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Distributionally Robust Model Predictive Control for Virtual Power Plants

Renewable Energy Sources (RES), such as solar and wind, increasingly contribute to the global energy production and mark an important pillar of the modern energy system. RES come with numerous advantages such as the provision of clean as well as broadly available energy production capacities. However, one major factor limiting the widespread adoption and penetration is the inherent underlying uncertainty of RES. Here, the concept of virtual power plants (VPP) can assist. A VPP is a collection of small-scale energy resources that, aggregated together and coordinated with grid operations, can provide the same kind of reliability and economic value as traditional power plants. To further increase the penetration and adoption of solar energy plants, intelligent strategies for VPPs are required to deal with the uncertainty of RES.

This PhD thesis addresses the uncertainty within the decision-making process of virtual power plants. In this context, uncertainties can derive from various sources such as the sunlight-dependent energy production, unpredictable (stochastic) market prices and variable energy demands. To tackle this problem, probabilistic forecasting is combined with strategies from control engineering. These control engineering strategies, particularly (Stochastic) Model Predictive Control and Distributionally Robust Optimization provide a promising approach in the context of VPPs as they allow i) to make informed decisions under uncertainty, ii) to hedge against quantifiable risks and iii) the end user to trade-off between conservatism and performance. By integrating these strategies, this research aims to enhance the decision-making capabilities of virtual power plants, ultimately leading to a more reliable and efficient integration of RES into the power grid.

Key Words: Virtual Power Plants, Distributionally Robust Optimization, Model Predictive Control, Uncertainty Quantification

Mathematical modeling of power system stability

In complex networks, such as power grids, a small perturbation, like losing the connection between two nodes, can have devastating consequences. Such small 'failures' can cascade through the network, possibly resulting in the entire collapse of the system. In real-world systems, small failures are inevitable; thus, developing strategies to prevent these from affecting the entire network is of interest. These strategies may involve altering the network topology — the way the network is interconnected — where different elements of a network are connected by special structures to contain a failure in the element where it originated, preventing it from affecting other elements in the network. On the other hand, locating 'fragile' nodes and 'immunizing' them against failure can prevent a small failure from cascading through the network. Interdependent networks pose a more realistic (and more complicated) situation, where two or more networks supply one another with resources, and the survival of one depends on the survival of the other. In my work, I will evaluate and develop strategies, mostly concerning network topology, to maintain complex networks alive and intact.

Modelling Alternative Land-Use Strategies for Solar PV Deployment

This PhD project explores how alternative land-use strategies for solar PV deployment can support Europe and Norway’s energy transition while balancing environmental and societal priorities. It integrates energy systems modeling, stakeholder engagement, and climate adaptation analysis to assess the viability of dual land uses such as agrivoltaics. The research investigates how solar PV expansion can be optimized to reduce land-use conflicts, protect biodiversity, and maintain agricultural productivity. By combining technical feasibility with social acceptance and climate resilience, the project aims to inform inclusive, land-efficient pathways for renewable energy development that align with long-term goals for energy security and food security.

Social Acceptance of Solar Energy in Norway

The global imperative for decarbonization has accelerated the deployment of renewable energy technologies, including solar photovoltaics (PV). While Norway’s energy system is predominantly based on hydropower, diversification toward solar energy is increasingly recognized as essential for achieving long-term climate and energy security goals. However, the expansion of ground-mounted solar PV introduces complex socio-environmental trade-offs, particularly concerning land-use conflicts, visual intrusion, and biodiversity loss/gains. Social acceptance has emerged as a critical determinant of renewable energy deployment, influencing both policy effectiveness and project implementation.

My doctoral study aims to systematically examine public preferences and attitudes toward solar PV development in Norway. Employing Discrete Choice Experiments (DCE), the study will elicit individual-level trade-offs across key attributes such as location, scale, environmental considerations, and economic benefits. The resulting data will be analyzed using advanced econometric models to identify preference heterogeneity and underlying behavioral drivers. By integrating these insights into policy and planning frameworks, the research seeks to contribute to evidence-based strategies that enhance societal legitimacy and minimize opposition, thereby supporting Norway’s transition toward a resilient and sustainable energy future

Silicon Recycling

Among renewable energy sources, solar energy is one of the most abundant. To convert this energy into electrical energy, photovoltaic (PV) cells are required, and since their lifetime is expected to be of at least 25 years, increasing the proportion of recycled materials used in the fabrication of solar cells should further enhance their positive environmental impact. Considering silicon-based solar cells dominate the market, focusing on silicon recycling would make the process more environmentally friendly and reduce costs.

On the other hand, when using recycled materials, it is necessary to consider the effect of impurities present in them, as they are known to negatively affect solar cell performance. Therefore, an important part of this project is to investigate their role, concentration, and distribution when recycled materials are used. Throughout this project, we expect to identify the effects of persistent impurities on solar cell efficiency, yield, and overall manufacturing throughput.

Investigation of PV spinel oxide thin films grown with ALD

The PhD project intend to investigate some spinel oxide thin films (AB₂O₄), like ZnFe2O4, CuFe2O4 and MnFe2O4 for photovoltaic applications. The films will be grown with ALD and characterized with techniques such as XRD, XPS, UV-Vis, ellipsometry, AFM, and Hall measurements to determine their structural, optical, and electronic properties. By controlling the ratio between the cations, the strain, and crystalline, the idea is to engineer appropriate bandgaps and electrical properties of these oxides. The overall goal is to understand the relatively unexplored spinel oxides that can exhibit high stability with desirable photovoltaic properties, contributing to sustainable and efficient solar technologies within FME SOLAR

Sustainable Materials for High Efficiency Solar Cells

The power conversion efficiency of silicon solar cells is fundamentally limited by the Shockley-Queisser limit. A proposed technology for achieving higher efficiencies is intermediate band solar cells, where a so-called intermediate band (IB) material is inserted between the p-type and n-type materials of a typical p-n-junction. The IB material has three bandgaps instead of one, which allows more efficient utilization of the incoming solar energy.

The aim of my project is to identify an IB material, under the constraint that sustainable production and use of the materials is feasible. This involves synthesizing samples of candidate materials, and performing experiments that show that it has the desired properties an IB material should have. The materials chosen for investigation are heavily doped oxides and sulfides, and double oxide perovskites, and thin films of these materials will be made using pulsed laser deposition. These thin films will then be characterized for their structural, chemical and optical properties. If any material shows promise for use in IB solar cells, prototype solar cell devices may be fabricated to test if a power conversion efficiency exceeding the Shockley-Queisser limit is found.

Connection on terms as a facilitator for colocalization of power production and consumption.

The power grid in Norway faces numerous challenges regarding power distribution. This master's thesis will examine the experiences of distribution system operators (DSOs) and customers with grid connections under specific terms. Furthermore, it will analyze how a power line in the grid will react when both a solar plant and a data center are connected under these terms in the same area. The aim is to provide insights into how local production and consumption can potentially reduce the need to wait for the DSO to expand the grid.

Mitigating overvoltage in the Norwegian distribution grid due to high PV penetration

This thesis analyzes how active power curtailment and battery storage can mitigate overvoltage in the Norwegian distribution network due to high PV penetration. Power flow simulations in Pandapower are conducted on a reference grid to evaluate different mitigation strategies and their impact on voltage regulation

Modelling of temperature for mono- and bifacial photovoltaics in Nordic conditions

The efficiency of a photovoltaic (PV) module is highly dependent on the operating temperature of the PV cells. Measurements of the operating temperature is not available in the design-phase of a PV system, and in some cases it is not even measured for operating systems. In these cases, we must rely on models which estimates the cell temperature from measurements of ambient temperature, irradiance and wind speed to assess the performance of the system. These models typically rely on a set of empirical coefficients which have been determined for locations that experience very different weather conditions than what is common in the Nordics. This master’s thesis is focused on assessing the performance of existing temperature models and carry out further model development to enhance the accuracy of temperature modelling of PV systems operating in Nordic conditions

Wave-induced loss for floating photovoltaics

Floating photovoltaics (FPV) is a promising solution for renewable energy generation in areas where land is limited or costly. For FPV systems, waves will, if present, induce movements in the floating structures that the PV panels are mounted on. These movements will cause changes in the incident irradiance on the panels, giving rise to wave-induced losses (WIL). This master’s thesis will expand on previous efforts to simulate WIL by including time series of wave, wind and irradiance data and wave models to improve the model and account for potential correlations between power production and sea states.

Machine Learning Correction of Horizontal Irradiance for Solar Production Forecasting

This thesis examines the relationship between global horizontal irradiance forecasts and actual
solar production from tilted photovoltaic installations, focusing on the dynamic self-shading of
solar roof tiles. Existing approaches for solar roof tiles rely on manual adjustments that fail to
capture real-time variations in shading. By integrating numerical weather prediction data with
environmental, geometric, and overhead mapping information, this research systematically
incorporates self-shading eƯects to enhance forecasting accuracy and support optimized
energy management.