## 1 Introduction  
The development and maturation of renewable energies are triggering a profound change in the current energy system, displacing and replacing traditional electric power systems based on fossil fuels [1; 2; 3]. The implementation of renewable energies is becoming the backbone for meeting the objectives set for the energy transition [4; 5; 6] as clean, sustainable, and accessible energy sources. In this context of change, microgrids are postulated as a major element in the design and adjustment of the electricity system [7; 8; 9; 10]. Where they are implemented, microgrids represent a solution that guarantees continuous and secure access to electricity based on the use of distributed renewable generation sources and distributed storage system [3]. Moreover, the recent development of communication, information, and artificial intelligence technologies and their integration into microgrids means a new evolution of the concept: smart microgrids [11; 12; 13]. Some major concerns about renewable energies still represent challenges for the operability and management of the SMG that must be solved [14; 15]. As in the grid, different phenomena can occur in the SMG [16; 17; 18; 19; 20], such as mismatches between generation and generated demand, power consumption peaks, frequency variations, voltage level variations, blackouts, etc. Of these, the one that will be studied in this article is the phenomenon of the mismatch between generation and demand generated by prolonged consumption peaks [21; 22]. Consequently, in order to guarantee the correct operation of microgrids, energy management strategies/methodologies need to be implemented in the SMG [23]. These strategies will vary according to the conditions of each microgrid: topology, available resources, needs, limitations... [24; 25], and/or the criteria used by each manager, always having as the main objective to guarantee the energy balance between generation and demand while maximising the efficiency of the system [26; 27]. However, the energy management of an SMG is a complex problem of a non-linear and non-convex nature, requiring control, management, and optimisation algorithms that perform this task according to the established strategies and priorities. However, the use of these complex and highly sophisticated systems does not necessarily improve the performance and economic viability of the whole, and it is up to the SMG manager to develop and implement them [28]. The most widespread management strategy focuses on optimising various economic aspects using cost analysis indicators and metrics. This strategy seeks to maximise system efficiency while minimising costs. The operating cost is one of the most widely used and studied indicators for the energy management of microgrids. There are also microgrid EMSs, which seek to minimise operating costs through load scheduling based on the prediction of both wind generation and load consumption, supported by energy storage systems (ESSs) and diesel generation [29]. Other EMSs employ minimising operating costs by reducing fossil fuel-based generation sources [30]. There are methodologies where, in addition to minimising the life cycle cost, the annual cost of load loss, and the life cycle cost of the equipment that make up the microgrid, are further explored [31; 32]. On the other hand, the EMS can optimise costs according to existing operating conditions to effectively coordinate both dispatchable and non-dispatchable energy resources to maintain the stability of the microgrid [33; 34]. Operational cost reduction can be complemented with other indicators and metrics, such as cost of energy or net present cost, among others. The combination of different indicators is a common practice in EMSs based on economic objectives. This combination was used in [35; 36] to determine the best microgrid configuration by minimising cost of energy as the main indicator. The cost of energy is used in other management strategies to compare the generation performance of a microgrid with the national electricity tariff [37].