Environmental and political factors determine long-term development for renewable generation around the world. The rapid growth of renewable generation requires timely changes in power systems operation planning, investments in additional flexible assets and transmission capacity.

The development trends of restructured power systems suggest that the current tools and methodologies used for investment planning are lacking the coordination between transmission and flexibility sources. Moreover, a comprehensive analysis is required for efficient investment decisions in new flexibility sources or transmission assets. However, literature does not provide an efficient modeling tool that will allow such a comprehensive analysis.

This dissertation proposes mathematical modeling tools as well as solution methodologies to support efficient and coordinated investment planning in power systems with renewable generation. The mathematical formulations can be characterised as large scale, stochastic, disjunctive, nonlinear optimization problems. Corresponding solution methodologies are based on combination of linearization and reformulation techniques as well as tailored decomposition algorithms. Proposed mathematical tools and solution methodologies are then used to provide an analysis of transmission investment planning, energy storage investments planning as well as coordinated investment planning. The analysis shows that to achieve socially optimal outcome transmission investments should be regulated. Also, the results of the simulations show that coordinated investment planning of transmission, energy storage and renewable generation will result in much higher investments in renewable generation as well as more efficient operation of renewable generation plants. Consequently, coordinated investment planning with regulated transmission investments results in the highest social welfare outcome.