Optimal operation and stochastic scheduling of renewable energy of a microgrid with optimal sizing of battery energy storage considering cost reduction

Abstract

In order to alleviate worldwide worries about environmental issues, power system operators and planning entities are looking for new energy sources that will produce fewer emissions than conventional fossil-fuel power plants. When it comes to powering their systems, utilities are increasingly choosing renewable energy sources (RESs). Here, microgrids (MG) would furnish the ideal conditions for incorporating RESs. Thus, this study presents an seasonal optimization framework for the short-term operation of an MG, including energy storage and solar photovoltaic (PV) systems, while thoroughly exploring the effects of varying climatic factors on the optimal scheduling of resources. The day-ahead MG scheduling is addressed in this model, taking into account the impact of varying irradiances across a year. The resulting single-objective optimization problem is then tackled with the help of the converged barnacles mating optimizer (CBMO), as a robust and effective optimization method. The simulation findings show that the total operating cost of the grid-integrated microgrid is alleviated when a PV model is used in a real-world setting to raise the accuracy of the energy management system. In case 1, the CBMO reduces the operating cost of the MG by 262.784 €ct/day, which is less than the results obtained by some other algorithms. Using the hybrid CBMO, the reported costs for Case 2 is 298.8513 €ct/day. The CBMO approach yielded the best results of 352.1964 €ct on a warm sunny day, 285.2851 €ct on a cold sunny day, 257.7912 €ct on a warm cloudy day, and 252.135 €ct on a cold cloudy day. After running the simulations, it has been found out that the suggested CBMO has the shortest mean simulation time by 114.217 s in Case 3, while other algorithms need longer simulation time such that the GA needs 120.364 s, the PSO needs 118.487 s, the DE needs 118.039 s and the ICA needs 116.287 s. Therefore, the CBMO takes significantly less time than other methods.