Author:

Keywords:

Energy-system planning, Generation expansion planning, power system economics, Science & Technology, Technology, Engineering, Electrical & Electronic, Engineering, generation expansion planning, power system modeling, wind energy integration, SYSTEMS, 0906 Electrical and Electronic Engineering, Energy, 4008 Electrical engineering

Abstract:

Due to computational restrictions, energy-system optimization models (ESOMs) and generation expansion planning models (GEPMs) frequently represent intra-annual variations in demand and supply by using the data of a limited number of representative historical days. The vast majority of the current approaches to select a representative set of days relies on either simple heuristics or clustering algorithms and comparison of different approaches is restricted to different clustering algorithms. This paper contributes by: (i) proposing criteria and metrics for evaluating representativeness, (ii) providing a novel optimization-based approach to select a representative set of days and (iii) evaluating and comparing the developed approach to multiple approaches available from the literature. The developed optimization-based approach is shown to achieve more accurate results than the approaches available from the literature. As a consequence, by applying this approach to select a representative set of days, the accuracy of ESOMs/GEPMs can be improved without increasing the computational cost. The main disadvantage is that the approach is computationally costly and requires an implementation effort.