Analysis of the Impact of Renewable Energy Sources and Energy Storage Systems on Multi-Stage Transmission Network Expansion Planning

This paper presents a mathematical model to solve the multi-stage transmission network expansion planning (MTNEP) problem considering renewable energy sources (RES) and the allocation of energy storage systems (ESSs). Given the stochastic nature of both renewable energy sources and demand, the implementation of representative periods (RPs) is proposed. RPs represent the behavior of the electrical power system over time series, including short-term variations, demand and RES correlations, and geographic factors, offering feasible computational complexity to solve the MTNEP problem. The proposed model is formulated as a deterministic equivalent mixed-integer linear programming (MILP) model for a set of RPs. The MILP model optimizes investments in transmission lines (TLs) and ESSs. Furthermore, the MILP model incorporates active power losses through piecewise linearization and $N-1$ security constraints. A method is developed to reduce the combinatorial search space to deal with high-dimensional problems. The Garver’s 6-bus system, a modified IEEE 24-bus test system, and the northern Colombian transmission system are used to validate the proposed model. An extensive analysis of the impact of RES and ESSs on MTNEP is presented. Simultaneous optimization of TLs and ESSs provides better and cheaper MTNEPs compared to traditional methods. The effectiveness of ESSs in integrating RES, shifting electricity, reducing/delaying investments (especially when considering $N-1$ security constraints) and alleviating grid congestion is evidenced. This shows that ESSs have great potential as reinforcement and flexibility elements in the MTNEP problem.