面向电力系统概率稳定性提升的风电虚拟惯量参数优化方法

Virtual Inertia Parameter Optimization Method for Power System Probabilistic Stability Improvement

利用转子中储存的机械能,风电机组能够为系统提供惯量响应,支持系统频率稳定。然而,虚拟惯量参数设置影响系统小干扰稳定性,不合理的参数设置可能导致系统失稳。为此,该文提出一种协同考虑系统概率稳定性的风电虚拟惯量参数优化方法。首先,考虑风电不确定性,以满足电力系统频率稳定性约束和小干扰稳定性约束的概率最大为目标,建立风电虚拟惯量参数优化模型。针对概率优化模型的求解难题,通过考虑系统允许最大扰动情况,将频率概率稳定目标转化为解析频率稳定约束;同时利用解析累积量方法推导小干扰概率稳定性的解析表达式。然后,针对概率稳定性目标函数与虚拟惯量参数灵敏度表达式难以直接获得的问题,以阻尼比作为中间变量推导目标函数与虚拟惯量的灵敏度,将灵敏度作为梯度,采用牛顿法对模型进行求解。最后,通过仿真分析验证了所提方法能够在保证系统频率稳定性的前提下有效提升系统小干扰概率稳定性。

With the mechanical energy stored in the rotor,wind turbine generators can provide inertia response to support frequency stability.However,the unreasonable settings of virtual inertia parameters may threaten the small-signal stability of power systems.Thus,this paper proposes a virtual inertia parameter optimization method that can satisfy system probabilistic stability.Considering the uncertainty of wind power,the virtual inertia parameters optimization model is established to achieve the maximum probability of satisfying frequency stability and small-signal stability.For the difficulty of solving the probabilistic optimization model,the frequency probabilistic stability objective is transformed into a determined frequency stability constraint by considering the extreme fault of the system.Besides,the analytical expression of small-signal probabilistic stability is established by the analytical cumulative method.Taking the damping ratio as the intermediate variable,the sensitivity of objective function and virtual inertia is solved.The Newton method with sensitivity as a gradient is used to solve the optimization model.Case studies demonstrate the proposed model can improve probabilistic small-signal stability while ensuring frequency stability.