## 1 Introduction  
With the rapid development of renewable energy recently, renewable generation will be extensively utilized in the smart grid of the near future to mitigate environmental pollution and promote sustainable development. However, owing to the stochastic volatility and intermittency of renewable energy, the integration of renewable energy sources would possibly exacerbate the uncertainty of power systems, which brings a series of new challenges to transmission network expansion planning (TNEP). Hence, it is necessary to present an economic and robust TNEP method to deal with renewable energy integration in the smart grid. In the literature, there have been several studies on the TNEP problem under uncertainty by adopting mathematical optimization methods. For instance, [1] employed methods of stochastic programming and probabilistic constraints for generation and transmission expansion planning with consideration of uncertainties in power system operation. In [2], a chance-constrained programming method was proposed to deal with uncertain load and wind power in TNEP. However, the stochastic optimization generally requires the accurate probability density function (PDF) of each uncertain parameter as well as the deterministic form of constraints associated with uncertainties, which are difficult to achieve in practice. Apart from stochastic optimization, robust optimization has also been widely discussed in TNEP. Since robust optimization only requires the lower and upper bounds of uncertain parameters, uncertainties such as load demand, renewable generation, and transmission investment cost [3,4] can be easily considered in TNEP through a robust model. Nevertheless, the robust TNEP method also has the issue of over-conservatism and may lead to uneconomic solutions. Recently several researchers have employed the multi-scenario method in TNEP, which considers uncertainties by multiple scenarios. The works by [5,6] adopted a scenario-generation method to deal with uncertain generation dispatch. [7] applied the Monte Carlo simulation (MCS) to simulate the uncertainties of outages and load forecasting in the coordinated generation-transmission expansion planning. In addition, the robust TNEP design based on extreme scenarios was adopted by [8,9] to improve the robustness of planning schemes. Nevertheless, the above multi-scenario methods still have limitations, as they tend to generate a large number of scenarios to achieve accurate simulation of uncertainties, which may cause a heavy computational burden. With the integration of large-scale renewable energy, the penetration level of renewable generation is generally high in the smart grid. Under such circumstances, the traditional TNEP methods, which cater for rated renewable power output, may lead to low utilization rates of transmission lines and poor economic efficiency [10]. In addition, since the power output of spatially dispersed renewable power plants tends to be strongly correlated in many geographical regions [11], the assumption that each renewable power plant is independent is no longer applicable. Recently several studies have emphasized the necessity of considering spatio-temporal correlation among renewable energy sources in various fields of the power system, such as wind power forecasting [11], power market [12], _etc._ Accordingly, the spatial correlation among different renewable power plants also needs to be considered in TNEP to improve the simulation accuracy of uncertain renewable generation. Although much research has been devoted to the TNEP problem under uncertainty, few studies have investigated the impact of the peak distribution characteristics of renewable generation on the economic efficiency of TNEP. Hence, the purpose of this paper is to provide economic and flexible planning schemes considering uncertainty. A novel scenario-generation method based on clustering analysis is proposed first to handle uncertain factors.