Configuration Cost and Link Efficiency-aware Routing in the Power Grid Composed of Hub Networks

Interest in next-generation power grids, such as smart grids and distributed networks, has grown due to the goal of achieving carbon neutrality. Renewable energy sources, like solar power, are key to this transition, but their inherent fluctuation poses challenges. Our research focuses on a Virtual Grid (VG) system, an autonomous, distributed network that utilizes renewable energy. The VG system connects multiple VG Hubs to synthesize and distribute direct current power. Ensuring the effective control of each VG Hub amid network fluctuations is essential for the success of the system. Our research aims to develop an algorithm that determines power sources and routes for new loads within the VG Hub network. Previous methods to optimize the overall flow for each load addition have encountered issues with configuration costs and deviations from optimal solutions. Our challenge is to create a hybrid method that balances configuration minimization with flow optimization. The proposed algorithm reduces the need for new configurations with each new load and performs flow optimization only if the flow optimality of the solution exceeds a predefined threshold. Simulations were conducted to test the effectiveness of the algorithm in various network scenarios. The results demonstrated that our algorithm excels in networks with a high average degree and in large-scale networks with a low average degree. In addition, the algorithm performs better in scenarios with multiple supply sources or when additional power sources are introduced, compared to scenarios with a single supply source. By focusing on the correctness of our methods, the clarity of our objectives, the engagement of our solutions, and the effective delivery of our results, our approach presents a significant advancement in the control and optimization of Virtual Grid systems.