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 first used to find the so-called Pareto front while minimisation of power losses and the total installed capacity of the BES units are simultaneous objective functions. A number of solutions are chosen and developed over one year to find the best schedule for BES utilisation taking into account the power factor (PF) of the charge and discharge modes. Results of studies on the IEEE reliability test system 1996 confirm the existence of an optimal solution 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 is shown that the power loss would decrease, even during charge hours, if PFs of BESs are optimally tuned.