Investment planning under uncertainty in energy systems: Modelling and algorithms

Abstract

This thesis applies operational research methods for the investment planning of energy systems under uncertainty for the energy transition. We develop new models and solution methods.

On the modelling side, we first focus on modelling hydrogen-based offshore energy hubs in an offshore energy system. A mixed-integer linear program is developed for the investment planning of offshore energy systems with offshore energy hubs. The model is then extended to (1) include uncertainty using a multi-horizon stochastic programming approach and (2) include the European onshore and offshore energy systems. Finally, some major extensions are made to the model, which leads to the REORIENT model. The REORIENT model is a multi-horizon mixed-integer linear stochastic program for integrated investment, retrofit, and abandonment planning of energy systems under short-term and long-term uncertainty. This is the first model that integrates different alternatives and investigates the role of existing energy infrastructure in the energy transition. The REORIENT model features the main modelling contributions in this thesis. In addition, we also extend the modelling of an existing model, EMPIRE, which is a stochastic linear program for the European power system investment planning, by modelling the heat and industry sectors with a strong focus on endogenous decisions regarding industry decarbonisation, hydrogen and carbon capture and storage.

On the methodology side, we develop algorithms that exploit the structure of multi-horizon stochastic programming. The algorithms developed can also be applied in general multi-stage stochastic programs. We develop enhanced Benders decomposition and Lagrangean decomposition algorithms. The enhanced Benders decomposition utilises adaptive oracles. We also propose to stabilise the adaptive Benders decomposition with (1) a novel dynamic level method and (2) a novel centre point strategy. Also, we propose parallelised Lagrangean decomposition with primal reduction. The scenario subproblems are solved in parallel, and the primal problem is reduced based on the structure of multi-horizon stochastic programming and solved in parallel. We apply the proposed algorithms to solve the REORIENT model and its variations and compare them with standard Benders, unstabilised adaptive Benders, and standard Lagrangean decomposition.

The proposed models and algorithms contribute to operational research and provide useful insights for the energy transition.