# Robust Dynamic Transmission and Renewable Generation Expansion Planning: Walking Towards Sustainable Systems

## TL;DR

This paper presents a robust, multi-level optimization approach for dynamic transmission and renewable generation expansion planning, enabling sustainable power systems by effectively managing uncertainties and system complexities.

## Contribution

It introduces a novel adaptive robust optimization model for joint transmission and renewable generation expansion planning, considering capacity changes and operational uncertainties.

## Key findings

- Model effectively handles large, realistic systems.
- Optimizes investment and operational decisions under uncertainty.
- Demonstrates potential for sustainable, reliable power system development.

## Abstract

Nowadays, the transition from a conventional generation system to a renewable generation system is one of the most difficult challenges for system operators and companies. There are several reasons: the long-standing impact of investment decisions, the proper integration of renewable sources into the system, the present and future uncertainties, and the convenience to consider an integrated year-by-year representation of both uncertainties and investment decisions. However, recent breakthroughs in Dynamic Transmission Network Expansion Planning (DTNEP) have demonstrated that the use of robust optimization might render this problem computationally tractable for real systems. This paper intends to consider not only the capacity expansion of lines, but the construction and/or dismantling of renewable and conventional generation facilities as well. The Dynamic Transmission Network and Renewable Generation Expansion Planning (DTNRGEP) problem is formulated as an adaptive robust optimization problem with three levels. First level minimizes the investment costs of transmission network and generation expansion planning, the second level maximizes system operational costs with respect to uncertain parameters, while the third level minimizes those operational costs with respect to operational decisions. The method is tested for two cases: i) an illustrative example based on Garver IEEE system and ii) a case study using the IEEE 118-bus system. Numerical results from these examples demonstrate that the proposed model allows making optimal decisions towards reaching a sustainable power system, while overcoming problem size limitations and computational intractability for realistic cases.