## 1 Introduction  
In recent years, the consequences of global warming have motivated the adoption of international policies to try to mitigate climate change [1]; consequently, it is necessary to move from current energy systems related to greenhouse gas emissions [2] to new systems based on renewable energies (REs) [3] denoted as clean generation sources, which are currently a priority theme in research. An alternative of this type of system frequently used is the use of solar energy through photovoltaic panels (PVPs) [4]. Research related to photovoltaic systems (PVSs) is basically focused on increasing the efficiency in handling the electrical energy generated; a technique used for this purpose is the application of control algorithms for maximum power point tracking (MPPT), which are responsible for taking advantage of the greatest amount of energy generated by the PVPs [5]. Some of the most used algorithms are:perturb and observe (P&O) [6; 7], machine learning [8], improved pattern search method [9], genetic algorithms [10], fuzzy control [11; 12; 13], periodic power hunt [14] and stochastic algorithms [5]. The typical structure of a PVS consists of an arrangement of PVPs connected in cascade with a power electronic converter (PEC) in its topology such as DC-DC boost and a load variable; the algorithms for the MPPT are in charge of modifying the boost duty cycle so that the greatest amount of energy is extracted as possible, even in the presence of changes in environmental [7]. However, testing the performance of systems with this structure represents a high manufacturing cost, a large area is required for the disposal of PVPs, environmental conditions are discontinuous, and the system needs to be experimented with different PEC configurations [15]. An alternative to carry out experiments without the need for the physical PVS is the use of an emulator, this behaves as a non-linear source that reproduces the current-voltage characteristic curve of the PVP; in addition, it allows for control and reproduction of environmental conditions at any moment of the day and without depending on external phenomena; in this way, the design and experimentation of PVS is facilitated, making it possible to obtain results in less time and more efficient systems. However, the use of commercial emulators that can reproduce the behavior of PVP is an alternative with a very high economic cost; for this reason; in recent years, alternatives have been studied for the design of emulators that reduce the economic cost [15; 16]. Among the designs studied are: emulation of the system with an industrial computer [9], the use of a linear controlled voltage regulator [17], a controlled PEC buck connected to a voltage source [18; 19; 20; 21], the use of a programmable power source [22; 23] or a current generator and a mosfet [24]. Another option for conducting experimentation under controllable, real and reproducible conditions using hardware is the hardware in the loop (HIL) real time simulation (RTS) [25] which has the advantages of predicting errors and failures in the control systems without the risk of damaging the real plant. Due to its advantages, this technique is considered an important stage before the experimentation stage in a system [26]. HIL technique consists of the implementation of the model of a system in embedded hardware that will represent the real system. The HIL simulations can be classified into two different sections, these depend on the type of embedded elements and the number of research infrastructures (RIs) used. When only one IR is used, the simulation is classified as monolithic simulation (MS), while if more than one IR is used and they are connected to each other via internet, the simulation is classified as geographically distributed simulations. Within these categories, three types of simulations are defined: RTS, power hardware in the loop (PHIL) and controller hardware in the loop (CHIL). If the power elements and their associated controllers are emulated, the simulation is classified as RTS.