The microgrid is described as a localized low-voltage power distribution system integrating DG units and ESS to supply electricity to some small or remote communities. In this respect, the ESS stores energy when demand is low and releases the stored energy during peak hours. Real-time power balancing remains a major issue for isolated micro-grids using intermittent renewable DG sources. Battery Energy Storage Systems (BESS) can solve the problem as they can offer reserve capacity to meet the load changes. However, battery degradation significantly affects the BESS lifetime performance because degradation depends upon the cumulative energy throughput which has units in terms of kilowatt-hours (kWh) or megawatt-hours (MWh). When there is degradation affecting capacity reduction, there is a direct impact on the energy delivered to the load, and therefore it must be considered in system optimization. To reduce the operation costs and to make the electricity prices affordable for the consumers, the degradation effects must be included while optimizing the microgrid operation. For this, more detailed simulation on an hourly basis of battery discharge profile needs to be performed so as to assess the degradation effects based on actual discharge patterns. Then degradation costs and life estimations are included in the optimization. It is observed that higher average kWh and actual MWh throughput parameters increase operation costs in general while lower the electricity cost for the end user. This study presents an optimization method for minimizing microgrid operating costs and customer electricity expenses over the 24-hour period under consideration, with explicit modeling of BESS degradation. Accelerated Particle Swarm Optimization (APSO), the Modified Jaya (M-JAYA) algorithm, and the Linear Programming Interior-Point (LP-IP) method are implemented to optimize parameters related to degradation. Comparative results showcase the ability of these algorithms with respect to BESS lifetime, degradation cost, system operating cost, and customer electricity cost.