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Background

Hydropower is an important part of the flexibility of the power system. The ability to plan the dimensioning of hydropower, as well as its short-term and long-term operation, has a direct impact on the possibilities to act flexibly. With more reliable models, it becomes easier to predict how much one can and wants to produce at a given time, and to assess future capacity. The following shortcomings have been identified in current calculation models.

• Existing calculation models for hydropower production that include stochasticity, for example uncertainty regarding future water flow (levels in water reservoirs, precipitation) and forecast models for the electricity price market, are too computationally intensive to be used in the analysis of entire river systems (for example the Lule River).

• Today, the effect of head (or reservoir levels) on the ratio between turbine water flow and produced electrical power is taken into account in models for hydropower production planning, in order to reduce calculation times.

PhD project

Existing optimization models that take head into account are far too complex to be truly useful for larger river systems — especially when applied to long-term studies when uncertainties must be taken into account in different ways. Vattenfall AB sees great value in developing such models, as it provides better planning conditions, leads to better profit margins and enables more stable operation of their plants.

• The project aims to develop mathematical models that take head dependence and stochasticity into account, and to develop efficient solution methods that can calculate solutions within practically usable time frames, even for entire river systems.
• A combination of scientific disciplines is necessary (expertise in flow dynamics, hydropower, simulation and optimization theory).
• Modeling stochasticity and uncertainty leads to the research areas of stochastic optimization and robust optimization, both of which are active research areas.
• To succeed in keeping computation times down, significant method development will be required, utilizing problem knowledge and model structures.

Purpose and goal

The purpose of the project is to contribute to an accelerated transition to a future logistics system that meets the national requirements to reduce emissions from domestic transportation. The project will describe, build and demonstrate the transport ecosystem consisting of electrified long-distance (or energy-intensive) truck transports that require high-power charging in order to understand and develop a basis for how Sweden can accelerate the transition to an emission-free logistics system.

Expected effects and result

The project has the ambitious goal of putting 200 trucks in electrified long-distance transport in commercial operation as early as 2027, which is a prerequisite for the continued rollout towards 3,000 vehicles in 2030. It will contribute to the expansion of critical MCS charging infrastructure and results and effects are expected to have a wide geographical scope. Over time, the results and effects will contribute to the development of sustainable transport solutions on a global scale.

Planned approach and implementation

The project adopts a systems engineering approach and is structured with vertical and horizontal work packages. A large number of logistics actors will develop, demonstrate and validate efficient electrified logistics flows. Experiences from these arrangements are then utilized in horizontal work packages with the goal of developing, in collaboration with all project parties, documentation regarding FFI´s five system dimensions in order to prepare the market for these types of vehicles.

 The project duration is November 2024 to December 2027. 

Research question

This project aims to leverage data-driven methods to visualize electrochemical changes in materials at the nanometer scale, supporting the design of electrocatalysts for sustainable technologies such as water splitting and COâ‚‚ utilization. Improving electrocatalytic activity and selectivity in oxygen reactions (OER/ORR) requires modifying the catalyst surface—specifically reducing the native oxide layer and forming a new, controlled oxide layer.

Although in operando Transmission Electron Microscopy (TEM) can visualize these processes, frame-by-frame analysis is time-consuming, and signal loss in liquid electrolytes limits resolution. To address this, we propose enhancing compressed sensing algorithms using prior imaging data and model knowledge. A deep neural network will be trained to identify characteristic features of electrocatalytic surfaces, guiding the reconstruction of high-quality TEM visualizations in real time.

Sustainability aspects

Technology based on direct conversion between chemical and electrical energy is crucial for a sustainable society and to reduce global warming. Electrochemical water splitting and recombination or CO2 utilization is key for electrolysers and fuel cells. However, current catalysts for oxygen reactions rely on scarce, expensive platinum group metals (e.g., Ir, Ru), limiting scalability. We have developed thin film electrocatalysts based on transition metals as a sustainable alternative. Modifying the native oxide layer is essential to expose active sites and enhance performance. Additionally, the process allows for post-use catalyst recovery. This project will develop a reconstruction model for TEM micrographs to visualize the formation and evolution of surface oxides and oxide particles, supporting the development of cost-effective, high-performance catalysts.

Project-related publications

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