This thesis aimed to uncover the magnitude and effect of residential E-cooling demand on the Dutch energy market. Currently, little is known about the subject even though, due to rising temperatures and the increase in the amount of heat pumps, the amount and with that the effects of residential E-cooling demand is expected to rise sharply in the upcoming decades. First, the magnitude and patterns of residential E-cooling were uncovered by developing a thermodynamical model of the average Dutch residential houses. The effects of E-cooling were then tested by implementing the cooling demand in Pandapower and Plexos, testing the effects on local physical grids and on the overall power market respectively. The results showed a doubling of the cooling demand between 2025 and 2030 and a maximum annual cooling demand of approximately 0.4 TWh. The maximum cooling demand amounted to 2 TWh when alternative weather data was used reducing weather data limitations. In addition, it was shown how the demand for residential cooling has the potential to decrease local power quality when more than 40% of households actively cool their houses simultaneously, increasing network costs. Finally, it was also proven how power prices could increase due to higher demand and how revenue for certain generation components could double, or decrease by 20% in our grid during heat waves when accounting for residential E-cooling demand. This thesis provided among the first in-depth analysis of the magnitude and consequences of residential E-cooling demand on the Dutch energy market. It showed how cooling demand is expected to increase and what the consequences are of this increase.

The pressing need to mitigate global warming and transition to sustainable energy solutions has accelerated the development of innovative energy systems. This thesis investigates the sizing and design of a photovoltaic thermal (PVT) system integrated with aquifer thermal energy storage (ATES) within a fifth-generation district heating network (5GDHN) for a case study in the Werfgebied district in Hilversum, Netherlands. The study focuses on the configuration, storage distribution, and optimisation of component sizing within the district heating network to minimise overall electrical power usage, thus reducing grid dependency and CO2 emissions. A Python model of the multi-energy carrier system is developed, embedding the physical principles underlying the thermal and electrical properties of the components. The research finds that an optimal configuration for the ATES and PVT combination involves a single ATES well rather than distributed thermal energy storage. The results indicate that the size of the aquifer significantly affects the overall operating temperature and its fluctuations. A larger ATES maintains a stable but relatively colder temperature. Optimal sizing is achieved at the maximum allowed operating temperatures of an ATES in these areas, resulting in the most favorable temperature for maximum COP in the heat pumps. This minimises grid exchange and CO2 emissions. The optimal ATES size is determined to be 380,000 m3, in combination with 800 PVT modules, leading to a total CO2 equivalent emission of 856 tonnes.

As demand for clean and renewable energy around the world increases, solar photovoltaic (PV) technology becomes substantially popular, especially in low-voltage (LV) distribution networks. However, the integration of PV in LV distribution networks requires careful planning as it introduces voltage violations. To maintain network voltage, distributed control of residential-scale battery energy storage systems (BESS) is a possible option. Previous studies considered only one-day simulations with limited testing conditions. However, it is important to evaluate voltage control capability over an extended period of time. Moreover, it is important to estimate battery lifetime for the economic feasibility evaluation of distributed control.

This work aims to present a distributed control method for BESSs at a residential scale to provide voltage support in a highly PV-penetrated LV network while providing insights into their lifetime estimation. A control method based on a consensus algorithm with the addition of SOC balancing control is proposed and tested on a modified CIGRE LV distribution network using MATLAB/Simulink. Evaluations on the voltage support capability and control behavior are performed in various testing conditions and are extended beyond one day of simulation. Moreover, a battery lifetime estimation is performed using the resulting cycling profile from the proposed control.

The proposed control strategy can provide voltage support in most case variations with the exception of cold seasons and extreme addition of PV power generation. Concerning battery lifetime, there is only a small observable capacity fade from the proposed strategy’s cycling profile. It is important to investigate calendar aging because of the small cycling current from the operating conditions presented in this work.

The Netherlands is currently undergoing an energy transition in an effort to decarbonize its electrical grid and build a more sustainable generation model. This transition is being led by the integration of non-controllable, sustainable generation sources such as wind and photovoltaic (PV) power. The Dutch wholesale energy market has an imbalance settlement period in which the TSO penalizes or rewards deviations from the last submitted trading program (based on whether the deviations aggravate or relieve the overall market imbalance), and therefore, non-controllable sources are at a higher risks of suffering undesired deviations. The consequences of this are twofold: on one hand, they impose a strain on the grid and an increased demand of ancillary services; and on the other, they risk the economic profitability of the plant.
This issue can be bridged by combining non-controllable generation sources with storage assets.

Although the the dispatch of non-controllable energy sources has been studied extensively, there is a research gap in the proposal of revenue-maximizing strategies for operating hybrid power plants (with wind and PV generation, and energy storage capabilities) in the Dutch wholesale energy market, that account for the stochastic nature of the weather resources and include financial contingency factors.
This thesis aims to bridge that gap by setting up an optimization-based dispatch, using Mixed Integer Linear Programming. The optimization was extended to a scenario-based stochastic optimization, and the Conditional Value at Risk was introduced to account for the intrinsic financial risk of the dispatch under random weather conditions. The resulting problem is a two-stage optimization which was solved using a modified Bender's cut. The intra-day optimizations were also adapted as rolling-horizon dispatches, permitting the operation with periodic updates to the weather forecasts.


The study case for this research was the SWITCH lab, a small-scale laboratory developed by TNO to conduct empirical research on the integration of renewable energies and storage into the grid. TNO also provided the basis for a non-optimized dispatch strategy based on price benchmarking, which was used to compare the performance of the optimized strategy.

The optimized dispatch proved to be an effective strategy for producing maximal-revenue trading programs on all market closings. The optimized revenue provided revenues between 85.8% and 260.1% higher than a generation-alone plant configuration; and an increase in revenue with respect to a generation-only baseline between 300.0% and 8962.9% compared to the non-optimized strategy. Operation under a hybrid configuration using the optmized dispatch also yielded the best economic outlook, having the highest 10-year Net Present Value projections, an average Internal Return on Investment 57.2% higher than the hybrid plant under a non-optimized scheme and a 47.5% lower payback time.

The optimized strategy provided the most profitable trading programs for both the case of deficit and surplus of generation at delivery, turning a positive revenue even under unfavourable market conditions. Conversely, the non-optimized dispatch had the lowest economic outlook of any configuration, with worse NPV, IRR, and payback times than the generation-only plant.

These results highlight the importance of developing dispatch strategies that consider the long-term behaviour of generation and prices, as opposed to here-and-now strategies whose performance was shown to be comparatively deficient; and the synergy between storage and renewable generation sources to bridge the non-controllability problem.

To enable affordable and environmentally friendly eletrical energy storage, a battery was designed using sea-water as electrolyte. The performance of this battery was tested through repeated cycling under a variety of circumstances. The Coulombic and energetic efficiency were around 80% and 68% respectively. The battery saw a strong drop in performance after ~60 cycles. This was corroborated with results gained from four separate cells. Efforts to model the battery were limited by the accuracy of the utilized model itself.

The majority of Dutch homes currently use natural gas boilers to meet their space heating demand. Changes in this significantly large sector are required to achieve the sustainability goals established by the Paris Agreement. Since most renewable energy sources produce electricity, transitioning residential heating systems might cause problems managing national electricity grids. Furthermore, the intermittent nature of renewable energy sources leads to an imbalance between supply and demand.

These challenges can be overcome by combining different storage techniques. An example of such a technique is the thermal energy buffer developed by Borg, which focuses on single-household use. This thesis looks into the feasibility of such a system by comparing different scenarios for residential heating systems.

Three scenarios were modelled using Simscape. In scenario I, a natural gas boiler provided all the heating demand of the house. In scenario II, the heating system consisted of Photovoltaic Thermal (PVT) panels and the thermal energy buffer. In scenario III, the house was heated by PVT panels, the thermal energy buffer, and a heat pump. In all scenarios, the same house was connected to the heating system.

In scenario II, the system sizes were 0, 1, 2, and 3 PVT panels, combined with buffer capacities of 0, 2, 4, and 6 m3. In scenario III, the system sizes were: 3 PVT panels with 6 m3 buffer capacity, 1 PVT panel with 4 m3 buffer capacity, and 3 PVT panels with 2 m3 buffer capacity.

The total yearly heat consumption of the modelled house was 5109 kWh. In scenario I, 378 kg of CO2 was emitted. In scenario II, CO\textsubscript{2} emissions were highly dependent on the sizing of the PVT system and the TESS and ranged from 20 to 405 kg, based on a heating demand of 9444 kWh.

Scenarios I and III maintained a comfortable temperature during the entire year. The heating system of scenario II was insufficient to heat the house throughout the year for all modelled system sizes; however, the scenario could be acceptable with larger PVT and TESS sizing. By comparing CO2 emissions and payback time, the optimal capacity of the thermal energy buffer was found to be 6 m3.

The uncertainty of the future gas price causes a challenge in comparing the cost of electrified heating systems to traditional heating systems. Additionally, it underlines the necessity of decarbonizing residential heating systems to secure comfortable, affordable housing.