Islanding Research Articles

Power distribution system restoration based on soft open points and islanding by distributed generations

Abstract A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced.

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

Coordinated Control of Intentional Islanding and Phase Sequence Exchange for Enhancing Transient Stability of Isolated Power Grid

An isolated power grid (IPG) is electrically weakly connected to the utility power grid, and intentional controlled islanding (ICI) can secure IPG from external faults. Phase Sequence Exchange (PSE) is a recently developed emergency control technology in which power electronic devices are used to switch the three-phase sequence, thereby reducing the power angle of the generator by 120° and preventing the system from losing stability. This paper proposed a coordinated control scheme for PSE and ICI. After the system is disturbed, the local measurement of the interconnection line between IPG and the utility grid is used to assess the system's stability. PSE is adopted to adjust the power flow direction to reduce the transient energy of IPG. At the same time, using the internal measurement of IPG, the transient energy of IPG is calculated in real-time, and the critical energy is rolling refreshed based on offline information. After PSE reduces the energy of IPG to lower than the critical energy, IPG is safely disconnected from the utility grid. The proposed scheme needless to adjust the internal generator or load of IPG and is verified on the real-time digital simulator (RTDS) platform with a PSE prototype.

Novel DC-link voltage regulation and seamless transfer control strategy of a battery-based microgrid system

The main challenge of modern microgrids (MGs) is the capability to operate in either grid-connected mode (GC) or islanding (IS) mode with DC-link voltage regulation for all the utilized parallel-connected DC-AC converters in the MG during all operating modes and, at the same time, is capable of transferring seamlessly between these two operating modes. This paper suggests a novel control strategy capable of operating a battery-based MG system in each of the GC and the IS modes and, at the same time, seamlessly transferring it between these two operating modes. A PQ controller is utilized to realize the GC operation, whereas a V/f controller is used for the IS operation. The smooth transfer between the two modes is accomplished by minimizing the transient variations in the MG voltage, current, frequency and phase at the point of common coupling (PCC). Additionally, the suggested strategy is qualified to deliver a novel regulation and a transient-free transition for the DC-link voltage of the used bi-directional DC-AC converter of the considered battery-based MG system. Therefore, the suggested control strategy can potentially improve the reliability of the considered battery-based MG system. The considered battery-based MG system is simulated and controlled by utilizing MATLAB/Simulink. The simulation results demonstrate that the suggested control strategy can effectively act during the operation of the considered battery-based MG system, either in the GC mode or the IS mode, and it can also effectively control the transitions between the two operating modes.

Extensive assessment of virtual synchronous generators in intentional island mode

Virtual Synchronous Generators (VSGs) are considered one of the most effective cutting-edge technologies for seamlessly integrating Renewable Energy Systems (RESs) into power grids. While various VSG control schemes have been proposed and analysed for different technical aspects, not many experimental demonstrations have been published about VSGs operating in Intentional Island Mode (IIM). Inversely, the IIM has become increasingly attractive, nowadays, to energize those remote areas of the world where entire districts are served by a small number of RESs operating off-grid. In this scenario, the main objective of this paper is to present an extensive performance assessment of the VSG operating in IIM. Different working conditions are considered, with special focus on: black start capability, grid-connected-to-IIM transition, grid reconnection, feeding of non-linear and asymmetric local loads, load sharing between multiple VSGs operating in parallel. In order to achieve this objective, an improved structure of VSG is implemented on 8kVA three-phase converters and significant experimental results are presented. Algorithms for black start capability and grid synchronization are illustrated in detail as well. Meaningful information is provided for engineers involved in activity of designing and testing islanded VSGs.

A tri-level Typhoon-DAD robust optimization framework to enhance distribution network resilience

Extreme natural disasters such as typhoon often cause failures in distribution networks within a short time, and even lead to large area blackouts. We propose a tri-level robust optimization framework that combines pre-disaster, in-disaster and post-disaster strategy comprehensively to enhance distribution network resilience. The typhoons are regarded as attackers, and a tri-level Defender-Attacker-Defender model for typhoon disaster is used to integrate multiple resilience resources. In the first level, line hardening and distributed generation unit commitment are used to improve resilience in pre-disaster. The second level contains attack budget, attack time, hardening budget, repair time and load loss. It couples with the third level iteratively to generate the worst failure scenario under typhoon. In the third level, the Nested Column-and-Constraint Generation algorithm is used to solve distribution network reconfiguration and intentional islanding. Simulation time is spanned to the entire 24-hour disaster day. In the end, the proposed framework is tested through a case study using real data from super typhoon “Mangkhut” (2018) in Yangjiang, China. The result shows that it can effectively reduce load loss under the worst typhoon scenario and enhance distribution network resilience with limited resources.

A resilient self‐healing approach for active distribution networks considering dynamic microgrid formation

AbstractA large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

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

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

Multiclass transient event classification in hybrid distribution network based on co-training of fine KNN and ensemble KNN classifier

ABSTRACT A new machine learning-based method to classify the different transient events in distributed generation (DG) system has been proposed in this article. An existing hybrid DG-based network which consists of three microgrids (MGs), i.e. thermal, wind, and solar power, is used as test network to create transient conditions for both the islanding and grid-connected circumstances. The transient case studies include the symmetrical and unsymmetrical fault at distribution line, intentional islanding, variation of power demand, switching of capacitor bank, addition of nonlinear load, motor starting condition, etc. This recommended methodology starts with generating the sampled voltage signals of three different phases of different locations, and each signal has been decomposed using discrete wavelet transform. The significant features are extracted from the computed energy values of detailed wavelet coefficient for co-training of fine K-nearest neighbor (KNN) and ensemble KNN classification in the following stage. The results and the performance indices of the trained classifiers prove that the proposed method has been detected and classified all the transient events with 98% accuracy. Such type of multiple transient event classification in MG by a single algorithm is truly beneficial with respect to the power quality issues of modern power system.

A platform for demand response and intentional islanding in distribution grids: The LIVING GRID demonstration project

An optimization-based platform is presented to request ancillary services and perform demand response. The work has seen a joint effort of the Italian TSO and DSO (Transmission and Distribution System Operators) and active local prosumers within the LIVING GRID innovation and demonstration project. The platform, a pure software architecture based on existing control and supervision equipment in the prosumer's plant, embeds optimization models, can communicate reference values for active and reactive power and perform intentional islanding and demand response in portions of the distribution grid and microgrids. Two possible configurations have been tested: Single PoC (Point of Connection) and Multi-PoC. The results of the optimization model and in-field experiments are presented and discussed for the considered pilot site: the Savona Campus Smart Polygeneration Microgrid (University of Genova, Italy).

Analysis of an Anti-Islanding Prevention Technique in a Grid-Connected PV System

In the present power system with distributed generators (DGs), there are some issues that are not resolved yet. One of these issues involves a fast and very accurate detection of islanding. The number of DGs introduced into electricity distribution systems is increasing day by day, and it is the most challenging concept in modern power system scenarios, since DG islanding can cause degraded proficiency, excellence, reliability and quality of supply. Hence, anti-islanding with fast response time is essential for a DG connected grid system. To overcome the challenges from islanding DGs, researchers proposed a numerous model that deals with the consequences of intentional islanding, and clears it as fast as possible.

An intentional controlled islanding strategy considering island frequency stability for power system with wind-power integrated

As the last defense line to avoid cascading failures, intentional controlled islanding (ICI) is of great significance to maintain the stability of power systems. However, with the increasing penetration of renewable energy, the system inertia and primary frequency regulation capacity have significantly decreased, and the adaptability and effectiveness of ICI have also been significantly reduced. Aiming at the above problems, an ICI strategy considering island frequency stability with wind-power integration is proposed. Firstly, a basic model of ICI is constructed through the collaborative optimization of load shedding, generator tripping, and the optimal intentional islanding boundary. Secondly, a frequency response model of the islanded system considering the primary frequency regulation of wind power is established, and the corresponding linear iterative algorithm is proposed. Finally, the established frequency stability constraints are embedded into the ICI model, forming a mixed integer linear program (MILP) model. The results and the effectiveness of islanding frequency control using the proposed strategy is discussed in the IEEE39 system compared with the traditional ICI strategy.

Systemic risk mitigation strategy for power grid cascade failures using constrained spectral clustering

Power grids are prone to damage induced by natural or anthropogenic hazard events that might disrupt the functionality of key/multiple grid components concurrently, resulting in a chain of cascade failures spreading throughout the grid. Through integrating grid operation-guided with structure-driven modeling strategies, the current study proposes an approach to manage the risks of such cascade failure (known as systemic-risks) to minimize the possibility of large-scale catastrophic blackouts. The operation-guided modeling strategy is implemented through dispatch and load shedding to rebalance power demand and supply after disruptive events. On the other hand, the grid structure-driven modeling strategy adopted intentional controlled islanding approach through employing a constrained spectral clustering algorithm. Introducing the latter algorithm within the integrated (operation + structure) cascade failure model facilitated identifying the optimal cut-set lines to separate the grid into a group of functioning sub-grids following initial failure and prior to cascade propagation. To demonstrate the utility of the developed systemic risk management strategy, an actual power grid was simulated using a high-fidelity physics-based model under different disruption scenarios to compare the cascade failure size with and without strategy implementation, considering different numbers of sub-grids. The simulations demonstrate that the integrated (dispatch & load shedding-controlled islanding) strategy can effectively boost the overall grid robustness, and subsequently its resilience, and effectively manage catastrophic blackout systemic risks.

Three-stage resilience-oriented active distribution systems operation after natural disasters

Increasing energy demand and the industrialization of communities have been the leading causes of climate change. As a result, natural disasters have increased blackouts in power systems. Climate change mitigation should be a global goal of communities. Since natural disaster occurrence has risen recently, rapid system restoration after natural disasters is essential. Mitigation of global warming is inextricably linked to the mechanisms and architecture of energy systems. Energy resilience is a multi-dimensional solution to this problem. In this research, resilient strategies for dealing with natural disasters have been examined. Implementing distributed energy resources (DERs), smart distribution networks, and defining load priority based on consumers’ preferences have made a viable solution to the post-disaster operation. Previous efforts cared more about the generation and transmission sectors. This study focused on active distribution systems (ADSs) because distribution systems, as a vital part of energy systems, have not sufficiently contributed to crisis management. A framework for resilience enhancement to smartening conventional distribution systems against natural disasters has been proposed. Three-stage socio-technical optimization was investigated, including optimal allocation of battery storage systems (BESSs), intentional islanding, and optimal power flow (OPF) in MATLAB and DIgSILENT. This optimization presents a decision-making tool for distribution system operators to quickly prepare the system against natural disasters. By applying eight scenarios in the proposed system, in the best scenario (CS4), the recovered power value (RPV) has increased from 38% to more than 99% (more than 61% increase in the resilience index). The amount of supplied load increased from 56% to 92%. Furthermore, prioritizing crucial consumers and reconfiguration are two primary crisis management tools. This approach creates a solid ground for avoiding the social consequences of natural disasters like electrocution and power outages of priority consumers. Failure to apply these two techniques would result in a 21% reduction in the RPV.

Two-Stage Robust Design of Resilient Active Distribution Networks Considering Random Tie Line Outages and Outage Propagation

This paper proposes a two-stage method for the robust design of resilient active distribution networks (ADNs) against high-impact and low-probability (HILP) events. The line hardening and the deployment of remote-controlled switches (RCSs) are considered as two powerful measures for resilience enhancement. Especially, the hardening of tie lines and the deployment of bilateral tie switches are emphasized as part of the resilient design. A novel progressive detection mechanism (PDM) is devised to estimate the potential propagation of outages and identify surviving nodes outside of the minimum outage area after intentional islanding. The proposed PDM method considers potential RCS locations among regular lines and tie lines on outage to calculate the global optimal design scheme. The two-stage robust design model is formulated as a mixed-integer linear programming (MILP). The nested column-and-constraint generation (nested C&CG) algorithm is customized to solve the proposed model. Numerical results on a modified IEEE 33-node distribution system demonstrate the effectiveness and the superiority of the proposed resilience enhancement method.

Intentional Islanding of Active Distribution Networks by GenSets: An Analysis of Technical Constraints and Opportunities

The willingness to improve the security and reliability of power supply to end-users, often pushed by prescriptions of national regulatory authorities, is bringing considerable challenges for distribution system operators. Islanding a portion of the public distribution network after a fault is considered a measure to mitigate the effects of service interruptions. This procedure is usually carried out by counterfeeding the grid through a generator set (GenSet). Even if this approach is widely adopted around the world, reenergizing the grid and keeping the electric island stable is not a trivial task. In this framework, the scope of this paper is to provide a set of technical guidelines for the usage of GenSets to supply public grids in emergency conditions. The goal is to highlight the static and dynamic limits of the GenSet operations and simplify their exploitation for the grid operators. The numerical analyses, which have been carried out through the RMS simulation tool of the DigSilent PowerFactory software, also aim to evaluate the technical constraints in the case of active networks, which involve distributed generation implementing regulations according to ENTSO-E and Italian technical standards.

Intentional Controlled Islanding Strategy for Wind Power Plant Integrated Systems

The concept of intentional controlled islanding (ICI) is introduced as a proactive measure to safeguard the power system against blackouts in the event of significant disturbances. It involves strategically partitioning the system into self-healing islands, thereby mitigating the impact of such disturbances. This study introduces a new framework for creating stable, controlled islands in power systems with large-scale wind power plants. The proposed islanding strategy takes into account the impact of wind power plants on the coherency grouping of generators as a constraint in the ICI problem. The proposed algorithm employs the Virtual Synchronous Motion Equation (VSME) model of asynchronous generators to replace wind power plants in power systems and groups all generators, including synchronous generators and wind turbine generators, based on their dynamic coupling. Support Vector Clustering is employed in the ICI problem to determine the coherent generator clusters as the cores of the formed islands. The algorithm can identify the optimal number of islands without prior information about the number of clusters. In this study, a Mixed Integer Linear Programming (MILP) model is formulated to address the ICI problem with the objective of minimising the power imbalance in each island after splitting while ensuring the transient stability and dynamic frequency stability of ICI. Simulation results on modified 39-bus and 118-bus test power systems demonstrate that integrating a Virtual Inertia Controller into the wind-integrated power systems results in a high-inertia power system that behaves similarly to a conventional power system with only synchronous generators during the islanding procedure.

An Optimization-Based Intentional Islanding Scheme for Service Restoration in Distribution Systems Considering Anti-Parallel Operation of Distributed Generations

An islanding operation of distributed generations (DGs) in emergencies due to a fault in distribution systems can be a means of power supply for important loads in outage areas by facilitating the self-sufficient capability of DGs forming microgrids. This paper presents an optimization-based intentional islanding scheme to derive a near-optimal service restoration (SR) plan. The anti-parallel operation of DGs is considered a new constraint that avoids more than two DGs in an island thereby, enabling simpler control and operation of the distribution system in an emergency. Each island is created by an island partitioning scheme based on the tree representation of the network and fast searching scheme for the tree structure considering load importance, and a genetic algorithm (GA) is utilized to explore possible SR solutions. Case studies on IEEE 69-bus distribution system according to various fault locations are conducted, and the simulation results show that the proposed scheme can restore more loads with higher priority in outage areas by the intentional islanding of DGs. Furthermore, the time for deriving the optimal solution can be reduced since the evaluations for infeasible solutions are not performed.

Proposal of a Master–Slave Control for an Isolated Microgrid after an Intentional Islanding

Proposal of a Master–Slave Control for an Isolated Microgrid after an Intentional Islanding

Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems

Establishing cleaner energy generation, therefore, improving the sustainability of the power system is a crucial task in this century, and one of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies, such as wind, solar, and nuclear. However, with the increasing number of heterogeneous components included, the complexity of the hybrid energy system becomes more significant, and the complex system imposes a more stringent requirement of the contingency plan to enhance the overall system resilience. Among different strategies to ensure a reliable system, intentional islanding is commonly applied in practical applications for power systems and attracts abundant interest in the literature. In this study, we propose a hierarchical spectral clustering-based intentional islanding strategy at the transmission level with renewable generations. To incorporate the renewable generation that relies on the inverter technology, the frequency measurements are considered to represent the transient response, and it has been further used as embedded information in the clustering algorithm along with other important electrical information from the system to enrich the modeling capability of the proposed framework. To demonstrate the effectiveness of the islanding strategy, the modified IEEE 9-bus system coupled with wind farms is considered as the test case.