基于自适应步长双闭环模型预测控制的主动配电网优化调度

Optimal Scheduling of Active Distribution Network Based on Adaptive Step Double Loop Model Predictive Control

为应对不确定性分布式可再生能源大量接入对主动配电网优化调度带来的挑战,提出一种基于自适应步长双闭环模型预测控制的主动配电网优化调度策略。首先,在常规模型预测控制的基础上建立双闭环反馈机制,通过充分利用主动配电网中各可控资源实时反馈信息,提升系统优化调度的可靠性;其次,在优化调度中动态调整模型预测控制的域参数,设计变步长决策指标以自适应改变时间步长,从而协调优化精度与计算时间的矛盾;然后,基于上述理论建立调度模型,并采用复合微分进化算法进行求解,使系统的运行效益达到最佳。最后通过算例分析,证明了所提策略的可行性和优越性。

In order to cope with the challenges brought by a large amount of distributed renewable energy accessed to the optimal scheduling of active distribution networks,this paper proposes an optimal scheduling strategy based on the adaptive step double loop model predictive control.Firstly,a double loop mechanism based on the conventional model predictive control is established to make full use of the feedback information coming from the controllable resources in the active distribution network in order to improve the scheduling reliability of the system.Secondly,the domain parameters are dynamically adjusted in the optimization scheduling,and the variable step decision index which may change the time step adaptively is designed to coordinate the contradiction between the optimization accuracy and the calculation time.Then,based on the above theory,the scheduling model is constructed,and is solved by using the compound differential evolution algorithm to optimize the operating benefits of the system.Finally,the results of the case study verify the feasibility and superiority of the strategy proposed here.