## 1 Introduction
In this context, numerous research papers have been published recently to assess the best management of energy and the (HC) to enhance the technical and financial performance of the MG. Paper [16] provides a decentralized power management strategy based on Model Predictive Control (MPC) technique for islanded AC micro grid for managing stored energy. A novel optimization approach for managing an isolated micro grid is presented by [PERSON] et al. in [17] that enables automatic grid connection to deliver ancillary services to the primary grid, such as selling excess renewable generation and acquiring electricity to charge the battery bank. The proposed optimization uses a mesoscale meteorological model's weather forecasts in a hybrid economic model predictive control system. The application of MPC in an island micro-grid with PV generation and a hybrid storage system consisting of a battery, supercapacitor, and regenerative fuel cell was assessed by [PERSON] et al. in [18]. A market-oriented EMS for a hybrid electrical system composed of a system for converting wind energy into electricity and a system for BESS is introduced by [PERSON] et al. in [19]. [PERSON] recommends an efficient energy-management method for direct-current (DC) [PERSON] et al. in [20]. The typical MG comprises two renewable energy sources (fuel cells and solar systems) and two energy storage components (lithium-ion battery and supercapacitor). With a power-sharing arrangement between the load and the sources, an EMS was suggested to guarantee ideal bus voltage. A flatness control approach-based EMS was presented by [PERSON] et al. in [21]. In order to provide a steady DC bus voltage and an ideal power-sharing procedure between the solar array and the battery, the suggested method aims to utilize non-linear flatness theory to create an effective EMS. To determine the ideal size for the BESS, [PERSON] et al. in [22] created a mixed integer linear model. The purpose of the above work is to increase the profitable use of batteries, regulate their charging life cycle, and ensure the safe operation of these batteries. For finding the ideal scale of various types of DGs to obtain economic benefits while taking uncertainties in grid disturbance, load, and renewable generation into account, [PERSON] et al. [23] suggested using a two-stage stochastic mixed-integer programming method. An optimization method based on the theorem of multiple-attribute usefulness was presented by [PERSON] et al. in [24] to identify the hybrid ESS (HESS) optimal capacity to take advantage of their complementing qualities. The formulation of the optimization issue uses both the isolated and grid-connected modes of MG operation. While increasing MG dependability was the target in the isolated mode, minimizing operation costs was the objective in the grid-connected mode. Additionally, some studies have focused on determining the ideal size and position of the ESS to improve the performance of the MG. To determine the optimal allocation of ESS while considering a demand response (DR) program, [PERSON] et al. [25] developed a mixed-integer non-linear model. The multi-objective function sought to minimize the expected load loss and overall investment and operating costs. In order to find the best placement and size for ESS, [PERSON] et al. [26] presented an optimization technique based on the search for symbiotic organisms. This strategy involves decreasing the total power loss of the MG while boosting the voltage stability and voltage profile of the MG buses. In order to supply customer demand and secure the MG, [PERSON] and [PERSON] developed a genetic algorithm-based optimization technique [27] for the MG's ESS and DG allocation and economic analysis. This strategy considers the dynamic capacity adjustment of ESSs and DGs to handle the non-smooth cost functions. Economic analysis and the best allocation were decided. However, there was no discussion of the HC while the ESSs were available. A two-stage stochastic planning model was developed by [PERSON] et al.