Single-Step Allowable Action Threshold Determination of Renewable Energy Automatic Generation Control Using Model-Based and Data-Driven Method
Source abstract
Abstract
Renewable energy automatic generation control (AGC) has the characteristics of rapid adjustment and flexibility, which play a critical role in frequency regulation. Abnormal outputs in renewable energy AGC may trigger frequency fluctuations and threaten grid security. To address the above problems in renewable energy, AGC, a combined model-based and data-driven method for determining the single-step allowable action threshold, is proposed. Firstly, an AGC model with multiple frequency-regulating units is built, and the threshold can be obtained through simulation considering system status parameters. Secondly, as the model-based method struggles to satisfy the requirement of rapidity, a data-driven model based on CNN-LSTM is employed to determine the threshold in real-time. The training data is provided by a model-based method. Considering the limited coverage and interpretability of neural networks, a statistical error-prevention method is proposed to avoid deviations. Then, an adaptive piecewise constant approximation algorithm is employezd to reduce threshold update frequency and the burden for dispatchers. Finally, an adaptive threshold adjustment method for extreme scenarios is proposed, ensuring the frequency regulation of renewable energy AGC under extreme scenarios. Through experiments, the reliability and validity of the proposed method in threshold determination and error prevention are validated.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Frequency Control in Power Systems · Power System Optimization and Stability · Wind Turbine Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Grid Henan Electric Power Company, Zhengzhou 450052, China
State Grid Henan Electric Power Research Institute, Zhengzhou 450006, China
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China
Funding
State Grid Henan Electric Power Company Technology
References (63)
Show source-provided references
1. Eliminating current sensor dependencies in dab converters using a luenberger observer-based hybrid approach10.1109/TIA.2024.3384465 ↗
2. Saxena, A., Shankar, R., Al Zaabi, O., Al Hosani, K., and Muduli, U.R. (2023, January 11–12). Fuzzy rule-based adaptive virtual inertia control for enhancing multi-region micro-grid dynamic response. Proceedings of the 2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT), Malé, Maldives.10.1109/GlobConHT56829.2023.10087873 ↗
3. Intelligent Load Forecasting and Renewable Energy Integration for Enhanced Grid Reliability10.1109/TIA.2024.3436471 ↗
4. A State-of-the-Art Review on Modern and Future Developments of AGC/LFC of Conventional and Renewable Energy-Based Power Systems10.1016/j.ref.2022.09.006 ↗
5. Hierarchical AGC dispatch with detailed modeling of energy storage system behaviors10.1109/TPWRS.2022.3178432 ↗
6. Improving deployment availability of energy storage with data-driven AGC signal models10.1109/TPWRS.2017.2780223 ↗
7. Bevrani, H., and Hiyama, T. (2011). Intelligent Automatic Generation Control, CRC Press.
8. State-of-the-Art Review on Frequency Response of Wind Power Plants in Power Systems10.1007/s40565-017-0315-y ↗
9. Continuous Random Process Modeling of AGC Signals Based on Stochastic Differential Equations10.1109/TPWRS.2021.3058681 ↗
10. A Novel Multi-Agent DDQN-AD Method-Based Distributed Strategy for Automatic Generation Control of Integrated Energy Systems10.1109/TSTE.2019.2958361 ↗
11. Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement10.1109/TPWRS.2023.3282368 ↗
12. Ondar, A.M., Tremyasov, V.A., and Kenden, K.V. (2025, January 24). Analyzing the Risk Management of Renewable Energy Use. Proceedings of the 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), Sochi, Russian.10.1109/ICIEAM65163.2025.11028565 ↗
13. (2025, October 20). Spain Blackout Reveals Limits of Renewable Solar and Wind Energy. Available online: https://www.independent.co.uk/climate-change/news/spain-blackout-renewable-energy-solar-wind-b2742000.html.
14. (2025, October 20). 2025 Iberian Peninsula Blackout. Available online: https://en.wikipedia.org/wiki/2025_Iberian_Peninsula_blackout#cite_note-ferris-9.
15. Error Prevention Adaptive Optimization Model for Power Dispatch under Time Redundancy Control10.1007/s00202-024-02436-2 ↗
16. Enhancing Resilience Level of Power Distribution Systems Using Proactive Operational Actions10.1109/ACCESS.2019.2941593 ↗
17. A Traffic Analysis and Node Categorization-Aware Machine Learning-Integrated Framework for Cybersecurity Intrusion Detection and Prevention of WSNs in Smart Grids10.1109/ACCESS.2024.3422077 ↗
18. A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks10.1109/ACCESS.2021.3131014 ↗
19. SECS/GEMsec: A Mechanism for Detection and Prevention of Cyber-Attacks on SECS/GEM Communications in Industry 4.0 Landscape10.1109/ACCESS.2021.3127515 ↗
20. False Data Injection Attack and Corresponding Countermeasure in Multienergy Systems10.1109/TPWRS.2023.3276736 ↗
21. A Review of Control Strategies for Automatic Generation Control in Power Systems with Renewable Energy10.1088/2516-1083/ad2706 ↗
22. Liu, D., Wu, Y., Kang, Y., Li, C., Ji, X., and Zhang, X. (2023). Graph Convolution Network for Real-Time AGC Dispatch with High-Penetration Renewable Energy. CSEE J. Power Energy Syst., 1–11.
23. Joint Contribution of RTEM and AGC System for Frequency Stabilisation in Renewable Energy Integrated Power System10.1049/esi2.12145 ↗
24. Efficient Learning of Power Grid Voltage Control Strategies via Model-Based Deep Reinforcement Learning10.1007/s10994-023-06422-w ↗
25. A Real Application of Measurement-Based Load Modeling in Large-Scale Power Grids and Its Validation10.1109/TPWRS.2009.2030298 ↗
26. Analysis and Augmented Model-Based Control Design of Distributed Generation Converters with a Flexible Grid-Support Controller10.1109/TPEL.2018.2876799 ↗
27. Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach10.1109/TSG.2019.2950115 ↗
28. Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., and Shakhnov, V. (2023). Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review. Energies, 16.10.3390/en16104025 ↗
29. Robust Data-Driven State Estimation for Smart Grid10.1109/TSG.2015.2512925 ↗
30. Oh, S., Kong, J., Choi, M., and Jung, J. (2020). Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards. Appl. Sci., 10.10.3390/app10144693 ↗
31. Understanding Automatic Generation Control10.1109/59.207324 ↗
32. Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System10.1109/TASE.2024.3359219 ↗
33. Stochastic assessment of AGC systems under non-Gaussian uncertainty10.1109/TPWRS.2018.2865502 ↗
34. Zhang, Y., Yu, Y., Zhang, Y., Chen, B., and Liu, Z. (2025). Frequency Regulation Performance of a Wind–Energy Storage Hybrid System During Turbine Shutdown Due to Extreme Wind. Processes, 13.10.3390/pr13113383 ↗
35. Detailed modelling and parameters optimisation analysis on governing system of hydro-turbine generator unit10.1049/iet-gtd.2017.0372 ↗
36. Analyzing the inertia of power grid systems comprising diverse conventional and renewable energy sources10.1016/j.egyr.2022.11.022 ↗
37. Tielens, P., and Van Hertem, D. (2012, January 16–17). Grid inertia and frequency control in power systems with high penetration of renewables. Proceedings of the Young Researchers Symposium in Electrical Power Engineering, Delft, The Netherlands.
38. The Power Grid Inertia with High Renewable Energy Sources Integration: A Comprehensive Review10.1155/je/7975311 ↗
39. A Multi-Agent Deep Reinforcement Learning Method for Cooperative Load Frequency Control of a Multi-Area Power System10.1109/TPWRS.2020.2999890 ↗
40. A novel sequence-to-sequence data modelling based CNN–LSTM algorithm for three years ahead monthly peak load forecasting10.1109/TPWRS.2023.3271325 ↗
41. Short-term wind power prediction based on multi-parameters similarity wind process matching and weighed-voting-based deep learning model selection10.1109/TPWRS.2023.3257368 ↗
42. Wind power forecasting enhancement utilizing adaptive quantile function and CNN–LSTM: A probabilistic approach10.1109/TIA.2024.3354218 ↗
43. Short-Term Load Forecasting by Using a Combined Method of Convolutional Neural Networks and Fuzzy Time Series10.1016/j.energy.2019.03.081 ↗
44. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects10.1109/TNNLS.2021.3084827 ↗
45. Real-Time Identification of Power Fluctuations Based on LSTM Recurrent Neural Network: A Case Study on Singapore Power System10.1109/TII.2019.2910416 ↗
46. Deep Learning Through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems10.1016/j.measurement.2021.109330 ↗
47. Robust Mean-Squared Error Estimation in the Presence of Model Uncertainties10.1109/TSP.2004.838933 ↗
48. Mean Squared Error, Deconstructed10.1029/2021MS002681 ↗
49. Robeson, S.M., and Willmott, C.J. (2023). Decomposition of the Mean Absolute Error (MAE) into Systematic and Unsystematic Components. PLoS ONE, 18.10.1371/journal.pone.0279774 ↗
50. Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. (2001, January 21–24). Locally adaptive dimensionality reduction for indexing large time series databases. Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, Santa Barbara, CA, USA.10.1145/375663.375680 ↗
51. Anomaly Detection Using Piecewise Aggregate Approximation in the Amplitude Domain10.1007/s10489-017-1017-x ↗
52. Towards a Transitional Weather Scene Recognition Approach for Autonomous Vehicles10.1109/TITS.2023.3331882 ↗
53. Intelligent Scene Recognition Based on Deep Learning10.1109/ACCESS.2021.3057075 ↗
54. Xu, A., Cong, W., Qiu, J., Yang, T., Wei, Z., and Peng, B. (2020, January 20–23). Early Warning Method of Power Grid Meteorological Disasters Based on Scene Recognition. Proceedings of the 2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Beijing, China.10.1109/ICPSAsia48933.2020.9208536 ↗
55. Multiple Event Detection and Recognition Through Sparse Unmixing for High-Resolution Situational Awareness in Power Grid10.1109/TSG.2014.2314116 ↗
56. A Hybrid Prediction Method for Realistic Network Traffic with Temporal Convolutional Network and LSTM10.1109/TASE.2021.3077537 ↗
57. A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over United States and Europe Using Ground-Based Measurements10.1109/TGRS.2016.2553085 ↗
58. Trend Time–Series Modeling and Forecasting with Neural Networks10.1109/TNN.2007.912308 ↗
59. Root Mean Square Error (RMSE) or Mean Absolute Error (MAE): When to Use Them or Not10.5194/gmd-15-5481-2022 ↗
60. A Comparison of the Efficiency of Using a Deep CNN Approach with Other Common Regression Methods for the Prediction of EGFR Expression in Glioblastoma Patients10.1007/s10278-019-00290-4 ↗
61. Mehtab, S., Sen, J., and Dutta, A. (2020, January 1–3). Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models. Proceedings of the Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, Singapore.10.1007/978-981-16-0419-5_8 ↗
62. Symformer: End-to-End Symbolic Regression Using Transformer-Based Architecture10.1109/ACCESS.2024.3374649 ↗
63. Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting