Capacity Remuneration Mechanisms for Decarbonized Power Systems
I.J. Sanchez Jimenez (TU Delft - Technology, Policy and Management)
L.J. de Vries – Promotor (TU Delft - Technology, Policy and Management)
M. Cvetkovic – Promotor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
In a future power system powered mainly by variable renewable energy (VRE), ensuring a reliable electricity supply during periods of low solar and wind output will be a central challenge. As the revenues of dispatchable technologies are expected to become increasingly volatile, investors may not be willing to invest in sufficient capacity to ensure resource adequacy in all circumstances, including rare scarcity events. To ensure sufficient generation capacity to meet demand at all times, capacity remuneration mechanisms (CRMs) have been increasingly implemented in Europe. This dissertation investigates whether a CRM will be needed and, if so, which mechanism will be most suitable for a decarbonized power system and a power system in transition based in the Netherlands.
This research was conducted within the scope of the Horizon 2020 TradeRES Project - (grant agreement No 864276). [1] The objective of this project was to test innovative electricity market designs that meet society’s needs with a (near) 100% renewable power system. Such market designs should provide efficient incentives for both system operation and long-term investment, with this research focusing primarily on the latter. The project was designed to employ agent-based modeling, as this approach enables the simulation of imperfect markets in which actors operate without perfect information, foresight, or coordination. Agent-based models are particularly well-suited to capture long-term dynamics, allowing agents to adapt their strategies over time in response to evolving market conditions....