1 Introduction

Different sizing approach can be used to make a choice among a variety of system [1, 2]. Combining more than a single renewable power source with an appropriate battery capacity help to minimize the intermittency of the output power, and then to reduce the grid dependency [3]. Moreover, the thoughtful size of the HS helps to minimize the cost of energy by choosing the minimal configuration able to ensure supplying with continuity [4]. In this regard, a literature review of the most recent techniques used for sizing an HS, a different technique is compared and adopted according to the size and the utility of the installation [5]. The deterministic method requires data such as electric load profile and weather to estimate the long-term energy output of a specific hybrid configuration; data must be provided as hourly times-series and related to the location of the hybrid PV wind plant. In contrary, the electric load profile and weather are considered as stochastic in the probabilistic method [6, 7]. A comparison study has been made in order to compare the feasibility use of sizing deterministic approach, it is found that the yearly average month method helps to provide an optimized configuration of an HS connected to the gird, otherwise, the worst month methods servers to estimate the optimized configuration in a location without a grid connection [8].

In the literature, several studies have focused on the use of the TRNSYS software [9] for the evaluation of susceptible renewable energy implantation. In the Polytechnic University of Valencia, a validation procedure that evaluates an experimental result using measured data of a PV installation along two successive years [10], these result was confronted with the result of TRNSYS simulation of long-term energy output and present an acceptable accuracy. Mondol et al. [11] proposed a new TRNSYS component to estimate the energy exchange between a PV system and grid; this component is integrated into a designed TRNSYS model which simulate the long-term output of PV system. Moreover, an experimental study has been made to measure the uncertainties prediction results of TRNSYS simulation, compared with the long-term measurement data during 3 years of the PV installation planted in Northern Ireland. As a result, the uncertainties between the simulation and experimentation result are less than 5%. Panayiotou et al. [12] designed a TRNSYS model to examined an HS installation located at Nicosia, Cyprus, and Nice, France. This designed TRNSYS simulates the long-term output performance of this installation. As a result, Nicosia presents a higher solar potential compared to Nice, which is explained by less PV Array 11.7 kWp installation than 15.3 kWp for Nice with the same storage capacity of 108 kWh in both cases. Moreover Nice shows better performance in the HS due to its potential of wind energy, as optimum hybrid configuration, it is found 9.9 kWp of PV array, two wind turbines of 2.4 kW and 108 kWh of storage capacity. Otherwise, the same configuration is conserved for Nicosia. Bakić et al. [13] proposed a TRNSYS design to simulate a hybrid PV wind configuration, and they presented a dynamic simulation of solar and wind potential in Belgrade city, moreover, they presented a long-term electrical production for a specific hybrid PV wind configuration and the estimation of CO2 reduction. As a result, HS represents a promising means of clean energy generation [14].

Providing an optimized configuration of the renewable hybrid power plant is the principal target of this manuscript. These renewable power plants must, first of all, ensuring a reliable power supply of the load demand and being more cost-competitive in terms of investment. In the current work, an appropriate design under TRNSYS and Matlab has been used to assess weather condition for the referee cities and to perform a simulation of the energy exchange of the HS configuration, after, a determined load demand profile of a laboratory prototype is used to describe daily energy consumption. Then a dynamic simulation of energy out-put performance for different hybrid configuration is analyzed during 1 year. Finally, a techno-economic investigation of the chosen optimized solution is presented as a result and discussed, as well as an environmental assessment.

In this paper, the methodology adopted for sizing HS start by defining the electrical load demand (ELD) of the LP as a first step. One ton of bitumen required to be maintained at 160 °C by compensating its heat losses and ensuring it electrical equipment consumption during one day, and then 1 year period. The second step consists of deep weather data analysis of the location under study, i.e.: solar radiation, temperature, humidity, and wind speed. It supposed that these parameters may or not influence and decide the energy output from a RES. Therefore, a deep investigation of solar and wind potential is required to validate this supposition. Then if one referred cities is investigated, it admits numerous HS configurations sizes that are composed of PV panel, WT and batteries sizes. Only one optimized HS configuration with the lowest investment cost is adopted. Dynamic simulation of the energy output vs ELD profile is required to validate the power reliability of this configuration. But for the other referee cites witch are characterized by a different weather condition, the optimized HS configuration may or not satisfied the ELD, otherwise, a techno-economic investigation is required for each referee cities.

2 Overview of the industrial laboratory prototype

The laboratory prototype (LP) is a miniaturized industrial process used for maintaining heated a ton of bitumen in a metal tank thermally insulated. The heat losses are compensated by the aid of a crew-on immersion heater. The main object of the LP is to experiment the behaviors of the process component and to integrate the renewable energy, i.e., solar photovoltaic and wind energy in order to supply the load demand (see Fig. 1).

Fig. 1
figure 1

Simplified scheme of the LP of the industrial process

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The LP is composed principally of:

  • Storage tank characterized by thermic isolation, which allowed feeding, evacuation, and agitation of bitumen.

  • Process control: Allowed to control various stratification temperature and level of the liquid, controls numerous valve and circuit breaker, ensuring the conventional temperature storage for the bitumen.

3 Modeling the system components

3.1 Photovoltaic panel model

The photovoltaic panel is considered as the widespread renewable power sources, it is composed of numerous solar cell matrixed to form a panel, and it converts solar radiation to electricity through p–n semiconductor junction technology. The output power can be influenced by two parameters: radiation and temperature and can be explained:

$$P_{pv} \left( t \right) = P_{rate} \left( {\frac{G}{{G_{stc} }}} \right)\left[ {1 + k\left( {T_{cell} - T_{stc} } \right)} \right]$$
(1)
$$T_{cell} = T_{amb} + \left( {\frac{NOCT - 20}{800}} \right)$$
(2)

Ppv(t) represent the output power of the PV panel, Prate describe the nominal output power of the PV panel, G is the solar radiation in W/m2, STC represent the standard condition, Gstc is the solar radiation at STC equal to 1000 (W/m2), Tstc is the cell temperature at STC equal to 25 °C, k define the temperature coefficient, Tcell is the cell temperature, Tamb is the ambient temperature of air (°C), and finally the NOCT represent the normal operating cell temperature (°C).

3.2 Wind turbine model

The wind turbine’s output power can be explained as a function of wind speed as:

$$\begin{aligned} & P_{WT} = C_{p} (\lambda ,\beta )\frac{\rho A}{2}V_{wind}^{3} \\ & V_{cut{-}in} \le V_{wind} \le V_{cut{-}out} \\ \end{aligned}$$
(3)

Cp is called power coefficient, it is a function of Lambda and Beta, and their corresponding relation is expressed by the following equation [15]:

$$\begin{aligned} C_{p} (\lambda ,\beta ) & = 0.35 - 0.0167 \cdot (\beta - 2) \cdot \sin \left( {\frac{\pi \cdot (\lambda + 0.1)}{14.34 - 0.3 \cdot (\beta - 2)}} \right) \\ & \quad - 0.00184 \cdot (\lambda - 3)(\beta - 2) \\ \end{aligned}$$
(4)

where PWT represent is the output power (mechanic) of the wind turbine, Cp represents performance coefficient, ρ is the air density, A is the swept area of WT, λ is the tip speed ratio β is the blade pitch angle, and finally, Vcut-in, Vcut-out, are respectively the cut-in, the cut-out wind speed.

3.3 Batteries model

To remedy the fluctuation of renewable energy sources Ep, batteries can store excess energy up to their capacity limit, and provide it when needed by load demand energy El. Batteries help to regulate the intermittency of the renewable sources by ensuring power reliability until its discharges.

The charging mode can be explained by the following Eq. (5) where is describing the available capacity at t hours [16]:

$$SOC\left( t \right) = SOC\left( {t - 1} \right)*\left( {1 - \sigma } \right) + \left[ {E_{p} \left( t \right) - \frac{{E_{l} \left( t \right)}}{{\eta_{inv} }}} \right]\eta_{bat\_char}$$
(5)

Ep represent the produced energies from the renewables sources and El is the load demand of the LP. SOC(t), and SOC(t − 1) represent the batteries charge levels of times t and t − 1, the self-discharge rate is described hourly by σ.

The discharging mode can occur when the load demanded to exceed the produced energy of the renewables sources; in this case, batteries compensate the energy difference between production and consumption.

$$SOC\left( t \right) = SOC\left( {t - 1} \right)*\left( {1 - \sigma } \right) + \left[ {\frac{{E_{l} \left( t \right)}}{{\eta_{inv} }} - E_{p} \left( t \right)} \right]/\eta_{Bat\_dischar}$$
(6)

\(\eta_{{{\rm{bat}}\_{\rm{char}}}}\), \(\eta_{{{\rm{Bat}}\_{\rm{dischar}}}}\) represent the efficiency of batteries charge and discharge respectively, and \(\eta_{\rm{inv}}\) is the efficiency of the used inverter [17].

4 Weather assessment of the referee cities

The main objective of sizing an HS is providing an optimal PV panel and wind turbine size for each potential location which can power the laboratory prototype. Moreover, the optimized configuration must ensure the uninterruptible power supply of the load demand. The adopted sizing method is the deterministic approach which is performed by analyzing the dynamic simulation of the output energy performance obtained by TRNSYS/Matlab. In this work, deterministic procedure witch use metrological data (1 h interval) helps to estimate the performance and provides the feasibility of the HS, then, time-series metrological data is containing the hourly physical rate of solar irradiation, wind-speed, and temperature to achieve the simulation for all probable system configuration [18]. The chosen locations are the six referee cities of the Moroccan weather area (Fig. 2).

Fig. 2
figure 2

The six referee cities of the Moroccan weather area

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Referring to numerous study and analyses of Moroccan weather, A government agency specialized in renewable energy and efficiency (NADREEE) [19] has defined a weather area. Each climatic zoning is represented by referee cities see (Table 1).

Table 1 Weather characteristics of Moroccan referee cities
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The hourly solar irradiations on a horizontal surface (W/m2), and wind speed (m/s) of the six referee cities are illustrated respectively in following (Figs. 3, 4, 5, 6, 7, 8) [20].

Fig. 3
figure 3

Hourly solar irradiations and wind speed of Agadir City

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Fig. 4
figure 4

Hourly solar irradiations and wind speed of Errachidia City

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Fig. 5
figure 5

Hourly solar irradiations and wind speed of Fez City

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Fig. 6
figure 6

Hourly solar irradiations and wind speed of Ifran City

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Fig. 7
figure 7

Hourly solar irradiations and wind speed of Marrakech City

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Fig. 8
figure 8

Hourly solar irradiations and wind speed of Tangier City

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The referee cities have a significant yearly average irradiation along the year with exceptions for December and January. It should be noted that Tangier knows an enormous wind field in comparison with other cities. Important data can be deduced from the dynamic simulation which concerns the average annual of irradiation, temperature and wind speed (Table 2).

Table 2 Annual average values of the referee cities
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The weather data analysis is based on a specific resource data file called TMY2. Each location understudy has a specific TMY2 file, it can be generated form Meteonorm software [21], or downloaded from the National Renewable Energy Laboratory (NREL), TMY2 files contain the time series weather data such as solar radiation and meteorological element during 1 year period with 1 h step. In other hand, TMY2 files are used as input for a developed TRNSYS design where the PV panel and WT are modeled. This TRNSYS design allows generating the time series energy output for each or both RES during 1 year period. These times series energy output is used on a Matlab program as input to decide the appropriate size of renewable energy sources able to satisfy the ELD of the LP. Dynamic simulation of the time series energy output of generated sizing solution is analyzed technically and economically to decide the appropriate hybrid configuration solution for each location under study and time-series energy output of HS configuration following the six referee cities are presented in (Fig. 9).

Fig. 9
figure 9

Typical vertical-cylindrical tank

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5 Result and discussion

5.1 The assumption on the energy demand

According to specifications of the considered industrial prototype, the temperature of bitumen must be maintained at 160 °C. Thus, allowing the quality of bitumen to be conserved and ensuring bitumen viscosity for pumping. Otherwise, by compensating the heat losses of the bitumen tank during a day work, the temperature can be maintained at the desired value, the idea is to calculate the thermal energy of the heat losses, adding other electric consumption (sensors, commutator, regulator, and agitator) during a day to express the behavior of the electric load demand of the LP.

The procedure used determines the heat losses from a vertical-cylindrical storage tank seated on the Ground-like the one in Fig. 9. It includes the effects of tank configuration, ambient temperature, wind speed, and temperature variations within the tank and between air and ground. The approach is to develop equations for calculating the heat loss from each of the four surfaces, and get the total heat loss. Thus:

$${\rm{For-dry-sidewall}}\quad {\rm{q}}_{\rm{d}} = {\rm{U}}_{\rm{d}} {\rm{A}}_{\rm{d}} \left( {{\rm{T}}_{\rm{v}} - {\rm{T}}_{\rm{amb}} } \right)$$
(7)
$${\rm{For-wet-sidewall}}\quad {\rm{q}}_{\rm{w}} = {\rm{U}}_{\rm{w}} {\rm{A}}_{\rm{w}} \left( {{\rm{T}}_{\rm{L}} - {\rm{T}}_{\rm{amb}} } \right)$$
(8)
$${\rm{For-tank-bottom}}\quad {\rm{q}}_{\rm{b}} = {\rm{U}}_{\rm{b}} {\rm{A}}_{\rm{b}} \left( {{\rm{T}}_{\rm{L}} - {\rm{T}}_{\rm{G}} } \right)$$
(9)
$${\rm{For-tank-roof}}\quad {\rm{q}}_{\rm{r}} = {\rm{U}}_{\rm{r}} {\rm{A}}_{\rm{r}} \left( {{\rm{T}}_{\rm{v}} - {\rm{T}}_{\rm{amb}} } \right)$$
(10)
$${\rm{Total}}\quad {\rm{Q}} = {\rm{q}}_{\rm{d}} + {\rm{q}}_{\rm{w}} + {\rm{q}}_{\rm{b}} + {\rm{q}}_{\rm{r}}$$
(11)

The electric load demand of the LP combines thermal and electric component. The thermal component expresses the heat losses to be compensated by an electric heater during 1 day period for 1 tonne of the bitumen storage tank. The heat losses during the night are more significant: superior to 0.8 kW/h (influenced by the drop in temperature during the night), compared to those during the day time (inferior to 0.8 kW/h), these losses are equal to an average of 20.6 kWh/day (see Fig. 10).

Fig. 10
figure 10

Electric load demand of the laboratory prototype (kWh vs. h)

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In another hand, to well handle and to control the properties of the bitumen during the day work, some electric equipment is required such as electric agitator, electric valve, sensors, and controller. These operations can generally be involved between 9 and 17 h which express an energy demand of 0.62 kW/h. While these operations are not required in the other period especially during the night, only sensor and controller are operating. Therefore, the energy demand is equal to 0.2 kW/h. As a result, the electric consumption during the day/night is estimated to 8.9 kWh/day. The total energy request by LP is composed of thermal and electrical part, and it is illustrated in figure bellowing (Fig. 10).

5.2 Time series simulation result

Numerous HS configuration for different size has been investigated in order to reach the power reliability, and the system costs constraints through performing numerous dynamic simulations using a designed TRNSYS/Matlab model for each referee city. Table 3 regroups the optimized HS configuration of each referee cities. The optimized HS configuration depends on several factors, the potential of solar deposit and wind field of the chosen location, the behavior of the electric consumption and the cost of the material. These configurations can ensure the load demand of the LP which is estimated around 10,522.85 kWh/year. The optimized configuration is practically closed for five cities with 1 kWp less in the cities of Marrakech and Errachidia, but for Tangier, which knew in a significant potential of the wind deposit of 4.9 m/s compared to the other cities, the optimal HS is (PV = 3 kWp, WT = 3 kW).

Table 3 Optimized HS configuration
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According to the designed HS model, many simulation results are presented concerning the long-term output of hybrid produced energy versus load demand (Fig. 11). The green curve represents the energy output from the HS for each 1-h witch concerns PV Array and Wind Turbine, while the orange curve illustrates the ELD. The excess of energy production is serving to charge the battery bank as first, and then inject the rest in the grid. The batteries can provide the necessary energy to cover a limited period relating to its capacity, the grid can be connected if the HS and batteries have not sufficient energy to power the ELD.

Fig. 11
figure 11

Energy production and consumption during a year (1-h step)

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5.3 Economic assessment

The economic study helps to evaluate the investment cost of the HS configuration. Each chosen location required a particular system component (Table 4). For the same ELD, the optimized HS configuration may be differenced according to the chosen referee cities.

Table 4 The cost of HS installation [22]
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The cost of HS installation consists of all service provision relating to several tasks such as providing PV modules and their support, wind turbine and its support, and even the necessary components such as equipment for the protection, disjunction, and signalization.

After analyzing these economic results, it is deductible that each chosen location has a specific configuration requirement as an optimal solution to the sizing problem, and even. This optimal size could be cheaper compared to conventional energy sources (Table 4). The lowest installation cost appears in Tangier with 14,645.00 €, it showed the power of hybridization of various renewable energy sources as a realistic form of electrical power energy.

5.4 Return on investment and impact of the environment

Evaluating a renewable energy project taking into account Return on investment and environment benefice remind necessary, indeed, in this study, payback period (Eq. 12) is evaluated as an essential parameter in return on investment as well as CO2 reduction (Eq. 13) as environment benefice.

$${\rm{PBP}} = \frac{{\ln \left( {\frac{{C_{\rm{s}} i_{\rm{f}} }}{{E_{\rm{s}} C_{\rm{f}} }} + 1} \right)}}{{\ln \left( {1 + i_{\rm{f}} } \right)}}$$
(12)

Kalogirou [23] proposed a mathematical formula (Eq. 12) to calculate the payback period of the renewable energy project, this equation takes into account, Cs the capital cost of the system (€) which is detailed in Table 4, Es the total energy saving during a year (kWh) which is detailed in Table 3, Cf Cost of electricity 0.097 (€/kWh) and if electricity inflation (8%).

By analyzing the payback period of a susceptible installation of one of the referee cities (Table 5), Tangier present the minimum period of 9.36 years to recover all investment cost of the renewable power plant. In another hand, even if the investment cost is lower (14,845.00 €) compared by other cities, Marrakech presents the longer payback period 9.82 year, it can be explained by lowest total value of energy-saving during a year period (10,592.75 kWh), the hot temperature registered in Marrakech except the winter season influence the PV energy production.

$$Q_{\rm{emission mitigated}} = CE_{\rm{s}}$$
(13)

where C is the emission factor and Es is the energy saved during the year. The quantity of CO2 reduction by each configuration is summarized in the following (Table 6).

Table 5 Technical and economics comparison result
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Table 6 Energy-saving and CO2 mitigation
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6 Conclusion

In this paper, the authors are focusing on sizing and integrating HS based of photovoltaic and wind turbine to power load demand of industrial laboratory prototype, The target of this sizing study is providing an optimized configuration (PV and Wind size), which ensures electrical supplying of the laboratory prototype with the cheaper installation cost. Six referee cities have been chosen for potential integration of the laboratory prototype. TRNSYS and Matlab have been used to execute a simulation of energy exchange of the HS configuration of the cities understudies. First of all, a Weather assessment has been released to determine the potential of solar irradiation and wind speed of these cities. As a result, it appears after a data analysis that each location is characterized by a specific weather parameter such as solar radiation, temperature, humidity, and only Tangier has a significant potential of wind speed compared to other cities. The potential of solar ration remains approximatively the same except the cities of Tangier, Fez, and Ifran where the solar radiation drops during rainy winter season. Moreover, various dynamic simulations had been performed in order to visualize the long-term electrical production vs load demand of laboratory prototype for a possible HS configuration, after that between these HS configurations, the lowest one has been chosen, and its dynamic simulation of time series energy output of HS is done to assess their power reliability during a year. From these HS configurations sizes, the lowest in cost investment is choosing. Finally, for each selected configuration, an economic evaluation has been made in order to estimate the cost of HS solution integration and even to calculate the return on investment and the environmental impact. After analyzing these economic results, it is deductible that each chosen location has a specific configuration requirement as an optimal solution to the sizing problem, and even. This optimal size could be cheaper compared to conventional energy sources. The lowest installation cost appears in Tangier with (14,645.00 €), it showed the power of hybridization of various renewable energy sources as a realistic form of electrical power energy. A combination of photovoltaic and wind turbine with batteries presented excellent opportunities to integrate these renewable energy sources in such industrial application, and especially when reducing integration prices, and improving the return on investment time.