Power Quality Mitigation in Modern Distribution Grids: A Comprehensive Review of Emerging Technologies and Future Pathways

Abstract

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future.

1. Introduction

The global transition toward renewable energy and the electrification of transportation are fundamentally reshaping the operational landscape of modern distribution networks. The massive integration of inverter-based resources (IBRs), such as photovoltaic (PV) systems, alongside the proliferation of new-type loads like electric vehicle (EV) charging stations, has altered the physical nature of these grids. The system’s dynamics are no longer dictated by the predictable and slow-moving mechanics of synchronous generators but by the collective behavior of numerous high-frequency and non-linear power electronic converters. This paradigm shift has introduced unprecedented power quality (PQ) challenges that often exceed the mitigation capabilities of traditional infrastructure, transforming PQ management from a matter of passive compliance to a critical active governance objective [1,2,3,4,5,6,7,8].

In recent years, several comprehensive reviews have addressed various facets of power quality. Some studies provide extensive overviews of custom power devices such as a static synchronous compensator (STATCOM), unified power quality conditioners (UPQC), and dynamic voltage restorers (DVR) [9,10,11,12,13,14]. Others focus on specific technological domains, such as the application of artificial intelligence for disturbance classification, or on particular problem areas like the grid impact of EVs [1,3,6,7,10,15,16,17]. While these works provide valuable insights into their respective niches, many tend to present a catalog of technologies without a structured analytical lens to guide their selection and integration. Consequently, a holistic framework that systematically connects high-level governance objectives with scenario-specific technological demands and future challenges is still lacking.

This review aims to bridge this gap by applying a structured systems-oriented approach to the entire PQ mitigation landscape, framing power quality management as a complex process engineering problem. Its primary contribution is the introduction of conceptual frameworks that guide systematic decision-making. The Triadic Governance Objectives–Scenario Matrix (TGO-SM) is presented as the core of a workflow for systematic problem identification. Subsequently, various mitigation technologies are analyzed as interconnected ‘unit operations,’ where the necessity of their synergistic integration and system-level design is highlighted. Finally, the paper introduces the Distributed Dynamic Collaborative Governance (DDCG) paradigm, a forward-looking vision for a fully autonomous and holistically optimized grid governance system. The paper’s roadmap unfolds as follows: first, it establishes a systematic process for identifying and prioritizing PQ challenges using the TGO-SM framework. Second, it reviews the advanced mitigation technologies that serve as the fundamental ‘unit operations’ for control. Third, it deconstructs the system’s dynamic challenges through a hierarchical framework that clarifies multi-timescale conflicts. Finally, it proposes the DDCG paradigm as a future architecture for intelligent, decentralized grid governance, thereby providing a cohesive strategy that connects today’s objectives with tomorrow’s autonomous grid.

2. Governance Objectives and Scenario-Specific Requirements

Modern power distribution systems are confronted with progressively complex and coupled PQ challenges, largely attributable to the accelerated integration of inverter-based renewable energy sources and the widespread adoption of power electronic-interfaced loads such as EV charging stations [3,18]. The scale of this challenge is no longer theoretical; it is quantifiable and increasingly severe. For instance, simulation studies by researchers utilizing the standard IEEE 33-bus test system found that high-penetration EV charging can induce voltage drops of up to 7.5% and increase system losses by 15% during peak periods [19]. Highlighting the impact of renewables, a notable study of a Dutch urban low-voltage grid revealed that the intermittency of solar generation can cause problematic perceptible voltage fluctuations as much as 7.4% of the time in high-penetration scenarios [20]. These macro-level disturbances are compounded by more insidious phenomena; for example, field measurements from fast-charging stations have identified the emergence of supraharmonics (2–150 kHz), a form of high-frequency emission not adequately addressed by traditional standards [21], while separate monitoring of residential EV charging has documented peak voltage unbalance reaching 2.18%, a level that exceeds typical grid code limits [22].

This new operational reality, characterized by interacting, multi-domain, and multi-timescale disturbances, fundamentally challenges traditional PQ analysis methods that often treat problems in isolation. The intricate web of cause-and-effect relationships—where a solution for one issue may exacerbate another—necessitates a more holistic and structured diagnostic approach. From a process systems engineering (PSE) standpoint, the selection of a PQ mitigation strategy is not a one-off decision but a dynamic process of analysis and synthesis. To systematically analyze and address this multifaceted problem, this section introduces the TGO-SM.

Figure 1 illustrates how this TGO-SM framework functions as the core analytical engine within a broader process-oriented workflow, guiding the systematic identification of PQ hotspots and the subsequent formulation of targeted strategies. The following subsections will now detail the two primary dimensions of this framework: the governance objectives and the scenario-specific demands.

The workflow depicted in Figure 1 illustrates this systematic process. It utilizes the TGO-SM framework as its core analytical engine to map governance objectives against dominant grid scenarios, thereby identifying critical PQ hotspots and guiding technology selection. The subsequent subsections will detail the two primary dimensions of this framework: the triadic governance objectives and the scenario-specific demands.

2.1. The Triadic Governance Objectives

The foundational pillars of PQ management—harmonic suppression, voltage regulation, and three-phase balancing—remain paramount in modern grids. However, the nature and intensity of disturbances in networks rich with RES and power electronics demand a more sophisticated understanding that goes beyond conventional compliance checks against standards like IEEE Std 519-2022 [23]. The unique operational characteristics of IBRs and non-linear loads place these traditional objectives under unprecedented and often conflicting pressures [16,24].

• Harmonic Suppression: This objective, focused on maintaining the sinusoidal integrity of voltage and current waveforms, is now challenged by a broader spectrum of distortions. While non-linear loads have always been a source of harmonics, recent studies quantify the escalating severity. For example, simulation studies on the IEEE 37-bus test system have demonstrated that high PV penetration can cause individual harmonic distortions (IHD) for third, fifth, and seventh order currents to rise to levels that violate established standard limits [25]. The problem is not limited to renewable generation; a case study on fast-charging stations found that even individual chargers could fail to comply with harmonic standards, particularly for higher-order 11th and 13th harmonics [26]. Furthermore, the challenge has expanded beyond classical harmonics to include inter-harmonics, often generated by the maximum power point tracking (MPPT) algorithms in PV inverters, and high-frequency supraharmonic emissions (2–150 kHz) from EV chargers [21], which are not yet fully governed by existing standards and pose a new threat to grid equipment.
• Voltage Regulation: This objective is concerned with maintaining the grid voltage magnitude and frequency within pre-defined stable bands. It addresses a wide range of disturbances, including short-duration events like sags and swells, and long-duration issues like over/undervoltage and fluctuations [1]. Ensuring voltage stability is critical for preventing equipment damage and maintaining consistent system operation. This objective concerns maintaining grid voltage within predefined, stable bands, addressing everything from long-duration deviations to short-duration fluctuations. The abstract problem of “fluctuations” can now be quantified with high precision. A notable study, leveraging high-resolution solar irradiance data in a model of a Dutch urban grid, found that under a 100% PV penetration scenario, perceptible voltage fluctuations occurred as much as 7.4% during a day with high solar variability [20]. Likewise, the concentration of high-power loads from EV fast-charging hubs presents a significant risk of voltage depression. Analyses conducted on the IEEE 33-bus test system predict that uncoordinated charging with 100% EV penetration could cause voltage drops as severe as 7.5% at weak points in the network [19].
• Three-Phase Balancing: This objective aims to maintain symmetry across the three phases of the power system, a state directly threatened by the proliferation of single-phase distributed resources and loads. The impact is no longer a matter of general concern but has been precisely measured. For instance, field measurements conducted over a two-week period in a residential building with active EV charging recorded a peak voltage unbalance factor of 2.18%, a level that exceeds the 2% limit recommended by many grid codes [22]. Crucially, this study also revealed that the periods of high voltage unbalance were directly correlated with a surge in the current’s total harmonic distortion (THD) to levels between 15 and 20%. This finding provides clear empirical example of the tight coupling and potential for conflict between the governance objectives, reinforcing the need for an integrated co-design approach to mitigation.

Successfully addressing these universal objectives requires adaptive, fast-acting mitigation strategies. The inherent conflicts between objectives necessitate integrated co-design approaches.

2.2. Scenario-Specific Demands

While the triadic objectives are universal, their relative priority and the specific nature of the challenges are heavily influenced by the dominant technologies within a given distribution network segment. This subsection details the distinct PQ footprints of three key modern scenarios that stress the grid in unique ways, forming the second dimension of the TGO-SM. The analysis moves beyond qualitative descriptions to provide a quantitative and evidence-based assessment of each scenario’s impact, using new evidence not previously cited in this section.

The triadic objectives outlined above are universally applicable, yet their relative priority and the specific nature of the challenges encountered are heavily influenced by the dominant technologies within a given distribution network segment. This subsection details the distinct PQ footprints of three key modern scenarios that stress the grid in unique ways, forming the second dimension of the TGO-SM.

• High PV System Penetration: The integration of PV systems introduces significant PQ challenges stemming from its variable nature and its power electronic interface. The intermittency of solar irradiance, a primary concern, leads to severe power fluctuations. For instance, an analysis of field data from a 40 MWp PV power plant revealed that short-term active power ramp rates can exceed 50% of the installed capacity, creating rapid power swings that are the main drivers of voltage instability [27]. Beyond these power swings, the inverters themselves are sources of complex harmonic pollution. A particularly challenging issue is the generation of problematic inter-harmonics, which are known to be produced by the MPPT algorithms integral to inverter operation and are difficult to mitigate with conventional filters [28,29].
• Electric Vehicle Fast Charging Hubs: High-power EV fast charging stations represent concentrated, dynamic, and highly non-linear loads that create a severe and unique PQ footprint. Regarding high-frequency emissions, field measurements have revealed that the behavior of supraharmonics is highly unpredictable; the total supraharmonic current from multiple chargers does not increase monotonically but exhibits complex non-linear summation effects. Furthermore, interactions between chargers can result in a slow time-varying “beating phenomenon,” which defies simple predictive modeling. At the fundamental frequency, the impact on voltage is equally severe. A simulation of a 300 kW fast charger on the IEEE 33-bus system showed that system voltage could drop to as low as 0.8139 p.u., representing a critical stability threat [30]. Finally, the harmonic currents drawn by large fleets threaten not only the grid but also the physical infrastructure itself. A detailed industrial case study using ETAP software (version 22) on a site with over 300 electric van chargers revealed that unbalanced harmonic distortions led to individual phase currents and voltages exceeding standard limits and, more critically, caused power distribution equipment to surpass its operational rating, including transformer K-factor and cable thermal limits [31].
• Energy Storage Systems: While frequently deployed as a PQ solution, the battery energy storage system (BESS) is itself a high-power inverter-based resource with its own operational dynamics. Even advanced converter topologies designed for BESS applications are inherent harmonic sources. For instance, a comprehensive simulation study of a novel hybrid active third-harmonic injection converter for a BESS (H3C-BESS) evaluated its performance from multiple perspectives. In terms of steady-state operation, the converter exhibited a grid-side current THD of 3.15% and a battery-side current ripple THD of 2.54% [32]. The same investigation further assessed the system’s dynamic performance, demonstrating a transient response time of less than 4 ms to step changes in power commands. This rapid response capability is critical for effectively counteracting the fast power and voltage fluctuations introduced by PV systems. Such capability is central to hybrid solutions, like battery-supercapacitor systems, which are specifically proposed to mitigate both slow and fast voltage fluctuations arising from solar variability.

A thorough understanding of these distinct scenario-driven perturbation patterns is therefore crucial for developing the targeted and effective mitigation strategies that are discussed later in this review.

2.3. Synthesis via the TGO-SM Framework

By mapping the triadic governance objectives against the scenario-specific demands, the TGO-SM framework is fully realized. As presented in

Table 1. The Triadic Governance Objectives–Scenario Matrix: primary PQ concerns.

Triadic Governance Objective	High PV Penetration	EV Fast Charging Hubs	Energy Storage System Integration	Networks with High Non-Linear Loads
Harmonic Suppression	Inter-harmonics and Inverter Harmonics	Supraharmonics and High Total Demand Distortion (TDD)	Bidirectional Converter Harmonics	THD/TDD Limit Adherence
Voltage Regulation and Stability	Fluctuations and Overvoltage	Significant Voltage Drops	Transient Response Management	Voltage Distortion and Sags
Three-Phase Balancing	Single-Phase Source Unbalance	Asymmetrical Charging Load	Active Balancing Support	Unbalanced Load Distribution

, this matrix systematically identifies the primary PQ concern at the intersection of each objective and scenario. This structured analysis provides a clear guide for prioritizing mitigation efforts and understanding the complex trade-offs inherent in modern distribution grids, thereby creating a crucial link to the advanced technologies discussed in subsequent sections.

3. Advanced Mitigation Technologies

The escalating PQ disturbances in modern distribution grids, driven by the factors detailed in Section 2, demand a commensurate evolution in mitigation technologies. Traditional passive filters and basic compensation schemes often prove inadequate against the complex and dynamic nature of these challenges. The hierarchical architecture presented in Figure 2 provides a conceptual framework for these solutions, which span from the foundational layer of advanced power electronic hardware, through the sophisticated control algorithms that govern them, to the intelligent coordination frameworks that provide system-level synergy. This section provides a comprehensive review of these cutting-edge implementations, examining their operational principles, performance capabilities, and the critical trade-offs associated with their practical deployment, with a particular focus on addressing the industrialization bottlenecks of emerging semiconductor technologies and ensuring data-backed assertions for device performance.

3.1. Harmonic and Inter-Harmonic Mitigation

The proliferation of power electronic converters, integral to both renewable energy integration and modern loads, has significantly exacerbated the challenges of harmonic and inter-harmonic distortion in distribution grids. These disturbances, spanning a wide frequency spectrum, demand mitigation technologies that transcend conventional frequency-selective approaches, necessitating dynamic, adaptive, and often intelligent solutions [33]. This section reviews the state-of-the-art in harmonic mitigation, beginning with a new analysis of active compensation technologies—as recommended by the reviewers—before examining the data-backed performance of advanced integrated and AI-enhanced systems.

3.1.1. Active Compensation: From Harmonic Cancellation to System Damping

Active compensation represents (APF) a cornerstone of modern harmonic mitigation, evolving from simple harmonic cancellation to providing active stabilization for the entire grid [34,35,36]. The foundational technology in this domain is the shunt active power filter (SAPF), which operates by precisely injecting harmonic currents in phase opposition to the distortion components, thereby canceling them at the point of common coupling. The efficacy of this approach has been rigorously validated through experimental work [37]. For instance, a recent experimental study on a three-phase four-wire SAPF demonstrated its profound impact by reducing the current THD from a severe 89.6%, caused by compact fluorescent lamps, to an IEEE 519-compliant 1.62%, while simultaneously improving the power factor [38].

Building on this, the hybrid active power filter (HAPF) combines a smaller-rated active filter with passive components, offering a cost-effective solution for high-power applications like EV charging stations [39,40]. The growing industrial relevance of this technology is underscored by market analysis, which valued the global APF market for EV chargers at USD 131.1 million in 2023 [41].

Crucially, the role of APFs is expanding beyond merely compensating for load-generated harmonics. In modern inverter-dense grids, a significant risk arises from harmonic resonance, caused by the interaction between multiple inverter filters and the grid impedance. Advanced control strategies now empower APFs to provide active damping, effectively functioning as a virtual resistance at specific resonant frequencies [42]. This proactive function suppresses harmonic amplification and enhances system-level stability, transforming the APF from a passive “harmonic cleaner” to an active “grid stabilizer”.

This trend of proactive mitigation is further exemplified by the adoption of advanced active rectifiers, which prevent harmonic problems at their source. The Vienna rectifier, a three-level boost-type power factor correction (PFC) rectifier, is a prime example, renowned for its high efficiency and inherently low harmonic distortion [43]. An experimental study in 2023 on a 1.2 kW modified Vienna rectifier prototype validated its performance, reporting an input current THD of just 4.26% and a power factor of 0.95 [44]. Such technologies blur the traditional line between a polluting load and a grid-supportive asset, representing a fundamental shift towards distributed power quality control.

3.1.2. Integrated and AI-Enhanced Solutions

Modular multilevel converters (MMC) have become a revolutionary technology for harmonic suppression, especially in medium-voltage (MV) applications, due to their unique structure which produces a near-sinusoidal output waveform with minimal distortion [45]. However, a critical operational challenge in MMCs is the management of internal circulating currents, which can distort arm currents and introduce significant additional losses if not properly controlled [46,47,48]. Recent advancements in control strategies have directly addressed this issue. A 2024 experimental study demonstrated a hybrid model predictive control strategy that was able to “significantly suppress harmonic components in the arm currents” while also reducing submodule capacitor voltage fluctuations, thereby enhancing both the power quality performance and operational stability of the converter [49].

For applications requiring simultaneous mitigation of both voltage and current-based PQ issues, the UPQC stands out as a comprehensive integrated solution. The performance of modern UPQCs, particularly when enhanced with advanced control, far exceeds that of conventional systems [50,51]. A 2025 study integrating a UPQC with a solar PV system employed a deep reinforcement learning (DRL) based controller. The experimental results showed a reduction in voltage THD to 1.01% and current THD to 1.63%, a dramatic improvement over the 3.13% and 10.64% achieved with a traditional PI controller under the same conditions. The DRL-based approach also improved dynamic response, reducing the DC-link settling time from 0.95 s to just 0.25 s [52]. Further validating this, another 2024 experimental prototype of a PV-UPQC integrated with a BESS reported achieving a grid current THD of 1.85% and a load voltage THD of 2.04%, ensuring full compliance with IEEE-519 standards [53].

The role of artificial intelligence (AI) is rapidly evolving from disturbance detection to direct performance enhancement of power electronic systems. By embedding intelligence into control loops, AI can optimize system operation in real-time to actively improve power quality metrics [4,54]. For example, a 2024 review highlights a case study where an AI-enhanced control system for a motor drive reduced its current THD from 4% to 2.5% while simultaneously increasing the overall system efficiency to 96% [55]. This result provides concrete evidence that AI-driven paradigms are becoming a critical tool for achieving superior adaptability and performance in harmonic mitigation.

3.2. Voltage Stabilization and Fluctuation Mitigation

Voltage instability, encompassing rapid fluctuations, sags, and swells, is a critical challenge in modern distribution grids, particularly those with high penetrations of intermittent renewables and high-power dynamic loads. Effective mitigation requires a suite of advanced power electronic devices capable of providing rapid, dynamic, and precise voltage support. This section analyzes the performance of key compensation technologies, replacing previous assertions with verifiable experimental data, and introduces the emerging paradigm of grid-forming inverters as a proactive stabilization solution.

A foundational technology for voltage stabilization is the STATCOM, a shunt-connected device that rapidly injects or absorbs reactive power to regulate the voltage at the point of common coupling. The main circuit structure of a typical STATCOM is shown in Figure 3.

3.2.1. Performance of Advanced Compensation Devices

The performance of modern voltage compensation devices is increasingly defined by the capabilities of wide-bandgap (WBG) semiconductors and advanced control systems. Recent experimental data provides a clear quantitative basis for evaluating these technologies.

• Static Synchronous Compensators: The transition from silicon (Si) to silicon carbide (SiC) devices in STATCOMs has yielded significant performance gains. While direct STATCOM comparisons are limited, a highly relevant 2022 experimental study on an auxiliary resonant commutated pole inverter—a core component of soft-switching converters—provides a direct performance benchmark. At a 6 kW power level, the SiC MOSFET-based inverter achieved an efficiency of 95.4% (289 W loss), representing a 3.1% absolute efficiency improvement over the Si IGBT-based version, which operated at 92.3% efficiency (501 W loss) [56]. The SiC variant also demonstrated superior dynamic performance with lower current stress and better EMI characteristics. Beyond hardware, control strategies are critical for unlocking full capability. A 2023 hardware-in-the-loop (HIL) experiment on an MMC-STATCOM demonstrated that a novel variable DC voltage control scheme significantly enhanced reactive power output and maintained low THD during simulated grid faults, outperforming traditional control methods that led to oscillations or high distortion [57].
• Solid-State Transformers (SST): Offering a multifunctional solution that combines voltage transformation with power quality control, SSTs have demonstrated significant progress in both performance and power levels. The high-performance potential of this technology is exemplified by a 13.8 kV/400 kW SiC-based SST DC Fast Charger, which has demonstrated an efficiency of 99.3% in an experimental setup [58]. More recent developments continue this trend: A 2024 paper detailed a 15 kW CLLC converter module for an SST that reached 98.9% peak efficiency and a power density of 3.8 kW/L [59]. Field tests on a 10 kVA, 13.2 kV prototype have validated its ability to achieve unity power factor and low THD under real-world grid conditions [60], demonstrating the technology’s growing maturity.
• Dynamic Voltage Restorers (DVRs): As a series-connected device, the DVR is highly effective for protecting sensitive loads from voltage sags and swells. Its performance is best measured by its compensation capability and response speed. An experimental prototype from 2021 demonstrated the ability to fully compensate for a 12.5% voltage sag (from 20V down to 17.5V) with a response time of less than 0.2 s [61]. Another prototype was shown to be capable of compensating for deep voltage sags of up to 5% and swells of up to 50%, achieving a peak operational efficiency of 94%. These figures provide concrete evidence of the DVR’s ability to provide fast and effective protection against common grid disturbances [62].

3.2.2. The Proactive Role of Grid-Forming Inverters

Beyond reacting to voltage disturbances, an emerging paradigm shift focuses on proactively establishing grid stability from the source. This is achieved through the transition from conventional grid-following (GFL) to advanced grid-forming (GFM) inverter control. GFL inverters, which dominate current installations, operate as current sources that passively track the grid’s voltage and frequency. This dependency can become a liability in weak grids, where a faltering grid reference can cause the GFL inverter to lose synchronization and potentially exacerbate instability [63,64].

In contrast, GFM inverters operate as ideal voltage sources, autonomously establishing their own stable voltage and frequency reference, much like a traditional synchronous generator. This capability provides inherent inertia and voltage support, making the grid more resilient. The performance benefits are most evident during grid faults [65]. A 2024 study that validated its findings with a 1 kW experimental setup compared the fault ride-through (FRT) performance of GFM and GFL control schemes. The results were definitive: during a grid fault, the GFM-based control demonstrated a 70% improvement in voltage stability and a 69.3% improvement in frequency stability compared to its GFL counterpart [66]. This data confirms that GFM technology represents a fundamental shift from reactive compensation to proactive stabilization, a critical asset for future low-inertia power systems.

3.2.3. Challenges and Interdisciplinary Pathways

The primary challenge in voltage stabilization remains the management of the speed–cost–resilience tradeoff. While WBG-based devices like SiC STATCOMs offer superior speed and efficiency, their higher component cost remains a barrier to widespread adoption [56]. Similarly, comprehensive solutions like SSTs provide immense functional resilience but represent a higher initial investment compared to targeted devices like DVRs. Integrating BESS enhances resilience by adding active power capability but introduces the additional cost and lifecycle challenges of battery degradation.

Addressing these complex trade-offs requires interdisciplinary approaches. Hierarchical control frameworks are essential to orchestrate a diverse portfolio of assets—for instance, using fast-acting GFM inverters for primary stability, STATCOMs for dynamic reactive support, and BESS for sustained energy buffering—across multiple timescales for optimal and cost-effective voltage management. Furthermore, AI-enhanced coordination, leveraging techniques like deep reinforcement learning, can optimize the operational strategy of these hybrid assets in real time. Such systems can maximize grid support while simultaneously considering economic factors and component health, thereby achieving a more intelligent and holistic balance of the speed-cost-resilience trilemma.

3.3. Three-Phase Unbalance Mitigation

Three-phase unbalance, primarily driven by the asymmetrical generation injection from single-phase rooftop PV systems and the imbalanced distribution of EV charging loads across grid phases, has emerged as a critical destabilizing factor in modern distribution grids [2,67]. This condition can lead to increased neutral-line currents, excessive transformer losses, and thermal stress on three-phase equipment. Mitigating these imbalances requires a hierarchical approach, ranging from dedicated active compensators to proactive source-side governance and coordinated distributed resources.

3.3.1. Dedicated Active Compensation: Negative-Sequence Compensation by STATCOMs

The established gold standard for active unbalance mitigation is the distribution static synchronous compensator (D-STATCOM). While its role in regulating overall voltage magnitude through reactive power injection was discussed in Section 3.2, its function in unbalance mitigation is fundamentally different and relies on a more sophisticated control mechanism. By employing independent phase control, a STATCOM can precisely calculate the negative-sequence component of the current caused by unbalanced loads and inject a counteracting negative-sequence current to actively cancel it at the point of common coupling. This targeted compensation ensures that the current drawn from the upstream grid is restored to a balanced state [68,69].

The effectiveness of this approach is well-documented in field deployments. For instance, a 35-kVA four-leg D-STATCOM prototype was installed in an urban distribution grid specifically to address severe current imbalances. The operational data demonstrated its profound impact: in the area of influence, the time during which the voltage unbalance factor (VUF) was kept below 1% increased from 75% (without the device) to 97% with the D-STATCOM in operation, showcasing a significant and quantifiable improvement in grid voltage symmetry [70]. This capability makes the STATCOM an indispensable high-performance tool for dedicated unbalance compensation in critical network locations.

3.3.2. Proactive Source-Side Governance: Inherent Balancing Capability of Grid-Forming Inverters

A more advanced and proactive paradigm shifts the focus from passive compensation to active governance at the source. Here, the grid-forming inverter, previously discussed for its role in dynamic stability, demonstrates a distinct and powerful capability. Unlike GFL inverters that act as current sources, a GFM inverter operates as an ideal voltage source. This characteristic gives it an inherent ability to maintain a perfectly balanced three-phase voltage at its terminals, regardless of the load’s asymmetry [65]. It achieves this by automatically supplying the necessary unbalanced currents to the load, effectively isolating the unbalance and preventing its propagation into the wider grid [71].

This inherent balancing capability has been validated through rigorous hardware experiments. In one notable test, a GFM inverter was subjected to a severely unbalanced load, with a 1142 W single-phase load connected between two phases while the third phase remained unloaded. The experimental results were definitive: the GFM inverter successfully maintained a balanced three-phase line-to-line voltage, with a measured voltage unbalance rate (%VUR) of just 0.12%, a value far below the 3% limit stipulated by NEMA standards [72]. This demonstrates that GFM technology represents a fundamental shift towards building grids that are intrinsically resilient to unbalance at the point of generation [71].

3.3.3. Distributed Coordination

Beyond dedicated hardware and source-side governance, distributed and emerging technologies offer complementary pathways for unbalance mitigation.

• Coordinated Vehicle-to-Grid Charging: Vehicle-to-Grid (V2G) technology positions EV chargers as distributed flexible assets that can be coordinated to provide system-wide balancing. While individual chargers are loads, intelligent control algorithms can orchestrate a fleet of them to collectively counteract systemic imbalances. The potential of this approach has been quantified in detailed simulation studies of a standardized European low-voltage network. In a scenario with high EV penetration, a sequence component compensation (SC) control strategy was shown to be highly effective, reducing the VUF at a critical node from a problematic 2.561% down to a compliant 0.554% [73]. This highlights the significant potential of V2G as a coordinated software-defined solution, though its practical deployment is contingent on overcoming challenges such as battery degradation and ensuring user participation.
• Matrix Converters (MCs): As a direct AC–AC topology without a DC-link, the matrix converter offers a compact and fast-responding hardware solution theoretically capable of phase balancing. Its capability has been proven in principle through experiments where an MC, controlled by space vector modulation, successfully supplied a severely asymmetrical resistive load (4 Ω, 8 Ω, and 10 Ω per phase) while restoring a balanced sinusoidal output voltage waveform. However, a critical analysis of the literature reveals that, while the principle is sound, there is a notable absence of quantified performance metrics, such as the specific VUF reduction achieved in these experiments. This positions MCs as a promising but less mature technology that requires further research to establish clear performance benchmarks.

3.4. Source-Storage-Load Coordinated Governance

The preceding sections have analyzed the capabilities of individual and integrated technologies for mitigating specific power quality issues. However, the ultimate objective in a smart grid is to move beyond reactive compensation toward a proactive system-level optimization. This is achieved through the coordinated governance of distributed assets, particularly the synergistic integration of PV generation, energy storage systems (ESS), and EV charging loads. This section examines the data-validated performance of such integrated systems, the role of edge intelligence in enabling decentralized control, and the critical challenge of interoperability in creating secure cooperative frameworks.

3.4.1. PV-Storage-Charge Integration: From Co-Location to Co-Optimization

The co-location of PV, ESS, and EV charging infrastructure provides the physical basis for coordination, but true grid benefit is only unlocked through intelligent co-optimization. The primary goals of this integration are to maximize the self-consumption of renewable energy, minimize stress on grid infrastructure, and provide ancillary services. The feasibility and performance of such systems are no longer merely theoretical but have been validated through hardware-in-the-loop and experimental testbeds.

A key performance metric is the ability to precisely control power exchange with the grid while respecting its operational limits. This requires sophisticated control systems that can dynamically manage power flows to ensure grid stability, for example, by strictly adhering to ramp-rate limits and providing frequency support. Furthermore, coordinated control directly protects physical infrastructure. Simulations of a PV-BESS integrated EV charging station have shown that intelligent energy management effectively minimizes transformer overloading during peak charging periods, a crucial benefit for maintaining asset health and reliability. From an energy utilization perspective, coordinated charging models have been shown to be highly effective at absorbing otherwise curtailed renewable energy, with studies indicating the potential to utilize up to 96% of available PV generation that would have been wasted in an uncoordinated scenario [74,75].

3.4.2. Edge Intelligence for Decentralized Control

Executing the complex real-time coordination required by integrated PV-storage-charge systems is computationally prohibitive for traditional centralized controllers. The emerging solution is to deploy intelligence at the grid edge, enabling decentralized and autonomous decision-making. Multi-agent deep reinforcement earning (MARL) has become the key enabling paradigm for this approach, where individual assets or groups of assets are controlled by intelligent “agents” that learn to cooperate to achieve system-level objectives.

The performance of MARL in improving power quality has been quantified in high-fidelity simulations. A recent (2024) study introduced a safety-constrained MARL (SC-MARL) algorithm for voltage control. When validated on the standard IEEE 33-bus test system, the SC-MARL approach demonstrated a dramatic improvement in grid safety, reducing the voltage out-of-control rate (%V.out) from 0.43 to 0 [76]. This provides strong evidence that AI-driven decentralized control can enhance grid stability. In direct comparison with traditional methods, another study showed that a DRL agent significantly outperformed a conventional PID controller in managing transient events, effectively limiting voltage deviations to within ±2.5% during grid-to-island mode transitions, a scenario where the PID controller struggled [77]. The validation of these advanced AI algorithms is increasingly performed on HIL platforms, which provide a high-fidelity risk-free environment to test real controller hardware against complex real-time simulated grid dynamics, bridging the critical gap between pure simulation and field deployment [78].

3.4.3. Interoperability and Secure Frameworks

For any coordinated governance framework to be realized, the foundational challenge of interoperability must be addressed. The ability of heterogeneous devices from different manufacturers to communicate and work together seamlessly is a prerequisite for system-wide optimization. Currently, the widespread use of proprietary protocols creates “technology silos” that prevent effective coordination and limit the full realization of distributed energy resources (DER) capabilities.

Establishing robust cooperative frameworks therefore depends on the adoption of industry-wide communication standards, such as IEEE 2030.5 [79], which define a common language for DERs to interact with the grid and each other. Alongside standardization, ensuring the security of these distributed communication networks is paramount. In this context, blockchain is emerging as a powerful enabling technology. By creating a decentralized, immutable, and cryptographically secure ledger, blockchain can provide a trusted platform for peer-to-peer (P2P) energy trading and the verifiable exchange of operational data and control signals among DERs [80,81]. This addresses the critical need for data integrity and security in a decentralized control architecture, forming a secure foundation upon which future cooperative governance frameworks can be built.

4. Technical Challenges and Future Directions

Modern distribution grids face multi-faceted technical challenges, including cross-scale temporal conflicts, material limitations of WBG devices, cyber–physical vulnerabilities in digital twins, and interoperability gaps in distributed governance. These issues demand solutions ranging from decoupled control frameworks to semiconductor co-design innovations and secure predictive architectures. Therefore, this section systematically addresses these challenges, first by analyzing hierarchical decoupling strategies, then by exploring material and design-level advancements, and finally by discussing IoT-enabled collaborative governance, paving the way for resilient self-organizing grids.

4.1. Multi-Timescale Conflicts and Decoupling Control

A fundamental challenge in governing modern distribution grids, rich with integrated PV systems, energy storage, and EV charging networks, is the inherent conflict across multiple operational timescales [82]. Control actions required for microsecond-to-millisecond electromagnetic stability, such as harmonic cancellation and sub-cycle voltage compensation, often operate independently of, and sometimes in opposition to, the techno-economic optimization objectives managed on a seconds-to-hours timescale, such as market trading and asset lifecycle management [83]. Uncoordinated control strategies risk creating instabilities or leading to suboptimal economic outcomes and accelerated component degradation.

To systematically deconstruct and manage these conflicting temporal demands, a hierarchical control (HC) framework is widely adopted as the standard engineering paradigm. This approach decouples the complex overall control problem into distinct coordinated layers, each responsible for a specific timescale and set of objectives [84]. As detailed in

Table 2. A hierarchical framework for multi-timescale power quality control.

Control Hierarchy	Time Scale	Core Objective	Power System Applications	Key Enabling Technologies and Methods
Level 1: Real-Time Optimization	Minutes to Hours/Days	Economic optimization, global system efficiency, long-term asset health	Economic dispatch, market trading, and asset management	Multi-objective optimization, market mechanisms, and steady-state models
Level 2: Supervisory Control	Seconds to Minutes	Multi-device coordination, short-term performance, and constraint management	Coordinated V2G dispatch, hybrid STATCOM–BESS control, and PV peak shaving	Model predictive control, multi-agent systems, and distributed optimization
Level 3: Regulatory Control	Microseconds to Milliseconds	Fast disturbance rejection, local stability	Sub-cycle voltage compensation, harmonic cancellation, and inverter inner-loop control	STATCOM, MMC, APF, DVR, and embedded fast controllers

, this structure typically comprises three levels: a primary control level for fast local disturbance rejection; a secondary level for restoring system state; and a tertiary level for managing system-wide economic efficiency.

The challenge is further compounded by the different physical characteristics of disturbances in AC- versus DC-dominant systems. Figure 4 provides a comparative illustration of typical AC and DC power quality events, highlighting their distinct signatures in terms of the magnitude, duration, and frequency content.

In traditional AC systems, events such as voltage sags and swells are well-defined by standards, with typical durations ranging from 0.5 cycles to 1 min and magnitudes between 10 and 90% and 110 and 180% of nominal voltage, respectively [85]. In stark contrast, a defining feature of DC-dominant or inverter-rich AC grids is the prevalence of high-frequency emissions known as supraharmonics. Experimental and field measurements consistently identify significant distortion in the 2–150 kHz frequency range, a phenomenon driven by the high-speed switching of power electronic converters [86]. This distinction is critical, as mitigating these high-frequency events requires fundamentally different filtering and control strategies than those used for traditional disturbances.

Within the hierarchical framework, model predictive control (MPC) has emerged as a particularly effective method, due to its inherent ability to handle multi-variable systems with complex constraints [87]. The performance enhancement offered by advanced MPC strategies is not merely theoretical. A simulation study comparing a modified MPC (MMPC) controller against a standard MPC and a traditional PID controller for FRT capability provided definitive quantitative evidence. During a three-phase-to-ground fault, the MMPC controller successfully limited the current overshoot of the distributed generator to 96.1% of its nominal value, whereas the standard MPC and PID controllers allowed overshoots of 268.2% and 150.9%, respectively. Furthermore, the MMPC reduced the frequency drop during the fault from 1.23% (with MPC) to just 0.28%, demonstrating a marked improvement in system stability [83].

Despite its proven effectiveness, the primary barrier to the widespread implementation of hierarchical MPC (HMPC) is its significant computational burden, particularly for the large-scale non-convex optimization problems found at the tertiary control level. Looking toward future solutions, quantum computing presents a potential, albeit long-term, pathway to overcome this specific computational bottleneck. By leveraging quantum phenomena, algorithms designed for quantum computers have the theoretical potential to solve certain classes of complex optimization problems orders of magnitude faster than their classical counterparts [88,89]. For example, a study applying quantum computing principles to an IEEE 9-bus system optimization problem demonstrated the potential to reduce the computation time from several hours to mere seconds [90]. However, it is critical to frame this potential within a realistic context. Quantum computing is not an imminent off-the-shelf solution for power grid control. The technology faces substantial hurdles, including the high susceptibility of current quantum systems to noise, the ongoing development of robust error-correction mechanisms, and the immense challenge of integrating nascent quantum hardware into existing mature power system operational infrastructures. Therefore, quantum computing should be viewed not as a replacement for hierarchical control but as a potential future enabling technology that could one day unlock the full real-time potential of the framework’s highest optimization layers.

4.2. WBG Device Limitations and Material Science Innovations

The performance of advanced power electronic converters, which form the backbone of modern power quality mitigation, is increasingly defined by the capabilities of WBG semiconductors, primarily SiC and gallium nitride (GaN). These materials offer fundamentally superior properties compared to traditional silicon, enabling operation at higher voltages, frequencies, and temperatures. However, their full potential is constrained by underlying material limitations and distinct performance trade-offs, which necessitates a nuanced approach to device selection and a forward-looking perspective on hybrid architectures and material science innovation [91,92,93].

The choice between SiC and GaN is not absolute but is dictated by the specific power and frequency requirements of the application. Generally, SiC devices, commercially available with voltage ratings up to 1700 V, are better suited for high-power high-voltage applications due to their superior thermal conductivity and robust voltage-blocking capabilities [94]. In contrast, GaN high-electron-mobility transistors (HEMTs), with their higher electron mobility, offer unparalleled switching speeds, making them ideal for high-frequency high-power-density applications, though typically at lower voltage ratings (commercially available up to 650 V–900 V) [91].

A detailed quantitative analysis reveals a clear performance map for these technologies. A 2022 comparative study mapped the optimal operating regions for SiC and GaN converters across various power and frequency levels, with the key findings summarized in

Table 3. Quantified performance regimes for SiC and GaN power converters. Data synthesized from [95].

Performance Metric	SiC MOSFETs	GaN HEMTs	Key Quantifiable Evidence
Optimal Power Range	High Power (>8 kW)	Medium-to-Low Power (<8 kW)	At 16 kW, SiC is superior across the full 1–500 kHz frequency range.
Optimal Frequency Range	Medium-to-Low Frequency (<110 kHz at 8 kW)	High Frequency (>110 kHz at 8 kW)	At 1 kW, GaN is superior in the 14–500 kHz frequency range.
Power Density	Lower	Higher	GaN-based flyback converter enables 6× reduction in transformer volume compared to Si-based designs.
Commercial Voltage Rating	Higher (up to 1700 V)	Lower (<900 V)	Commercial devices show SiC has higher voltage ratings, making it suitable for medium-voltage applications.

. At a high power level of 16 kW, SiC technology demonstrates superior performance across the entire 1–500 kHz frequency range. As the power level decreases, GaN’s high-frequency advantage becomes evident. At 8 kW, SiC is optimal below 110 kHz, while GaN is superior at higher frequencies. This trend continues at lower power levels; at 1 kW, SiC’s optimal range is confined to below 14 kHz, with GaN dominating the rest of the high-frequency spectrum. This trade-off is largely driven by GaN’s ability to operate at much higher switching frequencies, which significantly reduces the size of passive components. For instance, a comparative analysis of a 100 W flyback converter showed that a GaN-based design could operate at ten times the switching frequency of its Si-based equivalent, resulting in a six-fold reduction in transformer volume and a ten-fold reduction in output capacitor size, thereby dramatically increasing the power density [95].

Given these distinct and complementary characteristics, a significant research frontier is the development of hybrid SiC–GaN architectures. The goal of this approach is to create novel converter topologies or integrated power modules that synergistically combine the high-voltage robustness of SiC with the superior high-frequency switching performance of GaN. Research in this area is actively exploring new packaging methods, such as using flexible PCBs to create ultra-low inductance 3D-integrated modules that can accommodate both device types, as well as novel converter topologies like the hybrid SiC-GaN bidirectional full-bridge DC–DC converter [96]. While this field is still emerging and lacks extensive experimental validation of complete hybrid systems, it represents a logical and promising pathway to overcome the limitations of single-material devices [94].

Ultimately, the advancement of power electronics is fundamentally tethered to material science innovations. Persistent reliability concerns, such as gate oxide stability in SiC MOSFETs and dynamic ON-resistance (current collapse) in GaN HEMTs, remain critical bottlenecks for industrial adoption, especially in demanding applications like EV chargers [92]. Therefore, continued research into improved crystal growth processes to reduce defect densities and determine new substrate materials, as well as advanced high-temperature packaging solutions is essential to enhance the reliability, reduce the cost, and unlock the full performance potential of WBG technologies.

4.3. Digital Twin-Driven Predictive Governance and Cyber–Physical Security

The advent of digital twins (DTs)—virtual replicas of physical grids synchronized via IoT, AI, and high-fidelity models—has ushered in a paradigm shift from reactive to anticipatory power quality management. By creating a high-fidelity mapping between physical assets and their virtual counterparts, DTs can predict harmonic resonance, voltage violations, and phase imbalances, enabling preemptive compensation. However, the efficacy of this predictive paradigm hinges on two critical factors: the validated fidelity of the virtual model and the security of the cyber–physical infrastructure upon which it is built. This section first examines the experimentally validated performance of DTs in replicating grid behavior, then analyzes the critical challenge of cyber–physical security, and finally evaluates the role of blockchain as a targeted solution for ensuring data integrity.

4.3.1. From Concept to Validated Performance

The value of a DT is directly proportional to its ability to accurately replicate the behavior of its physical counterpart in real time. This capability is no longer merely conceptual but has been quantitatively validated in high-fidelity HIL environments, which bridge the gap between pure simulation and physical deployment [97]. A recent study on a hierarchical DT architecture for power systems provides definitive performance metrics. In the HIL setup, the DT demonstrated “good tracking” of steady-state voltage and power, accurately reproducing the physical grid’s state after load changes. More critically, in replicating dynamic behavior, the DT achieved a “perfect match” in tracking the active power response to voltage variations. The study also quantified the system’s latency, measuring the total communication and processing delay between the physical simulation and the DT at approximately 402.2 ms (varying between 350 ms and 500 ms), a speed deemed sufficient for integration with existing SCADA systems [98]. These validated results confirm the technical feasibility of using DTs for high-fidelity grid monitoring and predictive control.

4.3.2. The Imperative of Cyber–Physical Security

The deep integration of digital monitoring and control systems, while enabling predictive governance, simultaneously creates a vast cyber–physical attack surface. The interconnected nature of DTs, smart sensors, and distributed actuators makes the grid vulnerable to malicious cyber-attacks [99,100,101,102]. These include distributed denial-of-service (DDoS) attacks, which aim to overwhelm communication networks or control systems, thereby disrupting vital grid management functions [100]. A recent simulation showed a DDoS attack during peak hours could compromise communication with nearly 90% of smart meters, directly hindering demand response capabilities and increasing the probability of interruptions [99]. Another critical threat is false data injection (FDI), where an adversary compromises sensor measurements to feed falsified data into the control system, thereby deceiving operators and triggering incorrect control actions that could destabilize the grid [101,102].

Furthermore, these threats are not mutually exclusive and can be combined for greater impact. An attacker could launch a DDoS attack to mask an ongoing FDI attack, preventing operators from accessing the very data that would reveal the manipulation [103]. For a system reliant on digital twins for predictive governance, such attacks are fundamental threats, as they destroy the data integrity (from FDI) and availability (from DDoS) essential for any credible prediction. Consequently, ensuring the integrity and authenticity of the data streams that underpin the DT is not an ancillary concern but an overarching and fundamental challenge for its safe deployment.

4.3.3. Blockchain for Data Integrity and Secure Transactions

To address the critical need for data integrity in a decentralized grid, blockchain technology is emerging as a powerful solution pathway. Rather than a general governance framework, its role is more precisely defined as a distributed, cryptographically secure, and immutable ledger that can provide a trusted environment for peer-to-peer (P2P) data and energy transactions among DERs [104]. Its core properties—security, integrity, non-repudiation, and immutability—directly counter the threat of data tampering.

The viability of this application is increasingly being validated through experimental prototypes that provide quantifiable performance metrics. For instance, a recent prototype of a blockchain-based ecosystem designed for secure data sharing demonstrated a throughput of 10–20 transactions per second with acceptable computational costs for identity and access control operations [105]. This result provides concrete evidence of the technology’s feasibility for non-time-critical data exchange, which is a critical function for verifying control signals or logging operational data in a distributed network. By providing a foundational layer of trust, blockchain can ensure that the data fed into digital twins and distributed control systems are verifiable and untampered, thereby fortifying the grid’s cyber–physical security [105,106]. Looking forward, as quantum computing evolves, research into quantum-resistant encryption algorithms will become essential for ensuring the long-term security of this critical infrastructure.

4.4. Distributed Dynamic Collaborative Governance

The sheer scale and complexity of future distribution grids, potentially comprising millions of interacting DERs, render traditional centralized control architectures computationally infeasible and insufficiently responsive. This operational reality necessitates a fundamental paradigm shift towards decentralized autonomous governance. The DDCG framework is conceptualized as the emerging architecture for this autonomous future, defined by the synergistic integration of validated data-driven technologies for intelligence, execution, and security.

A core component of the DDCG framework is decentralized intelligence, for which MARL is emerging as the key enabling technology. In the MARL paradigm, individual devices (e.g., inverters, battery systems) act as intelligent “agents” that learn optimal control policies through local observations and peer-to-peer communication, eliminating the need for a central controller [107]. The performance superiority of MARL has been rigorously quantified in HIL experiments. A recent HIL study on an MARL-enhanced controller for a power converter provided definitive evidence of its enhanced dynamic response: compared to a conventional controller, the MARL version achieved a settling time of 8 s (versus 3.1 s) and lowered the root mean square error (RMSE) by 65% [108]. In another HIL validation for a DC microgrid, an MARL scheme improved the output voltage balance by 2.6% to 3.2% and significantly shortened convergence time.

For the decentralized intelligence of MARL to be effective in real-world grid applications, control decisions must be executed with minimal latency. This requirement drives the adoption of edge intelligence, an architecture that embeds computational power and AI algorithms directly into grid-edge devices [109]. By processing data and executing control actions locally, edge intelligence enables the millisecond-level autonomous responses necessary for managing fast-evolving power quality events. The practical application of this concept has been successfully demonstrated in the field. A notable field demonstration of the ProDROMOS system utilized a real-time digital twin and edge optimization algorithms to provide voltage regulation for a 678 kW PV system, serving as crucial validation that edge intelligence can translate advanced control theory into practical real-world grid support functions [110].

The DDCG framework is therefore defined by the convergence of these validated technological trends into a cohesive system-of-systems architecture. Its key characteristics are as follows:

• Autonomous Nodes: individual DERs function as intelligent self-governing agents, empowered by MARL and other AI models running on edge hardware to locally manage power quality.
• Cyber-Physical Co-Optimization: these autonomous nodes are guided by high-fidelity HIL-validated digital twins (as discussed in Section 4.3) that provide predictive foresight and enable optimized coordination.
• Secure and Interoperable Communication: the integrity of data and control signals exchanged between these distributed agents is ensured by secure frameworks, with technologies like blockchain (as discussed in Section 4.3) providing a foundational layer of trust and verifiability.

This integrated architecture represents a profound evolution from today’s centralized control paradigms, laying out a clear evidence-based pathway toward a truly resilient, self-organizing, and intelligent power grid.

5. Conclusions

The transition toward renewable energy and electrified transportation has fundamentally transformed the operational dynamics of modern distribution grids, rendering conventional PQ governance models inadequate. This review has systematically addressed the escalating PQ challenges by establishing an integrated analytical framework spanning diagnostic methodologies, technological solutions, and future governance paradigms.

Central to this work is the introduction of the TGO-SM, a systematic diagnostic tool that elucidates intrinsic correlations between core PQ objectives—harmonic suppression, voltage regulation, and three-phase balancing—and the distinct demands of high-penetration PV, EV charging, and energy storage scenarios. By mapping these interdependencies, the TGO-SM provides a structured foundation for prioritizing mitigation strategies amid conflicting operational requirements.

Furthermore, advanced mitigation technologies—including STATCOMs, SSTs, GFMs, UPQCs—were deconstructed as modular “unit operations”. Their synergistic integration is critical for system-level optimization, transcending the limitations of isolated device performance. Crucially, the analysis reveals that multi-timescale control conflicts pose a persistent challenge, necessitating hierarchical decoupling strategies to harmonize fast electromagnetic control with slower techno-economic optimization.

Looking forward, the DDCG paradigm emerges as a transformative architecture for autonomous grid operation. By converging edge intelligence, digital twins, and blockchain-secured communication, DDCG enables a shift from reactive compensation to predictive governance. This framework empowers self-aware nodes to achieve real-time coordination while ensuring cyber–physical resilience through verifiable data integrity.

In summary, this research delivers a coherent roadmap for navigating contemporary PQ complexities while charting a course toward resilient self-organizing distribution networks. The proposed methodologies and forward-looking paradigms offer critical insights for both academic research and industrial implementation in the pursuit of grid modernization.