Estimation of the Battery Capacity in the Microgrid of a Nearly Zero Energy Building According to the Desired Degree of Energy Autonomy

Abstract:
This paper proposes an integrated method based on the genetic-algorithm technique to estimate the proper capacity size of a battery storage system (BSS) that should be installed in a nearly Zero Energy Building (nZEB), in order to reach the desired degree of energy autonomy. The suggested method is referred to grid-connected nZEBs and seeks for a correct balance between the installation cost and the potential efficiency improvement of the nZEB’s microgrid. The sizing optimization method of the BSS is implemented by utilizing the genetic algorithm technique and the optimal solution is resulted through the minimization of a properly defined cost function. The effectiveness and the practicality of the suggested BSS estimation method are verified with results obtained by various operating scenarios from several real nZEB’s microgrids.