Optimal battery operation for the optimization of power distribution networks: An application of the ant lion optimizer

In this paper, we address the problem of optimal battery operation in power distribution networks in a scenario of high photovoltaic penetration and variable power demand during a typical day of operation. We mathematically modeled this problem using a Nonlinear Programming (NLP) model, which incorporates three different objective functions, each approached from a single -objective optimization perspective. The three objective functions we considered were (i) the reduction in operating costs, (ii) the minimization of energy losses, and (iii) the reduction in CO2 emissions. To solve the proposed NLP model, we implemented a master- slave methodology. In this methodology, the master stage uses the Parallel version of the Ant Lion Optimizer (PALO) to determine the batteries' charging and discharging operations at each time interval. The master stage, for its part, employs an hourly power flow analysis based on the successive approximation method to calculate the values of the objective functions and the constraints associated with the mathematical model. To validate the performance of the proposed methodology, the 33-node test system was adapted to the specific characteristics of power generation and demand in the city of Medellin in Colombia. The simulations conducted using MATLAB demonstrated that the PALO excelled in terms of solution quality, repeatability, and processing times when it came to minimizing the economic, technical, and environmental indicators under analysis. According to the results, the proposed methodology outperformed three other optimization approaches reported in the specialized literature, and used here for comparison purposes: the Chu and Beasley genetic algorithm, the particle swarm optimization algorithm, and the vortex search algorithm.