active power (f-P) and voltage- reactive power (V-Q) planes. The capability of the proposed control strategy to construct highly non-linear characteristics enables the DG to effectively meet different complex technical and economic constraints. The work carried out in this paper focuses on the f-P droop relation, and investigates the system performance under different physical and control parameters. The obtained results show that the proposed droop control is able to perform well under different operating conditions, shape the power sharing of the running DGs and stabilize the system performance.

Blockchain-based power trading system for microgrid

Direct trading between entities in the microgrid is the trend of micro-grid electricity trading. However, the lack of trust and endorsement among multiple entities in micro-grids makes it difficult to complete direct electricity transactions, which limits the green energy efficiency. To solve this problem, firstly, a blockchain-based microgrid power transaction level model and power transaction process management process are proposed. Secondly, an access interface between the microgrid smart terminal and the blockchain is designed to realize the connection between the blockchain and the underlying equipment. The system is implemented in an island microgrid, which realizes the peer-to-peer trading between power suppliers and users. The system builds a bridge among entities in microgrid and makes the power trading open, transparent and traceable.

Capacity Optimization of Clean Renewable Energy in Power Grid Considering Low Temperature Environment Constraint

Cold regions have complex and diverse environments with low temperatures and short sunshine times throughout the year. To rationally configure the capacity of the low-temperature environment microgrid system and improve the power supply reliability of the low-temperature environment microgrid system and energy utilization rate. Based on the characteristics of a low-temperature environment, a wind-hydrogen-storage microgrid capacity optimization model for hydrogen production from surplus wind power is proposed, and the real-time influence of low-temperature on the output of wind turbines, battery capacity and power load is considered, and the annual average cost is minimized. The annual load shortage rate is restricted, and the capacity of the microgrid power supply system in a low-temperature environment was optimized. In this study, a region in northeast China was the research object. According to the optimized model, because of the defects of the traditional particle swarm optimization (PSO) algorithm, the traditional PSO algorithm is improved to optimize the capacity of the microgrid system. The simulation results were analyzed from the aspects of economy and reliability, and the proposed results were verified. The reliability of the method provides a reference for the optimal configuration of the microgrid capacity in low-temperature environments.

Remote Synchronization of the Microgrid to the Utility Grid Without Access to Point of Common Coupling in the Presence of Disturbances

The article presents the method of remote micro-grid synchronization with the utility grid in the presence of voltage disturbances. The proposed algorithm and control devices synchronize different power systems on the basis of the instantaneous voltage phase angle in spite of voltage quality problems and without need of access to point of common coupling. In the presence of grid voltage disturbances, instantaneous grid voltage phase angle is estimated and transmitted remotely with a certain accuracy, which is varies in presence of disturbances of grid voltages or depends on the synchronization algorithm and data transmission rate. The paper focuses on the influence of voltage disturbances on the proposed remote synchronization strategy and the shape of the output current of the synchronized power converter on the micro-grid. The four types of grid voltage disturbances are presented: harmonics, one-phase sag, two-phase sag, and phase voltage jump. The final conclusions present the possibility of using the proposed remote synchronizations algorithm in micro-grid power converter applications in the presence of typical utility grid voltage disturbances.

Fractional Order Piλdµ Controller for Microgrid Power System Using Cohort Intelligence Optimization

Fractional Order Piλdµ Controller for Microgrid Power System Using Cohort Intelligence Optimization

Microgrid Power Sharing Framework for Software Defined Networking and Cybersecurity Analysis

Hierarchical control is a widely used strategy that can increase resilience and improve the reliability of the electrical network based on microgrid global variables. The large amounts of data required during transitions prompt the use of more reliable and flexible communications to achieve the control objectives. Such communications can involve potential cyber vulnerabilities and latency restrictions, which cannot be always addressed in real-time. To accurately capture the system’s overall operation, this paper proposes a co-simulation framework driven by flexible communications and a resilient control algorithm to regulate the frequency and voltage deviations in a networked microgrid. Model-based predictive control has been implemented, to avoid slow transient response associated with linear hierarchical control. Software-Defined Networking (SDN) is responsible for increasing the communication intelligence during the power-sharing process. The effects of critical communications and overall system performance are reviewed and compared for different co-simulation scenarios. Graphical Network Simulator (GNS3) is used in combination with model-based predictive control and SDN, to provide latency below 100 ms, as defined in IEC 61850. Testing of the proposed system under different cyber attack scenarios demonstrate its excellent performance. The novel control architecture presented in the paper provides a reference framework for future cloud computing-based microgrids.

Weibull distribution-based analysis for reliability assessment of an isolated power micro-grid system

This study investigates Weibull Distribution-based Analysis for reliability assessment of an isolated power Micro-grid system. The Mean Time to Repair (MTTR) reliability indices were calculated using the outage data obtained from the micro-grid site between 2015 and 2019. The Weibull probability function is widely used in reliability analysis; in this method, the shape and scale parameters were calculated using the Maximum likelihood Estimator (MLE). MLE technique was deployed to check the problem of the MTTR data analysis from data collected to estimate the Weibull study. The research involved the percentage, cumulative percentage, density, cumulative probability, survival probability, and hazard probability. The scale parameter, which was observed as 0.625195, equally represents the mean failure time of 0.99728, which influenced the overall reliability performance of the micro-grid power system.

Blockchain-based smart microgrid power transaction model

As a decentralized database technology, blockchain is increasingly being applied to smart grids. This paper proposes a blockchain-based smart microgrid power transaction model. The model realizes the power dispatching between users and agents in the microgrid through two-way auctions and point-to-point transactions and optimizes energy allocation through market regulation. And based on the Ethereum private chain, it ensures the openness, transparency, safety, and reliability of transactions. The simulation results show that the open and reliable features of the blockchain improve the efficiency of power dispatching, and verify the feasibility and effectiveness of the model.

Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach

Energy sharing has significant potential in improving system energy utilization, improving economic efficiency, and reducing the impact on power system. The multi-microgrids distributed coordinated operation model is proposed in this paper to provide a reference for the coordinated operation of multi-microgrids considering factors such as electric and heat demand response, multi-energy interaction and uncertainties. First of all, the system operation framework is introduced and the function of each part is analyzed. Secondly, the multi-microgrids cooperative operation model is established, and generalized Nash bargaining method is used to assign total operating cost to ensure the enthusiasm of each microgrid to participate in cooperation. Then, considering the uncertainty of wind power and load in microgrids, a stochastic programming model based on conditional value-at-risk is established. Furthermore, alternate direction multiplier method is used to solve the proposed model to protect the data privacy of each microgrid participating in cooperation. Finally, through the analysis of calculation example, it is verified that the proposed model can reduce the operating cost of each microgrid and maintain enthusiasm of each microgrid for cooperation. In addition, the convergence and accuracy of algorithm are explained, and the influence of different connection modes and the interaction limit of electric and thermal power on the operating cost of microgrids is compared. Moreover, stochastic programming model also provides a reference plan for the trade-off between economic benefits and risk level of multi-microgrids system.