Dynamic Modeling and Simulation of a Solar-Powered Water Pumping System for Irrigation in Kufri, Pakistan

This paper presents the dynamic simulation and performance evaluation of a solar-powered water pumping system designed for irrigation in Kufri, Khushab, Pakistan. The design is based on the previously developed in HOMER optimized model and extended into MATLAB/Simulink to analyze real-time behavior. The paper represents a design with an 8.75 kW photovoltaic array, a 48 V storage battery bank, a 7.11 kW inverter, and a 7.5 HP submersible induction motor pump. To assess its performance, the model integrates a Maximum Power Point Tracking (MPPT) control scheme, a DC–DC buck converter, a three-phase DC-AC inverter, and a step-up transformer. Simulation outcomes covering PV response curves, inverter voltage output, motor operational behavior, and battery charging and discharging profiles demonstrate steady performance, consistent irrigation of about 137–140 m3 per day, and strong tolerance to changes in solar irradiance. These findings verify that the previous study HOMER-optimized configuration is practically achievable and represents a sustainable approach for irrigation needs in rural agricultural areas.

Introduction

Pakistan has access to significant renewable energy resources, but only a fraction of this potential has been tapped so far [1]. Among the available options, solar energy is the most practical since the country lies in the global solar belt and experiences long hours of sunshine almost year-round [2]. In many parts of Pakistan, solar radiation is strong enough to make photovoltaic (PV) systems a workable alternative to grid electricity and other conventional sources [3]. Making fuller use of this energy could ease the demand for imported fuels, lower pressure on the national power supply, and help meet climate-related goals [4].

In Pakistan, agriculture continues to hold a central position within the national economy [5]. Irrigation in most rural areas, however, still depends on diesel-driven pumping units that are costly to operate, suffer from frequent breakdowns, and negatively affect the environment [6]. Using the nation’s tremendous solar potential to meet irrigation demands, solar pumping technology offers a dependable and ecological alternative. Earlier research work has already shown the technical and financial viability of such systems, often using optimization tools such as HOMER or Lorentz Compass to design them [7].

That said, most optimization-based studies tend to focus on overall energy balance and cost analysis over long periods [8]. They rarely examine short-term fluctuations or the way system components interact dynamically [9]. In practice, solar pumping systems are exposed to variations in sunlight, changes in water demand, and the electrical and mechanical responses of different components [10]. Capturing these effects is essential for realistic design and stability assessment. To respond to this need, the present research develops

a dynamic simulation model of a solar water pumping system. The model considers variable solar input, PV electrical response, motor operation, and hydraulic load, allowing a closer look at performance under real operating conditions. The outcomes are intended to provide guidance for engineers and policymakers working toward reliable solar irrigation solutions suited to Pakistan’s agricultural and energy context [11].

Literature Review

The dynamic simulation of solar-powered water pumping systems has received a lot of attention lately, especially in areas with high solar potential and water scarcity [12]. Memorial University of Newfoundland (MUN) scholars have made significant contributions in this area, concentrating on hybrid storage integration, techno-economic evaluations, and system reliability in a variety of operating scenarios [13].

Authors have proposed and evaluated a solar-powered pumping system for Kufri, Khushab, Pakistan, using HOMER Pro and LORENTZ Compass tools. Their analysis recommended an 8.75 kW photovoltaic (PV) array, coupled with 24 Trojan SPRE batteries and a 7.11 kW inverter, to meet irrigation requirements of 30,000 m² farmland with a daily demand of 137–140 m³ of water, whereas utilizing Homer PRO achieved an levelized cost of energy of $0.074 per kWh under an emissions-free scenario, while LORENTZ testing demonstrated that the pump reliably delivers 140 m³/day throughout the year. These results demonstrate viability for the irrigation demand [7].

A hybrid PV-battery-diesel system for an urban Karachi community has been proposed. Their MATLAB/Simulink model integrated a shared backup diesel generator with solar power generation and storage. In order to improve transient stability and facilitate load recovery under varying irradiance, the system included a DC–DC buck converter and a modified incremental conductance MPPT algorithm. Because of its stable voltage and frequency, reduced reliance on storage, and lower costs through generator sharing, this system seems to be viable for both urban and peri-urban applications [11].

At a larger scale, a larger solar-powered pumping system in Riyadh, Saudi Arabia, intended to supply 245 m³ of water per day for irrigation has been reported. Their Simulink model included an 11.5 kW PV array, dual-stage DC–DC boost converters, and a 5.5 kW DC shunt motor driving a centrifugal pump. Incorporating perturb-and-observe MPPT control and PID-based regulation [14].

Likewise, in 2022, Jahanfar created a hybrid storage-based solar water pumping system that integrated a water tank with battery storage. PV arrays, a buck converter with MPPT, a two-level inverter, and a centrifugal pump were all included in their MATLAB/Simulink model.

For irrigation in Iran and similar areas, the system proved to be a viable substitute for traditional diesel-driven systems because to its dependable performance under load disturbances, temperature changes, and variations in irradiance [10].

Collectively, these studies underline the critical role of dynamic simulation in assessing real-time performance and stability of solar water pumping systems.

Methodology

Site Location

The site chosen for this research is a remote village and far end from grid connectivity approx. 15 km located in Kufri, Khushab, Pakistan. The site selected for this study is Kufri, a rural area located in the Khushab district of Punjab, Pakistan (32°32.8 °′N, 72°5.5′E), as shown in Fig. 1.

Fig. 1. Site location Google map [7].

Previous Design Summary Homer Pro

The initial design for Kufri was carried out using HOMER Pro tool. HOMER recommended an 8.75 kW PV array, 24 Trojan SPRE 12-225 batteries (48 V system) and a 7.11 kW inverter. The schematic layout of the proposed system, as modeled in HOMER, is presented in Fig. 2 [7].

Fig. 2. Schematic of design in homer pro.

This paper mainly focus on design and development of a dynamic model in MATLAB/Simulink. All System components models are described below.

Photovoltaic (PV)

As the main source of electrical power, the photovoltaic (PV) unit is the central component of the system. The cell, the module, and the array are the three stages of hierarchy at which PV technology can be characterized.

Photovoltaic Cell

At the cell level, the most widely used representation is the single-diode model, which is derived from semiconductor physics and basic electrical laws. The equivalent electrical circuit of this model is illustrated in Fig. 3, where the photovoltaic (PV) cell can be represented by the single-diode model, consisting of a photocurrent source and a diode.

Fig. 3. Equivalent circuit of a solar cell.

The output current $I$ is given as [15]:

$$\begin{array}{r}I=I_L-I_D-\frac{V+I{R}_s}{R_{sh}}\end{array}$$

where:

• $I_L$ = Light-generated current (photocurrent)

• $I_D$ = Diode current

• $R_s$ = Series resistance (internal losses)

• $R_{sh}$ = Shunt resistance (leakage current path)

• $V$ = Terminal voltage of the PV cell

• $I$ = Output current of the PV cell

In Simulink, the array consists of two modules in series and thirteen parallel strings, tested under an irradiance of 1000 W/m² and at an ambient temperature of 25°C. The I–V (current–voltage) and P–V (power–voltage) characteristics of the PV array (Canadian Solar Inc. CS6U-345M) presents in Fig. 4 and PV system voltage in Fig. 5 and electrical specification in Table I.

Fig. 4. I-V & P-V curves of PV array.

Fig. 5. PV voltage systems output in MATLAB for a change in solar input.

Table I. PV Pannel Specification

Parameter	Value
Module	Canadian Solar Inc. CS6U-345M
Maximum Power (W)	345.186
Cells per module (Ncell)	72
Open circuit voltage Voc (V)	46.4
Short-circuit current Isc (A)	9.56
Voltage at maximum power point Vmp (V)	38.1
Current at maximum power point Imp (A)	9.06
Temperature coefficient of Voc (%/deg.C)	−0.3101
Temperature coefficient of Isc (%/deg.C)	0.036297

Power Conversion

As illustrated in Fig. 6, the photovoltaic system is arranged in two stages. Initially, a DC–DC converter is used to regulate the PV output and to perform MPPT so that maximum energy can be extracted. After this, a three-phase voltage source inverter converts the regulated DC link into AC power, which is then used to operate the irrigation pump with an addition of step-up transformer.

Fig. 6. Existing topology.

Buck Converter (vout < vinp)

The buck converter is a DC–DC topology that steps down a higher input voltage to a lower, regulated output with high efficiency and low ripple. In this work, it is used to condition the PV output to meet the DC link requirement of 48 V as shown in Fig. 7 ensuring $V_o<V_i$. Voltage regulation is achieved by controlling the duty cycle $D$ of the IGBT switch.

Fig. 7. Achieving voltage regulation output in MATLAB.

The main components include an IGBT, a freewheeling diode, and an LC filter that smooths current and voltage. The switch is driven by a PWM signal, which modulates the conduction interval and hence the average output voltage.

The steady-state relations are given by [16]:

(1) $$\begin{array}{ccc}& V_o=D{V}_i& \end{array}$$

(2) $$\begin{array}{ccc}& L=\frac{V_o(V_i-V_o)}{\mathrm{\Delta }{I}_L{f}_s\mathrm{\Delta }{V}_o}& \end{array}$$

(3) $$\begin{array}{ccc}& C_{out}=\frac{\mathrm{\Delta }{I}_L}{8f_s\mathrm{\Delta }{V}_o}& \end{array}$$

where $\mathrm{\Delta }{I}_L$ is the ripple in inductor current. Based on these relations, an inductor value of 2.2 mH is selected. To further minimize ripple and improve transient response, a larger capacitor of 5.6 mF with an ESR of 1 mΩ is employed in the model. Fig. 8 shows the buck converter circuit.

Fig. 8. DC-DC buck converter.

Three-Phase DC-AC Inverter

In this paper, a three-phase inverter was built in MATLAB/Simulink to change a DC supply into AC output. The circuit uses six IGBT switches, each paired with a diode, and they are connected in a bridge form. For control, sinusoidal PWM was chosen. Three sinusoidal signals, shifted by 120° from one another, were compared with a triangular carrier to produce the gate signals. The simulated output is given in Fig. 9. It shows the expected three-phase AC waveforms and confirms that the inverter is working correctly, giving a balanced supply across the three phases.

Fig. 9. Simulation inverter output of three-phase AC waveforms.

Maximum Power Point Tracking (MPPT)

PV modules exhibit non-linear I–V and P–V characteristics influenced by irradiance and temperature, which makes maximum power extraction essential. MPPT techniques adjust the operating point of the PV system to ensure maximum energy transfer. Among various methods, the Incremental Conductance (INC) algorithm is widely applied because of its fast-tracking response and straightforward implementation. Since the panel terminal voltage is adjusted based on its value in relation to the maximum power point voltage, the INC technique is more effective than the P&O approach. Thus, the features of the solar panel have no bearing on this technique. For the left of MPP, power increases with voltage, while for the right of maximum power point, power decreases with voltage. This condition can be expressed quantitatively [17]:

(4) $$\begin{array}{r}\text{P}=\text{V}\text{x}\text{I}\end{array}$$

(5) $$\begin{array}{r}\frac{dP}{dV}=\frac{d(I\ast V)}{dV}=I+V\frac{dI}{dV}\end{array}$$

(6) $$\begin{array}{r}\frac{dI}{dV}=-\frac{I}{V}\end{array}$$

(7) $$\begin{array}{r}\frac{dP}{dV}=0\end{array}$$

(8) $$\begin{array}{r}\frac{dI}{dV}=-\frac{I}{V}\end{array}$$

• Converter adjustment rule:

$$\begin{array}{cc}\frac{dI}{dV}>-\frac{I}{V}& \Rightarrow \text{Left of MPP }\to \text{increase duty cycle}\\ \frac{dI}{dV}<-\frac{I}{V}& \Rightarrow \text{Right of MPP}\to \text{decrease duty cycle}\end{array}$$

Fig. 10 illustrates the MPPT configuration for the photovoltaic (PV) system in Simulink.

Fig. 10. MPPT model in MATLAB.

Battery

A lead-acid battery bank rated at 48 V with a total storage capacity of 1350 Ah was implemented in MATLAB/Simulink to serve as the system’s energy backup and DC bus stabilizer. During periods of surplus generation, the batteries store the excess energy, while in times of shortfall they discharge to maintain supply. This process helps to hold the bus voltage within the required range. The simulation output shown in Fig. 11 indicates a voltage level maintained close to 48 V, demonstrating steady and dependable performance of the storage unit.

Fig. 11. Battery voltage result in MATLAB.

Step-Up Transformer

In this proposed model, the output voltage of the inverter has been raised from 33.94 V (line-to-line) to 380 V RMS at 50 Hz utilizing a three-phase, two-winding transformer. The grounded-wye (Yg–Yg) link between both windings is a common example of a balanced three-phase distribution configuration. According to the model, the transformer is a symmetrical system made up of three single-phase units, each of which has the same coupling core but is magnetically separated. The magnetization resistance and inductance were set at 500 pu to ensure minimal core losses and stable flux behavior. This configuration helps maintain voltage balance and provides proper isolation between the inverter and motor side, ensuring clean sinusoidal waveforms at the output.

Results and Analysis

The proposed solar water pumping system was simulated in MATLAB/Simulink under Standard Test Conditions (STC), corresponding to an irradiance of 1000 W/m² and a cell temperature of 25°C. The complete dynamic model is illustrated in Fig. 12, The complete system was modeled and simulated in MATLAB/Simulink to evaluate its dynamic performance. The PV array, DC–DC converter with MPPT control, battery storage, three-phase inverter, and step-up transformer were integrated to supply a balanced AC output. The transformer successfully steps up the inverter output, and the measured voltage and current waveforms demonstrate smooth sinusoidal characteristics with minimal distortion. Overall, the results validate that the proposed model can maintain voltage stability and balanced operation under dynamic conditions. The behavior of a three-phase inverter driving an AC motor through a Y-connected transformer.

Fig. 12. Complete model of solar water pump system.

A Three-Phase V–I Measurement block was used to record the output voltages of the inverter. The RLC branch has added a small capacitive reactive power of 1000 var to maintain system balance and improve the power factor. An RLC circuit with a grounded Y-connection, rated at 380 V RMS (phase-to-phase), and running at 50 Hz The transformer and motor were included to represent the inductive nature of an industrial drive system. The three traces (red: A–B, yellow: B–C, and blue: C–A) are sinusoidal with amplitudes close to ±500 V_peak and maintain a 120° phase shift. The simulated inverter–transformer configuration produced a balanced line-to-line voltage, sufficient to operate three-phase asynchronous (induction) motor such as a 5.5 kW submersible pump as shown in Fig. 13.

Fig. 13. Simulated phase-to-phase voltage.

Conclusion

This work presents the modeling and simulation of a solar-based pumping system integrated with a hybrid storage unit. The MATLAB/Simulink environment was used to evaluate system performance under changing solar and load conditions. Results demonstrate that the inclusion of hybrid storage not only ensures steady water output but also strengthens system reliability and reduces overall running expenses.

The simulation results also showed that the MPPT controller successfully tracks the maximum power point of the PV array, while the DC–DC converter and inverter stages maintain a stable DC bus and balanced AC output. These findings validate the suitability of the proposed system for irrigation purposes in Kufri, Khushab, Pakistan, where high solar potential and agricultural water demand make such systems highly beneficial. Although this model was designed for a specific region, the configuration can be applied to other areas with similar solar and irrigation requirements.

Conflict of Interest

The authors declare that there is no conflict of interest related to this research.