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Proceeding Paper

Review of Microgrids to Enhance Power System Resilience †

by
Jian-Hua He
and
Jhih-Hao Lin
*
Intelligent Manufacturing College, Fujian Polytechnic of Information Technology, Fuzhou 350003, China
*
Author to whom correspondence should be addressed.
†
Presented at the 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering, Yunlin, Taiwan, 15–17 November 2024.
Eng. Proc. 2025, 92(1), 82; https://doi.org/10.3390/engproc2025092082
Published: 27 May 2025
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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Abstract

As the frequency of extreme events keeps increasing, large-scale power system interruption is also increasing. Natural disasters cause more extensive damage than typical power outages or failures, and the system demands a longer recovery period. Accordingly, it is crucial and urgent for the power system to have resilience in addition to possessing strong robustness and reliability. For the power system resilience, time is a critical factor. The microgrid (MG) can be connected to the main grid or operate independently to significantly improve the flexibility of the system with great potential in enhancing the power system resilience. We summarize the important concepts of power system resilience and MGs to improve power system resilience. Useful references are provided in this article for power-related practitioners regarding efficient design schemes to improve the application of MGs in enhancing resilience.

1. Introduction

The number and severity of weather-related events have become serious. The annual average number of disaster events in the United States from 2019 to 2023 was more than twice that from 1980 to 2023, with the majority of them being severe storm events [1]. Extreme events cause significant damage to infrastructure, even permanent damage. Although infrastructure has been strengthened and disaster emergency plans have been carried out, it is extremely difficult to avoid the damage caused by disasters completely in the short term. Power systems are generally designed following reliability requirements; that is, the “N-1” or “N-2” criterion. That is, the system is ordinarily designed to endure low-impact/high-probability events. However, when confronted with high-impact/low-probability catastrophic events, the systems appear to be insufficiently prepared. Severe natural disasters cause cascading failures. For example, Hurricane Sandy was an “N-90” emergency [2]. Therefore, it is crucial to enhance the power system’s resilience. A resilient power grid bends like reeds in a storm [3].
MGs are different from traditional centralized power grids. They are mainly designed to integrate distributed energy resources (DERs) to enable local power generation, energy storage, and power supply. It is a small-scale power system that does not require expensive transmission facilities. MGs operate in coordination with external power systems, either in grid-connected mode or island mode. Even when disconnected from the power grid, they are capable of ensuring continuous local power supply during extreme events [4].
In this article, Section 2 describes the impact of extreme events on power systems. Section 3 introduces the concepts and evaluation of power system resilience. Section 4 reviews the concepts and advantages of MGs. Section 5 discusses the literature on MGs in improving the power system resilience. Finally, Section 6 concludes this article.

2. Impacts of Extreme Events on Power Systems

Extreme events lead to long-term power outages and destroy important infrastructure. For example, hurricanes might damage power transmission lines, overhead transmission towers, or high-voltage substations. In 2022, Hurricane Ian hit Florida, resulting in USD 40 billion in damage [5]. Earthquakes affect overhead and underground facilities and even trigger tsunamis. In March 2011, a huge earthquake and the subsequent tsunami occurred in northeastern Japan, resulting in power outages for 8.5 million users [6]. Floods also damage substations or power plants. In May 2022, more than 7 million people in northeastern Bangladesh were affected by the devastating floods [7]. Snowstorms significantly impact overhead transmission lines and conductors. In February 2021, winter storm Uri hit Texas, resulting in power supply problems for up to 11 million people [8]. Wildfires affect the exposed components of the power grid. In 2020, more than 4 million acres of land in California were burned due to wildfires [9].
Power outages by natural disasters harm transmission and distribution networks and key infrastructure, stop generator sets from operating, trigger cascading failures of components, and make the repair process difficult, compared with typical power outages [10]. Moreover, both the prediction and development of natural disasters are uncertain. Due to various reasons, the power system is bound to face more challenges in design.

3. Power System Resilience

3.1. Concepts of Power System Resilience

Although the probability of power systems being affected by extreme events is low, the risks involved are high. Therefore, a concept of resilience is introduced to reflect the survivability of the system when it is disturbed. The U.S. Department of Energy (DOE) defines resilience as “Resilience refers to the ability of energy facilities to quickly recover from damage to any of their components or any external system on which they depend. Resilience measures cannot prevent damage; on the contrary, enable the energy system to continue to operate and promote a rapid return to normal operation in the event of damage/interruption [11]”. The National Infrastructure Advisory Council (NIAC) defines resilience as “Infrastructure resilience refers to the ability to reduce the scale and/or duration of disruptive events. The effectiveness of resilient infrastructure or enterprises depends on their ability to anticipate, absorb, adapt to, and/or rapidly recover from potentially disruptive events [12]”.
The definition of resilience varies depending on the organization or association. A resilient system must have the ability to anticipate, resist, absorb, adapt to, and recover from disturbances. Anticipation refers to evaluating the potential hazard of a system before it is disturbed. Resistance means reducing the damage of an event when the system is under disturbances. Absorption refers to absorbing the impact of an event and minimizing the damage. Adaptation refers to reorganizing or modifying the system configuration. Recovery means the ability of the system to rapidly recover from disturbances [13].
Based on the above definition, the discussion of resilience in this study focuses on the flexibility and survivability of the system. As MGs are connected to the grid or operate independently, the flexibility of the system is significantly improved. Therefore, MGs have great potential to enhance power system resilience.

3.2. Resilience Assessment

The variations in system performance during the disturbance are shown in Figure 1. System performance Q(t) is expressed as a function of time t. The change in system performance is represented by the system’s load loss. Compared with traditional systems, a resilient system has less load loss. For a resilient system, t1 is the time when the disturbance occurs. Before time t1, Q(t) remains in a normal state. The system must be able to anticipate the potential hazards of the disturbance. After time t1, Q(t) drops rapidly. At time t2, Q(t) drops to the lowest level; that is, Qmin. In this period, the system must be able to absorb the impact of the disturbance and reduce damage. From time t2 to t3, the system formulates a recovery strategy to assess and deal with losses. From time t3 to t4, the system immediately takes recovery measures and restores to the normal state as soon as possible [10,14].
The load loss of a resilient system is relatively small. Therefore, the quantification of system resilience is expressed as the reciprocal of load loss as (1) [14,15].
R e s i l i e n c e = 1 L o s s
The load loss is calculated by integrating the relative deviation of performance during the degradation period (from time t1 to t4) (2).
L o s s = ∫ t 1 t 4 Q 0 − Q t Q t d t

4. MG

4.1. Concept

The U.S. Department of Energy and IEEE Standard 2030.7 define the MGs as “Loads and distributed energy sources interconnected within a clearly defined electrical boundary and operating as a single controllable entity relative to the grid. MGs can connect to or disconnect from the grid so that it can operate in grid-connected or islanded mode” [16,17]. The definition has no specific limitations or requirements on the scale or technology type of distributed energy sources. This definition indicates that MGs can be connected to the main grid or operate independently. The components connected to the MG are coordinated and controlled with each other. From this, it can be known that MGs can utilize different distributed energy sources and operate the local power grid independently when needed, which can enhance the power system resilience [18].

4.2. Structure

In terms of the power supply of the connected common bus, the structures of MGs are classified into alternating current MG (ACMG), direct current MG (DCMG), and hybrid MG [19].
  • ACMG features an AC power supply and an AC common bus. The AC common bus connects power generation sources, energy storage equipment, and other system components to the point of common coupling (PCC). The main advantage of ACMGs is their direct connection to the traditional AC power grid. ACMGs show relatively higher flexibility than other structures.
  • DCMG has a DC power supply and a DC common bus. The DC common bus connects power generation sources, energy storage equipment, and other system components. DCMG is connected to the PCC through a DC/AC converter. The main advantage of the DCMGs is that it experiences fewer power quality problems. It can provide better stability than the ACMGs and is conducive to integrating DERs.
  • Hybrid MG integrates ACMGs and DCMGs in the same distribution system. The AC units of distributed energy are connected to the AC grid, while the DC units are connected to the DC grid without the need for synchronization. Hybrid MGs combine the advantages of ACMGs and DCMGs, such as fewer conversion stages, reduced power loss, lower total cost, and higher reliability.

4.3. Control

To effectively control the operation of DERs, the control approaches of MGs are classified into centralized, decentralized, and hybrid as shown in Figure 2 [4,20].
  • Centralized MG: MGCC executes centralized control and is in charge of managing the operation of each distributed generation (DG). Each DG has a local controller (LC), which is directly connected to the central controller for data transmission. The centralized control approach offers a secure solution, but it demands considerable computing power and exhibits relatively limited flexibility in integrating new components.
  • Decentralized MG: DGs operate autonomously through LCs. The decentralized control approach cannot guarantee to provide the global optimal solution, but it has a relatively lower computational burden. In addition, new components can be easily integrated into the MG and can achieve plug-and-play operation.
  • Hybrid MG: DGs are regrouped and centrally controlled to improve overall performance. The hybrid control approach integrates the advantages of centralized and decentralized controls to obtain local optimal solutions and enhance its scalability and reliability.

4.4. Advantages

MGs integrate DERs to operate in grid-connected or island modes. In the grid-connected mode, MGs serve as backup power generation. In the island mode, it continuously and independently supplies power to local loads to guarantee the continuous operation of critical loads. Furthermore, bidirectional power flow and stable voltage and frequency are created by integrating MGs into the power distribution system, thereby reducing power fluctuations and disturbances [19,21,22]. In investment and operation, MGs are capable of reducing or deferring the investment demands for large-scale power stations and transmission systems, while also diminishing the overload and power loss of transmission and distribution lines. In the market, it is possible to reduce the cost of peak load and lower energy prices. In the environmental aspect, it alleviates environmental pollution and reduces greenhouse gas emissions. The advantages that MGs bring to the power system are as follows [23,24].
  • Reliability to backup power generation and continuous power supply;
  • Flexibility to plug-and-play and bidirectional power flow;
  • Power quality for stable voltage and frequency;
  • Investment to reduce investment;
  • Operation to decrease overload and power loss;
  • Market to lower energy prices;
  • Energy conservation and carbon reduction.

5. MGs for Resilience

Hardening measures and operational measures are conducive to enhancing power system resilience. Hardening measures mean strengthening infrastructure to make system components sturdy and reduce the physical impact of extreme events. Operational measures are classified as preventive and corrective actions based on the time in events. Preventive actions refer to the relevant strategies formulated before the occurrence of extreme events to reduce the impact of such events on the power system. Topologies and related emergency plans such as the backup plans formulated based on different situations are formulated. Corrective actions mean the improvement strategies adopted after the occurrence of the event to restore the load to operate the power system normally as soon as possible [2,25,26,27].
The crucial role of renewable energy as an alternative in enhancing the resilience of the power grid is emphasized [28]. DERs exhibit superior power-supply capabilities and flexibility in supporting resilience. MGs integrate DERs to enhance the efficiency of relevant measures. By integrating the coordination of hydrogen energy and renewable energy as well as hydrogen energy transportation, the MG construction strategy for hydrogen-penetrated active distribution networks containing hydrogen energy is proposed [29]. We reviewed various solutions for enhancing the power system resilience through the MG.

5.1. Topological Structure and Operation Strategy

A collaborative restoration method for AC/DC hybrid distribution networks is proposed in ref. [30]. By utilizing a topology search strategy based on DC lines, a fault restoration model is established. A hybrid optimization for MG scheduling enables the integration of mathematical programming and an arbitrary nonlinear constraint model through decision trees [31]. A resilient operation zone (ROZ) is introduced [32] to maintain the operation trajectory of the MG in the ROZ through emergency countermeasures. The load of distribution grids is quantified to enhance the recovery capability of power systems through the employment of the analytic hierarchy process (AHP) and mixed integer programming (MIP) [33]. With the integration of the active islanding effect and remote-control switches (RCS), a multi-stage resilience optimization method for electricity-gas coupled distribution networks is proposed [34]. The genetic algorithm and the machine learning algorithm are integrated to optimize the operation efficiency of ACMGs [35]. The affine arithmetic (AA) method is utilized to determine the uncertainty of power supply and demand and an energy management system (EMS) for multi-microgrid systems is proposed [36].

5.2. Energy Management and Control Schemes

The feasibility of a networked microgrid system (NMS) formed by connecting multiple MGs is examined [37], and an energy management and control framework for a networked MG with a hybrid AC/DC system is put forward. In ref. [38], a restoration optimization strategy based on the electric spring (ES) is proposed. The introduced model predictive control (MPC) strategy accomplishes the coordinated control of the ES and the water heating system (WHS). A management framework for networked MGs is presented to transition from centralization to decentralization and sustain the power supply to critical loads [39]. A dynamic microgrid-forming approach based on mixed integer linear programming (MILP) for the restoration of priority loads is presented in ref. [40]. A two-layer framework for ACMG management is introduced in ref. [41], including an energy management system layer that adopts the sparrow search algorithm (SSA) and a control layer that adopts particle swarm optimization (PSO) to enhance the overall efficiency of the MG.

5.3. Energy Allocation and Protection Design

A two-layer planning approach for networked MGs is put forward to determine the location of each DER and the solution for optimal load restoration [42]. A decision support strategy for determining the scale of DERs is proposed with resilience and cost being taken into consideration [43]. A wireless sensor network and a micro-controller-based network security architecture are introduced, and an MG design architecture capable of enhancing resilience is proposed [44]. Coordinating circuit-breaker protection and low voltage ride-through settings are used in ref. [45] to enhance the resilience of MGs under islanded configurations.

5.4. DCMG

A decentralized control scheme for DCMGs is used to address the resilient control issue of sensor and actuator failures [46]. Quadratic programming (QP) is utilized to put forward an operation optimization strategy for DCMGs [47]. A decentralized control framework for cyber-physical DCMGs is developed to enhance the resilience against cyber-attacks [48]. In ref. [49], droop control driven by artificial neural networks (ANN) is proposed for DCMGs to mitigate the influence of disturbances. DCMG with integrated nano grids is investigated for coordinated control and management in both off-grid and grid-tied states in a proposed control system [50].

5.5. Energy Storage System (ESS)

The hybrid multi-objective particle swarm optimization (MOPSO) method is employed to determine the optimal location and capacity of the battery energy storage (BES) [51]. A coordinated control strategy based on the battery energy storage system (BESS) is investigated to strengthen the recovery capability of MGs under disturbances [52]. An ESS based on supercapacitor (SC) is used in ref. [53] for realizing power management and DC voltage control of DCMGs. In ref. [54], an operation strategy of adopting underground energy storage systems (UESS) is used to enhance the grid resilience of renewable energy with high penetration. Taking into account the internal energy autonomy index and the grid supply point (GSP) resilience management approach, the optimal model for the capacity of the ESS is developed [55].

6. Conclusions

The discussion on resilience is important as the system must possess flexibility and survivability. MG integrates DERs to operate in either grid-connected mode or island mode and serve as a power source of the system. The research on MGs encompasses topological structure, operation strategies, energy management, control schemes, energy allocation, protection design, DCMGs, and energy storage systems to enhance power system resilience. In this article, we comprehensively review the significant concepts of power system resilience and MGs and the relevant literature regarding the role of MGs in enhancing power system resilience. The results provide a useful reference for power-related practitioners in formulating efficient design schemes.

Author Contributions

Conceptualization, J.-H.H. and J.-H.L.; methodology, J.-H.H.; software, J.-H.L.; validation, J.-H.H. and J.-H.L.; formal analysis, J.-H.L.; investigation, J.-H.L.; resources, J.-H.L.; data curation, J.-H.L.; writing—original draft preparation, J.-H.L.; writing—review and editing, J.-H.H.; visualization, J.-H.L.; supervision, J.-H.H.; project administration, J.-H.H.; funding acquisition, J.-H.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the major scientific research projects of Fujian Polytechnic of Information Technology. The grant numbers are YZDKJ23-05 and YZDKJ24-02.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Resilience curve.
Figure 1. Resilience curve.
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Figure 2. MGs control architectures: (a) centralized, (b) decentralized, and (c) hybrid.
Figure 2. MGs control architectures: (a) centralized, (b) decentralized, and (c) hybrid.
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He, J.-H.; Lin, J.-H. Review of Microgrids to Enhance Power System Resilience. Eng. Proc. 2025, 92, 82. https://doi.org/10.3390/engproc2025092082

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He J-H, Lin J-H. Review of Microgrids to Enhance Power System Resilience. Engineering Proceedings. 2025; 92(1):82. https://doi.org/10.3390/engproc2025092082

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He, Jian-Hua, and Jhih-Hao Lin. 2025. "Review of Microgrids to Enhance Power System Resilience" Engineering Proceedings 92, no. 1: 82. https://doi.org/10.3390/engproc2025092082

APA Style

He, J.-H., & Lin, J.-H. (2025). Review of Microgrids to Enhance Power System Resilience. Engineering Proceedings, 92(1), 82. https://doi.org/10.3390/engproc2025092082

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