Energy Conversion Technologies and Multi-Vector Systems

AI enabled Condition Monitoring and Fault Diagnostics for Distributed Energy Systems

There are several researchers in the energy group, lead by Professor Assadi, who are studying the use of data-driven methods, specifically Artificial Intelligence techniques, to improve the predictive capability and condition monitoring of small-scale Distributed Generation (DG) systems. Micro Gas Turbine (MGT) have been specifically targeted, due to their fuel flexibility and low emissions. However, AI-based data driven approaches has been applied to geothermal energy installations, heat pumps, offshore installations and more. Combining multiple methods in a hybrid structure have been investigated at system level, showing great potential to offset the limitations of using a single technique and thus improve accuracy of the prognoses, predictions, and more.

BHEsINNO – Innovation in Underground Thermal Energy Storages with Borehole Heat Exchangers

The project involves development of innovative setups of Borehole Heat Exchangers (BHEs). Structures tested as a part of the project will aim to maximize the energy effect (which is defined as a unitary power obtained in BHE, in Watt per meter). The innovative approaches cover new heat exchanger designs and materials, as well as tools for design and analysis of the system. Coaxial constructions will be analyzed and compared to the traditional, U-tube based ones. The coaxial construction enables heat extraction from deeper holes than the current standards for shallow geothermal. Research methodology is based on mathematical modelling of individual BHEs and fields consisting of multiple BHEs, taking into account their interference. Modelling will be verified by data gathered from in situ tests on BHEs.

Project ends April 2024.

Agastor – Advanced Gas and Carbon Dioxide Storage in Aquifer

Geological Carbon Storage (CGS) as an element of the CCUS/CCS processes is considered to be the most viable option for the storage of the large CO₂ quantities needed to reduce the CO2 concentration is the atmosphere and to combat the climate change effectively. Storage of natural gas and partially decarbonized gas (with addition of H₂) will play a vital role in the stability of energy supply in the EU. The innovative, guiding concept of the AGaStor project is based on the synergy between natural gas storage and CO₂ storage process. The main objective of the project is to facilitate the implementation of advanced Underground Gas Storage (UGS) using dynamic support of Carbon Dioxide Cushion (CDC) in saline aquifers. The project will produce practical guidelines and solutions for characterization of possible storage sites of UNGS with CDC (3D architecture of the storage complex, trapping mechanisms, reactive flow, CO₂/NG mixing process, risk assessment and sensitivity analysis) in selected regions of future deployment, improved monitoring and potential mitigation of CO₂ leakage. Combining CO₂ storage with UGS can bring economic and technological advantages to the industry and allow it to reduce the amount of anthropogenic emissions of CO₂. This new CCUS element may be an element of pro-climate action. A key issue of the AGaStor project will be knowledge exchange and enhanced cooperation between the Polish & Norwegian partners to determine the best technologies & application in the energy systems of partner countries.

Smart solutions for energy systems of the future

This project investigates tools and methods founded on artificial intelligence (AI) for monitoring, analysis and optimum design and operation of energy systems, mainly in buildings, considering both thermal and electrical energy and their coupling. Hence, the main objectives are:

1. studying advanced tools for real-time monitoring of energy systems;
2. analyses models development to predict the production, demand, and states of energy systems;
3. to evaluate how various energy resources, load profiles, and operating strategies affect the performance of multi-vector energy systems;

The work aims to address the following research questions:

1. In a multi-vector energy system consisting of heating, cooling, and power production technologies, how can the optimal operation of these technologies be determined?
2. What are the effects of an AI-based energy management system for both demand and supply side control on different indicators such as district energy consumption, costs, and GHG emissions?
3. How can an AI-based design and operation toolkit be developed to efficiently influence heating and cooling production of a heat pump system?

This Ph.D.-project is funded by the Research Council of Norway and Norconsult AS under the Industrial Ph.D. scheme, which is based on a company collaborating with a university on a doctoral project. The two regional energy projects Triangulum Central Energy Heat Pump Plant and Elnett21 – an emission-free and electric transportation system serve as the main reference cases for the PhD work.