Research Progress on Application of Supercapacitors in Grid Frequency Regulation
Abstract
1. Introduction
2. Overview of Supercapacitors
2.1. General Classification
2.2. Performance and Frequency Regulation Suitability of SCs
3. Key Technologies for SC-Based Frequency Regulation
3.1. Control Strategies
3.1.1. Virtual Droop Control
3.1.2. Virtual Inertia Control
3.1.3. Others

3.2. Topological Structure
3.3. Capacity Configuration Method
3.3.1. Conventional Configuration Methods
3.3.2. Optimization Algorithm-Based Capacity Sizing
4. Engineering Applications and Practical Cases of SCs for Frequency Regulation
4.1. Independent Frequency Regulation
4.2. Coordinated Frequency Regulation with Thermal Power Units
4.3. Auxiliary Frequency Regulation for Renewable Energy Stations
5. Conclusions and Prospects
- (1)
- Improvement of intrinsic device performance. Simultaneously achieving the high-power output required for instantaneous grid frequency regulation and the long-duration energy supply needed for sustained power adjustment remains the core bottleneck restricting the engineering application of supercapacitors in grid frequency regulation. From the perspective of materials and manufacturing processes, the pre-lithiation techniques of commercial LICs generally suffer from low lithium utilization. Therefore, high-efficiency and low-cost pre-lithiation strategies need to be urgently developed to reduce irreversible capacity loss inside cells and improve the initial Coulombic efficiency [130]. Meanwhile, developing high-specific-capacity cathode materials and high-rate anode materials to further boost energy density and power density is a key research direction for promoting the large-scale application of supercapacitors in the field of frequency regulation in the future [131].
- (2)
- Optimization and engineering promotion of control strategies. At present, most advanced control algorithms are only verified by simulation without field engineering tests under real grid power disturbances, which limits their practical promotion value. In the future, it will be necessary to accelerate the engineering implementation and scenario adaptation of advanced control algorithms. On the one hand, the composite frequency regulation framework combining virtual inertia and droop control should be improved and an SOC adaptive correction mechanism should be introduced. The output power is dynamically constrained according to the real-time residual capacity of supercapacitors to effectively avoid the risks of overcharge and overdischarge caused by continuous frequency disturbances and extend the service life of equipment [88]. On the other hand, aiming at the drawbacks of MPC, including complex iterations and long time delays, a lightweight real-time solution framework should be constructed to reduce computational overhead and meet the stringent millisecond-level rapid response requirements for primary grid frequency regulation. In addition, adopting machine learning to realize online adaptive tuning of controller parameters is also an important development direction [132].
- (3)
- Improvement of topological structures. In terms of topological schemes, the current low-voltage parallel boost topology boasts a simple structure and low control complexity. However, circulating current induces severe capacity degradation and energy loss, and its capacity expansion capability is restricted. The high-voltage cascaded topology requires no boost transformer and achieves higher theoretical efficiency, yet it is accompanied by great control difficulty. Moreover, the voltage imbalance problem arising from the series operation of large-capacity modules has not been effectively resolved. In the future, the integrated design of MMC submodules should be optimized to cut down converter switching losses and simplify the grid-connected architecture of medium- and high-voltage frequency regulation power stations [133]. Meanwhile, new cascaded topologies integrated with active voltage equalization control ought to be developed to real-time balance the voltage of individual cells within large-capacity series modules, mitigate inconsistent cell aging, and enhance the overall operational stability of energy storage stacks.
- (4)
- Optimization of capacity sizing methods. Conventional capacity sizing methods only take a single dimension of constraints into consideration at present. When applied to new power systems, they tend to lead to a dilemma between conservative schemes (excessive configurations resulting in poor economy) and radical schemes (insufficient frequency regulation capacity). Future research should strengthen the study of capacity configuration based on intelligent optimization algorithms. Global search strategies will be introduced to upgrade the optimization searching mechanism, improve computational efficiency and effectively avoid local optimal solutions so as to ensure that the final capacity configuration scheme meets the frequency regulation requirements while achieving the optimal full-life-cycle economic performance of the system [134].
- (5)
- Expansion of frequency regulation application scenarios. At present, SCs are mostly deployed for joint frequency regulation with lithium batteries, while coordinated operation with other energy storage systems remains limited. PHS features high technical maturity and large installed capacity, outperforming conventional thermal power units in terms of ramp capability and start–stop speed [135]. It can serve as a dominant resource for secondary frequency regulation to undertake medium- and low-frequency power regulation, and it can also participate in primary frequency regulation within a certain range to provide transient frequency support [136]. The coordinated operation of SCs and PHS allows SCs to undertake fast short-timescale regulation tasks, compensating the response deficiencies of pumped hydro units and unlocking their full frequency regulation potential. Economically, SCs have low per-unit power cost and are suitable for frequent short-duration high-power energy absorption and release; PHS enjoys outstanding per-unit-capacity-cost advantages and is ideal for long-duration large-scale energy dispatch. Their complementary strengths enable frequent participation in frequency regulation while optimizing the whole-life-cycle comprehensive cost. Technically, SCs and PHS exhibit favorable compatibility in AGC coordination, power allocation strategies and coordinated protection configuration [137]. Since the frequency response characteristics of pumped hydro units are similar to those of thermal power units and abundant practical experience has been accumulated for supercapacitor-assisted thermal unit frequency regulation, the corresponding control strategies and operational concepts are transferable and can provide valuable references for the synergistic application of SCs and PHS. In addition, the electrical topology for supercapacitor integration into pumped hydro stations can adopt the centralized grid connection scheme used in thermal power plants and renewable energy stations, with connection at the low-voltage side of step-up substations for centralized grid integration. Future research should further deepen the technical framework for joint grid frequency regulation by supercapacitors and pumped hydro units to fully tap their latent operational benefits.
- (6)
- Research on the digital operation and maintenance technology of supercapacitor energy storage frequency regulation systems. Large megawatt-scale supercapacitor frequency regulation power stations consist of thousands of capacitor cells connected in series and parallel. Under long-term high-frequency charge–discharge cycling conditions, discrepancies in the aging rate of individual cells will continuously accumulate and amplify, which may easily cause overvoltage and degradation failure of partial cells and further trigger potential safety hazards in equipment operation. The traditional operation and maintenance mode relies on offline regular shutdown inspections, which suffer from poor timeliness. It fails to grasp the real-time health status of each energy storage module and leads to delayed fault early warnings. Subsequent research needs to establish a high-precision full-scale digital model for supercapacitor energy storage systems [138]. Multi-dimensional operating data, including station operating power, module voltage, cell voltage and temperature, are collected and fused in real time to construct a cell-health-state evaluation model for accurately predicting the aging degradation degree and remaining cycle life of cells. Combined with machine learning-based intelligent fault early-warning algorithms, online precise localization of module faults and advance prediction of degradation trends can be realized. Predictive proactive maintenance is implemented to drastically reduce losses of frequency regulation benefits caused by unplanned station shutdowns and comprehensively improve the long-term continuous operational safety and reliability of supercapacitor frequency regulation power stations.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
| PFR | Primary frequency regulation |
| SFR | Secondary frequency regulation |
| FES | Flywheel energy storage |
| PHS | Pumped hydro storage |
| CAES | Compressed air energy storage |
| SCs | Supercapacitors |
| SMES | Superconducting magnetic energy storage |
| LABs | Lead–acid batteries |
| FBs | Flow batteries |
| LIBs | Lithium-ion batteries |
| AGC | Automatic generation control |
| EDLCs | Electrochemical double-layer capacitors |
| PCs | Pseudocapacitors |
| HSCs | Hybrid supercapacitors |
| LICs | Lithium-ion capacitors |
| CGDC | China Green Development Investment Group Co., Ltd. |
| SOC | State of charge |
| DFIG | Doubly fed induction generator |
| MPC | Model predictive control |
| FLC | Fuzzy logic control |
| PCS | Power conversion system |
| PSO | Particle swarm optimization |
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| Type of Energy Storage | Power Density (kW/kg) | Energy Density (Wh/kg) | Cycle Life | Efficiency (%) | Response Time | Applicable Frequency Regulation Stage | Virtual Inertia Support Capacity | Droop Control Adaptability |
|---|---|---|---|---|---|---|---|---|
| FES | 400–5000 | 20–80 | Over 20,000 cycles | 70–80 | 1–20 ms | PFR | Excellent | Excellent |
| PHS | 0.1–0.2 | 0.2–2 | 30–60 years | 60–70 | 10 s–4 min | SFR | Bad | Bad |
| CAES | 0.2–0.6 | 20–60 | 20–40 years | 40–50 | 1 s–1 min | SFR | Bad | Bad |
| SCs | 4000–10,000 | 10–20 | Over 100,000 cycles | 80–90 | 1–20 ms | PFR | Excellent | Excellent |
| SMES | 500–2000 | 1–10 | Over 100,000 cycles | 80–95 | 1–5 ms | PFR | Excellent | Excellent |
| LABs | 90–700 | 50–80 | 500–1000 cycles | 80–90 | >20 ms | PFR | Medium | Bad |
| FBs | 50–140 | 75–200 | Over 12,000 cycles | 60–85 | 20 ms–1 s | PFR&SFR | Good | Medium |
| LIBs | 200–300 | 200–400 | Over 10,000 cycles | 90–95 | 20 ms–1 s | PFR&SFR | Good | Good |
| Type | Energy Density (Wh/kg) | Power Density (kW/kg) | Cycle Life (Cycles) | Monthly Self-Discharge Rate (%/Month) |
|---|---|---|---|---|
| EDLCs | 5–15 | 10–100 | >100,000 | 1–3 |
| PCs | 5–30 | 1–10 | 10,000–50,000 | 5–10 |
| HSCs | 10–100 | 5–50 | 10,000–100,000 | 3–5 |
| Institution | Region | Location | Type | Energy Density (Wh/kg) | Power Density (kW/kg) |
|---|---|---|---|---|---|
| UCAP Power | USA | San Diego, CA | EDLCs | 8.6 | 18.9 |
| Man Yee Technology | China | Hong Kong | EDLCs | 4.1 | 3.2 |
| Skeleton Tech | Germany | Markranstädt | HSCs | 73 | 5.4 |
| Vinatech | South Korea | Jeonju | HSCs | 61.1 | 4.4 |
| Aowei Technology | China | Shanghai | HSCs | 100 | 9.5 |
| Musashi | Japan | Tokyo | LICs | 24 | 4 |
| JM Energy | Japan | Yamanashi-ken | LICs | 10 | 21.5 |
| AFEC | Japan | Tokyo | LICs | 10 | 17.2 |
| Shin-Kobe Electric Machinery | Japan | Tokyo | LICs | 8 | 7.4 |
| CGDG | China | Beijing | LICs | 17 | 15 |
| Configuration Method | Core Basis | Advantages | Limitations |
|---|---|---|---|
| Frequency regulation index-based | Grid primary frequency regulation and AGC assessment requirements | High compliance and qualification rate for frequency regulation | Insufficient consideration of economic benefits |
| System and device characteristic-based | Intrinsic parameters and operating characteristics of supercapacitors | Full utilization of device performance with high operational safety | May deviate from actual grid demand |
| Control strategy-based | Matching with the adopted control strategy | Strong compatibility with control logic and superior dynamic response of the system | Heavily dependent on control strategy parameters |
| Economy-oriented | Construction cost and revenue from frequency regulation | Clear return on investment, suitable for commercial operation | Vulnerable to fluctuations in policy and electricity prices |
| Intelligent optimization algorithm | Multi-objective optimization | Capable of resolving conflicts among multiple optimization objectives | Prone to falling into local optima |
| Power Plant Name | Scale and Related Equipment | Project Progress | Site Location |
|---|---|---|---|
| Huaneng Luoyuan | 5 MW SCs (4 min) + 15 MW/7.5 MWh lithium battery | Commissioned | Fujian |
| Guangdong Jinwan | 4 MWSCs (10 min) + 16 MW/8 MWh lithium battery | Commissioned | Guangdong |
| Datang Lubei | 4 MWSCs (30 s) + 5 MW/5 MWh lithium battery | Commissioned | Shandong |
| Huaneng Yangluo | 6 MW SCs (6 min) + 14 MW/14 MWh lithium battery | Commissioned | Hubei |
| Huaneng Zuoquan | 10 MW SCs (6 min) + 10 MW/10 MWh lithium battery | Commissioned | Shanxi |
| Huaneng Yimin | 16 MW SCs (10 min) | Commissioned | Inner Mongolia |
| Huaneng Tongchuan | 5 MW SCs (10 min) + 15 MW/15 MWh lithium battery | Commissioned | Shaanxi |
| Datang Shendong Thermal Power | 10 MW SCs (10 min) | Under construction | Liaoning |
| Datang Fuping Thermal Power | 5 MW/0.5 MWh SCs + 5 MW/5 MWh lithium battery | Under construction | Shaanxi |
| Datang Qinling Power Generation | 10 MW SCs (10 min) + 10 MW/10 MWh lithium battery | In preparation | Shaanxi |
| Performance Index | Energy Storage Out of Service | Energy Storage in Service | Improvement Ratio (%) |
|---|---|---|---|
| Regulation rate | 0.0488 | 0.9839 | 1916.19 |
| Regulation accuracy | 1.3228 | 5.1199 | 287.05 |
| Response time | 0.1859 | 0.7162 | 285.26 |
| Comprehensive performance index | 0.5406 | 0.8637 | 59.77 |
| Comprehensive performance requirement | 0.55 | 0.55 | — |
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Share and Cite
Quan, F.; Li, Z.; Zhang, Y.; Ye, B.; Zhang, T.; Zheng, Y.; Li, L.; Sun, X. Research Progress on Application of Supercapacitors in Grid Frequency Regulation. Batteries 2026, 12, 311. https://doi.org/10.3390/batteries12080311
Quan F, Li Z, Zhang Y, Ye B, Zhang T, Zheng Y, Li L, Sun X. Research Progress on Application of Supercapacitors in Grid Frequency Regulation. Batteries. 2026; 12(8):311. https://doi.org/10.3390/batteries12080311
Chicago/Turabian StyleQuan, Fengyun, Zilong Li, Yunfei Zhang, Bin Ye, Tong Zhang, Yong Zheng, Ling Li, and Xiaoxia Sun. 2026. "Research Progress on Application of Supercapacitors in Grid Frequency Regulation" Batteries 12, no. 8: 311. https://doi.org/10.3390/batteries12080311
APA StyleQuan, F., Li, Z., Zhang, Y., Ye, B., Zhang, T., Zheng, Y., Li, L., & Sun, X. (2026). Research Progress on Application of Supercapacitors in Grid Frequency Regulation. Batteries, 12(8), 311. https://doi.org/10.3390/batteries12080311
