Transmission Network Expansion Planning with Consideration for Optimal Integration of Renewable Energy

This paper proposes an optimization method for transmission network expansion planning (TNEP) under the uncertainty of renewable energy generation and load. Unlike traditional stochastic programming and robust optimization, this method combines both approaches using conditional value-at-risk (CVaR). The original problem model is a three-level structure, where uncertainty is eliminated through dual problems twice, and the three-level structure is consolidated into a single-level model. It is demonstrated that, when adopting a DC power flow model, the problem can eventually be reformulated as a mixed-integer linear programming (MILP) model, which is directly solvable by commercial optimization solvers. Numerical results for networks with uncertainties in both load and renewable energy generation show that the proposed method achieves better solutions compared to traditional TNEP design approaches.