Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid

Abstract

• This study presents an advanced Transactive Energy Management (TEM) approach designed to optimize scheduling and storage utilization within a grid-connected renewable energy microgrid. • The motivation behind this research is rooted in the need to develop robust, cost-effective solutions for managing renewable energy-based microgrids in grid-connected environments. • Through simulations in two main scenarios, one without energy storage and another with integrated storage, the study reveals that the SMA algorithm consistently outperforms traditional optimization methods. • In microgrids, the primary aim of Energy Management System (EMS) optimization is to minimize operational costs while satisfying various system constraints. This study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. SMA's adaptability enables effective management of renewable variability, maximizing energy efficiency while minimizing operational costs and emissions. The study evaluates SMA's performance through simulations of two scenarios: with and without battery storage. In the non-storage scenario, SMA reduces operational costs by optimizing distributed generation and grid transactions. However, in the storage-integrated scenario, SMA demonstrates substantial advantages, achieving 20–48% cost savings by leveraging optimal charging and discharging cycles. This underscores the critical role of energy storage in stabilizing costs and reducing reliance on grid power during high-price intervals. Additionally, the inclusion of storage contributes to 25–38% emission reductions by enhancing renewable energy utilization and minimizing dependency on fossil-fuel-generated electricity. Comparative analysis reveals that SMA consistently outperforms conventional methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of convergence speed and computational efficiency, making it particularly suitable for real-time energy management. SMA achieves faster convergence, ensuring timely decision-making even in dynamic market conditions. This research highlights the critical role of advanced energy management strategies and battery storage in improving economic and operational efficiency in renewable energy microgrids.

Topics & Concepts