Key Takeaways & Executive Findings
- •• • The proposed method achieves a minimum power angle deviation and maximum transient stability margin, as validated by simulations, directly enhancing grid stability under high renewable penetration. • • By optimizing renewable output with capacity reserve, the method increases renewable energy utilization while reducing system power angle oscillations, addressing the intermittency bottleneck. • • The virtual power angle model accurately captures the dynamic characteristics of renewable generators, enabling precise stability assessment under disturbances such as N-1 faults. • • The neural network-based multi-objective optimization algorithm effectively solves the trade-off between power angle deviation and stability margin, providing a computational framework for real-time control.
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Abstract
The increasing penetration of renewable energy in sending-end grids introduces significant power angle and voltage stability challenges due to the stochastic and fluctuating nature of renewable generation. This paper proposes an optimal control method for renewable energy generation to enhance the power angle stability of sending-end grids. First, a model of a renewable energy generation transmission system is established, and the output power of renewable sources is optimized based on sending-end grid stability. Second, the dynamic responses of power angle and voltage under disturbances are analyzed, and a virtual power angle model characterizing the dynamic behavior of renewable energy units is derived. Third, a power angle stability control model based on energy fluctuation is developed to analyze the impact of energy fluctuations on power angle stability. Finally, a multi-objective optimization algorithm based on neural networks is employed to minimize power angle deviation and maximize transient stability margin of the sending-end grid. Simulation results validate the effectiveness of the proposed method. The method demonstrates significant improvements in grid stability, renewable energy utilization, and reduction of system power angle oscillations, thereby effectively enhancing the power angle stability of sending-end grids with high renewable penetration.
1. Introduction
Existing commercial approaches for sending-end grid stability predominantly focus on reactive power compensation devices and voltage support, with limited attention to the energy-side dynamics of renewable generation. Coordinated control strategies between synchronous condensers and the power system remain scarce, failing to fully exploit the reactive power support capability of synchronous condensers. Furthermore, improper placement of FACTS devices can induce rotor angle instability and inter-area oscillations, as rotor angle stability is often not included as a constraint in optimization algorithms. These gaps result in suboptimal stability performance, particularly under high renewable penetration and N-1 contingencies.
This study addresses the power angle stability bottleneck by establishing a renewable energy transmission system model and optimizing renewable output based on sending-end grid stability. A virtual power angle model is derived to characterize the dynamic response of renewable units under disturbances, and a power angle stability control model based on energy fluctuation is developed. The multi-objective optimization problem, minimizing power angle deviation and maximizing transient stability margin, is solved using a neural network algorithm. Simulation results confirm the method's effectiveness in enhancing stability, increasing renewable utilization, and reducing oscillations, providing a robust solution for sending-end grids with high renewable shares.
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ZHAO Feng, ZENG Bing, TAN Beisi, CHEN Xiao, LI Zhi, ZHANG Wenchao (2026). Optimal Control Method for Renewable Energy Generation Based on Power Angle Stability of Sending-End Grid. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9706
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Frequently Asked Questions
What specific failure mechanisms under N-1 faults are mitigated by the proposed control method?
Under N-1 faults on AC transmission lines, the equivalent electromagnetic output power of renewable generation decreases, causing acceleration of synchronous generators and deteriorating power angle stability. The proposed method optimizes renewable output with capacity reserve, reducing the equivalent mechanical power increase and thus mitigating acceleration. Simulation results show that the method effectively suppresses power angle oscillations and maintains stability, as evidenced by the minimum power angle deviation achieved.
How does the neural network-based multi-objective optimization compare to conventional optimization algorithms in terms of computational efficiency and solution quality?
The neural network-based algorithm solves the multi-objective problem of minimizing power angle deviation and maximizing transient stability margin. While specific computational metrics are not provided in the extracted text, the method's effectiveness is validated through simulations, demonstrating its capability to handle the nonlinear and dynamic nature of the sending-end grid. Conventional algorithms often struggle with real-time application due to the complexity of power system dynamics; the neural network approach offers a promising alternative for online optimization.
What are the scalability bottlenecks when applying this method to larger grids with higher renewable penetration?
Scalability challenges include the increased complexity of the network equations and the need for accurate real-time data on renewable output and grid parameters. The method's reliance on a virtual power angle model and energy fluctuation analysis may require adaptation for larger systems with numerous nodes. However, the neural network's ability to learn complex mappings could potentially handle higher dimensions, though further testing on large-scale systems is necessary to confirm.
How does the proposed method ensure cost parity with legacy stability enhancement technologies such as synchronous condensers or FACTS devices?
The method leverages existing renewable generation capacity with reserve, avoiding additional hardware costs associated with synchronous condensers or FACTS devices. By optimizing control setpoints, it enhances stability without significant capital expenditure. While exact cost comparisons are not provided, the approach increases renewable utilization and reduces oscillations, potentially lowering operational costs and deferring infrastructure upgrades, thus offering a cost-effective alternative.
What are the key parameters and thresholds for the virtual power angle model, and how are they derived from the system dynamics?
The virtual power angle model is derived from the network equations (Equations 4-11), with stability coefficients KA and KB that depend on sending-end voltage and power fluctuations. The model uses the power angle δ, where δ < 90° indicates small-disturbance stability and δ > 90° indicates instability. These parameters are computed from node admittances, generator inertias, and power injections, providing a quantitative criterion for stability assessment.
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