Sustainable Crop Irrigation with Renewable Energy: A Case Study of Lethbridge County, Alberta

Abstract

The agriculture sector is a major contributor to the economy of Alberta, Canada, accounting for almost 2.8% of the total GDP. Considering its importance, implementing efficient and cost-effective irrigation systems is vital for promoting sustainable agriculture in semi-arid regions like Lethbridge County, Alberta, Canada. Although irrigation is primarily carried out using the Oldman River and its allied reservoirs, groundwater pumping becomes a supplementary necessity during periods of limited surface water availability or droughts. This research investigates the potential of renewable energy resources, such as wind and solar energy, to meet the energy requirements for crop irrigation. The study begins by identifying and calculating the water requirements for major crops in Lethbridge County, such as wheat and barley, using the United Nations Food and Agriculture Organization’s CROPWAT 8.0 software. Subsequently, energy calculations were conducted to meet the specific crop water demand through the design of a hybrid energy system using Homer Pro 3.16.2. A technoeconomic analysis of the renewable hybrid system has been carried out to demonstrate the efficiency and novelty of the proposed work. Outcomes revealed that the proposed system is both efficient and economical in fulfilling the crop water requirement through groundwater pumping, promoting sustainable agriculture, and helping to ensure food security in the region.

1. Introduction

Rapid climate changes, exponential population growth, and urbanization are some of the major challenges nations face in the efficient management of water resources for crop irrigation. Water scarcity is considered the second-largest issue after climate change by scientists in this century [1,2]. An increase in the gap between water supply and demand on a global scale is an emerging concern in the upcoming years [3,4]. Almost 87% of water consumed directly helps in fulfilling approximately 40–45% of the world’s food requirements [5].

Similarly, crops play a vital role in the economy of Canada, i.e., 7% of Canada’s GDP and 11% of jobs are related to the agriculture sector [6,7,8]. Out of this 7% GDP, Alberta’s agriculture sector incorporates a significant share of 2.8% [9]. Wheat and barley are the two major crops of the Alberta region, accounting for 28.4% and 52.2% of Canada’s harvested crops, respectively [10]. In 2022, wheat and barley produced in Alberta were estimated at approximately 416 million bushels and 247.13 million bushels, respectively [11].

Water usage for irrigation purposes in southern Alberta mainly relies on the Bow, Oldman, and South Saskatchewan River sub-basins (SSRB). Population and economic growth have significantly increased the pressure on these basins, which resulted in the closure of additional water license allocations in 2006. The water shortage has further increased in recent years due to increases in temperature, greater evapotranspiration, and changes in the timing and amounts of precipitation, which may be a part of local global climate change [12]. Evapotranspiration (Eto) is an important parameter of the hydrological cycle and plays a vital role in water management concerning irrigated and rainfed agriculture [13]. With the increase in Eto, crop water requirement also increases in a linear trend to maintain the quality of crops [14,15]. The Penman–Monteith equation is employed to estimate the reference evapotranspiration (ETo), which is a key parameter for determining the crop water requirement (CWR). The CWR is derived by multiplying ETo with a crop coefficient (Kc) that varies depending on the crop type and growth stage. The United Nations Food and Agriculture Organization designed a software named CROPWAT based on this method to calculate potential Eto using various parameters, such as temperature, humidity, wind speed, and irradiance.

Lethbridge County is one of the major stakeholders in the agriculture sector of Alberta. Being a semi-arid area, it relies on water from the Oldman River for the irrigation of crops. Due to water reduction from the river basins, groundwater can play a supplementary role in fulfilling the remaining water demand of Lethbridge County. However, groundwater pumping on a large scale is an energy-intensive task. Conventional energy resources are not economical in comparison to renewables and are considered one of the major reasons for abrupt climate change. Renewable energy resources such as solar irradiance, wind, and biomass are needed to shift from conventional power sources to clean and environment-friendly sources for groundwater pumping [16]. Solar photovoltaic systems are considered one of the most economical energy sources due to the decreasing cost over time. Dependency on solar photovoltaic systems has significantly increased all over the globe, shifting reliance from conventional to renewable energy resources [17]. Similarly, as stated by the Canadian Wind Energy Association, wind energy has recently become the primary source of renewable energy generation in Canada. The National Energy Board predicts that wind energy could account for 27% of all new power production between 2017 and 2040. In Alberta, wind energy has become the second most significant new source of electricity, attracting 1.2 billion USD in private investment for five new wind energy developments in recent years [18]. These renewable and clean energy sources can be vital in providing cost-effective and environmentally friendly energy for groundwater pumping.

In an application of CROPWAT [19], the authors presented the water requirement of various crops in Peshawar, Pakistan. Wheat, sorghum, and millet crops were considered for the study. The study emphasized the necessity of irrigation water to produce high-quality crops, as the available rainwater falls short of meeting the calculated crop water requirements for the region. Similarly, in Sudan [20], authors presented a comprehensive analysis on the potential of wind- and solar energy resources-based hybrid systems for water pumping required for irrigation of the agriculture sector. Different combinations of renewables were considered in designing a hybrid system at 12 different locations in Sudan using the soil and weather data. The outcome revealed that optimal hybrid system selection mainly relies on the cost and availability of renewable resources on specific sites.

In Saudi Arabia [21], authors stated that the reduction in rainfall due to climate change has a severe effect on crop production globally, and in some arid and semiarid regions, groundwater resources are diminishing very rapidly, a real threat to the ecosystem and environment [22,23]. To compensate for these changes and ensure food security, motorization of irrigation systems can help in decreasing the impact of droughts. Renewable hybrid systems have been considered for providing energy requirements for irrigation in Shaqra, Saudi Arabia. Crop water requirements for three crops have been calculated using CROPWAT and Homer Pro has been used to design the hybrid system and perform its techno-economic analysis for the understanding of system behavior. The result showed that the hybridization of PV systems and wind turbine setups can help in ensuring the required energy for irrigation purposes in the vicinity. The authors discussed the renewable energy and irrigation nexus concerning various parameters in the area of northwestern India [24]. The study indicates that 72% of the tube wells are electrically powered and consume almost 40% of the total electric power in the area. This has significantly increased the irrigated area by 36% due to low losses and economic energy factors. Due to the dependency on renewable energy sources, the yield of the area discussed is comparatively more economical than the Uttar Pradesh, Bihar, and Gujarat states. Authors in [25] stated that agriculture consumes almost 70% of the water available globally, and its 60% share is being wasted in processes that require efficient water management to ensure food security. The study has been carried out to analyze the irrigation water requirement for the specific crops in the area of Shaheed Benazirabad District, Sindh. CROPWAT software designed by FAO has been used for the water requirement of the area crops. Outcomes revealed that the use of efficient tools such as CROPWAT 8.0 and CLIMWAT 2.0 are beneficial in efficient irrigation and increase productivity.

Likewise, in [26], the authors presented a study on the assessment of crop water requirements for Maize to effectively manage the irrigation setup. CROPWAT was used in the study to analyze the water requirement for the specific crop using 16 years of available data for various parameters associated with water requirement calculations. The output of the research revealed the daily, monthly, and annual requirement of water at different stages of growth to avail high-quality crops. The authors evaluated suitable solutions to reduce agricultural green water scarcity due to rapid climatic changes [27]. The study mentioned that rain-fed agriculture is incorporating almost 60% of the food production on a global scale, which is being affected greatly due to current environmental pollution and its adverse effects in the form of low ratios of rain and even droughts. Green water requirements have been assessed in case of non-availability of rainwater. The study revealed that the green water adaptation strategies have the potential to reduce food production loss for up to 100 million people.

Furthermore, incorporating an Energy Storage System (ESS) can be beneficial for a Hybrid Renewable Energy System (HRES) to meet the fluctuating power supply or in the absence of solar and wind energy generation [28]. In addition to the growing need for sustainable agricultural practices, effective coordination among energy, water, and crop management systems has become increasingly critical. Recent studies on sustainable supply chains emphasize the role of coordination contracts—such as two-part tariffs, revenue sharing, and quantity flexibility—in aligning stakeholder interests and improving operational efficiency under uncertain demand conditions. These synchronization mechanisms not only enhance profitability but also support environmental goals like carbon emission reduction. Drawing on these principles, this research explores how a coordinated, renewable-energy-based irrigation system can promote sustainability in agriculture by optimizing both water use and energy efficiency [29].

Keeping in view the limited research works available on the adaptation of renewable energy resources for fulfilling the crop water requirement, this research work focused on presenting a solution to overcome the surface water scarcity in Lethbridge County, Alberta, Canada. Initially, the FAO-designed CROPWAT software has been used to estimate the crop water requirement of two major crops (wheat and barley) in the region. Using this crop water requirement, the energy requirement has been calculated through empirical calculations for groundwater pumping. Next, a renewable resources-based hybrid energy system was designed and analyzed in a technical and economic sense using Homer Pro software to fulfill the energy requirement for the irrigation of crops through groundwater pumping. Results revealed that hybrid systems are efficient and viable solutions in fulfilling the energy requirements for irrigation by keeping the environment clean.

2. System Design and Working Methodology

The area selected for the proposed study is an agricultural farm in Lethbridge County, Alberta, Canada, located at coordinates 49.696° N, 112.845° W, with an approximate elevation of 910 m above sea level. The County spans an area of approximately 127.19 square kilometers, and slight variations in elevation exist due to topographical differences. The map of Alberta specifying the county location can be seen in Figure 1. The main objective in designing the energy system is to understand the load requirement of any system. The primary task of the proposed research work is to design a hybrid renewable energy system-based groundwater-pumped irrigation mechanism for the specific area in Lethbridge County, Alberta. The block diagram of the proposed system can be seen in Figure 2. As the energy requirement for irrigation is entirely based on the crop water requirement, the crop water requirement has to be identified for the major crops (wheat and barley) in the area and converted into the required energy based on the available area to be irrigated and the water table depth in the specific area to estimate the power required for the pumps for water extraction.

Figure 3 shows that, being a semi-arid area, Lethbridge County has had the driest to below normal precipitation accumulation in the last ten years on average. This fact has increased the requirement of reliance on groundwater pumping to preserve the surface water. Keeping in view the renewable resources’ abundance in the area, high solar and wind potential can be analyzed from the Alberta map as shown in Figure 4.

The outcome of the work reported here was to find the most economical solution for fulfilling the energy demand of groundwater pumping for the irrigation of crops, as calculated by the crop water requirement. For this purpose, net present cost has been considered as the objective function to efficiently optimize the designed renewable resources-based energy system. Figure 5 explains the working methodology in the flowchart adopted to achieve the goal of the study.

As can be seen in Figure 4, Lethbridge ranked among the areas having the highest solar irradiance as well as wind speed potential in the region.

2.1. Crop Water Requirement Calculation Using CROPWAT

The United Nations Food and Agriculture Organization designed the CROPWAT software to calculate the crop water requirement. It works on the principle of calculating ET_O using the Penman–Monteith model [33] with the help of several parameters, such as the type of crops, soil data, rain, and temperature data, etc. Based on these several factors as mentioned, crop water requirement from planting till harvest time can be calculated for specific crops by using Equation (1).

$$E{T}_C=k_c\ast E{T}_O$$

(1)

where ${ET}_O$ and $k_c$ represent reference evapotranspiration and crop coefficient, respectively. ${ET}_O$ can be calculated using the Penman–Monteith equation as shown in Equation (2).

$$E{T}_O={\displaystyle \frac{0.408\Delta \left(R_n-G\right)+\frac{900}{T+273}{u}_2\left(e_s-e_a\right)}{\Delta +\gamma \left(1+0.3442\right)}}$$

(2)

In the above equation, $R_n$ and G represent net radiation and soil heat flux density in MJ/m²/day, T, $e_s-e_a$ represent daily air temperature and vapor pressure deficit of air in temperature context (kPa), respectively. Here, e_s represents saturation vapor pressure at a given temperature and $e_a$ represents actual vapor pressure. Whereas, Δ and γ represent the slope of the saturation vapor pressure curve with respect to temperature and the psychrometric constant, respectively.

Crop water requirement refers to the water required to maintain the moisture in the soil lost due to evapotranspiration. However, while calculating the crop water requirement, all water sources need to be considered, such as rainfall or any deep seepage [30]. Keeping in view the different sources, the net irrigation water requirement can be calculated using Equation (3).

$$NIWR=E{T}_O-R_{eff}$$

(3)

In the above equation, $R_{eff}$ represents effective rainfall. As stated above, wheat (spring and winter wheat) and Barley crops have been selected for the proposed study in Lethbridge County, Alberta, Canada. The area selected for crop water requirement calculations is 1000 acres (405 hectares). The Lethbridge Airport weather station has been used to acquire the required weather data. Considered data from the weather station can be seen in

Table 1. Climatic Data from Lethbridge Airport Weather Station for the Study [34].

Month	Min Temp	Max Temp	Humidity	Wind Speed	Sun	Rad	ETO
Units	°C	°C	%	Km/h	Hours	MJ/m2/day	mm/day
January	−10.5	0.5	0	11	8.7	7.2	0.12
February	−8.3	2.8	0	11	10.1	11.2	0.17
March	−4.4	7.2	0	10	12.0	17.4	0.74
April	0.5	13.9	0	10	13.8	24.1	1.86
May	5.5	18.9	0	10	15.4	29.2	2.95
June	10.0	22.8	0	9	16.2	31.4	3.53
July	12.2	26.1	0	8	15.8	30.2	4.04
August	11.1	26.1	10	8	14.3	25.7	2.56
September	7.2	21.1	0	9	12.5	19.4	1.37
October	1.6	13.8	0	10	10.7	12.9	0.39
November	−4.4	5.6	0	11	9.1	8.0	0.15
December	−9.4	0.6	0	12	8.2	6.0	0.13
Average	0.9	13.3	1	10	12.2	18.6	1.50

.

It can be seen in Table 1 that the temperature stays between −10.5 °C and 26.1 °C. Furthermore, rainfall is another important factor in calculating the crop water requirement. Monthly rainfall data can be seen in

Table 2. Rainfall Data for the Proposed Study.

Month	Min Rain	Max Rain
Units	mm/month	mm/month
January	2.5	2.5
February	0.0	0.0
March	5.1	5.1
April	17.3	17.8
May	42.4	45.7
June	55.0	61.0
July	29.0	30.5
August	29.0	30.5
September	26.7	27.9
October	12.4	12.7
November	2.5	2.5
December	2.5	2.5
Total Annual RainfallAverage (mm/year)	224.3	238.7

.

The total rainfall in the area is calculated as 238.7 mm, which is low, indicating a semi-arid area. Irrigation as a secondary source of water has been utilized historically. Following the closure of issuing water licenses for the Oldman River due to limited supply reservoirs since 2006, groundwater pumping has been recommended. Crop water requirements concerning wheat and barley have been made using CROPWAT, as shown in

Table 3. Crop Water Requirement for Wheat.

Month	Decade	Stage	Kc	ETc	Eff Rain	Irr. Req.
Units			coeff	mm/season	mm/season	mm/crop season
November	2	Init	0.40	0.6	0.4	0.2
November	3	Init	0.40	0.6	0.5	0.1
April	2	Deve	0.93	17.3	5.2	12.1
April	3	Deve	1.02	22.7	8.2	14.5
June	2	Mid	1.10	38.9	20.1	18.8
June	3	Mid	1.10	40.8	16.7	24.1
July	2	Late	1.09	46.3	8.5	37.8
July	3	Late	0.83	33.6	8.9	24.7
Total				447.1	196.9	268.3

and

Table 4. Crop Water Requirement for Barley.

Month	Decade	Stage	Kc	ETc	Eff Rain	Irr. Req.
Units			coeff	mm/season	mm/season	mm/crop season
May	2	Init	0.30	0.8	13.2	0.0
June	1	Deve	0.64	21.3	18.1	3.1
June	2	Deve	0.96	33.9	20.1	13.7
July	2	Mid	1.10	46.8	8.5	38.3
July	3	Mid	1.10	44.7	8.9	35.8
August	2	Late	0.92	23.5	9.7	13.8
August	3	Late	0.62	14.7	9.4	5.3
Total				328.2	149.6	190.5

. This data has been acquired by incorporating the climatic as well as rain data and other parameters, such as soil data of the study area, in the CROPWAT software.

The water requirement for both crops has been found to be 268.3 mm/decade and 190.5 mm/decade, respectively. It can be seen from Table 3 and Table 4 that the crop water requirement for both crops is significantly higher than the available rainwater. The data obtained from the software depict that crops having longer periods of growth need a high amount of irrigation, and the elevated water requirement during the summer months shows the need for crops due to dry weather and less precipitation. Furthermore, water evapotranspiration and a decrease in soil moisture are some of the major reasons behind the increased water requirement during these dry months. A better understanding of irrigation schedules and proper planning ensures efficient irrigation management by controlling the timing and amount of irrigation. For this purpose, the total irrigation requirement and irrigation schedule for both crops can be seen in

Table 5. Total Irrigation Requirement for Wheat.

Parameters	Value
Total Gross Irrigation (mm/season)	465.1
Total Net Irrigation (mm/season)	325.6
Actual Water Use by Crop (mm/season)	446.4
Efficiency of Irrigation Schedule	100%
Total Rainfall (mm/season)	208.8
Effective Rainfall (mm/season)	208.8
Efficiency Rain	100%
Moist Deficit at Harvest (mm/season)	62
Actual Irrigation Requirement (mm/season)	237.6

,

Table 6. Irrigation Schedule for Wheat.

Date	19 April	11 July	8 September
Day	220	303	End
Stage	Dev	Mid	End
Rainfall (mm)	0.0	0.0	0.0
Ks (fraction)	1.00	1.00	1.00
ETa (%)	100	100	0.0
Depletion (%)	55	56	21
Net Irrigation (mm)	157.6	168.0	-
Deficit (mm)	0.0	0.0	-
Loss (mm)	0.0	0.0	-
Gross Irrigation (mm)	225.1	240.0	-
Flow (litre/sec/ha)	0.12	0.33	-

,

Table 7. Total Irrigation Requirement for Barley.

Parameters	Value
Total Gross Irrigation (mm/season)	351.4
Total Net Irrigation (mm/season)	246.0
Actual Water Use by Crop (mm/season)	327.6
Efficiency of Irrigation Schedule	100%
Total Rainfall (mm/season)	165.0
Effective Rainfall (mm/season)	155.8
Efficiency Rain	94.4%
Moist Deficit at Harvest (mm/season)	35.7
Actual Irrigation Requirement (mm/season)	171.7

and

Table 8. Irrigation Schedule for Barley.

Date	26 June	31 July	8 September
Day	46	81	End
Stage	Mid	Mid	End
Rainfall (mm)	0.0	0.0	0.0
Ks (fraction)	1.00	1.00	1.00
ETa (%)	100	100	100
Depletion (%)	57	55	16
Net Irrigation (mm)	124.6	121.4	-
Deficit (mm)	0.0	0.0	0.0
Loss (mm)	0.0	0.0	0.0
Gross Irrigation (mm)	178.1	173.4	-
Flow (litre/sec/ha)	0.45	0.57	-

.

The above table shows the detailed analysis with respect to the total gross and total net irrigation requirement of crops before and after the consideration of effective rainfall, i.e., 237.6 mm and 171.7 mm for the wheat and barley, respectively. The irrigation supply scheme for the annual crop season comprising these two crops can be seen in

Table 9. Irrigation Supply Scheme for Complete Season.

	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec
Precipitation Deficit
1. Wheat	0.0	2.5	11.6	35.0	56.6	61.5	94.9	6.0	0.0	0.0	0.3	0.0
2. Barley	0.0	0.0	0.0	0.0	0.0	39.9	104.8	42.1	0.0	0.0	0.0	0.0
Net Scheme Irr. Req.
in mm/day	0.0	0.0	0.2	0.5	0.8	1.7	3.2	0.8	0.0	0.0	0.0	0.0
in mm/month	0.0	1.1	5.2	15.7	25.5	49.6	100.3	25.9	0.0	0.0	0.1	0.0
in L/s/h	0.00	0.00	0.02	0.06	0.10	0.19	0.37	0.10	0.00	0.00	0.00	0.00
Irrigated Area (% of total area)	0.0	45.0	45.0	45.0	45.0	100.0	100.0	100.0	0.0	0.0	45.0	0.0
Irrigation Required for Actual Area (L/s/h)	0.00	0.01	0.04	0.13	0.21	0.19	0.37	0.10	0.00	0.00	0.00	0.00

.

Irrigation water requirements for both crops were observed for the entire season. The finding reveals that crops having crop cycles across the year need high irrigation capacity. Furthermore, crops having a hot summer season in their cycle require more irrigation to fulfill their water demand as well as maintain the moisture of the soil.

2.2. Energy Calculations to Meet Crop Water Requirement

Based on the calculations of crop water requirements for the selected crops using CROPWAT software in the above section, energy calculations have been performed to design the renewable energy hybrid power system for groundwater pumping and fulfilling the crop requirements. Keeping in view the irrigation requirement as well as the depth of the water table at the site, waterpower has been calculated using Equation (4).

$$W_P={\displaystyle \frac{\mathrm{Net} \mathrm{Irr}.\mathrm{Required}\left(\mathrm{mm}\right) \ast \mathrm{groundwater} \mathrm{depth} \left(\mathrm{m}\right)}{76}}$$

(4)

Here, $W_P$ represents waterpower in hp. Groundwater depth has been considered 31 m to calculate waterpower on site, i.e., 168 hp for a selected land area of 1000 acres. From this waterpower and pump efficiency (60%), we can calculate the brake horsepower using Equation (5).

$${\eta }_P={\displaystyle \frac{W_p}{B_P}}$$

(5)

Here, ${\eta }_P$ represents pump efficiency and $B_P$ represents brake horsepower. Using this formulation, brake horsepower (280 hp) has been calculated as necessary to evaluate the motor input power to extract groundwater. Using Equation (6), motor input power has been calculated as follows;

$${\eta }_m={\displaystyle \frac{B_p}{I_P}}$$

(6)

In this equation, ${\eta }_m$ represents motor efficiency, and $I_P$ represents motor input power, respectively. By considering the motor efficiency of 65%, the motor input power has been calculated, i.e., 431 hp. It has been evaluated from the above calculations that at least 325 kW input power is required by the motors to extract the same capacity of groundwater as required by the crops on 1000 acres for a complete season. It is to be kept in mind that, keeping in view the limitations of motor power capacity, land area can be further subdivided into different sections to efficiently irrigate the land as required.

3. Renewable Energy-Based Hybrid System Design Using Homer Pro

Homer Pro software, developed by the USA’s National Renewable Energy Laboratory (NREL) has been utilized to design and analyze the techno-economic analysis of the renewable energy-based hybrid power system at the site to provide the required energy for the extraction of groundwater. Levelized cost of electricity and net present value are two of the major parameters among many to assess the most optimized and feasible hybrid scenario using Homer Pro [35]. Based on the calculations in the above section and approximations keeping in view the fluctuating nature of the renewables, a modeled load along with different capacity components has been incorporated in the software, and it provides the best-optimized design based on various parameters such as LCOE, NPV, OPEX, CAPEX, and GHG emissions, etc. The main objective of this software is to evaluate the most efficient and optimized system design by combining the conventional and renewable energy resources available on-site [36].

The potential site for the proposed hybrid energy system is a farm in Lethbridge County, Alberta, Canada, as can be seen in Figure 6.

The proposed grid-tied hybrid system comprises a PV system having a capacity of 250 kW and 100 kW wind turbine systems, respectively. Similarly, as calculations performed in the above section, an AC load having a capacity of 325 kW was considered for the proposed system. It is to be kept in mind that a battery backup system is not recommended for the designed system, as electric motors (AC load) to be used for pumping would require high initial torque or high in-rush current, which would significantly degrade the BESS lifetime and prove to be highly uneconomical.

The average monthly load profile can be seen in Figure 7. Furthermore, the working flowchart of the Homer Pro software can be seen in Figure 8.

3.1. Wind Speed Data

Lethbridge County, Alberta, Canada, faces strong winds throughout the year. The county’s wind speed data was collected from the NASA Prediction of Worldwide Energy Sources [37]. The average wind speed in the area was found to be 6.63 m/s at a height of 32 m wind turbine hub and can be seen in Figure 9.

3.2. Solar GHI Data

Similar to wind speed data, typical locations in Alberta have more than 2500 h of sun yearly, exceeding most other regions in Canada [38]. The annual average radiation was estimated to be 3.65 kWh/m²/day. The clearness index has been found to be highest in July and lowest in December and can be analyzed in Figure 10.

3.3. Mathematical Modeling

3.3.1. Solar Photovoltaic System

Using the Osterwald method for calculating the PV output power is the most authentic method, and Equation (7) for this purpose can be seen below [39]:

$$Ppv=N\times Am\times \eta g\times Gt$$

(7)

Here, Ppv, N, and A_m, represent PV’s generated power, number of solar PV modules, and area of the module, respectively. Similarly,$\eta$ is the generator’s efficiency, and G_t is the global radiation on the tilted plane (W/m²).

3.3.2. Wind Turbine System

Wind turbine output power can be calculated using Equation (8) as follows;

$$Power \left(P\right)={\displaystyle \frac{1}{2}}\times \rho \times A\times V^3$$

(8)

In this equation, P represents power generated from WT in kW, v denotes the wind speed (m/s), whereas ρ represents the density of the air (kg/m³), and A is the cross-sectional area of the wind in m².

3.3.3. Performance Factors for Techno-Economic Analysis

Various parameters such as LCOE, NPV, CAPEX, OPEX, and GHG emissions play a fundamental role in techno-economic analysis to find out the most optimized and feasible hybrid renewable systems.

Levelized Cost of Energy

The LCOE is the average cost per kilowatt-hour of electrical energy produced by a specific system. LCOE is determined by the division of total annualized cost by the sum of L_primeAC and L_primeDC from Equation (9) in HOMER [37].

$$LCOE={\displaystyle \frac{Total Annualized Cost}{L_{PrimeAC}+L_{PrimeDC}}}$$

(9)

In this equation, L_primeAC represents alternating current primary load, whereas L_primeDC represents the direct current main load.

Net Present Value

The current total of all the expenditures incurred by the hybrid system during its specified useful life, minus the salvage value over that useful life, is its net present value. Costs to be considered in NPV are capital, replacement, operating, and maintenance costs, as shown in Equation (10).

$$NPC=\sum C_{Capital}+C_{Replace}+C_{Maintenance}-C_{Salvage}$$

(10)

In the above equation, C_capital denotes the initial investment, C_replace is the cost of replacement, C_maintenance is the cost of upkeep, and C_salvage denotes the cost of salvage.

Total Annualized Cost

The total value occurs throughout the project’s expected timeframe. This cost also provides the net present value that was connected to the component cash flow order. Using HOMER Pro software, the capital recovery factor is multiplied after the entire annualized cost has been determined using NPC.

3.4. Grid-Tied PV-WT Hybrid Power System

As discussed, grid-tied PV-WT systems have been considered while proposing the optimal solution for the site. In this configuration, the power generation system’s optimization was carried out on a combination of both PV and Wind Turbine systems to provide renewable and environmentally friendly energy, as shown in Figure 11.

4. Rationale for Selecting CropWat and HOMER Pro

The reason for establishing CROPWAT relates to the research goal, which is to calculate exact amounts of irrigation water for various crops in various climates. Through this link, water and energy features are integrated, since crop water demand can be converted into the irrigation amounts needed daily to cultivate the crops, which directs the calculations for the use of energy [40].

HOMER Pro models time-series simulations, load management, economies, and technologies in order to simulate both community-connected and standalone situations. It gives a clear estimate of LCOE, NPC, emissions, battery cycle use, and other important factors integral to making decisions [36].

4.1. Comparison with Aqua Crop

Table 10. CropWat vs. Aqua Crop.

Feature	CropWat	Aqua Crop
Output Focus	Water requirement (ET, IR)	Crop yield vs. water use
Complexity	Simple to moderate	Moderate to high
Energy Integration	Direct link to energy modelling	Not directly linked
Applicability	Irrigation and pumping design	Productivity and food security

shows the comparison of CropWat with other Crop modelling software. Even though Aqua Crop from FAO allows for more advanced modeling of crops and water supply, it does not put as much emphasis on energy. While Aqua Crop can be best for output predictions, when planning and scheduling irrigation, CROPWAT is proven to be more robust [40].

4.2. HOMER PRO Comparison with Alternatives

While PVsyst, RETScreen, SAM, and Hybrid2 were scrutinized, they did not meet the needs of this study for using several energy systems. The comparison of different modelling tools can be seen in

Table 11. HOMER Pro vs. Other Modeling Tools.

Software	Strengths	Limitations
HOMER Pro	Full hybrid modeling, economic optimization	Commercial license, learning curve
PVsyst	Detailed PV simulation	No hybrid system modeling, limited economic tools
RETScreen	Policy/feasibility assessments	No dispatch or battery modeling
SAM (NREL)	Advanced solar finance modeling	Limited hybrid configuration, steep learning curve
Hybrid2	Early hybrid modeling tool (NREL)	Discontinued and lacks modern support

.

5. Results and Discussion

5.1. Techno-Economic Analysis and Comparison of System Configurations

Different renewable-based hybrid system configurations have been considered while analyzing the performance of the proposed design. Moreover, the outcomes of these different combinations have been compared along with the power generation through a utility grid-only system to evaluate the techno-economic feasibility and environmental soundness based on various important factors. The techno-economic analysis and GHG emissions considering all system configurations are presented in

Table 12. Technoeconomic Comparison of Various Hybrid Combinations of the Proposed System.

Parameter	Unit	National Utility Grid	Hybrid (PV-Grid)	Hybrid (WT-Grid)	Hybrid (PV-WT-Grid)
LCOE	CAD/kWh	0.320	0.231	0.204	0.128
Net Present Value (NPV)	CAD (Million)	3.73	2.82	2.55	1.82
Capital Cost	CAD (Million)	-	0.205	0.216	0.422
Replacement Cost	CAD	-	9843	34,542	44,385
Maintenance Cost	CAD (Million)	3.73	2.61	2.31	1.38
Salvage Value	CAD	-	−1852	−19,467	−21,319
IRR	%	-	42.2	50.5	43
ROI	%	-	37.9	46	39
Payback Period	Yrs	-	2.63	2.17	2.3
Renewable Fraction	%	0.00	35.2	40	65.4
Energy Purchased	kWh	901,568	631,072	579,092	398,176
Energy Sold	kWh	-	44,400	64,378	198,358
Total Emissions	kg/yr.	573,469	401,413	368,349	253,272

.

As can be seen in the above table, shifting to renewable resources from conventional pollutant resources has become the need of the hour from technical, economic, and environmental perspectives. It can be analyzed from Table 10 that the LCOE of hybrid systems is less than the rates/kWh provided by the conventional Utility Grid. The most optimized and efficient hybrid combination of conventional and renewable resources has been observed as the Grid-tied PV-WT system, as it has a comparatively lower LCOE than the WT-Grid, PV-Grid, and Grid-only systems, i.e., 0.128 CAD/kWh in comparison to 0.204 CAD/kWh, 0.231 CAD/kWh, and 0.320 CAD/kWh, respectively. This PV-WT-Grid configuration is most optimized because due to multiple renewable options, renewable generation is available throughout 24-h. This higher renewable penetration in the system significantly reduced the unit costs and fuel consumption directly associated with the reduction in GHG emissions. Furthermore, renewable penetration in the hybrid combination of PV-WT is higher than in the remaining scenarios as well. Variability in the CAPEX and OPEX costs has been observed between different combinations of hybrid systems. The CAPEX includes capital and replacement costs, while the OPEX includes maintenance and fuel costs.

In Table 10, the comparison between capital costs shows that the most optimized configuration (PV-WT-Grid) has the highest capital cost. This is due to the inclusion of the cost of equipment such as solar PV panels, wind turbines, and converters at the beginning of the project lifecycle. The capital cost in other configurations is comparatively less; they are using one renewable source or none at the least.

The above comparative study highlights the importance of the utility grid configuration from a CAPEX perspective; however, the high OPEX costs (mainly comprising time-to-time operational and maintenance costs due to several factors such as environmental impact, wear and tear, etc.) exponentially reduce its importance in the sense of energy generation in comparison to renewables for compensation of energy demand. Moreover, renewable systems have longer operational lives, typically with a payback period that makes the overall renewable-source-based systems more economical. The payback period of the optimized grid-tied PV-WT hybrid system is almost 2.3 years, compared to the grid-tied WT system’s 2.17 years, owing to the higher capital costs.

From the above simulation results, it can be concluded that hybrid renewable energy systems are more efficient, cost-effective, and environmentally friendly in compensating for the load demand required by the groundwater pumping system for efficient irrigation of the crops throughout the season. The environmental impact is reduced in the proposed hybrid configuration because the renewable fraction has improved.

5.2. Sensitivity Analysis

Various system variables have a distinct impact on the system’s performance. Through sensitivity analysis, the study evaluates how the proposed system performs under different conditions, particularly focusing on carbon emission penalties and grid unit price variations. As illustrated in

Table 13. Simulation Results of System Configurations with Current Electricity Unit Prices and Future Forecast Prices.

Parameter	Unit	Grid Only Configuration	Hybrid (PV-WT-Grid) with Current Unit Price	Hybrid (PV-WT-Grid) with Forecasted Unit Price
LCOE	CAD/kWh	0.386	0.128	0.147
Net Present Value (NPV)	CAD (Million)	4.5	1.82	2.09
Operating Cost	CAD (Million)	0.35	0.108	0.335
Maintenance Cost	CAD (Million)	4.503	1.38	1.64
Renewable Fraction	%	-	63.8	63.8
ROI	%	-	39	45
IRR	%	-	43	49

. variation in grid unit prices has a significant effect on the performance of the overall system. Despite the significant annual increase of 5.27 cents/kWh in utility grid prices [41], integrating renewable energy in hybrid systems has lowered overall unit costs. This shift towards renewable generation has not only reduced greenhouse gas emissions but also mitigated the impact of higher costs associated with imported grid power. The presented sensitivity analysis proves the importance and authenticates the feasibility of the proposed renewable-based hybrid configuration from the future perspective as well.

Similarly, a technoeconomic comparison has been carried out by using the no carbon penalties and a 50 CAD/ton carbon penalty set by the Canadian Government in 2022, with a 15 CAD/ton increase annually from 2023 to 2030 [42] to achieve the net zero emission goals according to the Paris agreement. As can be seen in

Table 14. Simulation results of system configurations with current zero penalties on carbon emissions and a 50 CAD/ton penalty with 15 CAD increase for the coming years.

Parameter	Unit	Grid Only Configuration	Hybrid (PV-WT-Grid) with Zero Penalties	Hybrid (PV-WT-Grid) with Carbon Emission Penalties
LCOE	CAD/kWh	0.386	0.128	0.152
Net Present Value (NPV)	CAD (Million)	4.5	1.82	2.18
Operating Cost	CAD (Million)	0.35	0.108	0.136
Maintenance Cost	CAD (Million)	4.503	1.38	1.72
Renewable Fraction	%	-	63.8	63.8
ROI	%	-	39	47
IRR	%	-	43	51

. LCOE per unit has been increased from 0.128 CAD/kWh in the case of no penalties on carbon emissions to 0.152 CAD/kWh with carbon emission penalties. Similarly, operating costs, as well as maintenance costs, have been significantly increased due to the imposed penalties on carbon emissions, which directly impact the ROI and IRR ratios of the overall project.

5.3. Comparative Studies

In the proposed research study, the real-time irrigation energy demand of an agricultural farm in Lethbridge County, Alberta, Canada, has been analyzed to design and optimize a hybrid renewable energy system for sustainable and cost-effective water pumping. A comparison of the levelized cost of electricity (LCOE) for the case study with global averages from different regions is presented in

Table 15. Comparative Study with the Previous Studies.

Country	Location	RE Sources	LOCE CAD/kWh
Yemen	Shafar	PV/WT/DG/Battery/Converter	0.200
India	Silchar	PV/Biogas generator/Pumped hydro energy	0.690
Colombia	Puerto Estrella	PV/DG/Battery/Converter	0.680
KSA	Rafah	PV/DG/Battery/Converter	0.240
India	Amritsar	PV/WT/DG/Battery/Converter	0.230
South Korea	Busan	PV/WT/Battery/Converter	0.560
Canary Islands	Lanzarote	PV/DG/Battery/Converter	0.570
Canada	Lethbridge	PV-WT-Grid	0.128

. The comparative study shows the significance of the major reliance on the renewable sources in achieving required energy with low COE and GHG emissions. It can be seen that due to efficient selection of renewable input sources, the LCOE of the presented study is the lowest among all. Even though cost variation can be due to the equipment’s pricing in different regions, the right choice of the input sources affects it significantly. Furthermore, most of the studies previously conducted have utilized diesel generators as secondary backup sources before the grid, which increases the overall GHG emissions.

6. Conclusions

The proposed research study presented a techno-economically viable solution to fulfill the irrigation requirements of crops throughout the season. An agricultural farm in Lethbridge County, Alberta, Canada, has been considered as the case study. Initially, the crop water requirement was calculated using the CROPWAT software. Results revealed that 410 mm/acre of water is required for both crops throughout the season. Then, the calculated irrigation water requirement has been converted into the energy required to pump groundwater for the said purpose. Later on, a hybrid grid-tied renewable energy system was designed using Homer Pro to assess the feasibility of relying on renewables to fulfill the energy demand.

The results revealed that the integration of wind energy with solar resources can very well reduce the dependency on the utility grid electricity. The proposed study demonstrated that shifting from conventional resources to renewable energy technologies is necessary to gain reliable, clean electricity for residential, commercial, agricultural, or industrial loads. The proposed design of the hybrid system resulted in approximately 52% cost reduction of the overall system. Similarly, the renewable energy penetration of the hybrid system is almost 65.4% highlighting the environmentally friendly nature of the proposed design while having a quite low LCOE of 0.128 CAD/kWh. The outcomes revealed that an optimized and efficient energy management system and irrigation scheduling are required to fully utilize the benefits of renewables throughout the day.

Keeping in view the application of renewables in the agriculture sector, a multidimensional approach with higher agricultural loads (farm machinery), as well as other renewable resources such as bioenergy, must be explored to capitalize on the higher benefits from this agro-energy integration. Some of the key findings of the research work are as follows;

• Renewable energy sources, such as wind and solar, can effectively meet energy demands for irrigation, reducing reliance on traditional grid electricity.
• A hybrid renewable energy system for agricultural irrigation is both cost-effective and efficient, with significant cost reductions and lower environmental impact.
• High renewable energy penetration reduces carbon footprints, demonstrating the environmental friendliness of renewable energy-based systems.
• Adoption of renewable energy for groundwater pumping ensures a reliable water supply for crops, contributing to food security in drought-prone regions.

Government incentives and policies can play a crucial role in influencing the agricultural sector’s adoption of renewable energy technologies. Some of the key effects are mentioned below:

• Incentives like subsidies, tax credits, and grants lower the upfront costs of renewable energy systems (e.g., solar panels, wind turbines, or biomass). This makes renewable energy more accessible and affordable for farmers, who may otherwise find the initial investment prohibitive.
• Interest-free loans or low-interest financing options can further reduce the financial burden, making renewable energy investments more feasible for agricultural operations.
• Policies that reward sustainable farming practices or renewable energy adoption, such as carbon credits or emissions trading schemes, incentivize farmers to invest in technologies that reduce their environmental impact.
• Government-funded research programs and extension services can educate farmers about the benefits of renewable energy, available incentives, and the technical aspects of implementing renewable solutions. This helps increase awareness and adoption.
• Governments can create favorable regulatory frameworks that support renewable energy deployment, such as easing grid access for farm-based energy producers or allowing for net metering where excess energy can be sold back to the grid.