风光互补发电系统的分级递阶分布式预测控制

Modern power systems are distributed energy network systems with a high degree of integration of renewable energy, clean energy and the information Internet. Among them, wind power generation and solar power generation systems are intermittent and subject to many external conditions. The coordinated optimization of wind power generation and solar power generation control can meet the load demand, reduce mechanical losses, extend the service life of the unit and ensure the safe and economic operation of the power grid. For large-scale and geographically dispersed wind-solar complementary power generation systems, this paper proposes a hierarchical distributed predictive control strategy. The upper-level optimization controller adopts an iterative distributed predictive control strategy, which not only realizes the optimal power distribution, but also can achieve the economic goals of reducing the mechanical losses of the low-speed shaft of the wind turbine and reducing the power generation cost of the system. The lower-level controller adopts a supervisory predictive control algorithm, which can simultaneously ensure the tracking performance and economic performance of the subsystem. The constructed hierarchical distributed predictive control strategy realizes the "plug-and-play" of distributed energy through the coordinated optimization among subsystems. Simulation experiments prove that the hierarchical distributed predictive control strategy proposed in this paper can effectively meet the requirements of the microgrid such as safety and reliability, high quality and high efficiency, and flexible interaction.

现代电力系统是可再生能源、清洁能源与信息互联网高度融合的分布式能源网络系统, 这其.中, 风力发电、太阳能发电系统具有间歇性且受到较多外部条件约束. 协同优化风力发电、太阳能发.电控制可以在满足负荷需求的同时, 减少机械损耗, 延长机组使用寿命, 保证电网的安全经济运行..针对大规模且地理分散的风光互补发电系统, 本文提出一种分级递阶分布式预测控制策略. 上层优.化控制器采用迭代分布式预测控制策略, 不仅实现功率优化分配, 而且能够实现减少风机低速轴机.械损耗、降低系统发电成本的经济性目标. 下层控制器采用监督预测控制算法可同时保证子系统的.跟踪性能和经济性能. 所构造的分级递阶分布式预测控制策略通过各子系统间的协同优化, 实现了.分布式能源的“即插即用”. 仿真实验证明, 本文提出的分级递阶分布式预测控制策略能够有效地实.现微网的安全可靠、优质高效、灵活互动等要求.