Name: Kris Poncelet


KU Leuven

Promotor / Supervisor

Promotor: Prof. dr. ir. William D'haeseleer

Copromotor: Prof. dr. ir. Erik Delarue

Samenvatting van het onderzoek / Summary of Research

This dissertation focuses on energy system optimization models (ESOMs). These are mathematical models which are used to generate possible transition pathways of the entire energy system in a single or multiple countries over a time horizon of multiple decades. Experimenting with different transition pathways allows gaining insights and can help in forming a long-term vision of a cost-effective transition towards a sustainable energy system. As such, these models form valuable tools for policy makers. 

Due to the large scope of ESOMs, solving these models quickly becomes computationally demanding. To limit the computational cost, ESOMs have historically used a low level of temporal and technical detail to represent the operation of the power system, i.e., intra-annual variations in demand and renewable generation are typically represented by 4-48 so-called time slices and the technical constraints faced by thermal power plants when changing their power output, starting up or shutting down are neglected. However, in the context of an increasing penetration of intermittent renewable energy sources (IRES), such as wind turbines and solar PV panels, of which the instantaneous electricity generation is weather dependent and therefore highly variable and limitedly predictable, this low level of temporal and technical detail might not be sufficient to grasp the economic and technical challenges related to integrating these IRES into the system. 

In this regard, the main objective of this dissertation is to assess the impact of this low level of temporal and technical detail on the results provided by ESOMs and to improve the modeling of the temporal and technical aspects related to the operation of the power system. The presented research shows that, due to the low level of temporal and technical detail, the transition pathways deemed optimal by ESOMs tend to have a bias towards baseload technologies (e.g., coal-fired power plants and nuclear power plants) as well as IRES (e.g., wind turbines and PV panels), while the value of more flexible technologies (e.g., gas-fired power plants and storage technologies) tends to be underestimated. Additionally, the low level of detail is shown to lead to an underestimation of the efforts required to achieve certain targets (e.g., a greenhouse gas emission reduction target). For high penetrations of IRES, particularly the low level of temporal detail is shown to have a strong impact on the obtained results. To overcome these issues, a novel method, based on selecting a number of representative historical days, to represent intra-annual variations in demand and renewable generation within a year is developed. This method is shown to strongly increase the accuracy of ESOMs without increasing the computational cost. Additionally, reduced formulations of the mathematical constraints which are used to model the technical constraints faced by thermal power plants when changing their power output are proposed. These reduced formulations are shown to be sufficiently accurate for long-term planning purposes while reducing computation time by a factor of 5-600 with respect to the model which integrates the detailed technical constraints.

Volledige tekst van het doctoraat / full text

Examencommissie / Board of examiners

Prof. dr. ir. William D'haeseleer (promotor) 
Prof. dr. ir. Erik Delarue (copromotor) 
Prof. dr. Carlo Vandecasteele (voorzitter/chairman) 
De heer Dirk Van Hertem (secretaris/secretary) , ESAT 
Prof. dr. Stefaan Proost 
Prof. dr. Goran Strbac , Imperial College 
Dr. Vera Silva , General Electric (GE)