This thesis investigates how a hybrid tariff model can be designed and implemented to support financially sustainable, transparent, and operationally efficient energy usage in decentralized smart grids, using the Slim Strandnet microgrid in Scheveningen as a real-world case study. As decentralized energy systems gain prominence in the energy transition, conventional tariff structures, designed for centralized, uni-directional energy flows, fall short in capturing the complexity and potential of these new configurations. This research addresses this gap by proposing and evaluating a novel tariff framework that distinguishes between energy-related and grid-related costs, and allocates them using a combination of dynamic pricing and cooperative cost-sharing mechanisms.

The central research question guiding this study is: How can a tariff model be developed for a local smart grid such as Slim Strandnet that incentivizes efficient use of the grid connection and distributes costs and benefits based on each participant’s contribution to balancing locally generated energy supply and demand? This overarching question is explored through three sub-questions focused on (1) the operational and financial benefits of collaboration in energy communities, (2) the applicability of cost allocation and pricing methods from the scientific literature, and (3) strategies to ensure financial risk mitigation for less flexible participants.

The thesis employs a dual-model approach. First, an operational energy model, based on agent-based decentralized optimization (ADMM), simulates energy flows and flexibility asset usage within the community. Second, a hybrid tariff model combines dynamic pricing for energy costs with ex-post allocation using Keys of Repartition (KoR) for grid-related costs. This layered structure allows the model to provide real-time behavioural incentives while ensuring fairness in cost and benefit allocation.

The Slim Strandnet case study includes eight simulation scenarios across three representative months (October, December, March), evaluating different combinations of tariff types and contractual arrangements such as Group Transport Agreements (GTA). The results demonstrate that collaborative operation significantly reduces peak demand and contracted grid capacity, leading to cost savings of up to 30%. Moreover, the hybrid RTP–KoR model effectively captures and redistributes flexibility benefits, with the KoR mechanism allocating shared costs in proportion to measurable contributions like peak load reduction and battery use. However, the findings also highlight that while RTP enhances system efficiency, it introduces price volatility that can disadvantage inflexible users.

To address this, the thesis proposes and evaluates financial protection mechanisms such as collective billing, community reserve funds, and flexibility credit schemes. These tools, although not yet implemented in practice, are shown to be compatible with the existing Slim Strandnet framework and supported by literature.

In conclusion, this thesis offers a scientifically grounded and practically tested tariff design that aligns operational incentives with fairness and financial security. It contributes both a methodological blueprint and empirical validation for future decentralized energy communities, demonstrating how collaborative tariff models can enhance the sustainability and resilience of local energy systems.