Optimal operation of battery storage systems in standalone and grid-connected DC microgrids using parallel metaheuristics optimization algorithms.

This study addresses the problem of optimal operation of batteries in standalone and grid-connected Direct Current (DC) Microgrids (MGs) that include photovoltaic (PV) generators operating at maximum power point. For that purpose, a mathematical model was formulated considering three objective functions: (1) the minimization of operating costs, (2) the reduction of energy losses associated with energy transport in DC MGs, and (3) the minimization of the total emissions of into the atmosphere produced by conventional generators. The model integrates a set of constraints that represent the operation of DC microgrids. Three parallel versions of well-known solution methodologies were used here: Parallel Particle Swarm Optimization (PPSO), the Parallel Vortex Search Algorithm (PVSA), and the Parallel Ant Lion Optimizer (PALO). These methodologies were implemented to optimize the hourly power flow method based on successive approximations and evaluate the objective functions and constraints. To validate the effectiveness of the solution methods proposed in this paper, two test systems were used: a standalone network and a grid-connected network (both in Colombia). Furthermore, to test the effectiveness of the proposed energy management system (regarding solution, repeatability, and processing times) each solution methodology was executed 100 times in each test system using MATLAB software. Based on the results of the simulations, the methodology with the best performance for standalone grids was the PVSA. In turn, the methodology that achieved the best results in the grid-connected network was the PALO. In both scenarios, the best average reductions in energy fixed costs, variable costs, energy losses, and
emissions were 0.0282%, 1.3039%, 9.1655%, and 0.1254%, respectively. Although these were important benefits in technical and environmental aspects, the results of the economic indicator suggest that the operation of electrical networks should consider variable costs in order to improve the impact of energy management systems in the future.