Reducing computing time of energy system models by a myopic approach

Abstract

In this paper, the performance of the existing energy system model PERSEUS-NET is improved in terms of computing time. Therefore, the possibility of switching from a perfect foresight to a myopic approach has been implemented. PERSEUS-NET is a linear optimization model generating scenarios of the future German electricity generation system until 2030, whilst considering exogenous regional characteristics such as electricity demand and existing power plants as well as electricity transmission network restrictions. Up to now, the model has been based on a perfect foresight approach, optimizing all variables over the whole time frame in a single run, thus determining the global optimum. However, this approach results in long computing times due to the high complexity of the problem. The new myopic approach splits the optimization into multiple, individually smaller, optimization problems each representing a 5 year period. The change within the generation system in each period is determined by optimizing the subproblem, whilst taking into account only the restrictions of that particular period. It was found that the optimization over the whole time frame with the myopic approach takes less than one tenth of the computing time of the perfect foresight approach. Therefore, we analyse in this paper the advantages and draw-backs of a change in the foresight as a way of reducing the complexity of energy system models. For PERSEUS-NET it is found that the myopic approach with stable input parameters is as suitable as the perfect foresight approach to generate consistent scenarios, with the advantage of significantly less computing time.

Optimisation of the capacity and the dispatch of decentralised micro-CHP systems: A case study for the UK

“…One approach to overcome this general weakness of linear programming techniques is to employ myopic methods, whereby each reference year is solved independently with the results from the previous one as input. This approach not only reduces the solution time under ceteris paribus conditions, it also enables a better consideration of shocks such as sudden price spikes [37] where the authors demonstrate that for scenarios without substantial ''shocks'' (i.e. sudden spikes) in the development of model-endogenous parameters such as fuel and CO 2 certificate prices, the results are much the same as in the case of the perfect foresight approach, but with a much reduced computation time.…”

Assessing CO 2 emissions of electric vehicles in Germany in 2030

“…The PERSEUS-NET-TS energy system model (Babrowski et al, 2014b) is an optimizing bottom-up model of the German electricity system. The model minimizes the system-relevant expenditures needed to satisfy the exogenously given electricity demand.…”

How to model the cycling ability of thermal units in power systems

“…The optimizing energy system model PERSEUS-NET-TS [24] is used to evaluate the advantages and drawbacks of the five different approaches to modeling the cycling ability. In principle, the myopic model is able to calculate the investment and dispatch plans for thermal generation plants in the German energy system up to 2040.…”

“…When optimizing energy system models, the calculation time is of high importance [24]. The number of considered time slots, respectively hours, for example, is always a compromise between precision and calculation time.…”

“…In order to understand the resulting inflictions, in this paper differences in the unit commitment are analyzed by applying five different approaches to depict the cycling ability of thermal units within the deterministic energy system model PERSEUS-NET-TS [24]. The following approaches are considered:…”