The large-scale grid connection of renewable energy causes great uncertainty in power system planning and operation. The power system flexibility index can quantify the system’s ability to adjust to uncertain events such as renewable energy, load fluctuations, and faults. Compared with traditional planning methods, the flexibility planning method can accurately evaluate the impact of various uncertain events on the system during the planning process, thus effectively ensuring the safe and economic operation of renewable energy systems. First, from the perspective of power transmission and safe operation, the flexibility index of the transmission line is defined. On this basis, considering the system’s economic operation strategy, aiming at the optimization of flexibility, investment cost, operation cost, and renewable energy consumption, a multi-objective transmission grid planning model based on flexibility and economy is proposed. The NSGAII optimization algorithm is used to solve the model. Finally, the simulation is performed in the modified Garver-6 and IEEE RTS-24 node systems to analyze the effectiveness of the proposed model. The results show that the planning model can meet the needs of flexibility and economy, improve the transmission capacity of power grids, reduce the probability of renewable energy abandonment or exceeding power flow, as well as enhance the flexibility, economy, and reliability of power systems.

The development of renewable energy represented by wind, photovoltaic and hydropower has increased the uncertainty of power systems. In order to ensure the flexible operation of power systems with a high proportion of renewable energy, it is necessary to establish a multi-scenario power system flexibility evaluation method. First, this study uses a modified k-means algorithm to cluster operating scenarios of renewable energy and load to obtain several typical scenarios. Then, flexibility evaluation indices are proposed from three perspectives, including supply and demand balance of the zone, power flow distribution of the zone and transmission capacity between zones. Next, to calculate the flexibility evaluation indices of each scenario—and according to the occurrence probability of each scenario—we multiplied the indices of each scenario by the scenario occurrence probability to obtain comprehensive evaluation indices of all scenarios. Based on the actual historical output data of renewable energy and load of a southern power system in China, a flexibility evaluation was performed on the modified IEEE 14 system and modified IEEE 39 system. The results show that the proposed clustering method and flexibility indices can effectively reflect the flexibility status of the power system.

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