Addressing the trends of digitalization, decentralization, democratization, and decarbonization, local peer-to-peer (P2P) markets have the potential to significantly accelerate decarbonization at the communal level. However, due to an increase in the number of energy consumers, such as electric vehicles or heat pumps, grid congestion can occur since actual low-voltage grids are not designed to transmit large loads. This paper introduces a novel concept for a platform to combine the advantages of P2P trading with the need for secure, automated low-voltage grid control, ensuring effective congestion management. Therefore, a dual local energy market has been developed, comprising a P2P energy market and a flexibility market with the latter ensures preventively managing congestion. Furthermore, mechanisms exist to provide curative, real-time congestion management. Additionally, the platform empowers prosumers, i.e. end users that produce and consume electrical energy, with intelligent market strategies to maximize their financial outcomes by participating in both markets. To provide a secure trading mechanism, the novel concept of Self-Sovereign-Identity is integrated into the platform. Tested in a smart grid laboratory at the University of Wuppertal, the platform provides financial gains for prosumers and effectively manages current- and voltage-related grid congestions.

In response to the energy transition to renewable resources, green hydrogen production via electrolysis is gaining momentum. Modular electrolysis plants provide a flexible and scalable solution to meet rising hydrogen demand and adapt to renewable energy fluctuations. However, optimizing their operation poses challenges, especially when dealing with heterogeneous electrolyzer modules. In this work, a combination of decentralized Multi-Agent Systems and the Module Type Package concept is presented that enhances the cost-optimized operation of modular electrolysis plants. This approach synergizes the individual strengths of Multi-Agent Systems in handling complex operational dynamics with the efficiency of the Module Type Package for integration and control capabilities. By integrating these technologies, the approach addresses the heterogeneity of electrolyzer modules and increases the adaptability, scalability, and operational flexibility of electrolysis plants. The approach was validated through a case study, demonstrating its effectiveness in achieving cost-optimized load scheduling, dynamic response to demand–supply fluctuations, and resilience against electrolyzer module malfunctions. In summary, the presented approach offers a comprehensive solution for the effective coordination and optimization of modular electrolysis plants.

The growing penetration of renewable energy sources and the increasing number of electrical consumers at the low-voltage level present significant challenges for controlling the low-voltage grid. This paper addresses these challenges by proposing a decentralized approach to energy dispatch and grid congestion management. While the global energy transition is driven by the urgent need to address the climate crisis, much of the current research remains focused on national solutions. Furthermore, centralized approaches to energy transition and congestion management pose risks such as single points of failure and limited scalability. To overcome these limitations, this paper introduces a decentralized method applicable to both German and Japanese systems. The proposed method is validated using the Comprehensive Analysis Tool for Distribution System and Distributed Generation for power flow analysis, combined with a road traffic simulation to model Japanese charging behavior patterns. Simulation results demonstrate the approach’s generalizability across various systems, highlighting the distinct country-specific characteristics of Germany and Japan. Additionally, the decentralized approach’s applicability to both countries is demonstrated and discussed. Furthermore, the decentralized control strategy is tested in a laboratory setting, integrating DC loads and sources, back-to-back inverters, and three-phase energy distribution. The results confirm that the decentralized control method effectively transmits control signals to real systems and that the underlying mathematical models accurately reflect real-world scenarios.

Effectively utilizing flexible energy resources requires optimizing their operation over time to balance dynamic demand and fluctuating supply from volatile renewable sources. Traditionally, this has been achieved through centralized optimization models, which suffer from scalability limitations, single points of failure, and limited flexibility when applied to decentralized and dynamically changing environments. Distributed models offer a promising alternative, providing enhanced flexibility, robustness, and computational efficiency by enabling parallel processing and reducing coordination delays. Thus, this work presents a methodology for the semi-automated transformation of centralized optimization models into distributed architectures, leveraging containerized multi-agent systems to achieve scalable and efficient optimization across multiple computing units. A case study involving 120 electrolyzers distributed across up to 5 optimization agents, including both homogeneous and heterogeneous configurations, demonstrates that the distributed approach accelerates computation time by a factor up to 27.42 compared to a centralized model while accepting a solution quality deviation of only 2.2 %. The optimization integrates site-wide and real-time optimization, ensuring adaptability to fluctuating renewable energy availability and improving system resilience. This combination enables long-term strategic planning while allowing real-time adjustments to maximize renewable energy utilization. The findings highlight the benefits of distributed optimization in modular energy systems and confirm that containerized multi-agent architectures enhance scalability and computational efficiency, making the approach well-suited for real-world applications in decentralized and modular energy networks.

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.