A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system

Abstract

Thoroughly assessing future energy systems requires examining both their end states and the paths leading to them. Employing dynamic investment or multi-stage optimization models is crucial for this analysis. However, solving these optimization problems becomes increasingly challenging due to their long time horizons – often spanning several decades – and their dynamic nature. While simplifications like aggregations are often used to expedite solving procedures, they introduce higher uncertainty into the results and might lead to suboptimal solutions compared to non-simplified models. Against this background, this paper presents a rigorous optimization method tailored for multi-stage optimization problems in long-term energy system planning. By dividing the solution algorithm into a design and operational optimization step, the proposed method efficiently finds feasible solutions for the non-simplified optimization problem with simultaneous quality proof. Applied to a real-life energy system of a waste treatment plant in Germany, the method significantly outperforms a benchmark solver by reducing the computational time to find the first feasible solution from more than two weeks to less than one hour. Furthermore, it exhibits greater robustness compared to a conventional long-term optimization approach and yields solutions closer to the optimum. Overall, this method offers decision-makers computationally efficient and reliable information for planning investment decisions in energy systems.