## 1 Introduction
This criterion is again of particular relevance [64], where different EMSs are compared, with the management methodology based on the battery charging cycle being the optimal one for this case study [65]. Finally, there are also cases of EMSs where energy management is aimed at battery care, seeking to extend battery life and optimise performance over the life of the battery [66; 67; 68; 69]. With stability of the microgrid as the main objective, the load following strategy is based on the adjustment of generation by the microgrid operator/manager to meet consumption demands at all times, responding in real time to variations and maintaining the energy balance. In the existing cases of isolated microgrids that have limited resources and generation sources [70], this type of strategy is the main management criterion for guaranteeing energy supply to loads/consumers [63; 71; 72]. In grid-connected microgrids, stability is not as sensitive as in the isolated case. However, it is true that it can be affected by phenomena coming from the grid. In connected cases where ensuring stability is a critical factor, the implementation of a load-following strategy to cover demand variations may be the sole objective of the EMS [34; 73]. However, this strategy is often complemented by other management criteria such as minimising costs, increasing equipment lifetime [56], meeting consumption forecasts [74], or optimising battery charging for specific equipment such as electric vehicles [75]. After exemplifying various EMS in microgrids, two methodologies focused on consumption peaks are shown below. One management strategy implemented to reduce the effects of this phenomenon on the microgrid is load shifting. The approach of this methodology is based on the identification of flexible and programmable loads in the system and their use to globally adjust the energy balance. The operating principle is based on scheduling the activation/start-up of peak-generating loads, which have previously been identified as manageable, in different periods of low consumption in such a way that the increase in demand does not generate mismatch peaks in these periods and can be assumed by the generation [76]. Therefore, as developed in the cases [77; 78], through the implementation of this strategy, the aim is to establish the best organisation and schedulingof loads in the microgrid, maximising efficiency and stability. These EMSs are especially interesting in microgrids as they allow the efficient integration of new loads such as electric vehicles [79; 80]. In parallel, this methodology can be combined with those seen above to meet criteria in addition to peak shaving. An EMS can be used to optimise load relocation in order to minimise operating costs [40; 81; 82]. Moreover, the EMS can establish the operation scenario that provides the most flexibility by studying its impact on the cost and stability of the network [83]. On the other hand, the peak shaving strategy is based on the total elimination or, failing that, the reduction of consumption peaks by managing the energy stored in the ESS [27; 77]. These peaks can have particularly detrimental effects on the microgrid, as they represent a threat to its stability by significantly unbalancing the relationship between generation and demand during time lapses that can last up to several hours [21], being particularly characteristic of certain time slots. Similarly, the most significant effect of peaks is the large increase in operating costs as they occur in characteristic time slots that coincide with high pricing periods where the price of energy is higher. In order for the grid not to collapse, the energy differential between demand and generation is taken from the grid. Therefore, this management strategy is employed to avoid this effect, as it is especially beneficial in technical and economic terms [84]. This application is the exact one implemented in EMSs, where cost of energy and operating cost have been minimised [85; 86; 87].