A Multi-Time Scale Hierarchical Coordinated Optimization Operation Strategy for Distribution Networks with Aggregated Distributed Energy Storage
Source abstract
Abstract
In recent years, with the steady growth of load demand in distribution networks, the fluctuation and uncertainty of power loads have significantly increased. Meanwhile, the rising penetration of photovoltaic generation has further exacerbated the challenges of power system accommodation capability. To enhance photovoltaic accommodation capability and realize the secure and economic operation of distribution networks, a multi-time scale hierarchical coordinated optimization operation strategy for distribution networks with aggregated distributed energy storage has been proposed. First, the regulation requirements of aggregated distributed energy storage are analyzed, and a distributed energy storage aggregation model is established based on an inner approximate Minkowski Sum. Subsequently, a multi-time scale optimization operation model considering source-load uncertainties for day-ahead, intra-day, and real-time stages is developed based on a stepped carbon emission cost model. Then, a power allocation method within the aggregated distributed energy storage based on the water-filling algorithm is presented. Finally, a practical distribution network in a demonstration county in China is used as a case study to validate the proposed method. The results demonstrate that the proposed strategy effectively reduces system operation costs while improving photovoltaic accommodation capacity and enhancing the reliability of system operation.
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Research context
OpenAlex topics: Optimal Power Flow Distribution · Power Systems and Renewable Energy · Smart Grid Energy Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Grid Henan Economic Research Institute, Zhengzhou 450052, China
Hainan Institute, Zhejiang University, Sanya 572025, China
Funding
Science and Technology Project of State Grid Henan Economic Research Institute
References (35)
Show source-provided references
1. Low-Carbon Scheduling of Power System with Wind Power Considering Uncertainty of Both Source and Load Sides
2. Research on Distributed Energy Storage Resource Management and Strategy Participating in Demand Response of New Power Systems
3. Duan, X., Wang, Y., Wei, Y., Li, Y., and Cui, X. (Mod. Electr. Power, 2024). DPV and HESS Joint Optimal Configuration in Distribution Network Considering Renewable Energy Consumption, Mod. Electr. Power, ahead of print.
4. Key Technologies and Prospects of Demand-Side Resource Utilization for Power Systems Dominated by Renewable Energy
5. Planning Method for Resource Aggregation of Virtual Power Plant Based on Dynamic Reconstruction
6. Multi-Time-Scale Resource Allocation Based on Long-Term Contracts and Real-Time Rental Business Models for Shared Energy Storage Systems10.35833/MPCE.2023.000744 ↗
7. Kwon, K., Lee, H.-B., Kim, N., Park, S., and Joshua, S.R. (2024). Integrated Battery and Hydrogen Energy Storage for Enhanced Grid Power Savings and Green Hydrogen Utilization. Appl. Sci., 14.10.3390/app14177631 ↗
8. Yin, Q. (2019). Scale Distributed Energy Storage Aggregation Modeling and Cooperative Optimized Regulation Strategy. [Ph.D. Thesis, North China Electric Power University].
9. Overview of Virtual Power Plant Modeling and Optimization Control for Distributed Resource Aggregation and Control
10. Analysis and Prospect of Distributed Energy Storage Business Modes
11. Aggregation Reference Model and Quantitative Metric System of Flexible Energy Resources
12. Research on Demand Side Resource Feasible Region Aggregation Based on Improved Zonotope
13. Flexible Aggregation and Coordinated Scheduling Strategy for Renewable Powered 5G Communication Base Station Clusters
14. A Concise, Approximate Representation of a Collection of Loads Described by Polytopes10.1016/j.ijepes.2016.05.001 ↗
15. Flexible Resource Cluster Response Based on Inner Approximate and Constraint Space Integration
16. Aggregate Feasible Region of DERs: Exact Formulation and Approximate Models10.1109/TSG.2022.3179998 ↗
17. Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation10.1109/TSG.2021.3068341 ↗
18. Lv, Y., Li, K., Zhao, H., and Lei, H. (2024). A Multi-Stage Constraint-Handling Multi-Objective Optimization Method for Resilient Microgrid Energy Management. Appl. Sci., 14.10.3390/app14083253 ↗
19. Multiple Time-Scales Source-Storage-Load Coordination Scheduling Strategy of Grid Connected to Energy Storage Power Station Considering Characteristic Distribution
20. A FDI Attack-Resilient Distributed Secondary Control Strategy for Islanded Microgrids10.1109/TSG.2020.3047949 ↗
21. Imitation Learning Based Real-Time Decision-Making of Microgrid Economic Dispatch Under Multiple Uncertainties
22. Two-Stage Day-Ahead and Intra-Day Rolling Optimization Scheduling Considering Joint Peak Regulation of Generalized Energy Storage and Thermal Power
23. Zhou, H. (2021). Optimal Dispatch of Wind Power System Considering Source-Load-Storage Joint Peak Shaving. [Ph.D. Thesis, Northeast Electric Power University].
24. Pan, P., Wang, Z., Chen, G., Shi, H., and Zha, X. (2024). Multi-Time Scale Optimal Dispatch of Distribution Network with Pumped Storage Station Based on Model Predictive Control. Appl. Sci., 14.10.3390/app142311122 ↗
25. Huang, X., Qi, D., Chen, Y., Yan, Y., Yang, S., Wang, Y., and Wang, X. (IEEE Trans. Smart Grid, 2024). Distributed Self-Triggered Privacy-Preserving Secondary Control of VSG-Based AC Microgrids, IEEE Trans. Smart Grid, ahead of print.10.1109/TSG.2024.3467392 ↗
26. Chen, Z., Li, Z., Lin, D., Xie, C., and Wang, Z. (J. Mod. Power Syst. Clean Energy, 2024). Multi-Time-Scale Optimal Scheduling of Integrated Energy System with Electric-Thermal-Hydrogen Hybrid Energy Storage under Wind and Solar Uncertainties, J. Mod. Power Syst. Clean Energy, ahead of print.
27. Long-Term Scenario Generation of Renewable Energy Generation Using Attention-Based Conditional Generative Adversarial Networks10.1049/enc2.12106 ↗
28. Trangbaek, K., and Bendtsen, J. (2012, January 10–13). Exact Constraint Aggregation with Applications to Smart Grids and Resource Distribution. Proceedings of the 2012 IEEE 51st Conference on Decision and Control (CDC), Maui, HI, USA.10.1109/CDC.2012.6426475 ↗
29. Source-Storage-Load Coordinated Economic Dispatch of An Active Distribution Network under Multiple Time Scales
30. Distributed Optimization for Joint Peer-to-Peer Electricity and Carbon Trading Among Multi-Energy Microgrids Considering Renewable Generation Uncertainty10.1049/enc2.12110 ↗
31. Fu, X., Yu, S., He, Q., Wang, L., Chen, C., Niu, C., and Lin, Z. (2024). Distributed Cooperative Dispatch Method of Distribution Network with District Heat Network and Battery Energy Storage System Considering Flexible Regulation Capability. Appl. Sci., 14.10.3390/app14177699 ↗
32. Kamgarpour, M., Ellen, C., Soudjani, S.E.Z., Gerwinn, S., Mathieu, J.L., and Müllner, N. (2013, January 25–30). Modeling Options for Demand Side Participation of Thermostatically Controlled Loads. Proceedings of the IREP Symposium Bulk Power System Dynamics and Control—IX Optimization, Security and Control of the Emerging Power Grid, Rethymno, Greece.10.1109/IREP.2013.6629396 ↗
33. Aggregation and Disaggregation of Energetic Flexibility from Distributed Energy Resources10.1109/TSG.2017.2761439 ↗
34. Taking into Account Carbon Neutrality Benefit and Clean Energy Consumption of Virtual Power Plant Two-layer Cooperative Optimization Dispatching
35. Distributed Optimization Management for Active Distribution Networks with Multiple Microgrids Based on Chance-Constrained Programming