Techno-economic approach for energy management system: Multi-objective optimization algorithms for energy storage in standalone and grid-connected DC microgrids

This document discusses energy management in storage systems connected to rural and urban direct current (DC) microgrids, to improve technical, economic, and environmental indicators proposing a mathematical model with three objective functions for a multi-objective approach: minimizing grid operating costs, reducing energy transport losses, and reducing CO2 2 emissions. The multi-objective model includes different operational constraints of the DC microgrid. This applies to scenarios of grid connection with both fixed and variable energy costs, as well as to isolated DC microgrids with diesel generators. All of this occurs within an environment with distributed energy resources, specifically photovoltaic generators and energy storage systems. Multi- objective optimization algorithms, such as Particle Swarm Optimization (MPSO), Grasshopper Optimization Algorithm (MGOA), Salp Swarm Algorithm (MSSA), and Ant-Lion Algorithm (MALO), are used to solve multi- objective problems. These algorithms are combined with an hourly power flow method based on successive approximations. The methodologies have been validated through two test scenarios. The first scenario had 27 nodes in a rural environment, while the second had 33 nodes in an urban environment. These scenarios were designed to represent average day generation and energy demand conditions in Colombia. Each scenario involved the integration of three distributed photovoltaic generators and three lithium-ion batteries. The objective was to assess the solution quality and processing times by iteratively running each algorithm 100 times.