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PDF(6883 KB)
考虑极端场景的输电-通信网络协同鲁棒扩展规划方法
Joint Robust Expansion Planning of Transmission Network and Communication Network Considering Extreme Scenarios
为了在满足经济性前提下,尽可能降低极端场景下电力和通信网络故障耦合引起的大面积停电风险,考虑双网结构融合和功能耦合特征,提出了信息物理协同双层(主层、子层)鲁棒扩展规划模型。首先,以投资经济性和极端场景下失负荷损失成本最小为目标,建立双网线路和通信业务路由协同优化的主层模型;其次,在子层模型中,计及线路结构耦合引起的共因失效模式,构建了电力/通信线路多重故障集,并考虑通信失效对电力系统应急调度的影响,基于max-min准则建立最恶劣极端场景最小失负荷损失成本计算模型;随后,采用列和约束生成算法实现两层模型解耦求解;最后,基于IEEE 14 节点信息物理系统进行仿真分析,结果表明,提出的协同规划模型能以经济合理的投资优化双网结构,降低通信系统故障级联影响,提高耦合系统面对极端场景的抵抗力。
In order to meet the economic premise and minimize the risk of large area outages caused by the coupling of failures in power and communication networks in extreme scenarios, this paper proposes a robust extended planning model with two layers (main layer and sub-layer) of cyber-physical cooperation, taking into account the structural convergence and functional coupling characteristics of the dual network. Firstly, the main-layer model for cooperative optimization of dual-network line and communication service routing is established with the objectives of economy and minimum loss of load under extreme scenarios; in the sub-layer model, the multiple fault sets of power/communication lines are constructed taking into account the common cause failure modes caused by line structure coupling, and the impact of communication failure on the emergency dispatch of power system is considered, and the calculation model of minimum loss of load under the worst extreme scenarios is established based on the max-min criterion. Then, the column and constraint generation algorithm is used to decouple the two-layer model. Finally, simulation analysis is conducted on the basis of IEEE 14-node information physical system. The results show that the collaborative planning model proposed in this paper can optimize the dual-network structure with economical and reasonable investment, reduce the impact of fault cascade in communication system, and improve the resistance of the coupled system to face extreme events.
electric cyber-physical system / extreme scenarios / joint expansion planning / robust optimization
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