Capacity Expansion (OSeMOSYS)
This is a least-cost optimisation model employed for long-term strategic planning. Its primary objective is to determine the optimal configuration of future capacity additions to an existing system over a defined planning horizon. This involves identifying the specific types, scales, and deployment schedules of new assets required to satisfy projected future demand while optimising a defined objective function, typically the minimisation of total discounted costs. Consequently, it serves as a critical analytical tool for evaluating alternative investment pathways and informing strategic infrastructure development decisions. The Capacity Expansion model used here is TZ-OSeMOSYS - a Python package developed by TransitionZero using the open source OSeMOSYS modelling framework as the basis.Dispatch (PyPSA)
Dispatch modelling focuses on determining the optimal operation of a power system’s resources (generators, storage, and controllable loads) to meet electricity demand at each point in time, typically over a short-term horizon (e.g., hourly or sub-hourly for the next day or week). The goal is to minimise operational costs (like fuel costs for generators) while satisfying demand and respecting various technical and network constraints. The Dispatch model used here is PyPSA. It provides a comprehensive and flexible open-source environment for conducting sophisticated dispatch modelling of power systems, considering technical constraints, economic objectives, and the increasing complexity introduced by renewable energy sources and sector coupling. It allows users to analyse short-term system operation and evaluate the impact of different dispatch strategies and technologies.Either of the two modelling frameworks are applied according to the research question being analysed. They can also be combined, with the Capacity Expansion results for a specific year fed into the Dispatch model.