Other

In recent years, the energy sector has undergone changes that have a high impact on the dynamics in power markets. One of the changes has been the shift towards energy production from intermittent sources, such as wind and solar. As a consequence of this shift, the amounts of energy that are traded at the short-term markets throughout a day are uncertain, as they depend on hardly predictable weather conditions.
This uncertainty increases the volatility of short-term energy prices, and thus makes it much more challenging to make economically viable energy trading decisions. One way to respond to this challenge is to leverage assets such as grid-level battery storage, and electrolyzers to have more flexibility when making trading decisions. The challenge then is how to optimally leverage such an asset to make viable trading decisions under high price volatility. This research project focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals with economically viable trading decisions.
This project is funded by EWE Trading.

The logistics sector in the Netherlands is a vital economic pillar, employing over 673,000 people and contributing €65 billion annually. However, the sector faces pressing challenges, such as reducing greenhouse gas emissions, ensuring supply chain resilience amidst disruptions, and overcoming infrastructure and workforce shortages. With freight transport projected to grow by 20% by 2030, these challenges require a shift from isolated logistics operations to collaborative, connected logistics networks. Upcoming policy measures, including kilometre chargers, CO2 caps, the Emissions Trading Scheme, and the Corporate Sustainability Reporting Directive, add urgency to this transition.
A promising framework for addressing these challenges is the Physical Internet (PI), which envisions a transformative shift in logistics systems. The PI concept aims to “do more with less” by enabling the sharing of assets within the freight and transport industries. This involves transitioning from the isolated scheduling of proprietary assets to the collaborative scheduling of shared resources in open, connected logistics networks.
This project is part of TNO’s Early Research Program on Future-Proof Smart Logistics. It aims to contribute to the realisation of the PI concept by developing advanced machine learning-based decentralised decision-making algorithms. These algorithms will enable logistics companies to collaborate effectively and optimise operational scheduling across multi-actor systems, ensuring sustainability and efficiency at both company and system levels.
This project is funded by TNO.