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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   (1272 HTML6 PDF(pc) (1718KB)(52)  

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.4 Value spaces of offshore wind power extreme scenarios and typical scenarios
Extracts from the Article
传统不确定极限场景的生成通常选取风电出力和负荷等不确定因素均取到边界极值时的状态,而实际运行状态中边界极值状态为出现率极低的最恶劣状态.引入Vague软集,可根据海上风电出力和负荷的变化调节隶属度,进而将极限场景的范围缩小在出现率较高的合理场景范围内.选取不确定因素集合中正逆指标的极限值,分别代入Vague值计算公式,辅变量λedk+,λedk-取0或1,取得Vague的区间临界值,即不确定性因素取到正负理想值.其中,当正逆指标与辅助变量全部取到极值时,等同于传统极限场景.如图4所示,矩形虚线范围为传统场景取值空间,矩形顶点及其所连接的虚线代表传统极限场景边界;实线边界为Vague场景集中的极限场景,较传统极限场景,Vague边界内场景发生可能性更高.实线曲线所包含的范围为Vague不确定集生成的场景取值空间.
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