In recent years, the yearly share of electricity generated in the Netherlands from renewable sources such as solar and wind has increased significantly, reaching 42% in 2023, and is projected to rise to 70% by 2030. However, production from these sources is highly weather-dependent, unpredictable, and often misaligned with typical periods of peak electricity demand. This mismatch leads to price volatility in electricity markets, emphasizing the importance of a reliable trading strategy for assets with flexible production or demand. Solar panels are partially flexible assets, as their production can be curtailed. However, their output is weather-dependent and cannot be predicted with complete accuracy. This creates a challenge in determining the trade volume in the day-ahead market, resulting in the highest profit. Trading conservatively in the day-ahead market reduces revenue but minimizes imbalance volumes through the possibility of curtailment. On the other hand, trading too much can result in unavoidable imbalances when actual production falls short. Existing literature focuses on minimizing imbalance volumes as extreme prices, high volatility, and lower average prices than day-ahead prices characterize the imbalance market. In these works, it is typically assumed that the real-time imbalance price is unavailable. However, real-time imbalance price predictions are available in this research, enabling optimal real-time decision-making. This provides the opportunity to profit from high imbalance prices while avoiding negative prices. In this study, a coordinated bidding strategy optimizes day-ahead bids, balancing revenue maximization and risk minimization as both the production and imbalance price are uncertain. The primary objective is to explore methods for incorporating uncertain imbalance prices into day-ahead optimization. Various methods from the literature are compared, and a novel decision-focused approach is introduced. Combined with solar generation forecasts, state-of-the-art day-ahead price predictions, and optimization models, monthly revenues are simulated using \ac{EMS} software. Results show that modeling uncertain imbalance prices using historical scenarios achieves the highest and most consistent revenues, especially when combined with \ac{CVaR} optimization. The novel decision-focused approach also performs among the best models, delivering consistently high revenues. Adding battery storage to the solar panels yields similar results, and further revenue increases are possible with improved solar forecasting. This highlights an important direction for future research.

Green hydrogen plays an important role in the energy transition. It can function as a storage medium, as well as a replacement for fossil fuels in transport or high-temperature heat processes. However, the economic feasibility of electrolysers has proved to be a problem. Even though a lot of research has been done to the electrolysis technology, very few research has been done to the implementation of an electrolyser.

For this research, a physical model of an electrolyser has been developed, as well as an Energy Management System (EMS). For this system, trading strategies for electricity markets have been developed. By trading on the imbalance and day ahead market, the contribution margin (hydrogen revenue minus electricity costs) has been significantly increased by over 27%. Seasonal hydrogen storage in salt caverns has proven to be a promising solution for producing more hydrogen and increasing revenue, depending on the storage costs that are applied. A Battery Energy Storage System (BESS) has been added to the system for its competence in dynamic behaviour on the electricity markets. For the addition of a BESS to an electrolyser, no conclusive proof of the benefits for the economic viability of green hydrogen has been found.

This paper describes a real-time energy management system developed for a solar park in the Netherlands using an alkaline electrolyser. The optimization problem is split into a two-step optimization, taking into account the specifications of the electrolyser and allowing the electrolyser to respond to changes in the imbalance market. The first optimization step determines the state of the electrolyser one day in advance. The second optimization step determines the electrolyser power, using the state of the electrolyser as an input. Simulations using data from 2020, 2021 and 2022 show that the use of the electrolyser is limited to a number of days in the year with a lot of solar generation, causing the day-ahead prices to be low. Different scenarios have been tested to get insight into how the use of the electrolyser is influenced by these changes. The type of electrolyser, being able to put the electrolyser on standby and allowing the grid to be used for the electrolyser hardly affected the results. At last, different hydrogen prices are compared. The higher hydrogen prices lead to more use of the electrolyser. This real-time EMS contributes to the ability of an alkaline electrolyser to respond to the sudden changes in the grid and by doing this making it possible to use alkaline electrolysers for balancing the grid and contributes to the use of green hydrogen in the industry for a competitive price.