# A Real Model of a Micro-Grid to Improve Network Stability
## 1 Introduction  
The initial period within the development of electricity systems began in the mid-19 th century. At this stage, electric energy was transferred via direct current (DC); the main obstacle consisted in the impossibility of producing and transferring higher electric power rates. Alternating current (AC) and its production perspectives then became known around 1870. However, within electrification through alternate current, an essential move forward only came later with the inventions of the synchronous alternator, three-phase system, transformer, and asynchronous motor (demonstrated by [PERSON] in 1887). Since then, transmission systems have exclusively used AC current, although generally in micro-grids and smart grids, DC current has recently re-assumed importance for energy transmission [1; 2; 3; 4] as it reduces line loss caused by reactive power [5]. The AC system is commonly used in households because most appliances are designed for this related type of feeding, but there still remains the question of whether DC-based appliances could be a more advantageous solution for home micro-grids. Obviously, the concept includes the possibility of switching between DC and AC loads via converters [6]; in this context [5], characterizes a controlled, full-wave AC/DC rectifier integrated in a closed control loop. The basic criterion for embedding DC appliances in DC smart grid consists in the economy of use [7]. Currently, direct current is being increasingly provided by photovoltaic sources and wind power plants. Both of these renewable sources, however, often reduce the stability of the grid, and control algorithms are thus required to maintain stable voltage and frequency. Grid control and optimization possibilities are outlined in many articles, using a wide range of simple and sophisticated methods [8; 9; 10; 11]. [PERSON] et al., for example, analyzed the stability issue by means of the Nyquist criteria, and with the impedance relation method [12]. [PERSON] et al. then describe micro-grid stabilization via central controller aggregates exploiting a genetic algorithm to ensure all the information necessary for optimal operation is present [13], and [PERSON] et al. utilize particle swarm optimization in conjunction with dynamic programming [14]. Another sophisticated method to optimize grid stability is enhanced bee colony optimization [15; 16]. The basic micro-grid control tool is embodied in the demand-response (DR) algorithm, which can be coupled with the optimization procedures described above. DR-based programs constitute an effective approach to resolving the problem of wind and photovoltaic power fluctuations [17; 18; 19; 20]. Further, [PERSON] et al. propose an operational algorithm for the individual agents of a test micro-grid that comprises a battery energy storage system (BESS) and an intelligent load [21]. The central purpose of a smart grid is to secure an effective way to manage the load growth, reduce the pollution of the environment, and improve the comprehensive energy efficiency and power supply reliability. The low carbon requirement is closely related to emission cutting in the automotive industry [22; 23; 24; 25; 26]. From the perspective of the demand-response strategy as well, it appears suitable to charge vehicles with a smart grid in order to help maintain the stability of the network [27]; for example, diverse strategies for the vehicle-to-grid (V2G) connection are discussed by [PERSON] [28], who set his proposals in the urban region of Florianopolis, Brazil. In micro-grids, the most frequent energy storage method relies on batteries [29; 30]. Another major option is to use a hybrid electrothermal storage system that employs a battery and a hot water tank along with an electrical water heater as a controllable load; all of these components then facilitate the actual energy management within the micro-grid [31]. The solution presented within this paper combines a battery with metal hydride-based storage.