水风光互补适应性调度的集合卡尔曼滤波方法北大核心CSCD

水风光互补是实现能源转型、助力碳中和的重要途径。传统的水风光互补中长期调度模型,忽略了气候变化和人类活动引起的水风光资源变异,得到的静态调度规则难以适应变化环境。为此,本研究采用集合卡尔曼滤波技术开展适应性调度研究。首先进行确定性优化调度,得到最优运行轨迹;其次识别调度函数的决策变量与自变量,构建线性调度函数;最后采用集合卡尔曼滤波动态更新调度函数的参数,形成适应性调度规则。以锦屏一级水电站及周边风、光电站为实例,研究表明适应性调度规则较常规调度和静态调度,在检验期年均发电量分别提高6.45亿k W·h和0.53亿k W·h,说明论文所提方法可有效追踪最优轨迹的变化来适应变化环境。

An ensemble Kalman filter method for adaptive operation of hydro-wind-solar hybrid power systems

The hydro-wind-solar hybrid power system is an important approach to achieving energy transition and carbon neutrality. Traditional medium- and long-term operation models for such systems ignore the variations in hydropower, wind and solar resources caused by climate change and human activities, so they often formulate operating rules that are static and difficult to adapt to the changing environment. In this work, we adopt an ensemble Kalman filter to study the adaptive operation of the system. First, an optimal operating trajectory is obtained using the deterministic optimal operation of the system; then, a decision variable and an independent variable are identified for the operating function, and a linear operating function is constructed. Finally, the parameters of the operating function are updated dynamically using the ensemble Kalman filter, forming adaptive operating rules. This procedure is applied in a case study of the Jinping I hydropower station along with the wind and photovoltaic power stations nearby. The result shows that in the validation period, the new adaptive operating rules achieve a power output increase of 645 and 53 million kW·h against the conventional and static rules, respectively. This demonstrates these new rules can adapt to the changing environment through tracking changes in the optimal operating trajectory.