Affinity-Based Power Flow Optimization in Reconfigurable Picogrid

• Published in: 2024 IEEE/SICE International Symposium on System Integration (SII) pp. 1164 - 1169
• Main Authors: Kokubu, Ryota, Oeda, Kaito, Kawakita, Yuusuke, Yokogawa, Shinji, Tobe, Yoshito, Ichikawa, Haruhisa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.01.2024
• Subjects: Load flow; Optimization; Power grids; Renewable energy sources; Routing; System integration; Universal Serial Bus
• ISSN: 2474-2325
• DOI: 10.1109/SII58957.2024.10417157

In recent years, there has been an increasing demand to adopt renewable energy resources to power grids for environmental issues and energy concerns. A virtual grid system, which is designed to utilize decentralized resources, is proposed as an approach. This system enables users to construct their power grids with virtual grid hubs (VG Hub) which interconnect via commercially available cables according to USB Power Delivery (USB PD). A prototype of VG Hub is capable of synthesizing and distributing DC power for connected devices, and the power flow control is managed by a microcontroller. Our previous study proposed a method called Minimum-Hop Power Path Routing (MHPPR). This method determines the power flow that uses the closest source in candidates which can fulfill the demand when a load connects to the network to minimize power loss due to transmission. However, this could result in unproductive power flow, such as a flow that consumes power stored in batteries prior to surplus power from generators. Therefore, we assumed that there are factors to value a source for a load other than demand and supply capacity, and inferred that the factor derives from the type of device. We defined this factor as the affinity between devices. The objective of this study is to solve the power path optimization problem while taking affinity into account. In this study, we discussed device classification and affinity between types and defined affinity coefficients. We then extended MHPPR by incorporating this affinity coefficient. The results have shown that the proposed method selected the sources following the priority defined by the affinity.