Exploring the value of load-shifting for cost-optimal power grids

A research case for the Dutch power grid in 2035 through analysis of possible scenarios

Master Thesis (2024)
Author(s)

M.P.A. van de Camp (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Simon Tindemans – Mentor (TU Delft - Intelligent Electrical Power Grids)

F. Lombardi – Graduation committee member (TU Delft - Energy and Industry)

Niels Janssen – Mentor (Witteveen+Bos)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2024
Language
English
Graduation Date
07-11-2024
Awarding Institution
Delft University of Technology
Programme
['Electrical Engineering | Sustainable Energy Technology']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

The energy transition towards fossil-free systems presents significant challenges in power grid management, particularly in handling distributed and intermittent energy resources. The Netherlands faces substantial grid congestion due to rapid Distributed Energy Resource (DER) integration without adequate flexibility solutions.

While Energy System Optimisation Models (ESOMs) enable robust system planning, they often oversimplify or neglect Demand Response, particularly load-shifting capabilities. This thesis validates modeling frameworks for load-shifting and proposes a novel framework for modeling aggregated load-shifting in large-scale ESOMs. This approach accurately accounts for load-shifting characteristics without requiring extensive process-specific constraints.

Case study results for the Netherlands demonstrate that load-shifting provides significant value in a fossil-free power grid by adding flexibility and reducing overall system costs, particularly in battery energy storage investments. However, controllable flexibility assets remain crucial for peak demand management and facilitating renewable energy integration. Furthermore, power grid configurations incorporating Demand Response showed greater robustness against weather and demand variations, suggesting that networks utilizing implicit flexibility are more economical and risk-averse than those relying solely on explicit flexibility solutions.

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