Adaptive Pricing-Based Optimized Resource Utilization in Networked Microgrids

Abstract

The performance of networked microgrids primarily depends on the design of internal market structure for maximum resource utilization, optimized power sharing, and enhanced economic efficiency. This article presents a novel framework for resource optimization within the networked microgrids. First, each microgrid is optimized using a local energy management system to compute power shortage/surplus depending on various parameters including local generation (solar photovoltaic), battery energy storage system, and load profile. The total shortage/surplus is obtained by aggregating the shortage/surplus of each microgrid, facilitating the calculation of adaptive internal trade price. The internal trade prices are responsive to load variations and time-of-use prices, thereby encouraging internal trading within the networked microgrids. The internal trading price is strategically set to be lower than buying price from the grid and higher than selling price to the grid to maximize overall revenue of the entire network. Subsequently, a central energy management system is formulated, determining optimized power sharing between microgrids based on the adaptive internal trading. The proposed strategy is validated on the IEEE-33 bus test feeder using improved accelerated particle swarm optimization, achieving an annual power loss reduction of 1795.8 kW and an overall cost reduction of 7.2%. These results confirm the practicality and effectiveness of the proposed framework in real-world scenarios.

Keywords

• Adaptive pricing
• battery energy storage
• energy management
• power sharing