The transition to a fully electrified energy grid in the Netherlands is essential for the country's energy transition strategy. However, the existing grid infrastructure struggles to support the increased demand due to electrification, leading to congestion and potential outages anticipated before 2030. Microgrids, decentralized networks where generation, heat, storage, and consumption are coordinated, offer a promising solution by providing the required flexibility for the integration of Distributed Renewable Energy Resources (DERs) and potentially reducing energy losses associated with long-distance energy exchange.

This study explores the implementation of Deep Reinforcement Learning (DRL) in a district-level microgrid in the Netherlands. The primary objective is to minimize grid connection dependency and reduce energy exchange distances within the microgrid cost-effectively, thereby enhancing grid reliability and sustainability, answering the research question: \textit{Main Research Question}: \emph{How can Distributed Energy Resources be deployed and managed in a cost-effective manner within an electrified microgrid to achieve a balance between energy consumption and production in order to minimize the burden on the central grid given fluctuating demand?}

A comprehensive framework was developed offering a structured approach for simulating and optimizing microgrid scenarios, providing practical solutions and frameworks for the efficient management and deployment of microgrids. The study demonstrates that DERs can be effectively deployed and managed within an electrified microgrid through a combination of simulation techniques with geographical data input, a DRL model with a Deep Q-Network (DQN) that controls the system, and dynamic mappings and visualization. While the DQN demonstrated its potential in optimizing microgrid configurations, challenges were noted due to the large action spaces required for routing optimization. To overcome these limitations, future work is proposed to combine DRL with Graph Neural Networks (GNNs). This novel method shows promise in enhancing the scalability and efficiency of the optimization process, enabling distance and routing optimization without an aggregated model.

Scenarios were created to test the influence of different components, demonstrating how various factors affect overall system performance. The results of the model were as expected, and undertakings to optimize the model further led to significant improvements in the variability of the learning curve and of the rewards.

The study concludes that the developed pipeline contributes to existing works by offering a structured approach for simulating and optimizing microgrid scenarios and testing different setups. While this research approach has proven effective, future work should enhance the realism of components and address some computational limitations encountered. Refining these methodologies by incorporating GNNs, integrating real-world data, and extending the model with additional algorithms for performance comparison will further advance the field. This effort ultimately supports the vision of a reimagined, sustainable, resilient, and efficient energy grid.