基于分布式模型预测控制的综合能源系统多时间尺度优化调度

采用具有滚动优化、反馈校正特点的模型预测控制是实现综合能源系统多时间尺度优化调度的关键技术之一.鉴于集中式模型预测控制实现系统整体在线优化的复杂性,文中提出一种基于分布式模型预测控制的综合能源系统多时间尺度优化调度方法,通过各子系统协调配合实现综合能源系统灵活调度.首先,建立以系统日运行经济最优、系统日运行费用及机组启停惩罚费用最小为目标的日前、日内滚动优化模型.然后,在实时阶段采用基于分布式模型预测控制的优化调度策略对整体优化问题进行分解,各子系统根据其他子系统前一时刻的输入序列进行状态估计并优化自身性能指标.最后,通过对各子系统的协调控制进而实现整个系统的在线优化,满足其动态调整需求.仿真结果表明,所提方法能够在改善系统控制性能的同时,提高系统运行的经济性.

Multi-time-scale Optimization Scheduling of Integrated Energy System Based on Distributed Model Predictive Control

The use of model predictive control with characteristics of rolling optimization and feedback correction is one of the key technologies to achieve multi-time-scale optimization scheduling of integrated energy systems. In view of the complexity of the centralized model predictive control to achieve the overall online optimization of the system, this paper proposes a multi-time-scale optimization scheduling method for the integrated energy system based on the distributed model predictive control, which realizes the flexible scheduling of the integrated energy system with the coordination of various subsystems. First, day-ahead and intra-day rolling optimization models are established with the objectives of optimal economy in daily operation of the system,the lowest system daily operation costs and unit on-off penalty costs. Then, at the real-time stage, an optimal scheduling strategy based on the distributed model predictive control is used to decompose the overall optimization problem. Each subsystem estimates the state according to the input sequence of the previous time of other subsystems and optimizes its performance index. Finally, through the coordinated control of various subsystems, the online optimization of the entire system is realized to meet its dynamic adjustment demands. The simulation results show that the proposed method can improve the economy of the system operation while improving the control performance of the system.