Uncertainty in Long-Term Grid Planning

Uncertainty in Long-Term Grid Planning: Approaching Transmission Expansion Planning through the Framework of Decision Making under Deep Uncertainty

Calon, Rob (TU Delft Technology, Policy and Management)

Chappin, Emile (mentor)
Kwakkel, Jan (graduation committee)
van Blijswijk, Martti (graduation committee)

Delft University of Technology

Engineering and Policy Analysis

2020-03-24

Motivations for sustainability are initiating an energy transition that is changing the European energy domain. The transition effectuated the adaptation of large volumes of wind and solar based generation capacity. The intermittent power-output of these Variable Renewable Energy Sources challenges the balancing operation of the electricity network in particular. Despite the availability of different solutions like storage, smart applications and infrastructure substitution, large investments in transmission capacity are inevitable. While the need for additional transmission capacity is evident, the realization of transmission capacity has become increasingly complex due to the uncertainty surrounding the future landscape in which this expansion would take place. The many possible pathways towards a sustainable future make it increasingly difficult to predict the development of generation and load profiles and thereby complicate the identification of capacity requirements within the electricity network. This raises the need for new approaches that address the high degree of uncertainty present within the electricity domain. Literature describes the framework of Decision Making under Deep Uncertainty as an alternative approach to addressing the role of uncertainty in Transmission Expansion Planning. In contrast to traditional scenario planning approaches, this approach focuses on the computational evaluation of large numbers of scenarios that are sampled from a constrained uncertainty space. The idea is to inform decision making by exploring the uncertainty space and identifying conditions under which certain outcomes occur. Consequently, decision makers are aware of the conditions under which interventions might succeed or fail and are therefor able to design strategies that perform in different futures. The potential of the framework of Decision Making under Deep Uncertainty in the context of Transmission Expansion Planning is explored through a proof-of-concept approach that focuses on Transmission Expansion Planning in the context of The Netherlands. In this approach a simplified integrated market simulation and network model are used to explore the effects of different quantities of wind and solar based generation capacity on the required transmission capacity within the electricity network. Instead of using merely three traditional scenarios, this thesis has evaluated and analyzed 20,000 different scenarios. The results of these analyses have been reviewed by domain experts during two workshop sessions. These sessions established that approaches to Decision Making under Deep Uncertainty could provide useful insights in relation to model sensitivity, the reduction of dimensional complexity of the uncertainty space and the development of scenarios that describe areas within the uncertainty space. The sessions furthermore established that the application of Decision Making under Deep Uncertainty in relation to Transmission Expansion Planning requires further development in order to become a viable alternative to traditional scenario planning in a corporate environment. The application of Decision Making under Deep Uncertainty approaches within the context of Transmission Expansion Planning provides a unique opportunity to make the uncertainty space more visible for Transmission System Operators. The approach provides the building blocks to design adaptive investment strategies which in turn are geared towards facilitating the energy transition in a robust manner.

Transmission Expansion Planning
Decision Making under Deep Uncertainty
Exploratory Modelling
Deep Uncertainty
Electricity Network
Scenario Discovery

http://resolver.tudelft.nl/uuid:381be1e3-2731-4149-a312-5abdcf26ceb3

Student theses

master thesis

© 2020 Rob Calon