Grid-connected Operation Modes Research Articles

Data-driven optimization for microgrid control under distributed energy resource variability

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

Smooth Transition Control Strategy for Operation Modes of Optical Storage Microgrid based on VSG

Addressing the challenge of suppressing voltage and frequency oscillations during mode transitions and achieving smooth transitions between grid-connected and islanded operation modes in peer-to-peer structured optical storage microgrids, is an urgent technical issue. This paper proposes a control strategy for smooth mode transitions based on numerical soft starters, focusing on the dynamic characteristics of voltage and frequency in optical storage microgrids during grid-connected and islanded operation modes. The numerical soft starter smoothens the input reference value of the current inner loop, reducing the impact of transient surge current on the current inner loop during transitions, thus ensuring the oscillations of bus voltage and frequency during microgrid mode transitions. In islanded operation mode, a VSG control strategy is introduced by constructing a small signal model to analyze the frequency response characteristics of the system under different inertia and damping characteristics, setting appropriate control parameters to provide strong frequency support for the system during islanded operation. Finally, an optical storage microgrid system is established using Matlab/Simulink to conduct simulation experiments under conditions such as grid-connected/islanded mode transitions, load fluctuations, and sudden changes in photovoltaic output power, validating the effectiveness of the proposed smoothing strategy.

Fault identification, classification, and localization in microgrids using superimposed components and Wigner distribution function

The integration of Distributed Energy Resources (DERs) into distribution grids has become increasingly feasible and sustainable due to the development of microgrids. However, the development of an effective protection strategy remains a challenge in the implementation of microgrids. To address this challenge, this paper presents a simple and novel microgrid protection method based on superimposed components, Wigner distribution function (WDF) and alienation index-based. The proposed method develops a new fault detection index (FDI) by applying the alienation coefficient and WDF on a superimposed current signal to detect faulty events in the microgrid. The scheme is inherently phase segregated because the FDI is obtained for each phase individually. In addition, the proposed strategy introduces a new fault zone identification method based on the superimposed positive sequence reactive power (SPSQ). After obtaining the complete fault information, a relevant trip signal is generated to isolate the faulty section from the rest of the grid. The proposed methodology is evaluated through simulations using MATLAB/SIMULINK software. Various fault types, with varying parameters are simulated to validate the proposed approach. The results indicate that the proposed methodology is capable of recognizing, classifying, and locating all fault types in both grid-connected and islanded modes of operation.

Research on Grid-Connected Optimal Operation Mode between Renewable Energy Cluster and Shared Energy Storage on Power Supply Side

The renewable energy cluster can reduce the total power deviation of renewable energy stations and also bring cooperative benefits to renewable energy stations. Shared energy storage can assist in tracking the power generation plan of renewable energy and has advantages in the scale of investment, utilization rate, and other aspects. Therefore, this article proposes a study on the grid-connected optimal operation mode between renewable energy cluster and shared energy storage on the power supply side. Firstly, for the complementary characteristics blurring each member’s contribution to the cluster power deviation, an improved Shapley value method is used to build a rational mechanism for apportioning the cluster power deviation penalty. Secondly, based on the Nash negotiation model and allocation mechanism, the revenue model of the renewable energy cluster is constructed, and an improved particle swarm optimization algorithm is proposed to solve the cluster operation strategy. Then, based on the charge and discharge demand of renewable energy cluster, with the goal of maximizing the benefits of shared energy storage, capacity pricing is optimized, as well as capacity allocation and participation in the frequency control auxiliary service market. Finally, the solving process of grid-connected optimal operation mode is proposed, and the rationality of the grid-connected optimal operation strategy between renewable energy cluster and shared energy storage is verified by example analysis. The results indicate that renewable energy cluster and shared energy storage can effectively increase both benefits, and a win-win situation for all parties can be realized. On the one hand, the cooperation mode and allocation mechanism can effectively guarantee the benefit of each renewable energy station. On the other hand, shared energy storage is beneficial for assisting in tracking the power generation plan of renewable energy and reducing the renewable energy power curtailment situation. There also has residual capacity in the shared energy storage system; it can participate in the frequency control ancillary service market, which can improve the capacity utilization rate and benefit of shared energy storage.

Optimal single settings based relay coordination in DC microgrids for line faults

A novel time–current-rate-based inverse characteristic curve for relays in a DC microgrid is proposed in this paper. Line current rise rate is used as actuating quantity, ensuring quick line fault clearing (relay operating time is in order of a few μs). The advantage of using line current rise rate as actuating quantity is that for a line short-circuit fault, it does not vary significantly for grid-connected and islanded modes of operation and varying network topologies of DC microgrid. Consequently, using the proposed characteristic, a single set of optimal relay settings is obtained using an optimization solver in MATLAB. The obtained settings ensure reliable and selective coordination between primary and backup relays for various pole-to-ground and pole-to-pole line faults under different operating conditions with multiple sources in two different 4-bus 400V low voltage DC microgrids. The maximum relay operating time for a high resistance fault with the proposed curve is 394.9μs for the first considered low voltage DC microgrid. Simulations in the Real Time Digital Simulator and comparisons with previous schemes (one comparison on the second considered DC microgrid), conventional standard inverse, and extremely inverse curves for DC microgrid protection indicate the effectiveness of the proposed scheme.

Data-Mining Model-Based Enhanced Differential Relaying Scheme for Microgrids

The article presents a data-mining model-based enhanced differential relaying scheme for fault detection and classification task in the microgrids. The proposed research work aims to provide a fixed setting, fast, and reliable scheme for accurately detecting and classifying faults in the microgrid. The presented scheme fetches the three-phase current signals from both ends of the distribution lines and computes the differential current phasor, including the distributed generation (DG) in the fault model. The deep neural network (DNN)-based data-mining models for fault detection and classification are built using differential current phasor datasets generated by wide variations in fault and operating conditions of the microgrid, including different types of faults with the change in fault location, fault resistance, DG penetration in grid-connected and islanding mode of operation with types of DG units. The developed DNN models are validated for their ability to detect the faults in a low-voltage CERTS microgrid and subsequently for a medium-voltage IEEE-34 bus microgrid on a MATLAB/SIMULINK platform. Furthermore, the proposed intelligent scheme is also tested on the Typhoon hardware-in-loop platform for real-time performance evaluation. The accuracy and speed in detecting and classifying faults indicate that the proposed DNN-based intelligent differential relaying scheme can be a potential candidate for providing reliable protection measures for the microgrid.

Power management of grid-connected PV wind hybrid system incorporated with energy storage system

Renewable Energy Sources (RES) such as solar photovoltaic (PV) and wind-based generation cannot meet dynamic load demand all the time because of their intermittent nature. Battery Energy Storage System (BESS) is integrated with RES to meet dynamic load demands. Proper power management is needed for the efficient and reliable performance of the system. This paper presents power management of PV-Wind- Battery hybrid system for both grid-connected and islanded modes of operation as well. The power balance is maintained by a power management system (PMS) under different environmental conditions and load conditions, and modes of operation. The control method for state-of-charge (SOC) and battery charge/discharge is applied to ensure efficient performance and safe operation of BESS. Different cases, such as surplus/deficit power available from RES with respect to load demand and battery capacity, have been considered and carried out to verify the performance of PMS using the MATLAB/Simulink platform.

A novel SVM based adaptive scheme for accurate fault identification in microgrid

Microgrids, which include distributed energy resources, provide a more reliable power supply. However, despite the fast advancements in microgrid protection, the microgrid’s dynamic behaviour challenges the protection. The large-scale integration of renewable energy sources (RES) and the addition of electric vehicle (EV) charging loads cause additional protection issues on the grid due to its volatile nature. Conventional fault prediction schemes do not provide reliable protection with the dynamic nature of microgrids. This article proposes an adaptive scheme that accurately detects the volatile changes in the microgrid under normal and fault conditions. The novelty of the proposed approach is that the general-purpose adaptive scheme is based on a two-level SVM classifier model to ensure accuracy and speed of operation. The SVM-1 model identifies the mode of operation, the variations in RES generation, the random addition of EV charging load, and the occurrence of a fault in the microgrid. SVM-2 identifies the exact type and location of fault that occurred. The SVM model uses RMS values of three-phase voltage and current measurements as data inputs. The efficacy of the proposed scheme is tested on an IEEE 9-bus microgrid test bed by simulation experiments using MATLAB/SIMULINK. The sensitivity analysis results prove the robustness of the proposed scheme. Moreover, the proposed method is validated using real-time experiments on Hardware-In-Loop (HIL) real-time simulator, OPAL-RT, and Raspberry Pi microcontroller. Accurate online detection of faulted lines and fault types is obtained within a cycle of AC in grid-connected and islanded modes of operation.

Techno-Economic Analysis and Optimization of Hybrid Renewable Energy System with Energy Storage under Two Operational Modes

Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 106 EUR, 4.515 × 106 EUR, and 4.538 × 106 EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 106 EUR, 4.868 × 106 EUR, and 4.885 × 106 EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements.

Multipurpose FCS Model Predictive Control of VSC-Based Microgrids for Islanded and Grid-Connected Operation Modes

This article presents an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids. The proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with the minimum inverter switching, a negative-sequence current controller, and a loop to identify the control system operation mode. All the controllers are designed using the multipurpose finite control set-model predictive control (FCS-MPC) strategy. Since these controllers use the dynamic current and VSC voltage, they can be applied in grid-connected and island operation modes and transferred between them. The method uses voltage–frequency control instead of power control for VSCs. One inverter controls voltage, and the other controls current. The conventional FCS-MPC is enhanced to reduce the computation power by eightfold. This improvement is significant because the maximum switching frequency is limited in practical implementations. Also, the superiority of the proposed multipurpose control scheme is proved theoretically. Simulation is implemented using MATLAB software and compared with methods in the literature. The simulation demonstrates that the presented control strategy is efficient, authentic, and compatible. The proposed method is also tested and validated in hardware experiments.

An Asymmetrical Dual Quasi-Z-Source Based 7-Level Inverter for PV Applications

This paper presents a new single-stage quasi-z-source (qZS) based power converter for Photovoltaic (PV) applications. This seamless topology is obtained by the fusion of two quasi-z-source networks with a 7-level cascaded multilevel inverter (MLI). The proposed converter requires fewer passive components and power switching devices compared to the seven-level topologies with impedance and quasi-impedance sources, reported in the literature. The proposed modulation technique achieves the required level of voltage boosting using the qZS networks while synthesizing seven levels in the output voltage waveform. While the proposed power converter can regulate the output voltage in the standalone mode, it can inject active power into the grid at Unity power factor (UPF) in the grid-connected mode of operation. This paper also aims to mitigate the leakage current, which is one of the major issues in transformerless PV inverters, by reducing the magnitude of voltage transitions in the parasitic capacitor and the suppression of higher-order harmonics in the ground leakage current. The steady-state and the dynamic performances of the proposed power converter during grid-connected mode of operation is assessed with simulation studies and are validated with experimental results.

$\Delta V-\Delta I$ Plane-Based Line Faults Detection and Classification in DC Microgrid

The high fault current rise due to instantaneous capacitor discharge necessitates quick line fault detection in DC microgrids. Hence, this paper proposes a communication-less <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Delta V-\Delta I$</tex-math></inline-formula> plane-based line faults detection and classification scheme in a Low-Voltage (LV) DC microgrid using local parameters. Simultaneous threshold violations of difference of successive line current and bus voltage samples, with sum of squared currents and disagreement of instantaneous line current to its average echo, aid the scheme to identify forward and backward faults and categorize Pole-to-Ground (PG) and Pole-to-Pole (PP) faults. The proposed scheme is time-efficient as it quickly detects and classifies the worst possible close-in and line-end PG and PP High Resistance Faults (HRFs) within 0.72 ms. The proposed method's feasibility is validated in Real-Time Digital Simulator (RTDS), Numerical Relay Development Environment (NRDE), and microcontroller-based hardware in loop setup. Comparison with previous techniques and tests on two LV DC microgrids indicate that the thresholds in the proposed scheme can aid in quick fault (even line-end HRFs) detection. Also, the proposed scheme is robust to grid-connected and islanded modes of operation, ring and radial configurations, noise, and non-fault events, like load switching, varying power output of sources, and AC side and internal converter faults.

Energy Management System for Microgrid System using Improved Grey Wolf Optimization Algorithm

An Energy Management System (EMS) is indispensable to monitor the power flow and load matching inside a microgrid during grid-connected mode (GCM) and islanded modes (IM) of operation. Many conventional optimization algorithms show poor reliability for real time optimization problem solving where an objective function is non-linear. An optimization technique is necessary to reduce the cost of energy obtained from the grid, generated inside the grid, and consumed by the load. This article presents, an optimization scheme based on the improved Grey wolf optimization (GWO) algorithm that considers replacement of wounded/injured wolves of one pack by strong wolves of other pack for an EMS in micro-grid. The GWO optimization algorithm's effectiveness is demonstrated forGCM and IM operation. The proposed GWO shows fast, lost cost and precise optimization of the real time EMS for the grid connected and islanded micro-grid system.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

Multi-objective optimization configuration of wind-solar coupling hydrogen production system

The existing research on the wind solar coupling hydrogen generation system lacks the evaluation of factors such as the reliability of power supply to the microgrid system. This paper establishes a multi-objective optimization configuration model of wind-solar coupling hydrogen generation system under grid-connected operation mode for hydrogen generation. From the perspective of system economy and stability analysis, the model optimizes the capacity of the wind power unit, photovoltaic unit, energy storage unit, and hydrogen generation unit to improve the economy and reliability of microgrid operation, to minimize load loss rate, system annual cost, renewable energy power generation loss rate, and energy surplus rate.

Reliability-Based Optimal Integrated Microgrid Scheduling in Distribution Systems

This paper proposes a reliability-based optimal scheduling model of integrated microgrids. The proposed model is capable of delivering optimal scheduling of each individual microgrid and aggregated system. In addition, it is compatible with both grid-connected and islanded operation modes. The developed problem formulation of the proposed model takes into account the reliability indices SAIDI, SAIFI, and CAIDI to evaluate the reliability of the integrated system. Numerical simulations on a modified IEEE-33 bus test system, involving four integrated microgrids, were performed to investigate the advantages and the robustness of the developed model. Furthermore, the impact of merging Battery Energy Storage Systems (BESSs) on the optimization outcome is examined, which demonstrates its significant effectiveness in improving the power systems' reliability.

Stability Analysis of Microgrid with Load Shedding Scheme using RTDS

A microgrid system was simulated using the powerful RTDS simulator, the modelling was done on the RSCAD software, and the microgrid comprised of a diesel generator, a photovoltaic (PV) system and a Doubly-Fed Induction Generator (DFIG) wind turbine system as DERs. The paper comprises the study on stability analysis of the microgrid in grid-connected and islanded modes of operation, along with a successful load shedding scheme which was implemented based on the loads distinguished as low priority and high priority loads in order to keep the system balanced during faults.

Voltage-sag mitigation by stability-constrained partitioning scheme

Microgrids have the ability to function in islanded or grid-connected modes of operation. The increased penetration of inverter-based Distributed Energy Resources (DERs) in the system promotes the concept of partitioning the system into self-governing and self-adequate microgrids. Most of the partitioning techniques determine virtual boundaries and did not consider the survivability of the constructed microgrids. In this paper, a stability-constrained partitioning scheme is proposed based on small-signal stability to ensure microgrids' survivability when physically partitioned. Moreover, a sensitivity analysis of active power droop gain is utilized to define a novel index for the microgrid’s marginal stability. The application targeted in this paper is the mitigation of voltage-sag events caused by low impedance faults. The system will be partitioned into clusters of survivable microgrids during faults to isolate the faulted zone that caused the voltage-sag event. By isolating the voltage-sag origin from the rest of the system, voltage-sag mitigation is accomplished. Also, a microgrid re-connection method is proposed. This method allows multiple droop-controlled DERs to adjust the frequency, phase, and magnitude of their output voltages to facilitate seamless re-connection of microgrids. Simulation results are given that show a seamless re-connection of two different microgrids when the proposed method is utilized. The effectiveness of the proposed mitigation algorithm is validated using a modified IEEE 33-bus distribution system simulated on MATLAB/SIMULINK platform.

Distributed Economic Dispatch for Microgrids Tracking Ramp Power Commands

When in grid-connected mode of operation, distributed generators (DGs) within the microgrid (MG) can coordinate to act as a single entity to provide services to the bulk grid. The DGs can coordinate their power production to minimize the total operating cost, which is known as the distributed economic dispatch. Various methods have been proposed to solve the MG economic dispatch problem (EDP) in a distributed fashion, under the assumption that DGs’ power output, in aggregate, follows a constant or slowly varying power command. However, when the MG is providing frequency regulation service, or the internal load is highly dynamic, the MG EDP becomes dynamic, and the state-of-the-art distributed approaches cannot guarantee optimality. In this paper, we propose a distributed economic dispatch algorithm for MGs providing frequency regulation service, as an example of a dispatch profile with ramp commands. A consensus protocol guaranteeing zero steady-state error for ramp inputs is integrated into the EDP to find the optimal solution in a distributed way. With the proposed algorithm, the MG is able to tightly follow a time-varying regulation signal while maintaining an optimal economic dispatch for all the DGs within. We validate the proposed method using regulation signals from PJM and demonstrate the algorithm on a hardware-in-the-loop testbed.

Research on Multi-Objective Optimization Model of Industrial Microgrid Considering Demand Response Technology and User Satisfaction

In the process of wind power, coal power, and energy storage equipment participating in the operation of industrial microgrids, the stable operation of wind-storage industrial microgrids is guaranteed by considering demand response technology and user satisfaction. This paper firstly sorts out the status quo of microgrid operation optimization, and determines the main requirements for user satisfaction considering three types of load characteristics, demand response technology, power consumption benefit loss, user balance power purchase price and wind power consumption evaluation indicators in the system. Secondly, the operation architecture of the windstorage industrial microgrid is designed, and the multi-objective optimization model of the wind-storage industrial microgrid is established with the comprehensive operating cost and user satisfaction as the target variables, and the corresponding solution method is mentioned. Finally, a typical wind-storage industrial microgrid is selected for simulation analysis, and the results show that, (1) Considering the demand response technology, the comprehensive operating cost of the wind-storage industrial microgrid per day is 5292.63 yuan, the user satisfaction index is 0.953, and the wind power consumption rate reaches 100%. (2) By setting four scenarios, it highlights that the grid-connected operation mode is superior to the off-grid operation mode. Considering the demand response technology, the load curve can be optimized, and the time-of-use electricity price can be fully used to coordinate the operation of each unit, which enhances the wind power consumption capacity. The compromise solution of the system comprehensive operating cost and user satisfaction under the confidence level of 0.95 is obtained, namely (5343.22, 0.94). (3) The frontier curve shows that in the process of model solving, it is impossible to optimize any sub-objective by changing the control variables, which proves that there is a close relationship between the comprehensive operating cost of the system and the confidence level, which can provide effective guidance for the optimal operation of industrial microgrids.