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

In order to alleviate worldwide worries about environmental issues, power system operators and planning en-tities 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 pre-sents 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 euroct/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 euroct/day. The CBMO approach yielded the best results of 352.1964 euroct on a warm sunny day, 285.2851 euroct on a cold sunny day, 257.7912 euroct on a warm cloudy day, and 252.135 euroct 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.

Automated summary

In order to address worldwide environmental concerns, power system operators and planning entities are seeking new energy sources with lower emissions. Utilities are increasingly choosing renewable energy sources, and microgrids provide an ideal platform for incorporating them. This study presents a seasonal optimization framework for the short-term operation of a microgrid, taking into account energy storage and solar photovoltaic systems, and analyzing the impact of climate factors on resource scheduling.