基于信息间隙决策理论的气?电耦合配网滚动调度方法

面向气?电耦合配网，提出一种基于信息间隙决策理论（information gap decision theory，IGDT）的多能协同优化调度方法。首先，采用二阶锥优化(second-order conic program, SOCP)松弛描述配电网与天然气管网的能量流特征，利用电/气/冷/热多能互补及协同转化作为运行灵活性提升的重要手段，以经济性最优为原则，建立气?电耦合配网调度的确定性优化模型。在此基础上，提出基于IGDT的多能协同滚动调度方法，生成时变的动态风险边界。最后，在基于IEEE 33节点配电网和比利时20节点天然气网络的多能耦合系统中对所提方法进行了测试验证。算例结果表明，所提调度方法可以充分发挥气?电耦合配网的多能互补优势，并克服了传统滚动调度方法对于可再生能源预测精度的依赖，有效提升了系统对不确定性风险的承受能力，进而在鲁棒性与运行经济性之间取得合理权衡。

A Rolling-horizon Scheduling Method for Integrated Electricity-gas Distribution Network Based on Information Gap Decision Theory

To full play to the advantages of integrated electricity-gas distribution network, a multi-energy coordinative optimization scheduling method based on information gap decision theory (IGDT) was proposed. Firstly, the second-order conic program (SOCP) formulation was developed to capture the energy flows of power distribution network and natural gas delivery system, and the multi-energy coordination and synergetic conversion were employed as an important measure to enhance the operational flexibility. Secondly, following the cost-minimization principle, a deterministic scheduling model was constructed for the integrated electricity-gas distribution network. Thirdly, to handle the uncertainty of renewable energy (RE) generation and multi-energy load variation, a multi-energy collaborative rolling-horizon scheduling method based on information-gap decision theory, which helped to yield the time-varying dynamic bounds for the risk-tolerance on RE forecast errors, was developed. Finally, the proposed model and scheduling approach were tested on an exemplary electricity-gas distribution network based on IEEE 30-Bus power system and Belgium 20-Bus natural gas delivery network. As indicated by the numerical results, the proposed rolling-horizon scheduling method based on IGDT achieves the full advantages of multi-energy complementary. Also, different from the traditional rolling-horizon method, the proposed method is less sensitive to RE forecast errors, which effectively improves systems’ risk-hedging ability to uncertain risks, and thus makes a trade-off between robustness and operational economics.