Fast Power Emulation Approach to the Operation of Photovoltaic Power Plants Made of Di ﬀ erent Module Technologies

: This paper gives a comprehensive approach to the emulation of photovoltaic (PV) plants made of di ﬀ erent module technologies as well as varying peak power through the advanced fast PV power emulation technique. Even though PVs are recognized as a technology for CO 2 emissions mitigation, the proposed emulation technique provides the opportunity to replicate PV plant operation without a carbon footprint because of its working principle. The process of PV power plant emulation consists of several stages which are described in detail. An algorithm for determining PV power plant conﬁguration based on the technical characteristics of the PV emulation system equipment is developed and presented, as well as an algorithm for preparing data on the current–voltage ( i–v ) characteristics used as input data into programmable sources that mimic the power plant PV array. A case study of a single day operation of PV power plants made of two di ﬀ erent topologies and technologies was carried out with the fast PV power emulation approach and the results are evaluated and presented.


Introduction
Fossil fuel depletion and its negative influence on the environment has spawned the opportunity for Renewable Energy Sources (RES) to take on a significant role in global energy distribution. Because of its huge potential and technology in terms of CO 2 emissions mitigation, solar energy has become one of the most promising energy sources to cover the growing electricity demand [1]. Supportive energy policies and treaties have generated huge investment flows into PVs that have resulted in technology advancement and a huge increase in the capacity integrated into the grid. According to REN21, the global installed PV capacity in 2019 reached 627 GW [2]. Due to the increasing integration of PV plants into the existing power system, intensive research is being carried out to improve the PV equipment characteristics as well as to reduce investment costs. Thus, one direction of research covers the different technologies of PV modules [3], while the other deals with the development of electronic equipment for PV plants, more specifically, inverter topologies used to connect DC PV modules and the AC power grid [4][5][6][7][8].
Testing of inverters for PV plants requires a repetition of the meteorological conditions to which PV modules were exposed. This task is not easy, given the sensitivity of the current-voltage (i-v) characteristic of the module to various influences such as discontinuous time-dependent solar irradiation and change in daytime ambient temperature [9,10]. In addition, to test the operation of a PV power plant, it is necessary to provide a large surface area for installing PV modules. All of this One of the advantages of a programmable power supply is the automatic process of PV emulation executed through the commands, which also shortens the PV emulation time. The main idea of this paper is to propose the procedure of an advanced PV emulation technique called fast PV power emulation of PV plants. Compared to standard PV emulation, the proposed technique, allows the emulation of PV plants of arbitrarily determined size and made of different technologies in a shorter period of time [25]. Special measurement data filtering and an arbitrary number of commands entered into programmable DC power supplies, written in the form of a script, enable time reduction of the PV emulation process.
The procedure of fast PV power emulation of the PV plants given in Figure 1 starts with measurements obtained by a DAQ system on PV modules made of different technologies, which are recorded simultaneously and continuously in real-time. Before the start of the fast PV power emulation process, measurements taken from the DAQ system referring to PV modules need to be adjusted to PV plant configuration by the algorithm for filtering measurement data. After that, programmable DC power supplies use commands in order to generate i-v characteristics of PV arrays. Programmable DC power supplies use an AC distribution network to generate DC output.
Energies 2020, 13, x FOR PEER REVIEW 3 of 18 idea of this paper is to propose the procedure of an advanced PV emulation technique called fast PV power emulation of PV plants. Compared to standard PV emulation, the proposed technique, allows the emulation of PV plants of arbitrarily determined size and made of different technologies in a shorter period of time [25]. Special measurement data filtering and an arbitrary number of commands entered into programmable DC power supplies, written in the form of a script, enable time reduction of the PV emulation process. The procedure of fast PV power emulation of the PV plants given in Figure 1 starts with measurements obtained by a DAQ system on PV modules made of different technologies, which are recorded simultaneously and continuously in real-time. Before the start of the fast PV power emulation process, measurements taken from the DAQ system referring to PV modules need to be adjusted to PV plant configuration by the algorithm for filtering measurement data. After that, programmable DC power supplies use commands in order to generate i-v characteristics of PV arrays. Programmable DC power supplies use an AC distribution network to generate DC output. The PV plant inverter connected to the output of programmable DC power supplies is equipped with Maximum Power Point Trackers (MPPT), which extract the maximum output power of PV arrays generated by programmable DC power supplies. It converts the PV array DC power supplied by programmable DC power supplies back to AC power. The efficiency of this system is up to 95%, since most of the energy used for generating the DC power of PV arrays is supplied back to the AC distribution grid. The only energy losses occurring in this process are created by nonideal MPPTs of the PV plant inverter and power conversion losses occurring in the power electronic switching components.

Data Acquisition System with the Database
Measurement data used for fast PV power emulation are obtained by the DAQ system developed by the Laboratory for Renewable Energy Sources at the Faculty of Electrical Engineering, Computer Science and Information Technology (FERIT) Osijek [26]. The DAQ system simultaneously and continuously measures electrical output parameters of the 10 kWp power plant and five PV modules made of different technologies and meteorological parameters at the test site. The PV plant and modules are installed on the roof of FERIT Osijek building, with a tilt angle of 7°. Measurement data are stored in the local and the cloud database for further analysis. Currently, the database contains measurement data obtained by continuous measurements since March 2017. The PV plant inverter connected to the output of programmable DC power supplies is equipped with Maximum Power Point Trackers (MPPT), which extract the maximum output power of PV arrays generated by programmable DC power supplies. It converts the PV array DC power supplied by programmable DC power supplies back to AC power. The efficiency of this system is up to 95%, since most of the energy used for generating the DC power of PV arrays is supplied back to the AC distribution grid. The only energy losses occurring in this process are created by nonideal MPPTs of the PV plant inverter and power conversion losses occurring in the power electronic switching components. This paper uses measurements of the power output of PV modules connected to micro grid-tie inverters. Inverters are equipped with MPPTs which extract maximum DC power from the PV module and then, convert it to AC power injected into a 230 VAC, 50 Hz distribution network. The PV modules under study are as follows: monocrystalline silicon (m-Si) Bisol BMO 250, polycrystalline silicon (p-Si) Bisol BMU 250, amorphous silicon (a-Si) Masdar MPV100-S, copper-indium-selenide (CIS) Solar Frontier SF150-S, and heterojunction with intrinsic thin layer (HIT) Panasonic VBHN240SE10. Technical characteristics of PV modules under study that are used for analysis in this paper are given in Table 1. A schematic diagram of the DAQ system used for measuring the PV module's electrical output analyzed in this paper is given in Figure 2. Five PV modules made of different technologies installed on the roof, a pyranometer for solar irradiance measurements, and PV module micro grid-tie inverters can be seen in Figure 3. The system measures the output DC current and output DC voltage of each PV module under study. Measurements are recorded every second and sent to the microcontrollers, which process the data further. After that, the data are sent to the main PC, which averages the measurements over a one-minute period and stores the data both locally and in a cloud database. Furthermore, every second, the DAQ system also measures meteorological data such as solar irradiance, ambient temperature, and cell temperatures of the PV modules, but that will not be the focus of this paper. A detailed description of the DAQ system can be found in [32].

Data Acquisition System with the Database
This paper uses measurements of the power output of PV modules connected to micro grid-tie inverters. Inverters are equipped with MPPTs which extract maximum DC power from the PV module and then, convert it to AC power injected into a 230 VAC, 50 Hz distribution network. The PV modules under study are as follows: monocrystalline silicon (m-Si) Bisol BMO 250, polycrystalline silicon (p-Si) Bisol BMU 250, amorphous silicon (a-Si) Masdar MPV100-S, copper-indium-selenide (CIS) Solar Frontier SF150-S, and heterojunction with intrinsic thin layer (HIT) Panasonic VBHN240SE10. Technical characteristics of PV modules under study that are used for analysis in this paper are given in Table 1. A schematic diagram of the DAQ system used for measuring the PV module's electrical output analyzed in this paper is given in Figure 2. Five PV modules made of different technologies installed on the roof, a pyranometer for solar irradiance measurements, and PV module micro grid-tie inverters can be seen in Figure 3. The system measures the output DC current and output DC voltage of each PV module under study. Measurements are recorded every second and sent to the microcontrollers, which process the data further. After that, the data are sent to the main PC, which averages the measurements over a one-minute period and stores the data both locally and in a cloud database. Furthermore, every second, the DAQ system also measures meteorological data such as solar irradiance, ambient temperature, and cell temperatures of the PV modules, but that will not be the focus of this paper. A detailed description of the DAQ system can be found in [32].

A Developed Approach to Fast PV Power Emulation
The process of fast PV power emulation is described in this section. An algorithm developed for determining PV plant configuration is described in the first subsection, which is a precondition for the second algorithm developed for fast PV power emulation measurement data filtering. In the last part of the section, fast PV power emulation validation indices are defined.

PV Plant Configuration Determination
The fast PV power emulation process begins with determining PV plant configuration. A system developed for fast PV power emulation in this paper assumes that a PV plant consists of the same nominal output power PV arrays (the same number of PV modules). These PV arrays consist of seriesparallel combinations of PV modules. Furthermore, it is assumed that every PV module in a PV array has identical characteristics, while line losses between PV modules are neglected. Figure 4 shows the algorithm for determining PV array configuration, which is used in the system developed for fast PV power emulation.
The algorithm starts with the selection of the PV module (technology) followed by the input of the desired nominal output power of the PV plant Pnom and the number of PV arrays i. If the desired nominal output power exceeds the limitations of the equipment (DC programmable power supplies or the PV plant inverter) determined by Pmax_emul, the algorithm redirects back to determining the nominal output power of the PV plant until this condition is satisfied. The next step is to determine the nominal output power of the PV array Parray by dividing Pnom with the number of PV arrays i, which is determined by the number of inputs of the PV plant inverter. The number of modules in a PV array n is calculated as the floor function of the Parray and Pmod_nom quotient, since this number must be an integer. Next, the algorithm calculates the number of parallels p and the number of modules in one parallel m, i.e., PV array configuration. For a better understanding, a visual representation of PV array topology and corresponding variables calculated by the proposed algorithm n, m, and p is given in Figure 5.
Calculation of the number of parallels p and the number of modules in one parallel m is constrained by the following conditions:

A Developed Approach to Fast PV Power Emulation
The process of fast PV power emulation is described in this section. An algorithm developed for determining PV plant configuration is described in the first subsection, which is a precondition for the second algorithm developed for fast PV power emulation measurement data filtering. In the last part of the section, fast PV power emulation validation indices are defined.

PV Plant Configuration Determination
The fast PV power emulation process begins with determining PV plant configuration. A system developed for fast PV power emulation in this paper assumes that a PV plant consists of the same nominal output power PV arrays (the same number of PV modules). These PV arrays consist of series-parallel combinations of PV modules. Furthermore, it is assumed that every PV module in a PV array has identical characteristics, while line losses between PV modules are neglected. Figure 4 shows the algorithm for determining PV array configuration, which is used in the system developed for fast PV power emulation.
The algorithm starts with the selection of the PV module (technology) followed by the input of the desired nominal output power of the PV plant P nom and the number of PV arrays i. If the desired nominal output power exceeds the limitations of the equipment (DC programmable power supplies or the PV plant inverter) determined by P max_emul , the algorithm redirects back to determining the nominal output power of the PV plant until this condition is satisfied. The next step is to determine the nominal output power of the PV array P array by dividing P nom with the number of PV arrays i, which is determined by the number of inputs of the PV plant inverter. The number of modules in a PV array n is calculated as the floor function of the P array and P mod_nom quotient, since this number must be an integer. Next, the algorithm calculates the number of parallels p and the number of modules in one parallel m, i.e., PV array configuration. For a better understanding, a visual representation of PV array topology and corresponding variables calculated by the proposed algorithm n, m, and p is given in Figure 5. At the end of the algorithm, new nominal output power of the PV array Parray is calculated, since integer division is performed and the remainder is neglected; therefore, this value can be different than the one originally determined by dividing Pnom by i.

Measurement Data Filtering for Fast PV Power Emulation
The next step of the fast PV power emulation process is measurement data filtering, aimed at satisfying all constraints imposed by PV emulation system equipment (a DC programmable power supply and a PV plant inverter). The algorithm starts with the selection of the measurement day with j samples, which is imported from the DAQ system described in Section 3. The number of samples Calculation of the number of parallels p and the number of modules in one parallel m is constrained by the following conditions: The short-circuit current of the PV array at STC I SC_array , calculated as the product of p and the short-circuit current of the PV module at STC I SC_mod , must be equal to or lower than the maximum output current of the DC programmable power supply and the maximum input current of the PV plant inverter I SC_max .
At the end of the algorithm, new nominal output power of the PV array P array is calculated, since integer division is performed and the remainder is neglected; therefore, this value can be different than the one originally determined by dividing P nom by i.

Measurement Data Filtering for Fast PV Power Emulation
The next step of the fast PV power emulation process is measurement data filtering, aimed at satisfying all constraints imposed by PV emulation system equipment (a DC programmable power supply and a PV plant inverter). The algorithm starts with the selection of the measurement day with j samples, which is imported from the DAQ system described in Section 3. The number of samples (current and voltage values of the i-v array characteristic) j in a chosen day should meet the constraint of the number of commands that can be executed by the DC programmable power supply, given with variable j max ; thus, measurement data should have as many time points as the DC programmable power supply can execute commands. This means that each sample represents the mean value of a certain parameter for the time period t, consequently enabling the fast PV power emulation process. Figure 6 shows the algorithm for the measurement data filtering process used in the system developed for fast PV power emulation.

Measurement Data Filtering for Fast PV Power Emulation
The next step of the fast PV power emulation process is measurement data filtering, aimed at satisfying all constraints imposed by PV emulation system equipment (a DC programmable power supply and a PV plant inverter). The algorithm starts with the selection of the measurement day with j samples, which is imported from the DAQ system described in Section 3. The number of samples (current and voltage values of the i-v array characteristic) j in a chosen day should meet the constraint of the number of commands that can be executed by the DC programmable power supply, given with variable jmax; thus, measurement data should have as many time points as the DC programmable power supply can execute commands. This means that each sample represents the mean value of a certain parameter for the time period t, consequently enabling the fast PV power emulation process. Figure 6 shows the algorithm for the measurement data filtering process used in the system developed for fast PV power emulation.   Since the DAQ system records only the MPP voltage and the current determined by the operating point of the PV module, coefficients κ and FF are determined to calculate the open-circuit voltage U OC_array, j and the short-circuit current I SC_array, j of the PV array for every measurement sample j, which are mandatory operating points for the input of i-v characteristics into DC programmable power supplies. Coefficient κ is determined by dividing the short-circuit current of the PV module at STC I SC_mod by the MPP current of the PV module at STC I MPP_mod , as given in (1). This ratio is considered as a constant, since the most important operating point for the DC programmable power supply is the MPP and this assumption does not affect the emulation accuracy significantly.
Furthermore, the fill factor FF is calculated with the PV module manufacturer data given in the datasheet for STC in order to calculate the open-circuit voltage of the PV array U OC_array, j , as given in (2). The MPP array voltage for the measurement sample j, U MPP_array, j , is calculated as the product of m and the MPP voltage of the PV module measured by the DAQ system U MPP_mod, j . An analogy can be drawn for the calculation of the MPP current of the PV array for the measurement sample j; only the number of parallels p is used. Upon the calculation of U OC_array, j , I SC_array, j , U MPP_array, j , and I MPP_array, j , the algorithm calculates output power of the PV array P MPP_array, j , AC output power of the PV plant P plant_AC, j , and electricity generation of the PV plant W plant, j for the measurement sample j, where η inverter is the efficiency of the PV plant inverter.
Before the algorithm proceeds to the next measurement sample j, it checks if the PV array output power P MPP_array, j is greater than the minimum value (threshold) of input DC power per array that should be produced by the PV array in order for the PV plant inverter to start converting DC to AC power, defined by P min_input . If it is not, the measurement sample is excluded from data filtering. This constraint excludes measurements at nighttime. Furthermore, the algorithm checks if the calculated data are within limits with respect to the following:

•
The maximum output voltage of the DC programmable power supply and the maximum input voltage of the PV plant inverter U OC_max .

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The MPPT voltage operating range of the PV plant inverter given by U MPP_min and U MPP, max .

•
The maximum output current of the DC programmable power supply and the maximum input current of the PV plant inverter I SC_max .
If the constraints are not satisfied, the algorithm returns to the selection of the day and the process starts again. If the constraints are fulfilled, the algorithm proceeds to the next measurement sample j until all measurement samples in a day are processed and total daily electricity generation by the PV plant W filtered is calculated. The PV emulation process is initiated in the last step of the algorithm.

Fast PV Power Emulation Validation
After the fast PV power emulation process is performed, the developed system extracts the output power of the PV plant inverter P inverter, j for each measurement sample j and calculates the real electricity generation of the PV plant during the fast PV power emulation process W inverter , as given in (3). Furthermore, the system extracts electricity generation measured by the electricity meter W counter in order to validate the accuracy of electricity generation measured by the inverter W inverter .
Energies 2020, 13, 5957 9 of 17 Due to time scaling, electricity generation measured by the electricity meter W counter should also be recalculated into a new parameter W meter , as given in (4). where: W meter -a recalculated value of electricity generation measured by the electricity meter; t-time interval of each measurement sample j; t c -time duration of one command (60 s).
Finally, percentage errors δ emulated and δ meter can be calculated as in (5) and (6) in order to evaluate the accuracy of the fast PV power emulation process.

Fast PV Power Emulation Results and Analysis
The system developed for fast PV power emulation is tested and the results are analyzed for two different PV plants with respect to their PV technology and nominal output power. The first case study refers to three PV plants with nominal output power of 6 kW made of a-Si Masdar MPV100-S, CIS Solar Frontier SF150-S and HIT Panasonic VBHN240SE10 PV modules, whereas the second case study refers to two PV plants with nominal output power of 12 kW made of m-Si Bisol BMO 250 and p-Si Bisol BMU 250 PV modules.

Fast PV Power Emulation Equipment Settings
Fast PV power emulation is performed in the Power Electronics Laboratory at FERIT Osijek. The laboratory is equipped with a PV emulation system that consists of two DC programmable power supplies, a PV plant inverter, and a digital electricity meter, which can be seen in Figure 7. Table 2 shows only technical parameters of the PV plant inverter Kaco Powador 12.0 TL3 and DC programmable power supplies LAB/HP 101000, which are used in this paper for the purpose of analysis. Each DC programmable power supply represents a single PV array.
In the emulation process, the PVsim mode and script mode of the DC programmable power supplies are used. The PVsim mode allows input of the PV i-v characteristic by four parameters: the open-circuit voltage, the short-circuit current, the MPP voltage, and the MPP current. Script mode enables control of the unit by the commands loaded from an SD card in the form of a script. The maximum number of commands that can be entered into one PV emulation system through the SD card slot is 35. The maximum duration of one command is limited to 65,536 ms [34].
When the fast PV power emulation process starts, the PV plant inverter starts a self-test process, which lasts 65 s, to become synchronized with the AC distribution grid. In order to avoid measurement error, which would arise if this constraint were neglected, a 65 s lasting compensation command is executed at the beginning of the PV emulation script. Furthermore, during the fast PV power emulation process, it was noticed that the PV plant inverter needs the minimum power of 100 W as a threshold of DC power per input, in order for the PV plant inverter to start converting DC power into AC power, which is also defined by the parameter P min_input in the algorithm.
Fast PV power emulation is performed in the Power Electronics Laboratory at FERIT Osijek. The laboratory is equipped with a PV emulation system that consists of two DC programmable power supplies, a PV plant inverter, and a digital electricity meter, which can be seen in Figure 7. Table 2 shows only technical parameters of the PV plant inverter Kaco Powador 12.0 TL3 and DC programmable power supplies LAB/HP 101000, which are used in this paper for the purpose of analysis. Each DC programmable power supply represents a single PV array.

DC side parameters (output)
Maximum output power 10 kW Voltage range 0-1000 V Current range 0-10 A Furthermore, it is observed that at the beginning of the fast PV power emulation process, the operating point of the PV arrays is not in the MPP. This is a result of the MPPT circuit inertia (sluggishness), which cannot set an operating point of the PV arrays immediately into the MPP at the beginning of their operation. The maximum time duration of the operating point set into the MPP by the MPPTs is determined by trial-and-error emulations and is 6 min. For the purpose of compensating for this behavior, an additional five compensation commands lasting 60 s each are executed at the beginning of the PV emulation script along with one compensation command executed due to the PV plant inverter self-test.
Due to six compensation commands executed at the beginning of every PV emulation script, the maximum number of real PV emulation commands decreased from 35 to 29. In every fast PV power emulation process, every command duration is set to 60 s. This means that the results obtained after performing fast PV power emulation need to be recalculated due to time scaling, as given in (3) and (4). Each fast PV power emulation process is performed three times with the same methodology in order to check the reproducibility of results.

Fast PV Power Emulation of a 6 kW PV Plant
Fast PV power emulation of the 6 kW PV plant is performed for PV plants made of a-Si Masdar MPV100-S, CIS Solar Frontier SF150-S and HIT Panasonic VBHN240SE10. PV plant array configurations for a-Si Masdar MPV100-S, CIS Solar Frontier SF150-S, and HIT Panasonic VBHN240SE10 are given in Table 3. Configurations of PV plant arrays made of a-Si Masdar MPV100-S and CIS Solar Frontier SF150-S are determined by means of the algorithm described in Section 4.1, while configuration of PV plant arrays made of HIT Panasonic VBHN240SE10 PV modules is slightly modified, since PV arrays have a different number of PV modules (13 and 12 PV modules) in order to satisfy nominal output power of 6 kW. The fast PV power emulation process is performed for 28 March 2018, 1 May 2018, and 2 September 2018, for which solar irradiance profiles are given in Figure 8. The process of fast PV power emulation is described in detail only for 2 September 2018. One-minute average values of output power of the a-Si Masdar MPV100-S, Solar Frontier SF150-S, and HIT Panasonic VBHN240SE10 PV modules on 2 September 2018 are given in Figure 9.
In order to satisfy the maximum number of commands that can be entered into the DC programmable power supply and the minimum input DC power per array that should be produced in order for the PV plant inverter to start working (parameter P min_input ), each measurement sample j extracted from the DAQ system represents a 30-min average value of a certain parameter, resulting in a total number of 24 commands, 18 j measurement samples and 6 compensation commands. This means that fast PV power emulation with known current and voltage values of i-v characteristics of the array obtained by various module technologies can make an estimation of electricity generation of the PV plant in approximately 25 min.
in order for the PV plant inverter to start working (parameter Pmin_input), each measurement sample j extracted from the DAQ system represents a 30-min average value of a certain parameter, resulting in a total number of 24 commands, 18 j measurement samples and 6 compensation commands. This means that fast PV power emulation with known current and voltage values of i-v characteristics of the array obtained by various module technologies can make an estimation of electricity generation of the PV plant in approximately 25 min.   Table 4 shows data extracted from the PV plant inverter after fast PV power emulation of the 6 kW PV plant made of Masdar MPV100-S PV modules and AC output power of the PV plant entered into DC programmable power supplies Pplant_AC, j for each measurement sample (command) j.   Table 4 shows data extracted from the PV plant inverter after fast PV power emulation of the 6 kW PV plant made of Masdar MPV100-S PV modules and AC output power of the PV plant entered into DC programmable power supplies P plant_AC, j for each measurement sample (command) j. In addition, fast PV power emulation results are given in Figure 10, which shows a comparison of AC output power of the PV plant entered into DC programmable power supplies P plant_AC, j and AC output power of the PV inverter P plant_AC, j for each measurement sample j. In addition, fast PV power emulation results are given in Figure 10, which shows a comparison of AC output power of the PV plant entered into DC programmable power supplies Pplant_AC, j and AC output power of the PV inverter Pplant_AC, j for each measurement sample j.    The results given in Tables 5-7 show technically acceptable errors δ emulated and δ meter for every day emulated. The days with sudden and pronounced changes in solar irradiation, i.e., partially cloudy days (Figure 8), were deliberately selected to test the most severe cases given the limitations imposed by electronic equipment. The maximum value of percentage errors, i.e., 6%, was recorded for the third day, which is technically acceptable for daily based emulation.

Fast PV Power Emulation of a 12 kW PV Plant
The second case study is fast PV power emulation of a 12 kW PV plant performed for PV plants made of m-Si Bisol BMO 250 and p-Si Bisol BMU 250. Configurations of PV plant arrays made of m-Si Bisol BMO 250 and p-Si Bisol BMU 250 are given in Table 8. Configurations of PV plant arrays are determined by means of the algorithm described in Section 4.1. The fast PV power emulation process is performed for 4 June 2017, for which the solar irradiance profile is given in Figure 11. One-minute average values of output power of m-Si Bisol BMO 250 and p-Si Bisol BMU 250 PV modules on 4 June 2017 are given in Figure 12. In order to satisfy the maximum number of commands that can be entered into DC programmable power supplies and the minimum input DC power per array that should be produced in order for the PV plant inverter to start working (parameter P min_input ), each measurement sample j extracted from the data acquisition system represents a 30-min average value of a certain parameter resulting in a total number of 29 commands, 23 j measurement samples and 6 compensation commands.
According to Figure 11, a day with no sudden changes in solar radiation, i.e., a sunny day for the second PV power plant, is deliberately selected to evaluate the fast PV power emulation process. The percentage error was expected to be smaller in comparison to the 6 kW PV plant case. Therefore, only one day was chosen, unlike the previous case when three days were selected. As expected for a sunny day, the percentage errors (δ emulated and δ meter ) are significantly reduced compared to days with a sudden change in solar radiation-a maximum of 2% (Table 9). The evaluations carried out for both cases indicate that with the data collected from only one module, fast PV power emulation can be used to estimate electricity generation from PV power plants based on PV modules made of different technologies. Depending on the number of commands, each fast PV power emulation takes up to a maximum of 30 min, which is required to estimate a one-day PV power plant electricity generation.
The second case study is fast PV power emulation of a 12 kW PV plant performed for PV plants made of m-Si Bisol BMO 250 and p-Si Bisol BMU 250. Configurations of PV plant arrays made of m-Si Bisol BMO 250 and p-Si Bisol BMU 250 are given in Table 8. Configurations of PV plant arrays are determined by means of the algorithm described in Section 4.1.
The fast PV power emulation process is performed for 4 June 2017, for which the solar irradiance profile is given in Figure 11. One-minute average values of output power of m-Si Bisol BMO 250 and p-Si Bisol BMU 250 PV modules on 4 June 2017 are given in Figure 12. In order to satisfy the maximum number of commands that can be entered into DC programmable power supplies and the minimum input DC power per array that should be produced in order for the PV plant inverter to start working (parameter Pmin_input), each measurement sample j extracted from the data acquisition system represents a 30-min average value of a certain parameter resulting in a total number of 29 commands, 23 j measurement samples and 6 compensation commands.   According to Figure 11, a day with no sudden changes in solar radiation, i.e., a sunny day for the second PV power plant, is deliberately selected to evaluate the fast PV power emulation process. The percentage error was expected to be smaller in comparison to the 6 kW PV plant case. Therefore, only one day was chosen, unlike the previous case when three days were selected. As expected for a sunny day, the percentage errors (δemulated and δmeter) are significantly reduced compared to days with a sudden change in solar radiation-a maximum of 2% (Table 9). The evaluations carried out for both cases indicate that with the data collected from only one module, fast PV power emulation can be used to estimate electricity generation from PV power plants based on PV modules made of different technologies. Depending on the number of commands, each fast PV power emulation takes up to a maximum of 30 min, which is required to estimate a one-day PV power plant electricity generation.

Conclusions
An advanced fast PV power emulation technique, called fast PV power emulation of PV plants based on different module technologies and different peak power, is proposed. The algorithm for selecting the number of modules for the desired nominal (peak) power of a PV power plant is elaborated in detail, depending on technical characteristics of the selected modules. An algorithm for filtering data collected from modules of different technologies has also been developed, considering the limitations of programmable sources as well as the limitations of the PV inverter.
Fast PV power emulation of PV plants shortens the time required for emulation in comparison to standard PV emulation. The fast PV power emulation process is demonstrated for two case studies, i.e., one for a 6 kW PV plant made of a-Si Masdar MPV100-S, CIS Solar Frontier SF150-S, and HIT Panasonic VBHN240SE10 PV modules, and the other for a 12 kW PV plant made of m-Si Bisol BMO 250 and p-Si Bisol BMU 250. The results indicate that the fast PV power emulation procedure can estimate one-day PV power plant operation in 30 min maximum. A prerequisite for carrying out fast PV power emulation is the i-v characteristics of the PV arrays obtained in our work by collecting current and voltage values from a single PV module of each technology, even though other methods are available. Percentage errors as indicators of the performed fast emulation accuracy for a partially cloudy day with variable (intermittent) solar radiation and for a sunny (clear sky) day are 6% and 2%, respectively, i.e., they are acceptable for PV power plant emulation on a daily basis.
Furthermore, the developed fast PV power emulation procedure has high efficiency of up to 95%, since most energy used for generating PV array DC power is supplied back to the AC distribution grid by the PV plant inverter. Energy losses occurring in this process are created by nonideal MPPTs of the PV plant inverter and switching losses in power electronic circuit components. Funding: The work of the authors is supported by the project Renewable Energy Sources for smart sustainable health Centers, University Education and other public buildings (RESCUE) funded by Interreg IPAII Croatia-Serbia, project no. HR-RS303 and by the J.J. Strossmayer University of Osijek Interdisciplinary research project "Establishment of interdisciplinary research group in the field of renewable energy sources and their integration into the smart future energy systems".

Conflicts of Interest:
The authors declare no conflict of interest.