基于配网潮流追踪的可再生能源有功调度与节点集群需求响应策略研究

Research on Renewable Energy Scheduling and Node Aggregated Demand Response Strategy Based on Distribution Network Power Flow Tracking

随着能源短缺和环境保护问题日益凸显,可再生能源的投入电网比例逐年提升;另一方面,随着电力系统用户负荷日趋多样化,以及通信和控制技术不断升级,需求侧可控柔性负荷比例也逐渐提升。主动配电网（activedistribution network,ADN）的核心技术之一就是合理利用需求侧可控负荷（demand side controllable load,DSCL）资源,实现与可再生能源的协同配合,为电网提供诸如波动平抑、负荷跟踪等多种辅助服务。该文提出一种面向“可再生能源–配电网络–需求侧可控负荷”互动,基于聚合信息的ADN分层协同调控框架,上层采用配网潮流追踪算法实现ADN可再生能源的有功调度,下层基于可控资源节点控制成本,设计以不同需求响应参与主体（电力公司和电力用户）为核心的优化函数,应用基于参数序列化的DSCL精准化控制策略,实现多种典型负荷资源最优需求响应,以到达平抑ADN联络线功率波动的目的。仿真结果表明：所提出的节点集群需求响应控制策略和不同优化函数,能够反应各主体的调控特性,基于所分配的调控目标,控制效果良好,对可再生能源集成起到积极作用。

With the energy shortage and environmental protection issues become increasingly prominent, the proportion of renewable energy input grid increases year by year. On the other hand, with the power system users＇ load is increasingly diversified, and communication and control technology continues to upgrade, the ratio of demand-side controllable flexible load is gradually improved. One of the core technologies of active distribution network （ADN） is rational use of demand-side controllable load （DSCL）, to achieve synergies with renewable energy, and to provide such as fluctuation stabilization, load following and other ancillary services for power grid. This paper presented a hierarchical collaborative control framework for ADN based on aggregated information, which is ＂renewable energy-distribution network-demand side controllable load＂ interaction. The upper layer adopts distribution power flow tracing （DNPFT） to realize the renewable energy active scheduling of ADN. The lower layer is based on node control cost （NCC）. Optimization functions which are at the core of different demand response participants （power companies and power users） were designed, to achieve a variety of typical load resources optimal demand response with precise control strategy based on parameter serialization for DSCL, to achieve the purpose of stabilizing the power distribution of ADN. The proposed node cluster demand response control strategy and different optimization functions can reflect the control characteristics of each subject, and play a positive role in the integration of renewable energy based on the assigned control objectives.