Islanded Mode Research Articles

Optimizing voltage references in a 3-leg power VSC under single-phase- to-ground faults

Distribution grid deals with several challenges such as the integration of the electrical vehicle, connection of renewable energy resources, management of storage systems, etc. These new factors can often produce some non-desirable effects in the grid such as electrical faults, which can damage the electricity supply quality. In order to improve the supply quality during electrical faults, power converters with specific control algorithms can be connected that modify the voltage on the loads connected to the grid during the duration of the fault. An optimal control can be ensured if a proper estimation of the voltage in the load is carried on. This paper calculates the voltage drop between the output of the power converter and the fault point so as to optimize voltage references in the power converter during single- phase-to-ground faults in islanded mode. Simulation results guarantee the successful performance of the proposal.

Dynamic load shedding and system restoration using wide area distribution management system

AbstractThe demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non‐renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply‐demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer‐based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink.

Modeling of hydropower plant in islanded mode for different operating conditions

Most countries have access to abundant water resources through rivers and canals. Utilizing this renewable resource, electricity can be generated in an environmentally friendly manner without causing pollution. In rapidly developing countries like India with the abundance of natural resources and diversities, the development of Hydropower is gaining in importance to meet the country’s demand. This work discusses the different operating conditions that may occur in real time of the standalone hydro power generation system. In this work, various operating conditions are considered in terms of faults and disturbances that occur on the load side. These effects of faults and disturbances may be caused in the generating side. It takes into consideration some major events from the load side i.e. small disturbance, load addition, load rejection, large disturbance. In this work, the above-mentioned objectives are achieved by creating a model of a hydro power plant in MATLAB Simulink and keeping its operating environment same, simulate different scenarios related to load side, and study its effect on the generator and generating system. This is achieved by changing the load side for different conditions like introducing a small fault into the system, changing the load on a larger scale, etc The conditions that are introduced are simulated in a period of 10 s time frame. The reaction of the generating side from these conditions is recorded and plotted on parameters that can show the effect directly on the generator.

Design of Universal Control Structure for Regulation of Voltage and Frequency in Hybrid Microgrid

Hybrid microgrid (HMG) control strategies reveal several critical research gaps that must be addressed. Current approaches often fall short in efficiency and reliability due to the inherent complexities of transitioning between grid-connected mode (GCM) and islanded mode (IM), resulting in increased losses and operational difficulties. Additionally, managing overloads, especially in IM, poses significant challenges for maintaining stable voltage and frequency regulation. This paper seeks to address these issues by introducing a novel universal control structure (UCS) for HMGs, which includes a frequency compensation unit (FCU) and a three-stage bidirectional AC/DC droop. It presents a streamlined yet robust solution for handling overloads, maintaining power balance, and ensuring precise voltage and frequency regulation while promising smooth maneuver in both GCM and IM. The proposed UCS provides flexible and reliable operation, allowing for the versatile placement of DC and AC sources and loads. This will reduce power transformation stages thereby lowering cost and increasing efficiency of system. Through experimental validation, this study demonstrates the effectiveness of the proposed control in addressing these critical research gaps and advancing the field of HMG control strategies.

Real time implementation of scaled droop control in hybrid microgrid with hydrogen storage for regulation of voltage and frequency.

The incorporation of renewable energy resources (RERs) into smart city through hybrid microgrid (HMG) offers a sustainable solution for clean energy. The HMG architecture also involves linking the AC-microgrid and DC-microgrid through bidirectional interconnection converters (ICC). This HMG combines AC sources like wind-DFIG with DC sources such as solar PV and solid oxide fuel cell (SOFC), supported by battery energy storage systems (BESS) and hydrogen storage units (HSU). The HSU can generate and store hydrogen during RER surplus. This stored hydrogen can be further employed for production of electrical power along with numerous other applications. The HSU is emerged as a competent tool which can be utilised alone/in combination with BESS to enhance the system reliability. Harvesting power from clean and green sources requires its optimal operation and control while feeding to the existing grid. The existing strategies of controlling ICC are complex and not efficient; hence, a novel intelligent scaled droop control structure (SDCS) is proposed, utilizing frequency, DC voltage, and active power. The SDCS regulate voltage and frequency in both islanded mode (IM) and grid connected mode (GCM) of HMG. Experimental validation demonstrates its simplicity and effectiveness, making it suitable for smart city environments, ensuring uninterrupted power for critical loads with improved air quality.

Dynamic security constrained AC optimal power flow for microgrids

To ensure the continuous operation of a microgrid, proactive planning is essential, especially when contemplating possible dynamic events that alter the scheduled scenarios for the islanded operation or during its transition from connected to islanded mode. This paper introduces an innovative mathematical programming model for the AC optimal power flow (AC-OPF) with dynamic security constraints (DSCs), considering two scenarios: the islanded operation and the transition. This model uses positive-sequence equations to represent the inverter-based resources (IBRs) for grid-forming and grid-following roles, along with a fourth-order synchronous generator model equipped with excitation and frequency control systems. They assess the dynamic response to specific events such as short-circuits, decreases in PV generation, increases in load demand during the islanded operation, and the transition itself. The DSCs are applied to dispatchable distributed energy resources (DERs), which react to variations in the microgrid, considering generation and opportunity costs to minimize the discrepancy between planned and secure operating points. The mathematical programming model is implemented using AMPL, and solutions are obtained through the nonlinear optimization solver IPOPT. The tests are conducted in an adapted version of the microgrid being developed at the University of Campinas that includes a synchronous generator, photovoltaic (PV) generation, and a battery energy storage system (BESS). Results demonstrate the model’s effectiveness in adjusting generation dispatch to withstand defined events and optimizing generation resources, even when limits are not reached.

Techno-economic microgrid design optimization considering fuel procurement cost and battery energy storage system lifetime analysis

The importance of microgrids (MGs) lies in their capacity to enhance energy reliability, integrate renewable resources, and bolster resilience, yet their optimal design and sizing pose intricate challenges in balancing investment and operational cost while dispatching the MG equipment efficiently. This paper presents the optimal MG design problem formulated as an integer linear program (ILP) aimed at minimizing total investment and operational cost of MG as well as optimal hourly dispatch of MG asset over the project lifetime. Furthermore, a comprehensive analysis of the fuel procurement cost and the impact of ESS technical parameters, such as depth of discharge (Dod), ESS lifetime, and battery C-rate, on the optimal MG design, ESS lifetime and relevant costs is presented in this work for a MG operating in islanded and grid-connected modes. The simulation results demonstrate that the optimal design of a MG with the maximum load demand of 300 kW operating in the grid-connected mode can save about 28 k$ equivalent to 14% in total annual cost compared with the islanded mode operation thanks to the flexibility provided by the utility purchases and efficient MG equipment sizing. This study’s unique contributions provide significant insights into the practical implications of MG design optimization, potentially influencing regulatory policies and business strategies.

Decentralized Smart Energy Management in Hybrid Microgrids: An Evaluation of Operational Modes, Resource Optimization, and Environmental Impacts in Nigeria

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids

A novel dynamic batching energy management strategy for multi-microgrid networks with enhanced privacy

This article presents a novel energy management approach termed Dynamic Batching EMS (DBEMS) designed specifically for day-ahead scheduling within multi-microgrid (MMG) networks. The prime aim of this strategy is to curtail operational costs within the MMG network, fortify the privacy of individual microgrids, and optimize the utilization of available resources, surpassing existing methodologies like centralized, decentralized, and nested management strategies. The emphasis is chiefly on orchestrating cost-effective operations while bolstering data privacy for each microgrid within the complex MMG framework. The efficacy of the proposed strategy is evaluated across both grid-connected and islanded modes, focusing on a network comprising four microgrids. The model formulation is executed utilizing the Mixed Integer Linear Programming method. In the islanded mode, optimization is conducted for scenarios both inclusive and exclusive of Battery Energy Storage Systems (BESS). Comparative assessments are made against centralized and nested strategies, considering parameters including operational costs, privacy enhancements, load-shedding, computation time, and computational burden.

Decentralized Operations of Industrial Complex Microgrids Considering Corporate Power Purchase Agreements for Renewable Energy 100% Initiatives in South Korea

With the rise of environmental policies and advanced technologies, power systems are transitioning from centralized to decentralized systems, incorporating more distributed energy resources (DERs). This shift has increased interest in the operational functions of microgrids (MGs). The “Renewable Energy 100%” (RE100) campaign is pushing companies to adopt renewable energy. In South Korea, industrial complex microgrids (ICMGs) aim to achieve RE100 through corporate power purchase agreements (PPAs) with renewable energy providers. ICMGs need to operate in both grid-connected and islanded modes, facing challenges in power transactions due to different operating agents. This study proposes a decentralized optimal power flow (OPF) method using the separable augmented Lagrangian relaxation (SALR) algorithm to solve these power transaction problems without disclosing internal information. The proposed method decomposes the centralized OPF problem into subproblems for each ICMG and solves them in a distributed manner, sharing only transaction prices and amounts. Numerical results from the case study validate the effectiveness of the proposed method.

Hydrogen-fueled microgrid energy management: Novel EMS approach for efficiency and reliability

Effective energy management within microgrids is crucial, especially given system uncertainties. This study presents a novel Energy Management System (EMS) designed for microgrids with diverse energy sources, notably hydrogen and fuel cells. The EMS integrates artificial intelligence algorithms to predict and adapt to rapid changes, enhancing energy resource efficiency and minimizing wastage. Responsive in nature, the EMS enhances efficiency, reliability, and cost reduction in microgrid energy, dynamically responding to varying conditions for improved stability and environmental protection. Employing a random pattern approach, the EMS focuses on operational efficiency and reliability in supplying electrical and thermal loads. Addressing uncertainties in the electricity market and renewable production, a stochastic framework with scenario generation enables operation in grid-connected and island modes. Enhanced optimization through an improved particle swarm algorithm ensures cost-effectiveness and system reliability in dynamic environmental conditions. This research pioneers innovative microgrid energy management, emphasizing hydrogen and fuel cell technologies to elevate efficiency and performance reliability.

ENERGY MANAGEMENT SYSTEM OF MICROGRID WITH RENEWABLE ENERGY SOURCES

The most recent development in the electrical system is the microgrid. Many scattered, networked energy storage, loads, and generation units make up a typical microgrid system. An energy system made up entirely of renewable resources is called a microgrid (MG), an energy storage device, and loads that can operate in grid-connected or islanded mode. The demand for the load and the power flow within MG should be managed by scheduling renewable resources. This paper presents a hybrid microgrid energy management system (MEMS) that combines grid-connected solar (PV), wind power from microturbines, and battery energy storage systems (BESS). Due to their increased energy efficiency, microgrid units are becoming more and more well-liked every day. Energy management for microgrids is required since renewable energy sources provide an imbalance in the Power System due to their stochastic behavior. Controlling the DC/DC bidirectional converter (DBC), which links the Ni-H battery to the DC bus of the grid-connected microgrid, is the primary goal of this study. The start-up of a wind/photovoltaic power generation system to the load is covered in this study. MATLAB/SIMULINK was used to model a microgrid system that is connected to the grid. Key Words: Microgrid, Photovoltaic (PV)

Distributed voltage unbalance mitigation in islanded microgrid using moth flame optimization and firebug swarm optimization

In recent trends, hybrid renewable energy sources (HRES) provide a better solution to meet energy demand, maximizing the productivity of electricity network. Due to the above-mentioned features, several researchers have given more attention to PV-wind-based HRES systems. A stable energy supply must be added, including batteries, diesel generator (DG), to meet demand in the event of a grid failure. To meet the voltage unbalance in islanded mode, the sizes of DG have need to be selected sensibly and HRES requires additional energy storage. To examine the voltage unbalance problems of an islanded microgrid, a hybrid optimization approach known as moth flame optimization (MFO) and firebug swarm optimization (FSO) is introduced. Due to various loads, harmonic distortion causes the voltage to unbalance, which can result in voltage collapse. To deliver a quick response in an island mode, a comprehensive algorithm called MFO-FSO control is proposed. MATLAB software is used to validate the results which demonstrate that the proposed MFO-FSO outperforms the conventional decoupling double synchronous reference frame (DDSRF) methods by reducing total harmonic distortion (THD) up to 1.21% and voltage unbalance factor (VUF) up to 1.427%.

Voltage and frequency control of microgrid in presence of micro-turbine interfaced to matrix converter

The active and reactive load changes have a significant impact on voltage and frequency. In this paper, in order to stabilize the microgrid (MG) against load variations in islanding mode, the active and reactive power of all distributed generators (DGs), including energy storage (battery), diesel generator, and micro-turbine, are controlled. The micro-turbine generator is connected to MG through a three-phase to three-phase matrix converter, and the droop control method is applied for controlling the voltage and frequency of MG. In addition, a method is introduced for voltage and frequency control of micro-turbines in the transition state from grid-connected mode to islanding mode. A novel switching strategy of the matrix converter is used for converting the high-frequency output voltage of the micro-turbine to the grid-side frequency of the utility system. Moreover, using the switching strategy, the low-order harmonics in the output current and voltage are not produced, and consequently, the size of the output filter would be reduced. In fact, the suggested control strategy is load-independent and has no frequency conversion restrictions. The proposed approach for voltage and frequency regulation demonstrates exceptional performance and favorable response across various load alteration scenarios. The suggested strategy is examined in several scenarios in the MG test systems, and the simulation results are addressed.

Coordination of SRF-PLL and Grid Forming Inverter Control in Microgrid with Solar PV and Energy Storage

Recently, there has been a huge advancement in renewable energy integration in power systems. Power converters with grid-forming or grid-following topologies are typically employed to link these decentralized power sources to the grid. However, because distributed generation has less inertia than synchronous generators, their use of renewable energy sources threatens the electrical grid’s reliability. Suitable control approaches for ensuring frequency and voltage stability in the grid-connected form of operation are established in this study, which offers dynamic, seamless power switching in the islanded mode of operation. In this research, effective Phase Locked Loop (PLL) techniques for grid-forming (GFM) and grid-following (GFL) converters are designed to achieve a smooth transition from grid-tied to islanded mode of operation. In this work, PLL configurations are implemented while considering the active and reactive power, frequency, voltage, and current parameters of the system, and ensuring voltage and frequency stability. The simulation results in a microgrid network that ensures a smooth transition of power transfer while switching between modes of operation, and supports the voltage and frequency stability of the system.

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

The increasing complexity of contemporary power distribution networks and the necessity to incorporate renewable energy sources have led to the rise in popularity of hybrid power distribution systems. A significant challenge for such systems is the reliable identification of multi-converter synchronous islanding mode, wherein numerous converters operate in synchronous islanded situations. Using state-of-the-art artificial intelligence ensemble techniques, we introduce "Vigil AI Assemble," a novel approach to detecting multi-converter synchronous islanding modes in hybrid power distribution systems. The proposed method enhances the precision and robustness of islanding situation recognition by combining various AI algorithms, each with its own distinct properties. By combining several AI-based techniques, the ensemble method can analyze system properties, waveform data, and communication in real-time.

АНАЛІЗ ПОТОЧНОГО СТАНУ ЕНЕРГОСИСТЕМИ ТА СТРАТЕГІЇ РОЗВИТКУ РОЗОСЕРЕДЖЕНОЇ ГЕНЕРАЦІЇ В УКРАЇНІ

The article analyzes the state of the Unified Energy System (UES) of Ukraine as of the beginning of 2023. The main focus of the article is on the issue of energy security and the consequences for the UES of Ukraine after mass attacks on energy facilities from the beginning of a full-scale invasion until the beginning of 2023. The assessment of the main shortcomings of the current model of the operation of the power system was carried out and the current short-term strategies for its modernization were considered in order to prepare for the heating season of 2023-2024. During the analysis of potentially possible short-term strategies, the main attention was paid to the recovery plan of Ukraine, available capacities of the Gas Transport System (GTS) of Ukraine, potential of using "Floating Power Plants", poten-tial possibilities of the operation of distribution networks (DM) in the "island mode" and stimulating an increase in the share of distributed sources generation (DSG) and energy storage systems (ESS). The current results of the implemen-tation of the above-mentioned strategies and the potential of their development after the end of a full-scale war in ac-cordance with the energy strategy of Ukraine until 2035 and the main theses of the energy strategy of Ukraine until 2050 were presented. Based on the results of the analysis, the recommendation regarding the implementation of the preparation of the UES of Ukraine for the next heating season were formed. Bibl. 10, fig. 2.

Seamless Switching Control Strategy for a Power Conversion System in a Microgrid Based on Extended State Observer and Super-Twisting Algorithm

Microgrids can operate stably in both islanded and grid-connected modes, and the transition between these modes enhances system reliability and flexibility, enabling microgrids to adapt to diverse operational requirements and environmental conditions. The switching process, however, may introduce transient voltage and frequency fluctuations, causing voltage and current shocks to the grid and potentially damaging devices and systems connected to the microgrid. To address this issue, this study introduces a novel approach based on the Extended State Observer (ESO) and the Super-Twisting Algorithm (STA). Power conversion systems use Virtual Synchronous Generator (VSG) control and Power-Quality (PQ) control when they are connected to the grid or when the microgrid is not connected to the grid. VSG and PQ share a current loop. Transitioning the reference current generated by the outer loop achieves the switching of control strategies. A real-time observer is designed to estimate and compensate for current fluctuations, disturbances, and variations in id, iq, and system parameters during the switching process to facilitate a smooth transition of control strategies. Furthermore, to enhance the dynamic response and robustness of the system, the Proportional–Integral (PI) controller in the ESO is replaced with a novel super-twisting sliding mode controller based on a boundary layer. The Lyapunov stability principle is applied to ensure asymptotic stability under disturbances. The proposed control strategy is validated through simulation using a seamless switching model of the power conversion system developed on the Matlab/Simulink (R2021b) platform. Simulation results demonstrate that the optimized control strategy enables smooth microgrid transitions, thereby improving the overall reliability of grid operations.

Energy Management Strategy for a Net Zero Emission Islanded Photovoltaic Microgrid-Based Green Hydrogen System

Investing in green hydrogen systems has become a global objective to achieve the net-zero emission goal. Therefore, it is seen as the primary force behind efforts to restructure the world’s energy, lessen our reliance on gas, attain carbon neutrality, and combat climate change. This paper proposes a power management for a net zero emission PV microgrid-based decentralized green hydrogen system. The hybrid microgrid combines a fuel cell, battery, PV, electrolyzer, and compressed hydrogen storage (CHSU) unit aimed at power sharing between the total components of the islanded DC microgrid and minimizing the equivalent hydrogen consumption (EHC) by the fuel cell and the battery. In order to minimize the EHC and maintain the battery SOC, an optimization-based approach known as the Equivalent Consumption Minimization Strategy (ECMS) is used. A rule-based management is used to manage the power consumed by the electrolyzer and the CHSU by the PV system in case of excess power. The battery is controlled by an inverse droop control to regulate the dc bus voltage and the output power of the PV system is maximized by the fuzzy logic controller-based MPPT. As the hybrid microgrid works in the islanded mode, a two-level hierarchical control is applied in order to generate the voltage and the frequency references. The suggested energy management approach establishes the operating point for each system component in order to enhance the system’s efficiency. It allows the hybrid system to use less hydrogen while managing energy more efficiently.

Characterization of Turbulent Fluctuations in the Sub-Alfvénic Solar Wind

Parker Solar Probe (PSP) observed sub-Alfvénic solar wind intervals during encounters 8–14, and low-frequency magnetohydrodynamic (MHD) turbulence in these regions may differ from that in super-Alfvénic wind. We apply a new mode decomposition analysis to the sub-Alfvénic flow observed by PSP on 2021 April 28, identifying and characterizing entropy, magnetic islands, forward and backward Alfvén waves, including weakly/nonpropagating Alfvén vortices, forward and backward fast and slow magnetosonic (MS) modes. Density fluctuations are primarily and almost equally entropy- and backward-propagating slow MS modes. The mode decomposition provides phase information (frequency and wavenumber k) for each mode. Entropy density fluctuations have a wavenumber anisotropy of k ∥ ≫ k ⊥, whereas slow-mode density fluctuations have k ⊥ > k ∥. Magnetic field fluctuations are primarily magnetic island modes (δ B i ) with an O(1) smaller contribution from unidirectionally propagating Alfvén waves (δ B A+) giving a variance anisotropy of 〈δBi2〉/〈δBA2〉=4.1 . Incompressible magnetic fluctuations dominate compressible contributions from fast and slow MS modes. The magnetic island spectrum is Kolmogorov-like k⊥−1.6 in perpendicular wavenumber, and the unidirectional Alfvén wave spectra are k∥−1.6 and k⊥−1.5 . Fast MS modes propagate at essentially the Alfvén speed with anticorrelated transverse velocity and magnetic field fluctuations and are almost exclusively magnetic due to β p ≪ 1. Transverse velocity fluctuations are the dominant velocity component in fast MS modes, and longitudinal fluctuations dominate in slow modes. Mode decomposition is an effective tool in identifying the basic building blocks of MHD turbulence and provides detailed phase information about each of the modes.