Controlling-Based Optimization Framework for Dynamic Grid Reconfiguration: Enhancing Resilience and Cost-Efficiency in Hybrid Renewable Microgrids with Peer-to-Peer Energy Trading

This study proposes an advanced framework for combining renewable energy resources with adaptive feeder restructuring to improve both local energy exchange and power system resilience. Instead of conventional supply driven operation, the model emphasizes the coordinated placement of solar photovoltaic (PV) units and wind turbines (WTs), along with intelligently optimized grid reconfiguration using Particle Swarm Optimization (PSO), is the best strategy for operating modern electricity microgrids. A distinctive feature of the design is the integration of a peer-to-peer (P2P) market, which allows prosumers within the microgrid to negotiate power transactions in real time, thereby further increasing trading efficiency and reducing reliance on the main grid. A multi-stage analysis method is used to compare three different system configurations: a baseline scenario of energy distribution with PV cells and renewable energy integration, a scenario integrating hybrid PV-WT, and a fully integrated PV-WT system with optimized grid reconfiguration. The Taiwan Power Company (TPC) distribution test system served as the benchmark for assessing critical indicators, such as voltage regulation, power loss, and cost of operation. The results show that the coordinated approach of feeder restructuring with P2P trading reduces network losses from 0.0024 to 0.002 MW and lowers the average operating cost from 2.3729 $/MWh to 1.954 $/MWh. This methodology provides a scalable and resilient solution for enabling secure, affordable, and sustainable electricity exchange in future smart distribution grids.

Distribution network reconfiguration; Energy trading; Power loss reduction; Renewable energy integration; Smart grid

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