Impact of forecasting on energy system optimization

Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems' design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.