**2.2 Usage of PVGIS for estimation of irradiance profiles**

The production of PV generators depends on installation conditions (location and tilt and azimuth of the PV modules) and on weather conditions (solar irradiance and temperature). The Photovoltaic Geographical Information System (PVGIS) [22] database is a free online tool; it permits to know the average daily irradiance and temperature profiles corresponding to each month of the year. The monthly profiles can be obtained for every location in Europe, Africa, and Asia starting from the definition of the location of the generator and the tilt and the azimuth of the PV modules. Additional parameters can be selected, such as the typology of solar radiation database and the calculation of irradiation profiles, also for tracking systems. In **Figure 2**, the home screen of PVGIS database is shown.

The PVGIS database provides a temperature profile and three irradiance profiles for each month. In particular, the irradiance profiles correspond to a clear sky day, an average day, and an overcast day. During the clear sky day, the global irradiation is maximum; in fact, it is mainly composed of the beam contribution, because no clouds are present. During the overcast day, the solar irradiation is minimum; in fact, in case of cloudy and rainy days, only the diffuse component of the solar irradiance is present. The average day is an intermediate situation: it is based on the average irradiance condition occurring in the month under consideration. In **Figure 3**, an example of the output profiles of the software is presented for January; the selected location is in Italy (Turin, 45.05° Nord, 7° 40' Est) and the PV modules are installed with an inclination of 15° and West oriented (azimuth = 90°, where South = 0°). Data are provided with a time step of 15 min.

In the present chapter, it is supposed to install a single device including both the PV converter and the BMS; the BMS will be equipped with additional hardware and software capable of accessing Internet and download data from the PVGIS database. After the installation of the PV generator, during the setting up of the converter, the input parameters requested by PVGIS to estimate the irradiance and temperature profiles are inserted in the software of the device.

**77**

**Table 1.**

*A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw…*

The device accesses the PVGIS database and downloads and elaborates the three above-described irradiation profiles for each month. Starting from these data and the rated power of the PV generator, the power converter calculates a total of 36 PV production profiles by an appropriate photovoltaic model, which will be described in detail in Section 2.3. Finally, the power generation profiles are integrated over the entire day: the result is a list of daily energy productions for each month in three different weather conditions. **Table 1** shows the daily energy production of a PV

*Irradiance and temperature profiles for January in Turin (Italy) from PVGIS database; PV modules have* 

generator with rated power of 1 kWp installed as defined in **Figure 3**.

*Daily energy production for each month in three different weather conditions.*

Regarding the PV power simulation, the AC power production *P*AC is calculated according to the model described in [23]. The inputs of the model are solar irradiance *G*, ambient temperature *Ta*, and rated power of the PV generator *P*PV,r.

**PV production (kWh/kWp) Clear sky day Average day Overcast day**

January 2.0 1.5 0.8 February 3.0 2.5 1.0 March 4.3 3.7 1.6 April 5.6 4.3 1.8 May 6.4 5.2 2.2 June 6.6 5.6 2.2 July 6.5 5.8 1.9 August 5.8 5.0 1.8 September 4.6 3.9 1.5 October 3.3 2.5 1.3 November 2.2 1.6 0.8 December 1.8 1.3 0.7

**2.3 Modeling of PV generators**

*inclination of 15° and West orientation.*

**Figure 3.**

*DOI: http://dx.doi.org/10.5772/intechopen.80562*

**Figure 2.** *PVGIS website.*

*A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw… DOI: http://dx.doi.org/10.5772/intechopen.80562*

#### **Figure 3.**

*Green Energy Advances*

The DC/AC converter connects the PV system and the BESS to the AC side, i.e., local loads and the grid. Moreover, the device is Internet-connected and downloads raw weather forecast of 1-day ahead, compares provisional load and production

The production of PV generators depends on installation conditions (location and tilt and azimuth of the PV modules) and on weather conditions (solar irradiance and temperature). The Photovoltaic Geographical Information System (PVGIS) [22] database is a free online tool; it permits to know the average daily irradiance and temperature profiles corresponding to each month of the year. The monthly profiles can be obtained for every location in Europe, Africa, and Asia starting from the definition of the location of the generator and the tilt and the azimuth of the PV modules. Additional parameters can be selected, such as the typology of solar radiation database and the calculation of irradiation profiles, also for tracking systems. In **Figure 2**, the home screen of PVGIS database is shown.

The PVGIS database provides a temperature profile and three irradiance profiles for each month. In particular, the irradiance profiles correspond to a clear sky day, an average day, and an overcast day. During the clear sky day, the global irradiation is maximum; in fact, it is mainly composed of the beam contribution, because no clouds are present. During the overcast day, the solar irradiation is minimum; in fact, in case of cloudy and rainy days, only the diffuse component of the solar irradiance is present. The average day is an intermediate situation: it is based on the average irradiance condition occurring in the month under consideration. In **Figure 3**, an example of the output profiles of the software is presented for January; the selected location is in Italy (Turin, 45.05° Nord, 7° 40' Est) and the PV modules are installed with an inclination of 15° and West oriented (azimuth = 90°, where

In the present chapter, it is supposed to install a single device including both the PV converter and the BMS; the BMS will be equipped with additional hardware and software capable of accessing Internet and download data from the PVGIS database. After the installation of the PV generator, during the setting up of the converter, the input parameters requested by PVGIS to estimate the irradiance and temperature

profile, and adopts the best strategy to reduce consumption peaks.

**2.2 Usage of PVGIS for estimation of irradiance profiles**

South = 0°). Data are provided with a time step of 15 min.

profiles are inserted in the software of the device.

**76**

**Figure 2.** *PVGIS website.*

*Irradiance and temperature profiles for January in Turin (Italy) from PVGIS database; PV modules have inclination of 15° and West orientation.*

The device accesses the PVGIS database and downloads and elaborates the three above-described irradiation profiles for each month. Starting from these data and the rated power of the PV generator, the power converter calculates a total of 36 PV production profiles by an appropriate photovoltaic model, which will be described in detail in Section 2.3. Finally, the power generation profiles are integrated over the entire day: the result is a list of daily energy productions for each month in three different weather conditions. **Table 1** shows the daily energy production of a PV generator with rated power of 1 kWp installed as defined in **Figure 3**.

#### **2.3 Modeling of PV generators**

Regarding the PV power simulation, the AC power production *P*AC is calculated according to the model described in [23]. The inputs of the model are solar irradiance *G*, ambient temperature *Ta*, and rated power of the PV generator *P*PV,r.


#### **Table 1.**

*Daily energy production for each month in three different weather conditions.*

The thermal losses and consequently the DC input power change, while the other sources of losses are considered constant:

$$P\_{\rm AC} = \,^P\_{\rm PV,r} \cdot \frac{G}{G\_{\rm STC}} \cdot \eta\_{\rm mix} \cdot \eta\_{\rm thermal} \cdot \eta\_{\rm DC/AC} \tag{1}$$

with

$$
\eta\_{\rm mix} = \eta\_{\rm direct} \cdot \eta\_{\rm refl} \cdot \eta\_{\rm mis} \cdot \eta\_{\rm MPPT} \cdot \eta\_{\rm cabl} \cdot \eta\_{\rm shad} \tag{2}
$$

Losses due to temperature ηtherm are due to the reduction in the voltage of the PV generation with increasing temperature. The power loss with respect to the standard test condition is linearly dependent on temperature with proportionality factor about *γth* ≈ 0.3 ÷ 0.5%/°C depending on the semiconductor of the photovoltaic generator [24]. According to [25], a value of 0.5%/°C, typical for c-Si PV modules, which is the most diffused PV technology for terrestrial applications, is used. In order to estimate temperature losses, at every time step, the temperature of the PV cells *Tc* is calculated starting from measured air temperature *Ta* by the following equation [26]:

$$T\_{\mathbf{c}} = \,^\*T\_{\mathbf{a}} \cdot \frac{\text{NOCT} - 20 \stackrel{\circ}{\text{C}}}{G\_{\text{NOCT}}} \cdot \mathbf{G} \tag{3}$$

*NOCT* is the normal operating cell temperature, generally provided by the manufacturer of the PV modules; in this work, it corresponds to a typical value *NOCT* = 45°C. *G*NOCT is the solar irradiance occurring at *NOCT* condition and it is 800 W/m<sup>2</sup> ; the overtemperature losses *η*therm (with respect to TSTC = 25°C) are calculated by the formula:

$$
\eta\_{\text{therm}} = \Upsilon - \chi\_{th} \cdot \left( T\_c - T\_{\text{STC}} \right) \tag{4}
$$

**79**

unitary.

*A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw…*

are quadratically dependent on the power output. For the sake of simplicity, an

A correct model of the storage is fundamental to evaluate energy flows. Many electric models are present in literature and they permit to simulate operation of batteries with different pros and cons [34–37]. The simplest model describes a battery by an equivalent voltage source in series with an internal resistance. The equivalent voltage can easily be determined by measuring the open circuit voltage of the battery, while the measurement of the internal resistance requires a further test performed during battery charge. Obviously, this model has a limited use, because the parameters are constant: the accumulator results in having an infinite capacity and there is no way to determine the *SOC*. An upgrade with respect to the basic model is obtained using an equivalent resistive-capacitive model [36]. The values of resistances and capacitances can be determined through impulsive test of the battery. The advantage of this model is that it permits to evaluate the charge and discharge transients with variable loads in time. However, the SOC dependence on the voltage, which has to be determined, requires careful preliminary measurements on the battery. Another possible model consists of the impedance model, where a voltage source is in series with a resistance and an inductance. An additional series impedance is used to represent the electrochemical characteristics of the battery. Nevertheless, the definition of this impedance is complicated; in fact, it can be obtained starting from an electrochemical impedance spectroscopy to obtain an equivalent impedance in the frequency domain. In addition, the impedance has

average value of *ηDC/AC* = 0.97 is considered in the present work [33].

to be characterized varying the state of charge and the temperature [35].

system with continuous measurements is required.

The most sophisticated models [38, 39] are developed to calculate also the state of health (*SOH*), which is a parameter useful to evaluate how the charge-discharge profiles affect the storage life and when the batteries have to be replaced. In fact, PV production is intermittent; thus, PV generators cannot guarantee the optimal charge-discharge cycles to have the longest possible life of storage and the highest efficiency. For example, the *real-time model* described in [39] is a blend of the previous battery models whose particular combination of components and dependencies eases the estimation of the equivalent parameters. In conclusion, this model permits to calculate the *SOC*, the *SOH*, and then the residual life. This information permits the evaluation of the economic investment of electrochemical storage system [40], taking in consideration the battery management. Nevertheless, the models that permit to estimate the *SOH* require a continuous measurement of battery parameters (i.e., voltage, current, and temperature of the batteries) [37]. For this reason, the calculation of the *SOH* cannot be performed with only simulations, but a real

The *energy model* is used in the simulations presented in this chapter, because it permits to simulate the *SOC* with a good approximation (a few percent points) without measurements and with a low computation effort (only the formulas (5) and (6) are used). The *energy model* permits to estimate the state of charge of batteries; i.e., how much energy is stored or can be stored in a battery with rated energy capacity *Cbat*, by the comparison with the limits imposed to preserve life of batteries. The calculation of the *SOC*(*t*) at the instant *t* is a function of the state of charge *SOC*(*t* − 1) at the previous time step, of the power exchanged *P*bat during the time step *Δt* (in this chapter, *Δt* = 1 min) and of the charge efficiency *ηbat*. During the charge phase, the batteries behave as a generator (*P*bat > 0) and it is considered a charge efficiency *ηbat* = 0.88; during discharge (*P*bat < 0), efficiency is considered

*DOI: http://dx.doi.org/10.5772/intechopen.80562*

**2.4 Modeling of electrochemical storage**

Losses due to dirt *η*dirt provide an average 2% of reduction in energy production for the deposit of dust and other materials on the glass of the modules. Thus, a typical value *η*dirt = 0.98 is used in the present work [27, 28]. Note that in case of horizontal modules, the cleaning made by rain is reduced, and in case of emission of pollution close to the plants from special industrial processes [29], losses can be more than 7%. Losses due to reflection from the glass on the front of the PV module are inevitable losses due to a not ideal transparency of the glass; according to [30], they can be considered equal to ≈3% (*η*refl = 0.97). Mismatch losses are due to nonuniformity in *I-V* characteristics of modules connected in series or in parallel. Thus, the conversion unit imposes to the whole PV generator a working point not perfectly corresponding to the optimum. According to [31], a typical value of *η*mis = 0.97 is used. Joule losses take into account dissipation of electrical energy into heat by Joule effect in the cables. During design phase, cables should be sized in order to keep Joule losses within 3% in nominal conditions [30]. Since the PV system operates at nominal conditions (maximum power) for a short period during the year, and in other conditions (partial load) losses are lower, Joule losses are estimated equal to an average value of 1% (*η*cab l = 0.99) [30]. Losses for shadings are due to external causes, as a wrong design; thus, in the performed simulations, these losses are neglected (*ηshad* = 1). Regarding the accuracy of the maximum power point tracking (MPPT) system, it causes losses, because the optimum value is generally not perfectly tracked, especially at low power: on average, this loss can be estimated ≈1%. (*ηMPPT* = 0.99) [32]. Finally, the DC/AC conversion introduces losses, which

are quadratically dependent on the power output. For the sake of simplicity, an average value of *ηDC/AC* = 0.97 is considered in the present work [33].
