Research on capacity and site selection optimization and integrated frequency regulation control strategy for grid-forming energy storage in high-proportion renewable energy power system
随着可再生能源在电力系统中的比例不断提升, 电力系统的调节与稳定性面临新的挑战。构网型储能作为提升系统调频能力和惯量控制的关键手段,其优化配置至关重要。文章提出了一种储能系统的定容选址优化方法,并设计了综合调频控制策略, 以实现储能系统的高效运行。分析了高比例新能源背景下储能参与调频的动态特性,提出有功-频率下垂控制和虚拟同步机控制两种策略,并将其协同优化形成综合调频方法, 以提高响应速度和频率抑制能力,增强系统稳定性。为优化配置,构建了以调频效果、电网脆弱性和储能经济性为目标的多目标优化模型, 引入自适应多目标进化算法(adaptive multi-objective evolutionary algorithm, AMEA)和改进的逼近理想解排序(technique for order preference by similarity to an ideal solution,TOPSIS)方法, 通过信息熵法赋予不同目标权重,得出储能容量与选址的最优配置方案。案例分析表明,相较现有方法,最优储能配置方案的频率偏差方均根降低至0.038 8,电网脆弱性指数为0.235,且储能成本控制在1.6亿元以内。此外, 虚拟同步机控制策略有效增强了系统的惯量响应能力,频率偏差方均根显著降低, 系统稳定性进一步提高。
英文摘要:
As the proportion of renewable energy in power system continues to increase, the regulation and stability of power system face new challenges. Grid-forming energy storage, as a key means to enhance frequency regulation capability and inertia control, is crucial for its optimized configuration. This paper proposes a method for capacity and site selection optimization of energy storage system, and designs a comprehensive frequency regulation control strategy to achieve efficient operation. Firstly, the dynamic characteristics of energy storage participation in frequency regulation under the background of high-proportion renewable energy are analyzed, and two strategies, active power-frequency droop control and virtual synchronous machine (VSM) control, are proposed. These strategies are synergistically optimized to form a comprehensive frequency regulation method, which improves response speed and frequency suppression ability, thereby enhancing system stability. For optimized configuration, a multi-objective optimization model is constructed, targeting frequency regulation performance, grid vulnerability, and the economic viability of energy storage. The model introduces an adaptive multi-objective evolutionary algorithm (AMEA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method, assigning different weights to objectives using the entropy method, resulting in the optimal configuration scheme of energy storage capacity and site selection. Case analysis demonstrates that compared to existing methods, the optimal energy storage configuration scheme reduces the root mean square (RMS) of frequency deviation to 0.038 8, the grid vulnerability index to 0.235, and controls energy storage costs within 160 million yuan. Moreover, the VSM control strategy effectively enhances the inertia response capability of system, significantly reducing the RMS of frequency deviation and further improving system stability.