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Two-Stage Robust Expansion Planning of Transmission Network Considering Uncertainty of Offshore Wind Power
TIAN Shuxin, HAN Xue, FU Yang, SU Xiangjing, LI Zhenkun
Journal of Shanghai Jiao Tong University    2024, 58 (9): 1400-1409.   DOI: 10.16183/j.cnki.jsjtu.2023.028
Abstract   (1269 HTML6 PDF(pc) (1718KB)(47)  

The complex and multiple uncertainties of offshore wind power pose great challenges to the safety and robustness of transmission grid structures. In order to improve the adaptability of grid structure to offshore wind power, a robust expansion planning method based on Vague soft set is proposed. First, Monte Carlo simulation is employed to construct the offshore wind Vague scenarios, which transform multiple comprehensive uncertainties of offshore wind power into uncertain parameter sets from true membership function, pseudo-membership function, and unknown information measure based on the Vague soft set theory. Then, a two-stage robust expansion planning model based on Vague scenario set is established for transmission network with offshore wind power penetration. The minimum total investment cost of offshore and onshore line and network loss is taken as the objective function in the first stage, while the minimum objectives of wind abandonment and cutting load for offshore wind power are proposed with the alternating current power flow constraint based on second-order cone relaxation in the second stage. Based on the expected values of wind abandonment and cutting load returned by the second stage model, the operation variables of the first stage model are modified to ultimately obtain the iterative transmission network robust planning scheme. Finally, the Gurobi mathematical optimization engine is used to analyze the Garver 6-node system and IEEE 39-node system to verify the effectiveness and feasibility of the proposed robust expansion planning method.


Fig.1 Vague scenario sampling process based on Monte Carlo simulation
Extracts from the Article
基于Vague软集的场景集将最恶劣海上风电极限场景范围缩小至出现频率较高的Vague集边界范围内,使得规划策略既在构建的场景集内经济较优,也可以在Vague集边界极限的情况下保持鲁棒安全.当校验场景的取值空间为无穷大时,将使鲁棒优化阶段相应出现无限个运行约束,直接导致模型难以求解.因此需要生成有限个离散场景来代替整个取值空间.采用蒙特卡罗模拟,首先确定采样参数,其次对初始数据即上述不确定因素转化为正逆指标后所对应的Vague软集度量函数值进行排序,再进一步进行Vague场景采样,每组正逆指标对应的Vague软集度量函数值对应一个场景,最后对场景数量进行判断,满足要求即停止采样.t1~tn时段内,海上风电Vague场景生成的采样过程如图1所示.
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