The European Commission’s roadmap aims to install 60 GW of offshore wind energy by 2030 and 300 GW by 2050, requiring substantial raw materials. Airborne wind energy (AWE) offers a promising alternative with lower material demand and environmental impact. This thesis assesses the environmental impact of a 100 kW soft-kite AWE system using life cycle assessment (LCA). The target market for these AWE systems includes off-grid remote areas. Thus, a comparative LCA of hybrid power plant (HPP) configurations was conducted using site data from a military base in Marseille, France. Results show the Falcon AWE system has a GWP of 8.6 kg CO2 eq/MWh and CED of 144.1 MJ/MWh, with the ground station being the most impactful component. For the HPP, including diesel generators and batteries reduces the oversizing of renewable components, enhancing sustainability. Future recommendations include developing AWE-specific databases and evaluating more impact indicators.

Airborne wind energy (AWE) is a wind energy technology in the development phase consisting of tethered kites that reach high altitudes consisting of relatively stable wind speeds. While no company has yet reached the point of commercial viability, a variety of AWE technology concepts and designs have been under development and are currently at low- to intermediate technology readiness levels. When AWE systems are grid-connected a power smoothing element is needed to smooth the oscillating power output of the system for the grid. In this thesis, a framework for modeling a grid-connected hybrid power system (HPS) consisting of AWE and batteries participating in the day-ahead market (DAM) has been developed in the MATLAB environment. The framework incorporates an existing AWE performance and cost model with power smoothing performance, battery degradation, and DAM storage arbitrage. Multiple use case scenarios are evaluated to test the economic performance of multiple configurations of the HPS. These scenarios are; an AWE system with an ultracapacitor (UC); an AWE system with batteries; a battery system operating in DAM arbitrage and an AWE system with batteries operating in DAM arbitrage. The configurations were evaluated using multiple performance metrics, primarily the internal rate of return (IRR) as a metric for economic performance. A simulation of the HPS participating in storage arbitrage was then used to determine the economic viability of using the battery system for both power smoothing and arbitrage. The arbitrage behavior of the storage system was determined by a heuristic selling logic model developed to simulate the combined use of a storage system for power smoothing and arbitrage. The arbitrage logic is based on price volatility and power smoothing constraints.

The battery power smoothing system resulted in significantly lower system cost overall and consequently an increase in profitability (IRR of 12.37%) compared to the more expensive UC power smoothing configuration (IRR of 10.20%). The HPS configuration with batteries used for power smoothing combined with arbitrage showed a marginal increase in economic performance with an IRR of 12.43%. This showed a potential value increase of the system when using excess capacity arbitrage but not at a significant rate.

Currently, in remote off-grid locations, electricity is primarily generated using diesel generators, which is expensive and has significant carbon emissions. Alternatively, these remote locations have a huge potential for utilizing renewable energy sources. In such locations, airborne wind energy (AWE) systems could have an advantage over conventional wind turbines. Since, the AWE systems operate at higher altitudes, stronger and more consistent wind energy can be harnessed. Moreover, they are more compact and have higher mobility, which can reduce installation, operation, and maintenance costs. The centralized controller of the HPP optimizes the energy sources’ dispatch based on the resource and demand forecasts. Due to the anti-correlation between the wind and solar resources, electricity can be generated more constantly on a daily and seasonal scale. The batteries can be smaller, and diesel generators rarely need to be used. The stronger the anti-correlation between the wind and solar resources, the better the HPP performs. The model of the HPP uses wind, solar, and load data as inputs. The hourly energy production is calculated, and combined with the load data, the battery capacity is determined. To find the optimal number of kites, modules, battery capacity, different combinations of kites and modules are put in the model. The levelized cost of electricity (LCoE) is evaluated for each configuration. The model’s output is the minimal LCoE with the corresponding capacity of the different components. The model is used to evaluate multiple case studies, which resulted in the following key findings. HPPs have a stronger case in off-grid markets than utility-scale grid-connected markets. The cost reduction by sharing the infrastructure is minimal. The security of the power supply is better maximized at lower costs than standalone installations. The LCoE has become so competitive that the use of diesel can almost wholly be replaced in suitable locations, which results in a significant reduction in carbon emissions.