Benefits of Decomposition Methods to Speed-up Energy System Modelling and Application to Stochastic Optimization

Kurzfassung

The transition of the energy system towards a sustainable supply with low carbon emissions requires the long term planning of power generation capacity expansion. Energy scenarios can give insight into the development of complex electricity systems in the coming decades. Each scenario includes a large number of external factors which influence the pathway of energy system development. However, due to the long term nature of the energy transition, these external parameters (e.g. fuel prices, technology development, weather influences etc.) contain large uncertainties. To a certain degree, cross-impact-balance analysis can evaluate the consistency and improve the holistic image of future energy scenarios, but a large degree of uncertainty remains. So far, this problem was usually tackled by deterministic optimization with subsequent sensitivity analysis. Recent development towards parallel computing allow for stochastic optimization over a large set of scenarios. When considering flexibility options like electrical energy storage, demand side management and electric mobility, high temporal resolutions of the modelled energy system are required. Consequently, the implementation of stochastic optimization into high resolution optimizing energy system models will lead to an increased complexity, due to the additional scenario dimension. This problem can be tackled using decomposition approaches like enhanced Benders decomposition in order to guarantee achieving the global optimum while taking computational restrictions into account. Challenges arise especially regarding CPU load balancing for the aspired migration to high performance computing. The presentation will discuss preliminary results from an extension of the energy system model REMix, developed at the German Aerospace Center (8760 h per year, developed in GAMS and solved as a LP or MIP using CPLEX). By implementing different decomposition techniques and improving convergence, computational constraints can be overcome and the number of evaluated scenarios can be increased. Aim of this analysis is to increase the complexity of the stochastic models and improve the quality and robustness of the modelling results.