Abstract
Guided by the global trends of energy consumption restructuring and the strategic goal of achieving “carbon peaking and carbon neutrality,” the construction of a new power system dominated by renewable energy sources is underway. This research focuses on the transient stability optimization of new energy sending-end grids, with a particular emphasis on the integration of battery energy storage (BES) systems. The objectives include enhancing the proportion of new energy in the interconnected system, ensuring the safety and stability of the sending-end system, and achieving economic efficiency.
1. Introduction
With the increasing penetration of wind power and photovoltaic power in power grids, several challenges arise. Firstly, the reduction of conventional power sources leads to decreased system inertia, frequency response characteristics, and voltage response characteristics, significantly increasing the operational risk of the power grid. Secondly, new energy power stations lack energy storage, which can lead to instability issues. To address these challenges, this research proposes a comprehensive study on the modeling, operation optimization, transient stability mechanism, and control methods of new energy sending-end grids with battery energy storage.
2. Modeling and Operation Optimization of Battery Energy Storage and New Energy System
2.1 Dynamic Models of New Energy Sources and Battery Energy Storage
The dynamic energy input and output models of wind power, photovoltaic power, traditional synchronous power sources, load, and battery energy storage are established. For battery energy storage, the control of the AC-side output power is realized by adjusting the d-axis and q-axis currents (i_d_ac, i_q_ac) of the inverter. The control model is illustrated as follows:
id_ac,iq_ac control model
2.2 Configuration Data of Battery Energy Storage Units
Table 1 summarizes the configuration data of battery energy storage units for the sending-end grid, including storage capacity, maximum charging/discharging power, and maximum ramping rate.
Table 1. Configuration Data of Battery Energy Storage Units
| Storage Unit | Storage Capacity (MWh) | Maximum Charging/Discharging Power (MW) | Maximum Ramping Rate (MW/s) |
|---|---|---|---|
| B1 | 1500 | 300 | 300 |
| B5 | 1000 | 200 | 200 |
| B6 | 1600 | 300 | 200 |
| B14 | 1100 | 500 | 100 |
| B24 | 900 | 500 | 100 |
3. Transient Stability Mechanism and Discriminant Model
3.1 Energy Balance Dynamic Model
The high proportion of new energy in the transmission-end power system is the research object. Based on the energy balance dynamic model and the operation mode optimization model under the target of energy transmission, the dynamic models of transient energy characteristics of traditional synchronous power supply, virtual synchronous power supply, and the DC side of battery energy storage are studied.
3.2 Lyapunov Discriminant Model
A Lyapunov discriminant model for the transient stability of the new energy sending-end system is established based on the energy dynamic characteristic characterization equation of each component. This model provides a foundation for the subsequent transient stability control methods.
4. Transient Stability Control Method Based on Energy Storage Coordination
4.1 Weak Area Identification Model
Based on the transient stability discriminant model and the identification model of weak areas in the sending-end network energy balance during transient energy propagation, the relationship between battery energy storage and the energy characteristics of various power sources is analyzed.
4.2 Multi-Objective Dynamic Coordination Control Strategy
A multi-objective dynamic coordination control strategy is proposed for the rapid suppression of imbalance between the total generation and load in the sending-end system, considering the coordination between battery energy storage and other power sources such as thermal power units, PV, and wind power generation systems.
5. Optimization of Battery Energy Storage System for Improving Transient Stability
5.1 Dynamic Robust Optimal Control Model
The dynamic robust optimal control model and method for transient energy, voltage, and frequency coordination of the sending-end system by battery energy storage are studied. This model considers the uncertainties in system steady-state operation and transient processes.
5.2 Planning Model Considering Multi-Dimensional Uncertainty
Considering multi-dimensional uncertainty and transient stability constraints, a dynamic optimization model and method for battery energy storage system planning are established. Table 2 compares the energy storage installation planning results using distributionally robust optimization (DRO), stochastic programming (SP), and robust optimization (RO).
Table 2. Energy Storage Installation Planning Results
| Method | Node 1 (MWh/MW) | Node 2 (MWh/MW) | Energy Imbalance Rate | Transient Stability Improvement | Solving Time (s) |
|---|---|---|---|---|---|
| DRO | 12790/2842 | 8060/1791 | 4.2% | 29.7% | 226.7 |
| SP | 12430/2762 | 7820/1737 | 5.3% | 28.8% | 259.2 |
| RO | 13450/2989 | 8480/1884 | 3.1% | 31.2% | 277.4 |
6. Conclusion
This research provides a comprehensive study on the transient stability optimization of new energy sending-end grids with battery energy storage. The modeling and operation optimization of battery energy storage and new energy systems are conducted. The transient stability mechanism and discriminant model are established. Furthermore, transient stability control methods and optimization models for battery energy storage are proposed, considering multi-dimensional uncertainties and transient stability constraints. The results demonstrate the effectiveness of the proposed methods in enhancing the transient stability of new energy sending-end grids.