The rapid expansion of artificial intelligence (AI) is driving an unprecedented increase in data center demand. This surge requires massive computational power and energy consumption, leading to massive constructions of data centers annually worldwide. While AI-driven solutions enhance efficiency, they also introduce challenges, particularly where greater efficiency may drive increased resource consumption rather than reducing it. Addressing these pressures is crucial to aligning technological growth with sustainability goals. Despite increasing industry focus on sustainability, the lack of standardized performance metrics limits meaningful progress. Inconsistent energy efficiency, emissions, and resource utilization measurements make it difficult to enforce sustainability initiatives across the European data center industry. Regulatory fragmentation exacerbates this issue, allowing companies to navigate loopholes in sustainability enforcement. Without harmonized policies, even technically sound performance standards struggle to gain widespread adoption.
This research identifies key adoption factors that can drive the meaningful adoption of performance standards by 2030. A structured multi-method approach was applied, integrating literature review, interviews, BWM analysis, and desk research. The findings confirm that coercive pressures (regulatory enforcement) are the most influential driver, followed by observability (industry recognition) and mimetic pressures (competitor adoption trends). Operational factors such as reliability, robustness, and resilience were also critical for ensuring long-term adaptability. Beyond these adoption factors, additional barriers, regulatory complexities, neglected lifecycle emissions, and geographic disparities, must be addressed to ensure meaningful adoption. The research highlights five essential focal points for achieving widespread adoption of performance standards in European data centers: 1) Regulatory coherence and enforcement, 2) Balancing simplicity and comprehensiveness, 3) Enhancing observability and transparency, 4) Aligning with technological innovations, and 5) Addressing geographic and economic disparities.

The Dutch electricity network is shifting from a centralized, unidirectional system to a decentralized, bidirectional one, driven by renewable energy integration and increased electrification. This transition poses challenges, particularly low-voltage grid congestion. The study investigates the role of smart appliances in mitigating congestion through demand response initiatives. It identifies regulatory uncertainty, insufficient financial incentives, and lack of standardized communication protocols as barriers to adoption. Using qualitative interviews and Critical Transactions Theory, the research recommends regulatory intervention, direct load control, and unified communication standards to enhance grid resilience. Future research should incorporate social factors and assess standards for smart appliances.

Currently, the electricity grid in the Netherlands is reaching its capacity, resulting in congestion issues. One of the factors that causes the electricity grid to become overloaded is the increasing use of EVs (electric vehicles). A situation in which a large number of EV users in a certain area charge their EV at the same time can significantly increase the risk of congestion in the electricity network. To avoid such situations it is necessary to change the charging behaviour of the EV users.

The literature shows that smart charging systems, such as charging schemes, are having a high potential to solve these grid capacity problems. In the context of this study, a charging scheme is defined as a contract between an EV user and the charge card provider/electricity supplier stating, among others, at what times an EV user could charge his/her EV. However, to ensure that charging schemes can effectively contribute to solving the grid capacity problems, it is important that many EV users are willing to participate in such schemes.

Therefore, the research objective is to study how EV users will react to various charging schemes. In particular, the aim is to assess the effectiveness of appealing to someone's intrinsic motivation versus providing extrinsic incentives. First of all, with regard to the extrinsic incentives, this study specifically concerns monetary incentives. By presenting different discounts, it is possible to test whether a monetary compensation influences the willingness to participate in a charging scheme. Secondly, with regard to someone’s intrinsic motivation, this study focuses on appealing to someone's solidarity as a form of an intrinsic motivation. EV users with a certain degree of solidarity can be motivated to participate in a charging scheme when solidarity incentives are obtained (social recognition, togetherness and the development of friendships). By emphasizing the social benefits of a charging scheme, solidarity incentives could possibly increase the willingness to participate in a charging scheme.

Since the objective of the study is to research how EV users will react to various charging schemes, a survey containing a discrete choice experiment is used. To be able to examine the effectiveness of providing an extrinsic incentive (monetary compensation) versus appealing to someone's solidarity by emphasizing the social benefits of a charging scheme, the survey contains two charging scheme versions, an incentive-based version and an intrinsic-based version. By using two survey versions, it is possible to examine separately whether it is more effective to appeal to someone's intrinsic motivation versus providing a financial compensation.

The results show that the willingness to participate in a charging scheme was generally higher in the intrinsic-based version than in the incentive-based version. Based on this, it can be concluded, within the boundaries of the selected attribute ranges, that appealing to someone's solidarity by emphasizing the social benefits is more effective than offering a financial compensation. This is also substantiated by the fact that the financial compensation attribute included in the incentive-based charging schemes does not influence the willingness to participate.

To match supply from intermittent renewable energy sources (RES) with demand, it is proposed in literature to introduce flexibility in the electricity market of the future. Flexibility can be provided by energy storage, demand response and cross-border transmission. In this thesis flexibility is modeled explicitly through the price mechanism of demand and supply.
This price mechanism can be made explicit with the principles of Economic Engineering. With that price mechanism a price-dynamic bond graph model of the electricity market of the future is built. This model can be used with the various tools that control engineering has to offer to aid investors and regulators in designing the electricity market of the future.
It can aid specifically in determining the adequate generation capacity, but also in determining the necessary power and energy capacity of storage, demand response and cross-border transmission.

As an example of application, this thesis demonstrates the use of the price-dynamic model by simulating a future scenario. By simulating trading behavior of a market participant the change of prices for a market with flexibility can be quantified. It is shown that passive control does not represent realistic trading behavior, so optimal control is used. To this end, an Economic Model Predictive Controller (EMPC) is designed to simulate how market prices change when a trader maximizes his profits through energy arbitrage. Based on these price changes it is advised that the Transmission System Operator (TSO) implements an energy storage reserve market to account for risk and ensure grid stability in the electricity market
of the future.