Showing papers on "Islanding published in 2021"
TL;DR: This paper proposes a new structure and control scheme for future microgrid-based power system, which is designed to achieve a seamless operation in both islanded and grid-connected modes, while the load is appropriately shared by all units.
Abstract: This paper proposes a new structure and control scheme for future microgrid-based power system, which is designed to achieve a seamless operation in both islanded and grid-connected modes, while the load is appropriately shared by all units (i.e., renewable sources, energy storage systems and the grid). The proposed method, which involves physical separation of the microgrid from the grid by using AC/DC/AC converters, ensures safe, secure and seamless operation of both modes. Such a “buffered” structure enables reduction in the transmission losses by reducing the exchanged energy with the grid through using a dead-zone in the control of the buffering AC/DC/AC converter. An inverse-droop control technique has been implemented to control the voltage magnitude and frequency, using current control in the dq -frame. PSCAD/EMTDC software has been used to validate the proposed method through simulating different scenarios. The solution provides a simple, smooth, and communication-free decentralized control for multi-sources microgrids. Moreover, the proposed buffered structure separates the dynamics of the microgrid and the grid, which enables a faster microgrid voltage and frequency control and protects the grid and the microgrid from faults on the other side.
45 citations
TL;DR: A novel islanding detection method (IDM) for grid-connected photovoltaic systems (GCPVSs) through a disturbance injection in the maximum power point tracking (MPPT) algorithm that endorse timely and accurately detection with negligible non-detection zone (NDZ) as well as no false tripping in non-islanding disturbances.
Abstract: This paper proposes a novel islanding detection method (IDM) for grid-connected photovoltaic systems (GCPVSs) through a disturbance injection in the maximum power point tracking (MPPT) algorithm. When an absolute deviation of the output voltage exceeds a threshold, the applied disturbance shifts system operating point from its maximum power point (MPP) condition. This leads to a sharp active power output reduction and consequently, a significant voltage drop in islanded mode beyond the standard voltage limit. The proposed algorithm is defined in a way that the distributed generator (DG) can be restored to MPP after islanding classification. It is thereby effective in microgrid in where the power injection at maximum level to cater the critical loads and maintain the stability of the isolated area are pursued. An intentional time delay has also been considered to avoid nuisance tripping in short-circuit faults which do not require tripping. The assessment of the proposed technique has been conducted for a sample network containing two GCPVSs in a real-time platform including actual relays in hardware-in-the-loop (HiL). The provided results under extensive islanding scenarios defined in islanding standards endorse timely and accurately detection with negligible non-detection zone (NDZ) as well as no false tripping in non-islanding disturbances. The comparative analysis of the presented scheme with a few recent IDMs for GCPVS highlights its overall superiorities, including very small NDZ, fast detection, thresholds self-standing determination, no adverse effect on power quality, and simple and inexpensive integration.
39 citations
TL;DR: A heuristic algorithm, known as the collective decision-based optimization algorithm, is utilized to overcome the non-convexity and nonlinearity of the problem, and a deep learning gated recurrent unit technique is designed to forecast renewable power output for mitigating the uncertainties in renewable energy components.
Abstract: This paper investigates the impact of uncoordinated, coordinated, and smart charging of plug-in hybrid electric vehicles (PHEVs) on the optimal operation of microgrids (MGs) incorporating the dynamic line rating (DLR) security constraint. The DLR constraint, particularly in the islanding mode, influences the ampacity of MG feeders, when distribution lines reach their maximum capacity. To overcome any line outage or contingency situation, smart PHEVs are utilized to help improve the grid security. However, using PHEVs can cause higher power losses and feeder overloading issues. To address these concerns, a reconfiguration technique is employed in this paper. A heuristic algorithm, known as the collective decision-based optimization algorithm, is utilized to overcome the non-convexity and nonlinearity of the problem. The unscented transform technique is employed to model DLR uncertainties caused by solar radiation, load demand, and weather temperature, as well as PHEVs’ uncertainties caused by varying charging strategies, numbers of PHEVs being charged, charging start time, and charging duration. Moreover, a deep learning gated recurrent unit technique is designed to forecast renewable power output for mitigating the uncertainties in renewable energy components. A modified IEEE 33-bus test network is deployed to evaluate the efficiency and performance of the proposed model.
39 citations
TL;DR: The presented IDM has the advantages of self-standing thresholds determination, no improper effect on the output power quality, and simple and inexpensive structure, and the fast MPP restoration of the proposed scheme after islanding identification boosts the chance of seamless reconnection and DG autonomous operation in microgrid.
Abstract: This article proposes a fast and reliable two-level islanding detection method (IDM) for grid-connected photovoltaic system (GCPVS)-based microgrid. In the first level of the proposed IDM, the magnitude of the rate of change of output voltage (ROCOV) is computed. If this variable exceeds a predefined threshold, a disturbance is injected into the duty cycle of DC/DC converter after a given time delay to deviate the system operating point away of its maximum power point (MPP) condition. This leads to a substantial active power output and voltage reduction in an islanded mode. Therefore, the ROCOV and the rate of change of active power output (ROCOP) indices, measured in the second stage, pose great negative sets at the same time in islanding states. However, the variation of at least one of these variables is near-zero in non-islanding switching events. The assessment of the presented algorithm has been conducted under extensive islanding and non-islanding scenarios for a case study system with two PV power plants using hardware-in-the-loop (HiL) simulation tests. The provided results remark precise islanding classification with an eminently small non-detection zone (NDZ) within 510 ms. The presented IDM has the advantages of self-standing thresholds determination, no improper effect on the output power quality, and simple and inexpensive structure. Moreover, the fast MPP restoration of the proposed scheme after islanding identification boosts the chance of seamless reconnection and DG autonomous operation in microgrid.
35 citations
TL;DR: A deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem by using a neural network to approximate the nonlinear function between system operating condition and frequency nadir.
Abstract: In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.
32 citations
TL;DR: A risk-constrained stochastic framework is presented for joint energy and reserve scheduling of a resilient microgrid considering demand side management and conditional value-at-risk metric is incorporated with the optimization model to control the risk of profit variability.
Abstract: In this article, a risk-constrained stochastic framework is presented for joint energy and reserve scheduling of a resilient microgrid considering demand side management. The optimization problem is formulated to schedule the system operation in both normal and islanding modes by addressing the prevailing uncertainties of islanding duration as well as prediction errors of loads, renewable power generation, and electricity price. In a normal operation mode, where the grid-connection is available, the energy and reserve of local resources and energy trading with the main grid is scheduled to maximize the operator's profit considering feasible islanding. In a resilient operating mode, which is triggered by a disturbance in the main grid, the local resources should be scheduled to supply loads with the lowest emergency load shedding. To balance the economy and security requirements under uncertainties, the optimal scheduling is done properly through a security-constrained power flow method by considering system's objectives and constraints. Moreover, to properly handle the uncertainties of the problem, conditional value-at-risk metric is incorporated with the optimization model to control the risk of profit variability. The proposed scheme is implemented on a test microgrid and various case studies are presented to verify its effectiveness in normal and resiliency operating conditions.
31 citations
TL;DR: A novel nested restoration decision system (NRDS) is proposed, which aims to minimize unused capacity of distributed generation for service restoration due to contingency during islanding stage, based on the concept of nested microgrid formation.
Abstract: This paper proposes a novel nested restoration decision system (NRDS), which aims to minimize unused capacity of distributed generation (DG) for service restoration due to contingency during islanding stage. The proposed algorithm is based on the concept of nested microgrid formation, where the network control schemes are framed on a layered structure. The power-sharing strategy between neighboring microgrids in the network is from outer to inner microgrid loads, where the outermost microgrid exchanges power with utility grid only. The first stage refers to pre-fault scenario, which finds a solution for networked microgrid distributed generation as the initial setting. Furthermore, the second stage calculates additional redistribution requirements based on respective microgrid's deficiency using a solution index matrix. The proposed control has been evaluated on a networked microgrid system, while performing islanding.
31 citations
TL;DR: The developed algorithm is tested with a 10-kW grid-connected photovoltaic system to monitor the changes in voltage and power mismatch at the point of common coupling (PCC) and classify the state of the system efficiently.
Abstract: This article develops an islanding classification technique by adapting signal processing and machine learning techniques. The proposed method trains with all the possible islanding conditions, by extracting their features and classifying them. The performance of the proposed method was tested on a single-phase grid-connected photovoltaic system simulated using MATLAB/Simulink environment. The classifier achieved 98.1% training and 97.8% testing efficiency and can effectively detect islanding under 0.2 s with low misclassification. Further, the developed algorithm is tested with a 10-kW grid-connected photovoltaic system to monitor the changes in voltage and power mismatch at the point of common coupling (PCC) and classify the state of the system efficiently.
31 citations
31 citations
TL;DR: In this article, a three layers hierarchical control of inverter-based microgrid was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme.
Abstract: Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards.
29 citations
TL;DR: A new approach is introduced to accurately detect slow coherent generators by effectively minimizing generic normalized cuts and an enhanced version of the inertial generator aggregation method allows to produce accurate dynamic equivalents even if the selected number of generator groups is relatively low.
Abstract: Identifying generator coherency with respect to slow oscillatory modes has numerous power system use cases including dynamic model reduction, dynamic security analysis, or system integrity protection schemes (e.g., power system islanding). Despite their popularity in both research and industry, classic eigenvector-based slow coherency techniques may not always return accurate results. The multiple past endeavors to improve their accuracy often lack a solid mathematical foundation. Motivated by these deficiencies, we propose an alternative consistent approach to generator slow coherency. Firstly, a new approach is introduced to accurately detect slow coherent generators by effectively minimizing generic normalized cuts. As a by-product, the new approach can also guide the choice of the number of slow coherent groups. Secondly, it is shown that the combination of the the proposed slow coherency approach and an enhanced version of the inertial generator aggregation method allows to produce accurate dynamic equivalents even if the selected number of generator groups is relatively low.
TL;DR: Comparison analysis shows that the suggested method is more acceptable and the NDZ is reduced significantly, and proper performance even for small power mismatch is illustrated by presence of variety of non-islanding events and improvement of power quality is achieved in comparison active method.
Abstract: This paper proposes hybrid islanding detection for inverter-based distributed generation (DG) units. Conventionally, passive islanding detection methods may fail when power mismatches are nearly zero. Also, the difficulty of setting threshold is another common problem. To overcome this drawback, a new procedure for islanding detection is proposed based on the combination of the rate of change of voltage (ROCOV) and the rate of change of active power (ROCOAP). The ROCOAP is only activated when the islanding condition is suspected by ROCOV. When the power mismatch is negligible, some high-impedance fault or non-islanding events occur, the proposed method can identify islanding, thus presenting a combination of advantages of each passive/active method. Therefore, it is useful to appropriately detect islanding situation in all-inclusive of scenarios. Consequently, the proposed method under several conditions, such as pulsating loads, pre-energized transformers have been considered. To illustrate the effectiveness of the proposed approach, various tests such as UL1741 have been investigated. Comparison analysis shows that the suggested method is more acceptable and the NDZ is reduced significantly. In addition, proper performance even for small power mismatch is illustrated by presence of variety of non-islanding events and improvement of power quality is achieved in comparison active method.
TL;DR: An unintentional islanding transition control strategy based on artificial emotional reinforcement learning (AERL) for a three-/single-phase MMG and ensures the uninterrupted power supply of critical loads is proposed.
Abstract: Distribution network failures can cause unintentional islanding of three-/single-phase multimicrogrids (MMGs). The transient impulse that occurs during the unintentional islanding period can affect the stability of the voltage and frequency in an MMG. To address this problem, this article proposes an unintentional islanding transition control strategy based on artificial emotional reinforcement learning (AERL) for a three-/single-phase MMG. First, to solve the three-phase unbalance problem that occurs during the unintentional islanding period, a three-phase combination method based on a merge sort is proposed to realize the combination optimization of the single-phase source-load-storage. Second, a load-shedding strategy based on AERL is proposed to deal with the tie-line power shortage caused by the distribution network failures. This strategy can quickly eliminate power shortages and ensure the uninterrupted power supply of critical loads. Finally, the performance of the proposed transition strategy is verified in a three-/single-phase MMG model based on a modified IEEE 37-bus system and a modified IEEE 118-bus system. During the unintentional islanding period, the proposed transition strategy reduces the frequency recovery time by 21.74%, 14.29%, and 10%, the voltage recovery time by 16.22%, 13.89%, and 8.82% compared with the mixed-integer second-order cone programming method (MISOCPM), the implicit enumeration method (IEM), and the compound storage-regulating and load-shedding method (CSLM) in the modified IEEE 37-bus system, respectively; the proposed transition strategy reduces the frequency recovery time by 25.64%, 21.62%, and 12.12%, the voltage recovery time by 19.15%, 15.56%, and 7.32% compared with the MISOCPM, IEM, and CSLM in the modified IEEE 118-bus system, respectively. The test results show that the proposed transition control strategy realizes the seamless transition of a three-/single-phase MMG and ensures the uninterrupted power supply of critical loads.
TL;DR: Results show that by controlling the budget of uncertainty, the MG operator can achieve an almost 20% reduction in the operating cost, compared to a fully robust schedule, while achieving 0% probability of shedding more demand than expected.
Abstract: This article introduces a Robust Mixed-Integer Second Order Cone Programming (R-MISOCP) model for the resilience-oriented optimal scheduling of microgrids (MGs). This is developed for MGs that are islanded due to a scheduled interruption from the main grid, where minimizing both operational costs and load shedding is critical. The model introduced presents two main benefits. Firstly, an accurate second order cone power flow model (SOC-PF) is used, which ensures global optimality. Through a comparison with a piecewise linear power flow model on a modified IEEE 33 bus network, it is demonstrated that failure to accurately model power flow equations, can result in a significant underestimation of the operational cost of almost 12%. Secondly, uncertainty is modelled using a robust approach which allows trade-offs between the uncertainty that a MG operator is willing to tolerate, and performance. In this article, performance criteria considered are operational cost and load shedding. Market price, demand, renewable generation and islanding duration are considered as uncertain variables. Results show that by controlling the budget of uncertainty, the MG operator can achieve an almost 20% reduction in the operating cost, compared to a fully robust schedule, while achieving 0% probability of shedding more demand than expected.
TL;DR: An integrated framework to increase the resiliency of distribution system is proposed as tri-level mixed integer optimization problem and column constraint generation algorithm is utilized to make them computationally obedient and implemented on well-known IEEE 33-bus and 69-bus systems to prove their effectiveness and applicability.
TL;DR: An online learning-based adaptive secondary controller with extended state observer (ESO) is proposed for regulating the voltage and allocating the reactive power in an islanded microgrid using the active disturbance rejection properties of the ESO technique.
Abstract: In this brief, an online learning-based adaptive secondary controller with extended state observer (ESO) is proposed for regulating the voltage and allocating the reactive power in an islanded microgrid. With the active disturbance rejection properties of the ESO technique, two delicately designed neural networks via online learning approaches are proposed to compensate for the unknown/uncertain dynamics (UD) in the microgrid and attenuate the impact of external disturbance. Moreover, a vector of switching factors is introduced to unify the voltage recovery of the critical bus and accurate reactive power-sharing into a single framework. It is also worth noting that the devised approach is deployed in a distributed fashion, which enables the microgrid to acquire the plug-and-play capability. Extensive simulations are conducted to verify the effectiveness of the proposed controller for several cases including islanding and sudden load changing.
TL;DR: A novel HPE is proposed based on the frequency-domain sampling theorem, where the harmonic phasor is modeled based on several imaginary exponential functions, which has the following three advantages: it has an easier scheme for model parameter selection; it has zero-error results under nominal frequency condition; and it has higher accuracy under harmonic frequency deviation and harmonic modulation conditions.
Abstract: Harmonic phasor estimation can be used both in power system protection and monitoring applications, such as islanding detection, high-impedance fault location, and harmonic state estimation. A key challenge for harmonic phasor estimation is that different harmonics have different harmonic frequency bandwidths. As a result, a harmonic phasor estimator (HPE) that can adapt to such variations is needed. Although the sinc interpolation function-based estimator (SIFE) has such ability, it has disadvantages that: 1) it needs numerous simulations to select the model parameter and 2) it cannot obtain zero-error results under nominal frequency condition. In this article, a novel HPE is proposed based on the frequency-domain sampling theorem, where the harmonic phasor is modeled based on several imaginary exponential functions. The proposed HPE is compared with the SIFE in frequency response, model parameter selection scheme, and simulation tests. The results show that the proposed HPE has the following three advantages: 1) it has an easier scheme for model parameter selection; 2) it has zero-error results under nominal frequency condition; and 3) it has higher accuracy under harmonic frequency deviation and harmonic modulation conditions.
30 Jul 2021
TL;DR: In this article, the performance of a bi-directional converter took care of grid-associated framework with C-filter and π filter in forward and invert mode, the results are looked at regarding ripple voltage.
Abstract: These days, a Micro-Grid system is being considered as one of the answers for the energy worry the world over and it is acquiring consideration recently.-A Battery-Energy Storage-System (BESS) can be utilized in different parts of the power-frameworks.-When an islanding activity happens in a micro grid where a DG or a gathering of DGs keep on providing the micro grid framework which is isolated from the micro grid, the framework needs to have the expert generator which can give voltage and recurrence support.-When there is no simultaneous generator, converters interfaced batteries can go about as the expert control. This work suggests the demonstrating, investigation & simulation of bi-directional converter took care of grid-associated framework with C-filter and π filter in forward and invert mode. The results are looked at regarding ripple voltage.
TL;DR: Simulation and experiment results demonstrate that the passive method with adaptively threshold has high reliability against islanding and non-islanding events.
TL;DR: In this article, an overview of the shift in research from traditional schemes to intelligent islanding schemes is presented, which highlights the major obstacles, challenges, advantages and disadvantages of intelligent schemes.
Abstract: Detection of unintentional islanding, defined as inadvertently separation of distributed generators (DGs) from the utility grid, is a major challenging issue for modern distribution networks. Islanding detection becomes problematic especially when the local generation matches or closely matches the local load. Therefore, there are strict requirements for accurate, fast, and reliable islanding detection of renewables and DG-based systems. Various islanding schemes have been proposed in the literature, which can be categorized as remote, local, and intelligent-classifier-based schemes. Recently, intelligent schemes have gained attention due to their superior properties and advantages relative to traditional approaches. This paper overviews the shift in research from traditional schemes to intelligent islanding schemes. It also highlights the major obstacles, challenges, advantages and disadvantages, and future research directions of intelligent schemes. In this study, the intelligent-classifier-based islanding detection schemes presented over the last decade are analyzed objectively and comprehensively from all aspects of islanding detection. This research further highlights feature selection schemes and the most common parameters used for islanding detection. Finally, based on a detailed and critical analysis, the findings and potential recommendations are presented.
TL;DR: In this paper, an interval reduced kernel PCA-based Random Forest (IRKPCA-RF) approach is proposed for fault detection and diagnosis in grid-tied PV systems.
Abstract: This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors,…) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. The failures encompassed in this study are the open-circuit/short-circuit, islanding, output current sensor, and partial shading faults. In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. The main contribution of the proposed approach is to provide a good trade-off between low computation time and high classification metrics. The performance of the proposed IRKPCA-RF approach is assessed using a set of emulated data of a grid-tied PV system operating under healthy and faulty conditions. The presented results show that the proposed IRKPCA-RF approach is characterized by enhanced diagnosis metrics, classification rate, and computation time compared to the classical techniques.
TL;DR: This article presents a reliable islanding detection scheme for distributed generation (DG) to minimize the nondetection zone using a pattern-recognition method and the performance of the scheme is assessed through reliability analysis and it is compared to other machine learning techniques.
Abstract: This article presents a reliable islanding detection scheme for distributed generation (DG) to minimize the nondetection zone using a pattern-recognition method. A hybrid time–frequency signal decomposition along with a machine learning processes the voltage signal retrieved at the DG to make final decision. Amalgamation of time-varying filter and time domain decomposition obtains a modified intrinsic mode functions (MIMF), which enhances the time–frequency resolution of nonstationary signals. Moreover, the adaptive nature of the proposed hybrid signal decomposition makes it more advisable over other decomposition techniques to frame the input feature vector. Further, the random subspace ensemble framework based on ensemble k-nearest neighbor classifier is used among different machine learning techniques to identify the islanding condition by applying the feature vector generated using MIMF. The proposed scheme is thoroughly verified on two standard test systems for identifying the typical islanding condition of zero power mismatch and the proposed scheme discriminates the islanding from large scale disturbances such as capacitor switching and faults. The performance of the scheme is assessed through reliability analysis and it is also compared to other machine learning techniques.
TL;DR: In this article, the authors proposed a passive islanding detection scheme for synchronous distributed generations (SDGs) based on the combination of (dδ/dt) and (dPm/dt).
TL;DR: A new passive islanding detection method by using an advanced signal decomposition technique, i.e., time-varying filter-based empirical mode decomposition (TVF-EMD), which is more desirable over other decomposition techniques for enhanced resolution-based time-frequency analysis of nonstationary signals.
Abstract: This article presents a new passive islanding detection method by using an advanced signal decomposition technique, i.e., time-varying filter-based empirical mode decomposition (TVF-EMD). In the TVF-EMD, the adaptively tuned and predefined parameters and variation of the cutoff filter frequencies with respect to time make it more desirable over other decomposition techniques for enhanced resolution-based time-frequency analysis of nonstationary signals. The voltage signal measured at the distributed generation is processed through the TVF-EMD to decompose the signal into various intrinsic mode functions (IMF). The energy of the IMF is further extracted through the Teager energy operator. The computed energy is then used to identify the islanding scenario. In order to assess the performance of the proposed TVF-EMD, a number of simulation studies are performed on two standard test systems under varying islanding (i.e., different power mismatches and load quality factors) and critical nonislanding (i.e., capacitor switching and nonlinear load switching) conditions. The obtained results prove the efficacy of the proposed method.
TL;DR: A new approach using General Type-II Fuzzy controller to control smart island in combination with a novel modified optimization algorithm to increase the load sharing throughout the DGs operating in an islanding mode is presented.
TL;DR: The proposed methodology results have proved that the required energy is provided by optimal I-PV-BESS backup for a daylong islanding operation and its adaptability for practical situations.
Abstract: In current times, there is a need to do power system planning to endure situations of any kind. An islanding operation is one such unavoidable situation that may be required in many cases for both technical and economic reasons. First and foremost, this paper focuses on the determination of the best allotment of Interline-Photovoltaic (I-PV) system as per Electric Vehicle (EV) load penetration in the network. With different operational constraints, a multi-objective optimization using real power loss and voltage deviation index is formulated and solved using the Coyote Optimization Algorithm (COA).The paper highlights the computational efficiency of COA with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), in addition to various literary works, and the results suggest the superiority of COA by its global optima. The required battery energy storage system (BESS) capacity for supplying an islanded network's entire load demand for a day is determined in the second stage. The simulations were carried out on the IEEE 33-bus electrical distribution network (EDN) contemplating different levels of EV load penetration. The proposed methodology results have proved that the required energy is provided by optimal I-PV-BESS backup for a daylong islanding operation and its adaptability for practical situations.
TL;DR: In this article, a learning-based decision-making framework for the economic energy dispatch of an islanding microgrid based on the cloud-edge computing architecture is proposed to address the nontrivial task of economic dispatch in microgrids in the presence of uncertainties of renewable generations and loads.
Abstract: The paradigm of the Internet of Things (IoT) and cloud-edge computing plays a significant role in future smart grids. The data-driven solution integrating the artificial intelligence functionalities brings novel methods to address the nontrivial task of economic dispatch in microgrids in the presence of uncertainties of renewable generations and loads. This article proposes a learning-based decision-making framework for the economic energy dispatch of an islanding microgrid based on the cloud-edge computing architecture. Cloud resources are utilized to solve the optimal dispatch decision sequences over historical operating patterns. It can be considered as a sample labeling process for the supervised training that can implement the complex mapping of input–output space through an advanced machine learning model. Then, the well-trained model can be adopted locally at edge computing devices keeping the long-term parameters unchanged for implement the real-time microgrid energy dispatch. The key benefit of the proposed solution is that it effectively avoids the prediction of multiple stochastic variables and the design of sophisticated regulation strategies or reward policy functions for real-time dispatch. The solution is extensively assessed through simulation experiments by the use of real data measurements for a set of operational scenarios and the numerical results validate the effectiveness and benefit of the proposed algorithmic solution.
TL;DR: This paper introduces a passive islanding detection approach that uses deep learning combined with the continuous wavelet transform and hence no need for identifying the islanding features a priori as in the existing islanding Detection approaches.
TL;DR: A fault location algorithm for three-terminal transmission lines connected to an industrial microgrid that does not need the detection of faulty section, fault classification, and transmission line parameters and is independent of line impedances is presented.
TL;DR: A three-stage SHA to enhance resiliency of distribution systems can efficiently restore maximum PLs via network reconfiguration (NR) without intentional islanding during blackout with multiple line faults in the system.