# A Comparison of Energy Management System for a DC Microgrid
## 1 Introduction  
During the last two decades the development of energy management systems (EMSs) has been of interest by the energy research community. This interest is due to the continued and improved development of renewable energy systems (RE), and the EMS optimization techniques with advanced energy storage systems [1]. The efficient use of energy in any field has become paramount due to environmental, economic and operational aspects, therefore, the study of energy management in a microgrid is vital [2]. Microgrid can be classified according to their operation in autonomous and non-autonomous, the use of an autonomous microgrid can be seen in rural areas and in the use of equipment that needs energy autonomy for their operation as space vehicles or other sea vehicles where it cannot connect to the power distribution grid [3]. The non-autonomous grid can be seen in the residential, industrial grid and currently it is also connected to a general energy distribution grid either to export or import energy for many different applications [4]. However, the efficient use of the microgrid is difficult due to the presence of distributed energy resources, which can be controllable (or dispatchable), such as gas turbines or diesel engines, but also not controllable (or non-dispatchable), such as wind turbines or photovoltaic (PV) generators. Tidal generators whose production depends largely on weather conditions, which in turn can be predicted for long periods of time with some uncertainty [4]. Microgrids contribute to the preservation of the environment and energy sustainability as they reduce losses in the distribution lines and also mitigate the expansion of the main network and can be usedto provide electricity to rural areas, space vehicles and autonomous sea vehicles and many other applications [5]. [PERSON] et al. [6] conducted a comparative study of the different energy management strategies and examined the operation modes via simulation studies. [PERSON] et al. [7] presents a comparative and critical analysis on decision making strategies and their solution methods for microgrid energy management systems. To manage the volatility and intermittency of renewable energy resources and load demand, various uncertainty quantification methods are summarized. A comparative analysis on communication technologies is also discussed for cost-effective implementation of microgrid energy management systems. Finally, insights into future directions and real-world applications are provided. An overview of the DC microgrid system and the estimation of the energy savings of the DC microgrid over conventional AC systems are described in [8]. The study examined the system performance in locations across the United States for several commercial building types and operating profiles. It is found that the DC microgrid uses generated PV energy 6%-8% more efficiently than traditional AC systems. [PERSON] et al. [9] presents a comparative study of different optimization techniques, the genetic algorithm, the pattern search method and the interior point algorithm are used to optimally schedule the energy required from the thermal power generators to meet the load demand by taking into account the power from the renewable energy resource, a comparison of the optimal cost values that each optimization method obtained is made, the emission comparison is also performed and finally the time i uses to converge each method. In [10] the author illustrates the utilization of the particle swarm optimization (PSO) method for cost-efficient energy management in multi-source renewable energy microgrids. PSO algorithm is used to find out optimal energy mixing rates that can minimize daily energy cost of a renewable microgrids under energy balance and ant islanding constraints.