PROJECT TITLE :

Power factor-based scheduling of distributed battery energy storage units optimally allocated in bulk power systems for mitigating marginal losses

ABSTRACT:

This study addresses the problem of multi-objective optimal allocation and management of multiple battery energy storage (BES) units. The multi-objective genetic algorithm is initial used to seek out the therefore-known as Pareto front while minimisation of power losses and the full put in capability of the BES units are simultaneous objective functions. A number of solutions are chosen and developed over one year to search out the most effective schedule for BES utilisation taking into account the ability issue (PF) of the charge and discharge modes. Results of studies on the IEEE reliability check system 1996 ensure the existence of an optimal resolution for loss reduction. Optimal tuning of the charge and discharge PFs has also proven effective for marginal loss reduction and saving energy every day of the year. Finally, it's shown that the facility loss would decrease, even throughout charge hours, if PFs of BESs are optimally tuned.