Resilient Microgrids Research Articles

SoC–Based Inverter Control Strategy for Grid‐Connected Battery Energy Storage in AC Microgrid

The successful integration of battery energy storage systems (BESSs) is crucial for enhancing the resilience and performance of microgrids (MGs) and power systems. This study introduces a control strategy designed to optimize the operation of BESSs. This control strategy optimizes the BESS operation by dynamically adjusting the inverter’s power reference, thereby, extending the battery cycle life. This approach incorporates a droop control mechanism that adjusts control actions in response to state‐of‐charge (SoC) fluctuations of the BESSs, thereby, enhancing system performance. The effectiveness of this SoC–based control strategy is demonstrated through Matlab/Simulink. It shows its capabilities in regulating power, voltage, grid synchronization, and stability. The paper utilizes a modified CIGRE MG benchmark for system evaluation. It presents case studies to demonstrate the effectiveness of the proposed control modifications. Additionally, a comparative analysis with a power–voltage (P–V) control strategy is presented. This analysis highlights the advantages of the proposed strategy in ensuring stable voltage regulation within the MG. This research provides a robust foundation for future developments in optimizing BESS integration. It offers a roadmap to advance the efficiency, reliability, and longevity of battery‐based solutions in the evolving landscape of sustainable energy systems. Additionally, it sheds light on the potential for scalability and applicability across diverse MG configurations and operational scenarios.

A resilient microgrid formation framework: mobile battery-swapping station deployment for effective load restoration and voltage collapse prevention

A resilient microgrid formation framework: mobile battery-swapping station deployment for effective load restoration and voltage collapse prevention

Innovative approaches to microgrid resilience: Leveraging EVs for energy management

Innovative approaches to microgrid resilience: Leveraging EVs for energy management

Optimal Network Reconfiguration and Scheduling With Hardware-in-the-Loop Validation for Improved Microgrid Resilience

Optimal Network Reconfiguration and Scheduling With Hardware-in-the-Loop Validation for Improved Microgrid Resilience

Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data

This paper focuses on enhancing the resilience of microgrids—localized power systems that integrate multiple energy sources—against challenges such as natural disasters, technological obstacles, and human errors. It begins by defining the specific connotation of microgrid resilience and then proposes an innovative solution centered on the use of advanced sensor technology to continuously monitor the microgrid and its operational environment, ensuring accurate and timely data collection under dynamic conditions. Subsequently, a decision risk assessment framework is constructed, integrating data quality evaluation and operational risk considerations, to drive strategy optimization through in-depth data analysis. At the application level, this framework is successfully applied to two critical decision-making scenarios: the first is to optimize the power allocation strategy between solar energy and the auxiliary grid, aiming to maximize cost efficiency and minimize power outage losses; the second is to develop low-risk maintenance plans based on the predicted failure probabilities of microgrid components with uncertain information. Both decision processes skillfully utilize Monte Carlo simulation and multi-objective genetic algorithms to effectively manage the uncertainty risks in the decision-making process, thereby significantly enhancing the overall resilience of the microgrid.

Market clearing in an islanded microgrid considering the impact of demand response programs and consumer discomforting

ABSTRACT The traditional power system paradigm, characterized by fixed tariffs and a lack of consideration for dynamic wholesale values, is faced with inherent challenges impacting its efficiency and resilience. These challenges are addressed by introducing a novel model for market clearing in islanded microgrids. The limitations of existing systems, including the absence of dynamic pricing, insufficient consideration for consumer discomfort during peak hours, and inefficient resource utilization, are taken into account. The primary objective of this research is to mitigate customer discomfort through the incorporation of Demand Response Programs (DRP) and the use of the Particle Swarm Optimization (PSO) algorithm for market clearing. Simulation results demonstrate the effectiveness of the model, notably improving transmission line loading and overall resource utilization. This innovative approach not only provides a solution to existing challenges but also advances sustainable and resilient microgrid operations.

Resilience and Reliability of Solar-Powered Microgrids in Disaster-Prone Areas

Resilience and Reliability of Solar-Powered Microgrids in Disaster-Prone Areas

Artificial Intelligence for Microgrid Resilience: A Data-Driven and Model-Free Approach

Artificial Intelligence for Microgrid Resilience: A Data-Driven and Model-Free Approach

Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

Identification of Multi-Microgrid Clusters Using Terminal Spectral Clustering Algorithm

In response to the increasing impact of extreme weather on power distribution networks (PDNs), prioritizing resilience is imperative. This study introduces an innovative k-means spectral clustering algorithm to define the boundaries of microgrids (MGs) within a multi-microgrid (MMG) system. The aim is to improve reliability by clustering PDNs into resilient MGs. The power systems are modeled with nodes representing buses, and connections are represented as edges. The analysis involves computing the adjacency matrix, degree matrix, Laplacian matrix, and applying k-means clustering to group buses based on terminal point features. Silhouette coefficients (SC) are calculated to assess the quality of the clustering. The proposed method is tested on three IEEE distribution systems: IEEE 33, 69, and 118 bus systems. Findings reveal distinct clusters within each system with SC values above 0.68, particularly emphasizing the significance of terminal points as the basis for assisting power engineers in decision-making for predetermined grid partitioning.

Optimization-Based Approaches for Boosting Microgrid Resilience to Fault Events

Optimization-Based Approaches for Boosting Microgrid Resilience to Fault Events

Data-driven evaluation for quantifying energy resilience in distribution systems with microgrids and P2P energy trading

Electrical distribution systems (EDS) face the major issue of complete interruption of power when the supply end of the electrical grid encounters extreme events. The integration of distributed energy resources (DERs) at the residential level has opened the path to building energy resilience within these systems, enabling them to withstand such incidents at the load end. To further enhance energy resilience, peer-to-peer (P2P) energy trading networks could be established at the community level. While several researchers have developed models to implement energy trading in the P2P network with DERs, enabling prosumers to benefit from cost savings, the impact of both DERs and P2P energy trading on the system’s resilience has not been evaluated with a quantifiable measure. In this work, we propose a methodology introducing a quantifiable measure for evaluating energy resilience in electrical distribution systems with DERs and P2P energy trading by calculating the percolation threshold (PT) using complex networks for both univariate and multivariate data. We employ cooperative game theory to model P2P energy trading, ensuring that all prosumers participate rationally. We consider the formation of microgrids in a standard IEEE-123 node test feeder system integrated with renewable energy sources. The improvement in energy resilience with trading of energy in the P2P network is analyzed for the most resilient microgrid in the distribution system. The results demonstrate a 67.91% total cost benefit and a 25.07% improvement in the resilience of the microgrid due to the integration of DERs and P2P energy trading for a period of one year.

Resilient microgrid formation considering communication interruptions

AbstractDistribution system (DS) communication failures following extreme events often degrade monitoring and control functions, thus preventing the acquisition of complete global DS component state information, on which existing post‐disaster DS restoration methods are based. This paper proposes methods of inferring the states of DS components in the case of incomplete component state information. By using the known DS information, the operating states of unobservable DS branches and buses can be inferred, providing complete information for DS performance restoration before full communication recovery.

A Novel Protection Design Process to Increase Microgrid Resilience

A Novel Protection Design Process to Increase Microgrid Resilience

Adaptive identification of critical nodes for fault‐on voltage support in islanded microgrids

AbstractThe shedding of critical distributed energy resources during faults in an islanded microgrid may induce widespread voltage drops, potentially triggering a cascade of reactions leading to the collapse of the entire system. Accurately identifying critical nodes is the key technology to improve the resilience of microgrids. However, multi‐source coupling and the uncertainty in fault‐induced voltage sag can diminish the accuracy of node importance identification. To address this, this paper proposes an adaptive node identification method designed for quick and accurate identification of nodes that cope with various fault scenarios. This method introduces an index for evaluating voltage support capability based on the equivalent voltage drop range. This index adapts to fault uncertainty while integrating electrical parameters with spatial position. Furthermore, a higher‐order transition matrix reconstruction strategy with power propagation characteristics is proposed to reduce the higher‐order complexities arising from remote end faults' current flowing path length. Ultimately, the transition matrix is optimized by integrating it with the PageRank algorithm and highlighting the importance of source nodes. The proposed method is validated by numerical computation and time‐domain simulation results in a benchmark test microgrid, demonstrating its remarkable identification accuracy in a variety of fault scenarios.

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

The rapid integration of renewable energy sources (RES) and the electrification of transportation have significantly transformed modern energy infrastructures, emphasizing the need for efficient and flexible energy management systems. This study presents an intelligent, variable-fed, Type-2 Fuzzy Logic Controller (IT2FLC) designed for optimal management of Hybrid Microgrid (HMG) energy systems, specifically considering different modes of Electric Vehicles (EVs) integration. The necessity of this study arises from the challenges posed by fluctuating renewable energy outputs and the uncoordinated charging practices of EVs, which can lead to grid instability and increased operational costs. The proposed IT2FLC is based on comprehensive mathematical modeling that captures complex interactions among HMG components, including Doubly Fed Induction Generator (DFIG) units, photovoltaic (PV) systems, utility AC power, and EV batteries. Utilizing a yearly dataset for simulation, this work examines the HMG’s flexibility and adaptability under dynamic conditions managed by the proposed intelligent controller. A Simulink-based model is built for this study to replicate the dynamical operation of the HMG and test the precise and real-time decision-making capability of the proposed IT2FLC. The results demonstrate the IT2FLC’s superior performance, achieving a substantial cost avoidance of nearly $3,750,000 and efficient energy balance, affirming its potential to sustain optimal energy utilization under stochastic conditions. Additionally, the results attest that the proposed IT2FLC significantly enhances the resilience and economic feasibility of hybrid microgrids, achieving a balanced energy exchange with the utility grid and efficient utilization of EV batteries, proving to be a superior solution for optimal operation of hybrid grids.

A Framework to Assess and Analyze Enhancement Options for Microgrid Resiliency against Extreme Wind

A Framework to Assess and Analyze Enhancement Options for Microgrid Resiliency against Extreme Wind

Optimizing multi-objective design, planning, and operation for sustainable energy sharing districts considering electrochemical battery longevity

The urgent need to address the global warming crisis has led to a paradigm shift in energy production from traditional fossil fuels to renewable sources such as geothermal, solar, and wind. This transition necessitates efficient energy management strategies to enhance renewable energy utilization, reduce microgrid dependency, and establish a more stable power grid. This study explores the novel integration of interactive energy sharing networks utilizing electrochemical battery storage, emphasizing detailed modeling of battery degradation, smart energy management, and multi-criteria decision-making. This research involves an innovative method combining Monte Carlo Tree Search technique and a multi-criteria approach for energy management in district communities. The research delves into the complexities of integrated multi-energy systems, considering structural designs, advanced modeling methods, and multi-criteria predictions. It also employs powerful machine learning methods to predict battery depreciation, demonstrating their superiority over standard semi-empirical models. The study also proposes deterministic and stochastic methods for intelligent energy management, considering unpredictable factors like weather and energy demand profiles. The research incorporates a comprehensive examination of interactive energy sharing networks, encompassing renewable-to-vehicle and vehicle-to-building interactions. Through an in-depth analysis this research highlights the advantages of multi-agent systems, time-of-use energy shifting, demand profile flattening, and micro-grid resilience.

A reliable hybrid autoregressive integrated moving average and deep reinforcement machine learning strategy for resiliency enhancement in microgrid

Machine learning algorithms are crucial for energy transition and climate change system resilience. This paper presents the essential issue of improved microgrid resilience after catastrophes, with a focus on the Root Mean Square Error (RMSE) assessment metric. By using a Hybrid Autoregressive Integrated Moving Average- Deep Reinforcement Learning (HARIMA-DRL) based machine learning microgrid restoration method for post-disaster power recovery, resilience in the operation aspect is enhanced. The local power grid's resilience during a simulated disaster is examined using a quantitative resilience measure that is based on two significant quality functions. When the hybrid HARIMA-DRL model final resilience value is 3.07 %, 7.78 %, and 11.51 % higher for 5000, 8000, and 10000 training datasets, as compared to Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) and Gauss Reinforcement Memory (GRM). Simulation findings comparing the proposed HARIMA-DRL method to CNN-LSTM and GRM models trained on separate datasets show that the HARIMA-DRL enhances grid instantaneous and average resilience by 3.38 % over T1 and 4.96 % over T2. Flexible kernel settings and smooth nonlinear prediction behaviour allow the proposed HARIMA-DRL approach to provide the highest resilience enhancement and lowest prediction error.