Design and Simulation of a Renewable Energy-Based Smart Grid for Ma'an City, Jordan: A Feasibility Study

In this study, Ma’an governorate has been chosen to be the case study to present an integration method of RESs. As it is a suitable area for wind farms and PV farms. Whereas the average Specific Produced Energy (SPE) is 1870 kWh/kWp/year [25-28], and the average wind speed at an altitude of 10 meters is 5 m/s [29]. To calculate the energy consumption in Ma’an, the population is estimated in 2040. Where Ma’an population in 2019 is estimated to be 175200 people [25] increasing by 2.03%/year, with an average consumption of 2100 kWh/year/person [26]. The proposed project should be cover the energy consumption of 20 years. Thus, the electrical demand could be estimated to be 561.099 GWh/year, so, the gross electricity demand in Ma’an city will be covered by the designed wind and PV farms. An estimation of the percentage of both solar and wind energy required to cover the consumption of this city has been considered. The proposed model integrates renewable energy sources of PV and wind as the primary sources of energy, and a battery bank as the secondary source of energy. In summary, a mathematical model of a detailed design and sizing of the PV and wind farms has been implemented. Also, the use of MISO converter in order to integrate the designed RESs has been considered and tested. The chosen converter has the benefits of simple configuration, fewer parts, high performance, and a high conversion ratio. In order to simulate the proposed model with the aid of the converter; the inputs and output in terms of inductor (L), capacitor (C), and the duty cycle (D) of the power switches (IGBT) are mathematically related. PID controller has been used to control MISO converter where the simple and low cost controller structure is always in demand for most industrial and high performance applications.

In order to test the designed farms performance a PVsyst platform has been used for PV system simulation, while for shade analysis SketchUp software has been utilized. Also the Photovoltaic Geographical Information System (PVGIS) has been used to determine the Sun Peak Energy (SPE) and solar data of Ma'an’s city. Furthermore, SketchUp and MATLAB platforms have been used to model the wind turbines within the wind farm and to determine the layout of the farm. In addition, all the required weather data has been imported to the MATLAB package using the Global Wind Atlas and Global Solar Atlas, in order to determine the electrical output profiles of the mix of renewable sources which will be ready to be integrated to the SGs.

In the following sections, a detailed of technical analysis of the designed PV and wind farms will be discussed. Where the percentage of the covered energy demands has been chosen for each renewable energy source in order to obtain the lowest energy costs within an allowable range of reliability in the selected location [27]. Thus, in this study 60% of overall Ma’an’s energy demands will be covered by the PV farms and the remaining energy will be covered by the wind farm, also, in case of emergency loads the storage batteries will be sized to cover 20% of the overall Ma’an’s energy demand.

4.1 Study area and data set

In order to design the proposed wind farm and to select the optimal wind turbine in the selected location, a detailed weather information in Ma'an city is required. Ma'an a city located in southern Jordan, with geographical coordinates between (30.456, 36.696), and with elevation ranges from 1100 - 1140 m [29]. Ma'an prevails a desert climate, with temperatures in summer rising up to 35 degrees Celsius, and in winter temperatures down to 15 degrees Celsius. As for the precipitation rain, with a maximum of 60 mm [29]. The city is affected in summer by the southeastern desert winds, and in winter by westerly winds, which are the cause of rain. As mentioned before, Ma'an’s energy demand has been defined to be 561,099 GWh per year [25]. In this study 40% of the demand will be covered by the designed wind farm.

4.2 Target energy

Energy produced from the wind farm through Wind Turbines (WTs) will be:

$\begin{gathered}\mathrm{E}_{\mathrm{WT}}=40 \% \times \text { Target Energy }=40 \% \times 561.099 \mathrm{GW} / \text { year }=224.44 \mathrm{GW} / \text { year }\end{gathered}$                  (4)

To calculate the number of wind turbines that should be installed in the proposed farm, the determination of WT type, rated power, specifications, and the capacity factor are required. The Vestas V90-3.0 turbine has been chosen as the model on this study due to its relatively high rated power as listed in Table 3. The wind turbine cut-in speed is 4 m/s and the cut-out speed of 25 m/s, with a rotor diameter of 90 m, and with up to 1,000 V rated voltage.

Table 3. Vestas V90-3.0 turbine specifications [30]

To calculate the estimated annual energy produced by the selected WT a capacity factor approach could be used. At 100 m height the average wind speed in Ma’an city is 7.68 m/s, then the Capacity Factor (CF) could be calculated using Eq. (5) as follows:

$C F=0.087 \times v-\frac{P_{\text {rated }}}{(D)^2}$                     (5)

where, P is the WT rated power, D is the rotor’s diameter and v is the average wind speed.

$C F=0.087 \times 7.68-\frac{3000}{(90)^2}=0.2978$               

Then the total required number of WT is:

$\begin{gathered}\text { Number }_{\text {turbines }}=\frac{\text { Annual Energy }}{\mathrm{P}_{\text {rated }} \times 8760 \mathrm{~h} / \mathrm{y} \times \mathrm{CF}} =\frac{224.44 \mathrm{GWh}}{3000 \mathrm{~kW} \times 8760 \times 0.2978}\end{gathered}$   

Number $r_{\text {turbines }}=29$ turbines.              (6)

The required area for each WT should be considered in order to prevent wind damage within the farm, so the area could be assumed to be:

Area of one turbine $=4$ Dbetween towers in row $\times 7$ Dbetween row $=28 D 2=28 \times 902=226800 \mathrm{~m}^2$ $=0.2268 \mathrm{~km}^2$                (7)

Thus, the total area needed for the wind farm would be:

$\begin{aligned} \text { Total area } & =\text { Number } r_{\text {turbines }} \times \text { Area of one turbine } \\ & =30 \times 0.2268 \mathrm{~km}^2=6.8 \mathrm{~km}^2\end{aligned}$                   (8)

Then the proposed WTs layout configuration within the farm could be as shown in Figure 3.

tu_pian_3.png

Figure 3. Wind farm layout

• Feasibility study of the wind farm

The payback period of the designed wind farm is:

$$

\begin{aligned}

\text { Payback Period } & =\frac{\text { System Cost }}{\text { Saving Rate }}=\frac{114,750,000}{32,869,973}=3.5 \text { years } \approx 42 \text { months }

\end{aligned}

$$                (9)

Then the total saving rate of the PV and wind systems will be 79,993,973 JD/year with net profit of 1.4 Billion JD which make this project worthy to install.

$\begin{aligned} \text { Total saving rate } & =\text { PV saving rate } + \text { Wind saving rate }\end{aligned}$            (10)

$\begin{aligned} \text { Total saving rate } & =47,124,000 \mathrm{JD} / \text { year } +32,869,973 \mathrm{JD} / \text { year } =79,993,973 \mathrm{JD} / \text { year }\end{aligned}$

Net Profit $=542,354,555 \mathrm{JD}+888,287,400 \mathrm{JD}$

Net Profit $=1,430,641,955 \mathrm{JD} \approx 1.4$ Billion JD

4.3 PV system simulation using MATLAB

As mentioned before, Ma’an has a comparatively high daily Direct Normal Irradiation (DNI). These solar data are transferred to MATLAB for simulation purposes in order to generate a variable output based on the time of the day for each month of the year as shown in Figure 4.

tu_pian_4.png

Figure 4. Solar irradiance in Ma’an city during the day [31]

The process of determining the maximum output power of the PV farm during the day is known as the Maximum Power Point Tracking (MPPT), which could be obtained through the utilized solar inverter or the current –voltage (I-V) curve of the PV panels’ performance. Figure 5 depicts the PV array modeling using MATLAB-Simulink software. The module data that has been used is determined as shown in Figure 6.

tu_pian_5.png

Figure 5. PV array modeling

tu_pian_6.png

Figure 6. PV module specifications

The I-V curve of the PV module represents its energy transfer capability under operating conditions of irradiance and temperature, so as a result the current-voltage and power voltage curves are shown below in Figure 7.

tu_pian_8.png

Figure 7. I-V and P-V curves

The MATLAB simulation output voltage of the proposed PV array could be obtained as shown in Figure 8.

tu_pian_7.png

Figure 8. PV array output voltage

As a result, the profiles of the output voltage and the output current of simulated PV farms have a good correlation with those obtained via mathematical modeling. And this output could be connected to the MISO converter to be integrated with the other sources. So that, these profiles are important to be defined for each hour of the day.

4.4 Wind farm simulation using MATLAB

A wind power plant of 90 MW was simulated with Simulink-MATLAB, this section included 30 turbine every one of those with capacity of 3MW as mentioned before. To reduce the working area of Simulink-MATLAB each block consists of three WTs. This is because the wind farm is very large, so to reduce the software issues and to reduce the time required for simulation decided to connect every three turbines in parallel within one block. So, ten blocks (strings) of wind turbine each block with a capacity of (3×3 MW) to obtain a simulated 90 MW, through 10 strings in the proposed farm as shown in Figure 9. Each string has a special control system and specific database of the wind for the preselected location.

Every string connected to a transformer 400V/25kV, with 1 km transmission line loss is included as shown in Figure 10.

From the simulation results it was clear that the Power Factor (PF) quality could be improved by adding a capacitor bank to the system in order to ease the integration of the REs to SG.

qqjie_tu_20231229120654.png

Figure 9. 90 MW wind power plant-MATLAB

tu_pian_10.png

Figure 10. String design of 9 MW wind power

4.5 Wind farm simulation with control strategy

PID Controller for each string has been designed for protection purposes of the wind turbine by shutting the WT down during any fault. In this farm, for controller a number of monitored devices have been used such as: over wind speed, under wind speed, DC overvoltage, AC voltage and unbalance (Zero-sequence). Figure 11 depicts the protection system parameters’ that have been obtained during this simulation. Thus, for each string of the designed farm overcurrent protection system has been implemented as shown in Figure 12.

To test the robustness of the proposed controller within the designed wind farm; a three-phase AC fault has been applied at each string as shown in Figure 13. Where the three-phase AC fault parameters have been obtained in Figure 14.

As a simulation result, when the AC fault is detected within the system the turbine goes to shut down after 5 seconds from the fault acquired and this result demonstrates the robustness of the implemented controller. Figure 15 shows the simulation results of each subsystem of the wind power plant which consists of 2 strings (6 × 3MW). The results include the active power (MW), reactive power (MVAR), three-phase current (I_abc) (per-unit/18MVA), three-phase voltage (V_abc)(per-unit) and V (per-unit) (base voltage= 25kV).

qqjie_tu_20231229135845.png

Figure 11. Overcurrent protection system for wind power plant

tu_pian_12.png

Figure 12. Monitores protection parameters – MATLAB

666666.png

Figure 13. String design wind power plant with AC fault

tu_pian_14.png

Figure 14. Three-phase AC fault parameters

tu_pian_15.png

Figure 15. Wind power plant simulation results without fault

After applying the AC fault in the system, the output of the wind power plant will be shown in Figure 16. Which again this explains the effectiveness of the implemented control strategy in the system.

tu_pian_16.png

Figure 16. The simulation result of the wind power plant with AC fault

4.6 Batteries simulation using MATLAB

Designing an entire city to run on renewable energy sources calls for a storage system. Where the importance of having a storage system comes from, storing the surplus energy and using it when it is needed. Because it is known that these sources have volatile productivity as they depend on the surrounding environmental conditions. A 626 MWh battery system which represents 20% of the total Ma’an city demand has been designed using MATLAB. Figure 17 depicts the storage system scheme that has been implemented in this study as a secondary source of energy for emergency loads and to enhance the proposed system’s reliability. As integrating REs may cause insecurity of supply so, having a battery bank could solve this challenge.

tu_pian_17.jpg

Figure 17. The batteries scheme-MATLAB

This work is supported by Zarqa’a University and is fully funded by this institution.