Electric Power ›› 2022, Vol. 55 ›› Issue (1): 168-177.DOI: 10.11930/j.issn.1004-9649.202007274

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Multi-objective Flexible Planning of Transmission Network Considering Wind Power and Load Uncertainties

WANG Juanjuan1, WANG Tao1, LIU Zihan2, ZHU Hainan1, LI Fengshuo1, SUN Huazhong1   

  1. 1. State Grid Weifang Power Supply Company, Weifang 261014, China;
    2. School of Electrical Engineering, Shandong University, Jinan 250061, China
  • Received:2020-08-15 Revised:2021-08-10 Online:2022-01-28 Published:2022-01-20
  • Supported by:
    This work is supported by the Science and Technology Project of State Grid Shandong Electric Power Company (No.520604190002), the National Key R&D Program of China (No.2018YFA0702200)

Abstract: Large-scale centralized integration of wind power into power grid significantly enhances the uncertainty of power output in transmission network. In addition, most of the current transmission network planning models adopt DC power flows, which affects the accuracy of planning results. Based on AC power flow, this paper constructs a two-layer model for multi-objective flexible planning of transmission networks considering the uncertainty of wind power and load. The upper-level model is a multi-objective planning model of transmission networks, with consideration of the objective of the annual cost of construction investment, annual network loss and operating efficiency. The non-dominated sorting genetic algorithm-II is used to solve the problem, and the Pareto optimal planning scheme set is transmitted to the lower level. The lower-level model is a multi-scenario checking model with the minimum wind curtailment and load shedding as objective, which is used to test the endurance capacity of the Pareto optimal planning scheme set to the uncertainty of wind power and load. The constraints of the upper model is modified according to the wind curtailment and load shedding expectations, thus transmitting the impact of wind power and load uncertainty to the upper-level model. The AC power flow constraints of the lower-level model is conducted for second order cone relaxation, and the nonlinear nonconvex planning model is transformed into a second order cone planning model with convex feasible region, which is effectively solved using the Cplex solver. Finally, the effectiveness of the proposed model is verified by an adjusted 39-node system.

Key words: wind power and load uncertainty, AC power flow, second-order cone planning, multi-objective flexible planning, two-layer model