Leveraging the transportation algorithm for energy flow optimization in smart households

This paper presents the application of a transportation algorithm to optimize energy flow within a smart grid context. By leveraging this well-established optimization technique, it is demonstrated that energy efficiency can be enhanced and costs lowered in individual households equipped with smart appliances and connected to both traditional and renewable energy sources. Simulation studies have shown that the algorithm can effectively determine optimal energy consumption patterns, leading to significant energy savings. Additionally, the algorithm can provide valuable insights into network congestion and energy demand forecasting, enabling distribution system operators to make informed decisions. The proposed solution aligns well with the concept of smart homes. By integrating with smart devices, such as smart sockets and thermostats, energy consumption can be optimized based on real-time pricing and renewable energy availability, ultimately leading to lower energy bills and increased user comfort. Extending this approach to the distribution network level, by applying the transportation algorithm to optimize energy flow at the medium and low voltage levels, could further enhance grid stability and facilitate the integration of renewable energy sources.