Investigations on Strategies for Profit Maximization in Networked Microgrids

Abstract

Microgrids, acknowledged as small-scale power distribution systems integrating loads, energy storage systems, and distributed generation, represent a key component in the evolution of future power systems. With a dual operating capability, namely islanded and grid-connected modes, microgrids play a crucial role in managing the intermittent nature of renewable energy sources and addressing associated uncertainties. However, the intrinsic complexities related to generation and load patterns necessitate the for mation of networked microgrids, allowing for enhanced resource sharing, minimized transmission losses, and improved overall system reliability. The concept of networked microgrids has emerged as a response to the challenges posed by individual micro grids, emphasizing coordinated energy management, bidirectional energy trading, and optimized resource scheduling. This framework facilitates energy exchange not only between the grid and microgrids but also among interconnected microgrids, thereby fostering an efficient, collaborative energy ecosystem. In this regard, diverse strate gies such as hour block-based demand response programs are proposed in comparison to hour-to-hour demand response to optimize energy trading and minimize operational costs. Each hour block is formed based on generation and load imbalance, the role of microgrids and the Time-of-Use tariff system. Both techniques are evaluated in terms of time and complexity using the Particle Swarm Optimization method. Optimal capacity sizing of renewable energy sources and battery energy storage systems with the objec tive of cost reduction and the enhancement of overall reliability within the networked microgrid framework is proposed. To facilitate energy trading within the networked mi crogrids, both peer-to-peer and peer-to-grid approaches are employed, with the former utilizing a proposed "proportional trading method"; managed by a networked microgrid manager or aggregator. This study addresses these concerns through the development of a multi-objective optimization problem. Multi-Objective Particle Swarm Optimiza tion is applied to solve the formulated optimization problem. Different strategies for scheduling the individual energy storage system in networked microgrid environment is proposed to emphasize the dynamic nature of energy trading within microgrids with the objective to maximize the individual profits of each microgrid while ensuring collective benefits for the entire networked microgrid. Moreover, the integration of shared energy storage systems within networked microgrids is identified as a cost-effective solution to mitigate space constraints and minimize deployment expenses compared to individual energy storage system. Economic analyses further affirm the feasibility and financial vi advantages of implementing such strategies, highlighting the potential of networked mi crogrids in shaping sustainable and resilient energy solutions for the future