Open Source Grid Expansion Models for the EU Project Spine

Abstract: Modern power systems are at the forefront of addressing the transition towards a decarbonized energy system, with increased integration of renewable energy sources becoming the key means to achieve the same. In this context, an expansion planning problem provides a decision support framework for investments in new generation and transmission assets over a long time-frame, addressing a wide range of technical and economic criteria aligned with national policies. It is thus an important but complex problem to solve, involving a number of modelling challenges and uncertainties to be considered. These include the treatment of operational variability due to the intermittent nature of renewable sources like wind or solar, while also considering uncertainties such as demand growth, technological developments impacting future investment costs among others, that define the long-term dynamics in the expansion. In this regard, one of the goals of the EU Project Spine is to create open source models to study investment scenarios for the expansion of power systems. This thesis work aims to offer insights for the Spine project, by identifying and exploring various requirements pertinent to a planning problem, along with method development. The primary objective of this thesis is to develop an expansion planning model, that determines the optimal location, size or capacity and time of investment for different candidates in generation and transmission assets, including short-term energy storage. A Mixed-Integer Linear Programming (MILP) optimization problem is formulated for the same, with both investment and operational sub-problems solved together. Operational variability has been modelled in a reduced form using profiles of representative days, while also incorporating contemporary requirements in the planning problem such as penetration targets for renewable generation. The developed model has been evaluated using a case study done on a small test network, in which different expansion scenarios involving varying demand growth and phase-out of conventional generators are investigated. Also, a two-stage stochastic optimization is performed to consider long-term uncertainties in demand growth and the quality of the stochastic solution is analyzed. It is inferred from the results that the expansion solution is indeed different for different scenarios, and stochastic optimization proves to be important in addressing long-term uncertainties, as reflected by a high value of stochastic solution (VSS).