Green hydrogen is increasingly recognised as a key enabler of the energy transition, offering a carbon-free alternative for hard-to-abate sectors. However, its large-scale deployment remains economically challenging due to high capital expenditures (CAPEX) and production costs. The main cost driver is electricity procurement, given the reliance on affordable renewable energy and the challenges posed by its intermittent and uncertain nature. This underscores the importance of smart power sourcing and energy management strategies to reduce power-related expenses.

This thesis investigates the operational and economic performance of a hybrid renewable energy system designed for green hydrogen production. The system integrates an electrolyser with electricity procured via a wind Power Purchase Agreement (PPA), battery energy storage, and participation in the day-ahead electricity market. Hydrogen is assumed to be sold under a fixed-price offtake agreement, placing hydrogen production at the centre of operational decision-making. The primary objective is to determine the optimal dispatch strategy to maximise the long-term profitability of the hydrogen production plant, and to evaluate whether the addition of battery storage and market-based electricity sourcing enhances economic performance under different system configurations and external conditions.

To determine profit-maximising operational strategies, a mathematical optimisation framework is developed, comparing three solution methods with varying assumptions about foresight and uncertainty. The first method uses a deterministic formulation with perfect foresight, expressed as a mixed-integer linear programming (MILP) problem. This is extended into a rolling horizon approach with limited foresight, and a two-stage stochastic optimisation method that captures forecast uncertainty through correlated scenarios of wind generation and electricity prices. These methods are used to analyse how electricity sourcing strategies, system configuration and uncertainty treatment influence operational and economic indicators such as electrolyser load factor, system profitability, and the levelised cost of hydrogen (LCOH).

The results highlight the potential role of battery storage in reducing electricity costs and unlocking additional revenue by strategically charging and discharging in response to market price fluctuations. However, the associated cost savings are not sufficient to outweigh the high investment costs, making battery storage an unprofitable option under the assumed conditions. Another key finding is that allowing for supplementary electricity sourcing from the day-ahead market, in addition to the PPA, enables cost savings by taking advantage of low or even negative market prices. Nonetheless, to comply with green hydrogen certification requirements, the system must maintain a sufficiently large PPA to ensure a predominantly renewable electricity supply.

Regarding modeling approaches, the deterministic perfect foresight model achieves the highest system profitability, as it is able to optimise operations over the entire planning horizon with full knowledge of future conditions. In addition, it has the lowest computational burden, making it an attractive option for long-term planning and design studies. Although the rolling horizon and stochastic models provide a more realistic representation of uncertainty, the difference in profitability outcomes remains modest, with a maximum deviation of approximately 15 percent. For long-term profitability assessment and system design studies, the deterministic model proves to be sufficiently accurate while offering the advantage of lower computational effort.

By comparing different system configurations and optimisation approaches, this thesis provides insights into the trade-offs between model accuracy, economic performance, and operational robustness in integrated hydrogen systems, offering practical guidance for the design and operation of future green hydrogen facilities.

The COVID-19 crisis heavily affected financial stock markets. In March 2020 stock prices dropped immensely and markets became extremely volatile. In this report we model three European stock markets before and during the COVID-19 crisis to determine whether the dynamics of financial markets changed structurally compared to previous periods. Stock markets play a big role in our economy and can cause economic disruption when they crash. Therefore, it can be very useful to understand the dynamics of the stock market. Econometricians are nowadays often asked to model the non-constant volatility (conditional heteroscedasticity) of financial time series. This report uses Generalised AutoRegressive Conditionally Heteroscedastic (GARCH) models that are known for their precise modeling of conditional heteroscedasticity. They are also known for their ability to capture the key stylised facts, the common empirical properties applicable to all types of stock markets. This report includes general definitions and characteristics of the GARCH(p, q) process and covers topics such as autocorrelation of returns, kurtosis, leptokurticity and volatility clustering. For estimating the GARCH models this report uses the statistical program R which estimates the parameters by the Quasi-Maximum Likelihood Estimation (QMLE) method. We modeled different periods before and during COVID-19 and compared the estimated parameters with the corresponding 95% confidence intervals. To test the accuracy of the estimations we performed parametric bootstrapping. Throughout the report, models for the Dutch Amsterdam Exchange (AEX) index, the French Cotation Assistée Continu (CAC 40) index and the German Deutsche Aktien (DAX) index are analysed and compared. It seems that European markets may experience the impact of stock market crashes differently. The DAX index shows significant changes in the dynamics of the stock market due to the COVID-19 crisis whereas the AEX and CAC 40 index do not.