Techno-Economic and Environmental Assessment of Solar Photovoltaic Systems for Dairy Farms: A Comparative Analysis

Abstract

Integrating renewable energy into agricultural systems has emerged as a critical strategy for reducing the sector’s greenhouse gas emissions. However, limited research has examined how farm-specific operational patterns influence the techno-economic performance of solar photovoltaic (PV) systems. This study presents a comprehensive techno-economic and environmental assessment of grid-connected solar PV systems for two types of dairy farm operations: spring-calving and winter-calving. Using detailed farm-specific energy consumption profiles and solar irradiance data, system performance was evaluated under Ireland’s policy framework, including the Targeted Agricultural Modernisation Scheme grant and the Clean-Export tariff. The spring-calving operation achieved superior economic performance (payback period: 3.25 years; levelised cost of electricity: EUR 0.091/kWh) compared to the winter-calving operation (3.83 years; EUR 0.099/kWh). This superior performance is due to better seasonal alignment between solar generation and electricity demand. Sensitivity analysis reveals solar irradiance, grid electricity cost, and grant funding as main economic viability influencing factors. Environmental analysis demonstrates CO₂ emission reductions of 77% for spring-calving and 61% for winter-calving operations. The findings demonstrate that solar PV systems are both economically viable and environmentally beneficial for dairy farms. These results provide actionable insights for farmers and policymakers seeking to promote clean energy adoption and emission reduction in agriculture.

1. Introduction

The European Union (EU) aims to achieve net-zero emissions by 2050, with member states collectively reducing emissions by 9% in 2023 [1]. Within this framework, Ireland targets a 22–30% reduction in agricultural emissions, a 62–81% reduction in the electricity sector, and an 80% renewable electricity share by 2030 relative to 2018 levels [2]. The agricultural sector is the largest contributor to Ireland’s greenhouse gas (GHG) emissions, accounting for 38.4% of the national total [3]. This sector faces a dual energy challenge: rising fossil-fuel-based electricity costs and the need to adopt clean, sustainable, and low-carbon energy sources. Dairy farming is among the most energy- and emission-intensive sub-sectors, requiring substantial energy for milking, milk cooling, and water heating. This high energy demand makes dairy farms highly suitable for renewable energy integration. In particular, solar photovoltaic (PV) systems can substantially offset this energy burden, with recent studies reporting short payback periods (PBPs) and significant carbon dioxide (CO₂) emission reductions [4]. However, there are variations in the adoption of these systems across regions due to local climatic conditions, policy frameworks, and economic incentives [5]. Solar PV systems hold immense potential in advancing a shift toward renewable energy sources, particularly in tandem with supportive policy environments. By harnessing energy from the sun, solar PV technology offers a sustainable, clean, and low-carbon solution to meet growing energy demands while reducing reliance on fossil fuels. The widespread deployment of solar PV systems not only contributes to climate change mitigation but also strengthens regional energy security. Integrating solar PV systems into the agriculture sector (dairy farming) demonstrates scalability and the potential to deliver both environmental and economic benefits.

Despite these advantages, comprehensive solar PV assessments in the dairy sector remain limited, thereby demonstrating a necessity for this study. This research is critical in order to contribute to some research gaps evident in this area. In particular, the impact of different seasonal calving patterns (such as spring- and winter-calving) on techno-economic viability is underexplored. Such operations exhibit distinct energy-use profiles and seasonal dynamics, which are important issues to consider when modelling solar PV systems as they strongly influence renewable energy integration strategies. In addition, existing studies tend to focus on general agricultural applications without differentiating between dairy farms operating in distinct locations in temperate climates [6,7,8]. Furthermore, no prior study has examined the combined influence of a specific country’s policy landscape, including time-of-use (ToU) electricity pricing, export tariff, and grant funding, together with seasonal variations in farm energy demand. The methodology adopted in this study addresses these research gaps by modelling solar PV and battery systems tailored to climatic conditions, policy environment, and the specific energy use characteristics of both spring- and winter-calving dairy operations. Essentially, this study models the technical requirements of solar PV and battery systems suitable to meet the specific energy needs of real-life case farms that adopt contrasting dairy operations (spring- and winter-calving) and function under distinctive climatic conditions (varying location-specific solar irradiance data). These systems are then used to assess the economic and environmental impact of adopting them, based on the policy landscape (ToU electricity pricing, export tariff, and grant funding) in existence. No prior study has examined all these parameters within the same study, thereby demonstrating the critical research gap that this study addresses.

In summary, the main objective of this study is to present a comprehensive techno-economic and environmental assessment of grid-connected solar PV systems for two types of dairy farm operations: spring-calving and winter-calving. This is undertaken through a detailed assessment of the viability of solar PV integration across these contrasting operational models. Additionally, a comprehensive sensitivity analysis is performed to assess the impact of key financial and technical parameters on system outcomes. The contributions of this study include the following:

• It is the first comparative assessment of solar PV system performance for spring- and winter-calving dairy operations internationally.
• It develops a comprehensive evaluation framework integrating all relevant parameters, including: climatic data, dairy farm energy profiles, and policy incentives (capital grants, export tariff, and ToU pricing).
• It quantifies the environmental benefits (CO₂ emission reductions) for both calving operations.
• It determines key techno-economic factors by conducting a comprehensive sensitivity analysis for effective system performance.

The findings from this study offer actionable insights for farmers, policymakers, and stakeholders to accelerate renewable energy adoption and optimise solar PV integration across diverse dairy operations. Such action will contribute to many sustainable development goals (SDG’s), most specifically affordable and clean energy (SDG-7), and the development of economically viable farm enterprises will assist in combating zero hunger, achieve food security and promote sustainable agriculture (SDG-2). This paper is structured as follows: Section 1 introduces the background, policy incentives, and the literature; Section 2 describes the materials and methods; Section 3 presents the results and discussion; and Section 4 concludes this paper and outlines future research directions.

1.1. Policy Context: Renewable Energy and Agriculture

Many countries promote solar PV adoption in the agricultural sector through financial incentives under targeted policy schemes [9]. As shown in

Table 1. Summary of grant support schemes for solar PV adoption in agriculture internationally.

Country	Main Program(s)	Grant Level and Conditions
United Kingdom	Improving Farm Productivity (IFP) Grant	Up to 25% of eligible costs; minimum application GBP 15,000 and maximum GBP 100,000 for solar-only projects.
Ireland	Targeted Agricultural Modernisation Scheme (TAMS-III)	Up to 60% capital grant for solar installations; investment ceiling of EUR 90,000 per farm.
United States	Rural Energy for America Program (REAP)	Grants cover 25–50% of total project costs under the Inflation Reduction Act (IRA).
Canada	Agricultural Clean Technology (ACT) Program	50% funding for eligible clean energy project costs; up to 75% available for under-represented groups.
New Zealand	Solar on Farms Initiative	Up to 20% support for PV panels and 40% for inverters/batteries; maximum NZD 200,000 per farm.
Spain	CAP + European Recovery and Resilience Facility (RRF)	Farmers receive 30–40% support for land hosting solar installations.

, grant levels range from 20% (New Zealand) to 60% (Ireland), reflecting varying national strategies.

In Ireland, the renewable energy policy landscape includes several overlapping initiatives targeting different system scales and user categories. The Targeted Agricultural Modernisation Scheme (TAMS-III) offers up to 60% capital grant aid for solar PV systems (maximum investment ceiling of EUR 90,000 per farm). The Clean-Export Tariff (CET) allows small-scale generators (6–50 kWp) to receive EUR 0.15–0.25/kWh for surplus electricity exported to the grid (contracts up to 15 years). Recent updates permit TAMS-funded systems to also access CET payments, improving project returns [10]. Together, these schemes reduce capital and operating costs, thereby enhancing feasibility for dairy operations. Other supports including the Renewable Electricity Support Scheme (RESS), Microgeneration Support Scheme (MSS), and Non-Domestic Microgen Grant (NDMG) exist but fall outside this study’s scope. TAMS-III and CET provide the most relevant framework for assessing solar PV feasibility in Irish dairy farming.

1.2. Literature Review

The existing literature on solar PV in dairy farming can broadly be categorised into three main areas: technical, economic, and techno-economic studies. Technical studies primarily examine system performance, energy output, and reliability. In Brazil, a study [11] evaluated the performance of solar PV systems for small, medium, and large dairy farms under different solar radiation scenarios. The study concluded that although performance was dependent on climate, herd size, and panel area, solar PV generation met the electricity requirements for milking operations. Similarly, a prior study [12] assessed the performance of standalone solar PV systems on large dairy farms in temperate climates of New Zealand. The economic analysis focused on the financial viability of solar PV adoption without extensive technical modelling. Economic studies often focus on the financial viability of solar PV adoption without extensive technical modelling. In Tunisia, a study [13] compared three milk-cooling configurations (no cooling, grid-powered, and solar-powered) where solar PV systems were found to be the most cost-effective based on internal rate of return (IRR), net present value (NPV), and PBP indicators. Additionally, a study in Germany [14] showed that smaller PV systems optimised for summer use achieve higher self-consumption and lower PBPs due to low CET rates and seasonal alignment of generation and demand.

Techno-economic studies combine performance and cost analysis, and provide a more comprehensive understanding. For instance, a study [15] in India demonstrated that an off-grid solar PV and battery system reliably supplied 9.5 MWh annually at a cost of USD 0.0259/kWh, highlighting its competitiveness over fossil-based electricity. In Germany, a study [16] evaluated a hybrid solar PV-thermal system that reused heat from milk coolers and achieved a PBP below six years with a solar thermal fraction of 52%. Other comparative studies in Turkey and Brazil [17,18] confirmed that solar PV systems can reduce both energy costs and carbon emissions for dairy operations if they are techno-economically optimised. Recently, environmental considerations on solar PV adoption have become increasingly prominent [19]. For example, some studies [4,20] advocated for incorporating carbon footprint metrics as sustainability indicators alongside techno-economic benchmarks. Similarly, an open-source techno-economic optimisation model was adapted to analyse a hybrid solar PV biogas polygeneration plant serving a cooperative of 30 small dairy farms in [21]. The model optimised electricity, refrigeration, biogas use, and fertiliser recovery, yielding an energy cost of USD 0.044–0.070/kWh and CO₂ savings between 109 and 127 tonnes annually. This study emphasised the importance of simultaneous thermal and electrical energy supply systems in Bolivia. Similarly, another study in Pakistan [22] evaluated hybrid solid oxide fuel cell with solar PV and battery storage for a rural dairy farm. The authors reported 328 MWh/year of electricity and 513.6 MWh/year of thermal energy, with a PBP under 5 years. Their system reduced CO₂ emissions by up to 41% compared to conventional fossil fuels. Despite a growing number of studies, research on comprehensive, multi-dimensional evaluation remains limited, particularly that tailored to regional contexts.

While most existing studies focus on warmer or high-irradiance countries such as Brazil, Tunisia, India, Turkey, and Germany, research tailored to the Irish context remains limited. Few studies have examined solar PV adoption under Ireland’s temperate climate conditions. For instance, a study [23] applied Q-learning for battery management to reduce grid imports on Irish dairy farms but did not include economic or environmental assessments. Similarly, a study [24] developed an agent-based model to analyse investment behaviour under varying electricity prices and capital costs, yet excluded the influence of CET and grant schemes. Recently, another study [4] evaluated 11 Irish dairy farms to assess PV-driven cost savings and GHG reductions. That study reported average PBPs of 5 years for small systems and 8.5 years for larger installations, with GHG intensity reduced by 29%. However, that analysis relied on a cycle-charging dispatch process to charge batteries from grid electricity, a strategy that can only partially maximise the environmental benefits of solar energy utilisation.

Although previous studies demonstrate the potential of solar PV in dairy farming, key knowledge gaps remain. Understanding how seasonal calving patterns, local climatic conditions, and national energy policies influence solar PV system performance and investment feasibility is crucial. Including all these parameters allows for a comprehensive techno-economic and environmental assessments of solar PV integration in dairy farming. The configurations, key parameters, and limitations of the reviewed studies are summarised in

Table 2. Overview of existing solar PV studies for dairy farm applications.

Reference & Year	Location	System Components	Tech.	Econ.	Env.	Limitations
[16], 2023	Germany	PV, Grid	✓	✓	×	Assumes full PV utilisation without battery or grid export, which may overestimate savings.
[17], 2022	Turkey	PV, Grid, Batteries	✓	✓	×	Time-of-use tariffs not included; analysis assumes constrained optimal conditions.
[15], 2024	India	PV, Batteries	✓	✓	✓	Only off-grid scenario analysed; grid-integration impacts not evaluated.
[7], 2020	Ireland	PV, Grid	✓	✓	✓	Battery storage not evaluated; analysis limited to direct PV-grid integration without energy storage optimisation.
[4], 2024	Ireland	PV, Grid, Batteries	✓	✓	✓	Night-time grid charging assumed cost-effective without comparing to solar charging.
[24], 2023	Ireland	PV, Grid	✓	✓	×	Impact of export tariff not analysed.
[21], 2023	Bolivia	PV, Biogas, Thermal	✓	✓	✓	PV degradation and payback period not analysed.
[22], 2024	Pakistan	PV, Biogas, Batteries	✓	✓	✓	Only off-grid analysis; not suitable for modern high-demand dairy operations.

.

Table 2 highlights that of the prior studies that have examined solar PV for dairy farm applications, few have combined a technical, environmental and economic assessment in their analysis. While all the identified studies combine a technical and economic assessment, the absence of the associated environmental impact is noteworthy in many studies. Table 2 also underscores that, of the prior studies that have combined a technical, environmental and economic assessment, significant limitations are evident. It is also important to highlight that only one of the prior studies that have combined a technical, environmental and economic assessment has adopted a methodology including battery, grid and solar PV in its system components. In this context, the authors of the current study argue that more research adopting such a methodology is warranted and, in doing so, a critical research gap is addressed.

2. Materials and Methods

This study conducts a techno-economic and environmental assessment of solar PV systems for dairy farms. The methodology involves selecting representative sites, assessing solar resource availability, and analysing electricity consumption profiles of typical farm operations. A solar PV system with battery storage is then modelled under local conditions, incorporating an operating strategy that prioritises on-site consumption and optimises energy storage. System feasibility was assessed using technical, economic, and environmental indicators. A sensitivity analysis was also conducted to test the effect of changes in solar radiation, equipment cost, electricity tariffs, grant support, and export levels on project outcomes. This approach allowed for an evaluation of how economic and policy variations influence the performance and viability of solar PV integration in dairy farming. This approach provides valuable insights into the robustness and adaptability of solar PV and battery systems across varying operational conditions. For example, as evident in Table 1, the level of grant funding can vary between countries and the sensitivity analysis conducted allows the impact of such variations to be assessed.

2.1. Site Selection, Solar Resource, and Load Analysis

Two representative dairy farm operations in western Ireland were evaluated: a spring-calving farm at Ballymoe, County Galway (53.6916° N, −8.4897° W), and a winter-calving farm at Athenry, Co. Galway (53.2787° N, −8.8506° W). These sites reflect common calving operations in Irish dairy farming. The spring-calving operation utilises an automated milking and grazing management system, which raises electricity demand during peak milking periods. It supports 80 cows under a largely pasture-based regime. The winter-calving operation, with 74 cows, uses robotic milking with continuous indoor housing during colder months, increasing electricity demand. Monthly energy consumption profiles for both operations are provided in

Table 3. Monthly electricity consumption (kWh) of case dairy farms.

Month	Spring-Calving Operation (kWh)	Winter-Calving Operation (kWh)
January	1993	2191
February	2800	4399
March	2962	4709
April	3284	2873
May	4955	2972
June	4285	2999
July	3111	3098
August	3635	2477
September	2872	2399
October	2388	3272
November	2700	3167
December	1970	2265
Total	36,955	36,821

. The consumption profiles based on actual electricity bills data were analysed and scaled to monthly values due to varying billing periods. Major energy-consuming operations common to both opeartions include milking machines, milk cooling, water heating, water pumps, manure scrapers, and lighting. Despite differences in calving operations and technology integration, the annual energy consumption of both operations is comparable, with seasonal variations corresponding to their distinct dairy management cycles.

Monthly solar irradiance data for both locations were obtained from the NASA POWER database (https://power.larc.nasa.gov, accessed on 12 February 2025), which provides reliable satellite-based solar resource estimations. Average global horizontal irradiation for each site is shown in Figure 1. The Athenry site receives slightly higher annual solar irradiance than Ballymoe, but both exhibit similar seasonal patterns. The irradiance levels are highest between May and August, and lowest from November to February, with moderate values during the spring and autumn months (March/April/September/October). This study employs monthly average electricity consumption and solar generation data for two key reasons. First, the analysis focuses on long-term economic viability over the 25-year system lifetime, where annual and seasonal trends dominate financial outcomes more significantly than intra-day variations. Monthly resolution adequately captures the seasonal alignment—or misalignment—between solar generation and farm electricity demand patterns, which is the primary driver of economic performance differences between spring-calving and winter-calving operations. Second, the monthly approach is consistent with the data availability from actual farm electricity bills, which are typically issued monthly. This reflects the real-world information accessible to farmers when making investment decisions, enhancing the practical applicability of this study. For studies requiring detailed analysis of short-term grid interactions, peak demand management, or real-time control strategies, hourly or sub-hourly resolution is more appropriate, as demonstrated in [25]. However, for the strategic investment decisions and policy evaluations that form the core objectives of this study, monthly resolution provides an appropriate balance between analytical rigor and practical applicability. This analysis of solar availability, alongside dairy operation usage patterns, provides the foundation for evaluating the performance of solar PV and battery systems under Irish dairy farm conditions.

2.2. Proposed System Configuration

The proposed solar PV system for both operations is illustrated in Figure 2. It consists of three main components: a solar PV array, a battery bank, and an inverter. The battery system mitigates the intermittent nature of solar generation, while the inverter converts direct current (DC) from both the solar PV array and the battery into alternating current (AC) suitable for farm operations. As the dairy farms are connected to existing low-voltage grid infrastructure, there is no requirement for additional high-voltage conversion equipment, simplifying system integration and reducing installation costs. The arrows in Figure 2 indicate the direction of power flow. For the battery bank, the bidirectional flow represents both charging and discharging operations. Similarly, the two-way arrows at the grid interface represent both grid import (electricity drawn from the grid to meet the demand) and grid export (surplus solar PV energy sent back to the grid). It is assumed that the solar PV panels will be mounted on the rooftops of existing farm buildings, thereby optimising space utilisation, eliminating the need for additional land, and protecting the panels from damage by livestock. Compared to ground-mounted systems, rooftop installations offer a more practical and seamless integration with the existing farm infrastructure [26].

2.2.1. Solar Photovoltaic Array

Solar PV panels capture solar radiation and convert it into electrical energy, which can be estimated using the following equation adapted from [27,28]:

$$E_{\mathit{Panel}}=G\times A\times \eta$$

(1)

where G is the solar irradiance in kWh/m², A is the solar panel area in m², and $\eta$ is the panel efficiency. The total output of the solar PV array comprising multiple panels is given in Equation (2):

$${\mathit{E}}_{\mathit{PV}}=N_{\mathit{Panels}}\times E_{\mathit{Panel}}$$

(2)

where ${\mathit{N}}_{\mathit{Panels}}$ represents the total number of solar panels in the system as determined according to the total energy required on the farm.

2.2.2. Battery Bank

As solar PV generation and on-farm demand do not always coincide, particularly during evening peaks, battery storage is essential for improving self-consumption and reducing grid dependence. The total battery capacity is estimated using Equation (3):

$$E_{Bat}={\displaystyle \frac{min(E_{PV}\times f)}{\eta }}$$

(3)

where $E_{\mathit{Bat}}$ is battery capacity (in kWh), $\mathit{f}$ shows the fraction of solar PV energy allocated to battery storage ($0\le f\le 1$), and $\eta$ is the round-trip efficiency of battery storage. Equation (3) allows the battery bank to be sized in proportion to the capacity of the solar PV system and the intended allocation of PV energy for storage. However, the energy stored in the battery bank varies with the charging and discharging cycles. Charging occurs when solar PV generation exceeds demand, and discharging occurs when the load surpasses solar PV production. The charging and discharging energies are constrained by the maximum allowable battery capacity. This behaviour is adapted from [29] and described in Equations (4) and (5).

$$E_{Bat,ch}=\left\{\begin{array}{cc}{E}_{Bat,max},\hfill & \mathrm{if}(E_{PV}-E_{LD})>E_{Bat,max}\hfill \\ E_{PV}-E_{LD},\hfill & \mathrm{if}0<(E_{PV}-E_{LD})\le E_{Bat,max}\hfill \\ 0,\hfill & \mathrm{otherwise}\hfill \end{array}\right.$$

(4)

$$E_{Bat,dis}=\left\{\begin{array}{cc}{E}_{Bat,max},\hfill & \mathrm{if}(E_{LD}-E_{PV})>E_{Bat,max}\hfill \\ E_{LD}-E_{PV},\hfill & \mathrm{if}0<(E_{LD}-E_{PV})\le E_{Bat,max}\hfill \\ 0,\hfill & \mathrm{otherwise}\hfill \end{array}\right.$$

(5)

where $E_{Bat,ch}$ and $E_{Bat,dis}$ represent battery charging and discharging energy, respectively, $E_{LD}$ is the load demand, and $E_{Bat,max}$ is the maximum permissible charge/discharge per cycle. This formulation ensures that the battery operates within its technical constraints while maximising the utilisation of surplus solar PV energy to minimise grid dependency. Equations (3)–(5) represent monthly-averaged energy flows rather than time-series state-of-charge (SoC) tracking [29]. This simplified approach has some limitations as it cannot capture daily charge–discharge cycles and SoC variations. However, the $E_{Bat,max}$ constraint prevents energy throughput overestimation, and monthly aggregation adequately captures cumulative system transactions (energy supply, energy storage and load transactions) over the operational lifetime. Time-series SoC modeling would be necessary for battery degradation analysis or real-time dispatch optimisation. For the comparative techno-economic assessment presented here, the monthly energy-balance approach provides computational efficiency while maintaining adequate accuracy for the study objectives.

2.2.3. Inverter

The inverter converts DC power from the PV array and battery into AC electricity for on-site use. The AC energy output is given in Equation (6).

$$E_{\mathit{Inv}}=E_{\mathit{DC}}\times \eta$$

(6)

where $E_{\mathit{Inv}}$ is the energy output after conversion, $E_{\mathit{DC}}$ is the total DC input, and $\eta$ is the inverter efficiency (in percentage).

2.2.4. Renewable Energy Fraction

The renewable energy fraction (REF) represents the proportion of total electricity demand to be met by renewable sources such as solar PV array and battery bank. In hybrid systems, REF serves as a key metric for sizing renewable system components as outlined in [30] and expressed in Equation (7).

$$\mathit{REF}=\left({\displaystyle \frac{E_{\mathit{PV}}+E_{\mathit{Bat}}}{E_{\mathit{LD}}}}\right)\times 100$$

(7)

A REF of 0% indicates the entire load demand met by the grid without any contribution from renewables, whereas a REF of 100% represents complete renewable supply. Higher REF values require appropriately sized PV and battery systems to balance technical and economic feasibility. In this study, Trina Vertex S TSM-400DE09.08 solar panels from Trina Solar Co., Ltd. (Changzhou, China), a BYD B-Plus H battery module from BYD Company Ltd. (Shenzhen, China) and a Sofar-30KTLX-G inverter from Sofar Solar Co., Ltd. (Shenzhen, China) are considered. The specifications and costs of these components are considered from up-to-date market prices and a prior Irish study [4,31,32,33]. Capital cost assumptions are derived from publicly available supplier sources in euros based on 2025 market data. The Balance of System (BOS) costs, which cover mounting structures, cabling, meters and breakers, are also included, with reference to values reported in [34]. The technical and economic parameters incorporated into the simulations are given in

Table 4. Technical and economic parameters considered.

Component	Parameter	Value
Solar PV	Rated capacity (Wp)	400
	Solar PV efficiency (%)	20.8
	Initial/capital costs (EUR/kWp)	450
	Expected lifetime (years)	25
Battery	Rated capacity (kWh)	1.28
	Charging/discharging efficiency (%)	95.3
	Initial/capital costs (EUR/kWh)	570
	Replacement costs (After 10 years) (EUR)	570
	Expected lifetime (years)	10
Inverter	Rated power (kW)	30
	Inverter efficiency (%)	96.6
	Initial/capital costs (EUR/kW)	67
	Replacement costs (After 10 years) (EUR)	2000
	Expected lifetime (years)	10
Other Important Parameters	Project lifetime (years)	20
	Discount rate (%)	6.4
	Solar PV degradation rate (%)	0.6
	Balance of System costs (EUR/kWp)	200
	Clean-Export Tariff (EUR/kWh)	0.20
	Grid electricity cost (EUR/kWh)	0.27

.

2.3. Economic Criteria

In the literature, several criteria are commonly used to evaluate investments in renewable energy projects, including levelised cost of electricity (LCOE) and PBP [35,36]. Other studies have adopted NPV as an economic indicator [20]. However, in this study, the focus was on LCOE and PBP as they provide clear insight into cost competitiveness and investment recovery time. The LCOE expresses the average cost of electricity generation (EUR/kWh) over the system lifetime. It is obtained by dividing the discounted total lifetime costs by the discounted total energy output according to [37] given in Equation (8).

$$\mathit{LCOE}={\displaystyle \frac{\mathit{Total}\mathit{Lifetime}\mathit{Costs}}{\mathit{Total}\mathit{Lifetime}\mathit{Energy}\mathit{Production}}}=\sum _{n=1}^N{\displaystyle \frac{\mathit{Costs}/{(1+r)}^n}{\mathit{Energy}/{(1+r)}^n}}$$

(8)

where r is the discount rate, n is the project year, and N refers to system lifespan. The total lifetime cost comprises the initial capital investments, annual operation and maintenance (O&M) expenses, and component replacement costs. The detailed formulation is given in Equation (9).

$$\mathit{LCOE}={\displaystyle \frac{I_0+{\sum }_{t=1}^n\frac{O_t}{{(1+r)}^t}+\frac{R}{{(1+r)}^n}}{{\sum }_{t=1}^n\frac{E_t}{{(1+r)}^t}}}$$

(9)

where $I_0$ is the initial investment, $O_t$ is the annual O&M costs, and $R_t$ shows any replacement costs. The denominator accounts for the energy generated each year ($E_t$), with the values adjusted for the time value of money using the discount rate (r). The LCOE allows for a comparison of the economic viability of solar PV energy against alternative energy sources. While LCOE solely focuses on cost per unit of electricity, the PBP measures the period required to recover the initial investment through accumulated savings and export revenue. This discounted analysis is expressed in Equation (10).

$$\mathit{P}BP=\sum _{t=1}^n{\displaystyle \frac{{\mathit{Revenue}}_t}{{(1+r)}^t}}\ge I_0$$

(10)

where ${\mathit{Revenue}}_t$ represents the net annual savings or earnings generated by the system, $I_0$ is the initial investment cost, and r is the discount rate. In this study, savings result from reduced electricity purchases, and earnings arise from energy exported under the CET scheme.

2.4. Environmental Criteria

The reduction in CO₂ emissions related to electricity consumption is a widely recognised environmental measure [36,38]. In this study, the analysis quantifies avoided emissions from displaced grid electricity and estimates the system’s lifetime contribution to GHG mitigation within the Irish dairy farming context.

2.5. System Operational Framework

The operational framework, as illustrated in Figure 3, outlines the structured process used to evaluate the technical, economic, and environmental performance of solar PV systems for both spring-calving and winter-calving operations. The framework begins with the collection of all required input data, including solar irradiance, detailed farm-specific energy consumption profiles, component efficiencies, capital and operating costs, and related policy schemes such as CET, ToU electricity prices, and applied grant funding (TAMS-III). Using these inputs, simulations estimate lifetime solar PV generation, battery operation (charging and discharging), and associated grid interactions. The operating strategy prioritises on-site solar PV utilisation to meet the farms’ demand. Any surplus (PV surplus 1) charges the battery until it is full. The additional excess electricity, if available (PV surplus 2), is exported to the grid at the applicable tariff (CET). During periods of insufficient solar PV generation, the battery discharges to support the load. The grid supplies electricity only when both solar PV and battery outputs cannot meet demand. After simulating the energy flows, the framework evaluates system performance using three indicators, including LCOE, PBP, and CO₂ emissions. Finally, sensitivity analysis examines how variations in key factors such as solar irradiance, component costs, grant funding, and grid electricity prices affect overall system performance.

3. Results and Discussions

MATLAB (R2024a) simulations were conducted for the spring- and winter-calving dairy operations using their respective load profiles. The system design and operational strategy described in Section 2 were implemented to maximise on-site solar PV utilisation and reduce grid dependence. The solar PV arrays were sized to achieve a REF of 80%, ensuring that most of the farms’ electricity demand is supplied from renewable sources. The battery bank was designed to store 30% of solar PV energy to provide sufficient storage capacity to shift surplus generation to high-demand periods, particularly during morning and evening milking. The simulation period was set to 20 years, alligned with the typical operational lifespan of solar PV panels. The yearly energy profiles for both operations, following the integration of solar PV arrays and battery storage, were analysed over this period.

3.1. Spring-Calving Farm Operation Analysis

The simulated monthly energy profile of the spring-calving operation investigated with 30.4 kWp solar PV arrays consisting of 76 solar PV panels and 20 battery units is shown in Figure 4. The energy breakdown includes contributions from solar PV, battery discharge, grid imports, and grid exports. Seasonal variations in solar radiation and farm load significantly influence energy distribution. From April to August, solar PV energy generation reaches its highest levels, with values exceeding 4000 kWh per month, significantly reducing reliance on the grid. Notably, grid imports drop to zero in April, June, and July, with excess solar energy exported to the grid after battery charging. Conversely, from November to February, energy generation from solar PV drops below 1000 kWh, resulting in increased dependency on grid electricity. Battery discharge is also reduced during this period due to limited solar input. The transitional months of March, September, and October experience moderate solar PV generation (≥1500 kWh) and battery support, keeping grid imports relatively low. Over the year, the solar PV arrays generated a total of 29,882 kWh, of which 21,635 kWh was supplied directly to the farm load and 6685 kWh was discharged from the battery. An additional 1562 kWh was exported to the grid. Out of the total annual electricity consumption of 36,955 kWh, only 8635 kWh was imported from the grid. The results confirm that the proposed solar PV–battery system effectively meets most of the operations electricity demand, enhances self-consumption, and significantly reduces dependence on the grid during periods of high solar availability.

3.2. Winter-Calving Farm Operation Analysis

The simulated monthly performance of the winter-calving dairy operation equipped with 27.6 kWp solar PV arrays consisting of 69 panels and 25 battery units is shown in Figure 5. The monthly energy profile includes contributions from solar PV, battery discharge, grid imports, and grid exports. Seasonal variations in solar radiation and farm load strongly influence energy distribution. From April to August, energy generation from solar PV reaches its annual peak, exceeding 3000 kWh per month, eliminating grid imports and allowing for surplus grid exports. In contrast to the spring-calving operation, the winter-calving operation exhibits increased energy consumption during October to March. However, generation drops to around 1000 kWh, increasing dependence on grid supply and reducing battery support. Annually, solar PV generated 29,687 kWh of total energy. Of this, 15,040 kWh was directly supplied to the load and 7512 kWh was supplied via battery discharge. Accordingly, 14,269 kWh was imported from the grid to meet the total demand of 36,821 kWh, while 7135 kWh of surplus solar PV energy was exported. Overall, the system achieved strong self-consumption and notable grid independence despite seasonal challenges.

3.3. Comparison and Observation

A summary comparison of both operations is provided in

Table 5. Summary of techno-economic metrics for case farms.

Metric	Spring-Calving Operation	Winter-Calving Operation
PV Size (kWp)	30.4 (76 panels)	27.6 (69 panels)
Battery Capacity (kWh)	25.6 (20 units)	32 (25 units)
Initial Cost before Grant (EUR)	36,280	38,090
Initial Cost after 60% Grant (EUR)	14,512	15,236
O&M Cost 1% of Initial Cost (EUR/year)	363	381
Replacement Cost (EUR at Year 11)	16,520	20,150
Total Discounted Cost (EUR)	27,434	29,677
Total Discounted Energy (kWh)	302,661	300,450
Total Discounted savings plus earnings (EUR)	57,622	51,511
LCOE (EUR/kWh)	0.091	0.099
PBP (years)	3.25	3.83
CO2 Emission Reduction (%)	77	61

. The spring-calving operation employed a 30.4 kWp solar PV system, while the winter-calving operation used a slightly smaller 27.6 kWp system. Both configurations were designed to achieve an 80% REF based on a prior academic study [30] by adjusting system capacity to balance high self-consumption and CO₂ savings against the cost of oversizing. The sizing differences between the two operations arise from seasonal alignment between solar availability and energy demand. The spring-calving operation’s peak demand coincides with higher solar irradiance, which allows for more direct solar PV utilisation and requires fewer batteries. Conversely, the winter-calving operation’s higher demand during low-irradiance months necessitates more batteries to ensure adequate energy supply.

3.3.1. Technical Impact

The total annual solar PV energy generation was comparable for both dairy operations: 29,882 kWh for the spring-calving operation and 29,687 kWh for the winter-calving operation. However, their utilisation patterns differ significantly. In the spring-calving farm, 72% of the solar PV energy (21,635 kWh) was directly consumed by the load, while 23% (6685 kWh) was stored in batteries, and only 5% (1562 kWh) was exported to the grid, implying that 95% of solar PV energy is consumed effectively for self use. This high self-consumption rate reflects strong on-site utilisation and reduced grid dependence. In contrast, the winter-calving farm consumed 51% (15,040 kWh), stored 25% (7512 kWh), and exported 24% (7135 kWh). The higher grid export rate indicates a seasonal mismatch between energy generation and demand, as shown in Figure 5. Peak consumption during low-irradiance months limits self-use and increases exports, which is validated by the annual contribution of energy sources shown in Figure 6. For the spring-calving operation, solar PV, battery, and grid imports contributed 59%, 18%, and 23% of total energy, compared to 41%, 20%, and 39% for the winter-calving operation. Also, the winter-calving operation contributed a higher grid export of 19%. These findings highlight a lower utilisation of on-site solar PV energy due to seasonal mismatch for the winter-calving operation.

3.3.2. Economic Impact

Economic performance was evaluated using a 60% capital grant and a CET of EUR 0.20/kWh, with a 6.4% discount rate [4]. The grid electricity cost adopted for this analysis is EUR 0.27/kWh, representing the average of day and night tariffs in Ireland. Tax incentives were excluded, as these vary across individual farm circumstances. Under these assumptions, the LCOE for the spring-calving operation was calculated to be EUR 0.091/kWh, while it was EUR 0.099/kWh for the winter-calving operation. Both values are significantly lower than the prevailing grid electricity prices in Ireland and confirm the strong economic viability of integrating solar PVs in dairy farms.

After LCOE, the discounted PBPs for both operations were calculated. For this purpose, the discounted cash flows including the yearly revenues and costs were analysed. Figure 7 presents the discounted cash flows over a 15-year horizon (although the project lifespan is set to 20 years, discounted cash flows and PBP analysis are assessed over a 15-year period, considering the Irish government’s 15-year guaranteed incentives under the CET scheme). As evident, the spring-calving operation achieves a PBP of 3.25 years, whereas the winter-calving operation reaches its PBP in 3.83 years. These short PBPs are attractive given Ireland’s 15-year guaranteed CET policy, which provides long-term financial security for renewable energy investments. The cumulative financial benefit comprising opportunity cost savings from reduced reliance on high-cost grid electricity and earnings from exporting excess solar energy to the grid over the 15-year CET period amounts to EUR 57,622 for the spring-calving operation and EUR 51,511 for the winter-calving operation. Interestingly, despite receiving higher annual solar irradiance (1077 kWh/m²) than the spring-calving operation (985 kWh/m²), the winter-calving operation exhibited marginally lower economic performance due to the seasonal mismatch between energy demand and solar availability.

The findings of this study align with previous research confirming the strong economic potential of solar PV systems. International studies have reported varying LCOE and PBP values across different regions and economic contexts. For instance, LCOE values of USD 0.065/kWh in Turkey with an 8.2 year PBP [20], and USD 0.289/kWh in Pakistan with a 4.9 year PBP [22] have been reported, while a German dairy farm study achieved a PBP of under 6 years [16]. These variations reflect regional differences in solar resource availability, equipment costs, labour expenses, policy incentives, and electricity tariffs, yet collectively demonstrate the global economic viability of solar PV systems. Compared with previous Irish studies, the proposed systems show significantly improved performance. A recent Irish study [4] reported PBPs for solar PV systems ranging from 5 to 8.5 years, depending on farm scale and load profiles, under a 40% TAMS grant scheme, while another Irish study [39] achieved a PBP of 5.4 years. In comparison, the PBPs achieved of 3.25 years for the spring-calving operation and 3.83 years for the winter-calving operation are favourable. These positive results are mainly due to two important considerations. First, prior studies applied a CET of EUR 0.135/kWh with a 40% TAMS grant, whereas this study reflects the latest Irish policy incentives by employing a higher CET of EUR 0.20/kWh alongside a 60% TAMS grant. This combination significantly improves revenue streams, reduces capital burden, and shortens PBP durations. Second, battery charging in this study relies exclusively on surplus solar PV generation and avoids the need for night-time grid charging, which was incorporated in [4]. This approach incurs higher operational costs, as Ireland’s off-peak electricity price (EUR 0.18/kWh) remains above the calculated LCOEs of EUR 0.091/kWh and EUR 0.099/kWh. The PV-only charging strategy adopted here maximises cost effectiveness and contributes to the superior economic performance of the proposed system compared to previous studies.

3.4. Environmental Impact

The energy-related emissions in Ireland reached their lowest level in over 30 years in 2023 at 31.4 MtCO₂eq, which represents an 8.3% reduction compared with 2022 levels [40]. This sustained decline reflects both supportive policy measures and the growing integration of renewable energy into the national grid. During the same period, the grid carbon intensity decreased from 300 g CO₂/kWh in 2022 to 255 g CO₂/kWh in 2023 [41]. Using this updated factor, the environmental impact of the proposed solar PV systems was assessed.

Without solar PV, the annual grid electricity consumption of the spring- and winter-calving operations (36,955 kWh and 36,821 kWh) resulted in emissions of 9.43 and 9.39 tonnes of CO₂ per year, respectively. Following solar PV integration, grid imports decreased to 8635 kWh and 14,269 kWh, reducing emissions to 2.20 and 3.64 tonnes of CO₂ per year. These values represent emission reductions of 77% and 61% for the spring- and winter-calving operations, respectively. The higher reduction achieved by the spring-calving operation is primarily due to its greater solar self-consumption ratio of 95% compared with 76% for the winter-calving operation. When compared to previous studies [4,7,42], the results of this study demonstrate significantly improved environmental performance. A prior Irish study [4] reported GHG emission reductions between 31.9% and 39.3% for a 63-cow dairy farm using 11–18 kWp solar PV with battery storage. In contrast, this study achieved substantially greater reductions of 61% and 77%, with 27.6 kWp and 30.4 kWp solar PV systems for 74-cow and 80-cow farms, respectively. This improvement arises from the optimised operational strategy that maximizes on-site solar utilisation. These findings highlight the importance of effective system design and operational control in realising the full environmental potential of solar PV adoption in dairy farming.

3.5. Sensitivity Analysis

While LCOE and PBP are key indicators of solar PV and battery system economic performance, their values depend on various input parameters. Sensitivity analysis was therefore conducted to examine how variations in these parameters influence results and to test the robustness of the proposed systems. Each variable was adjusted by ±10%, ±20%, and ±30% with separate analyses performed for LCOE and PBP for both operations. The corresponding trends are illustrated in Figure 8 and Figure 9, with numerical results summarised in

Table 6. LCOE sensitivity analysis for various parameters for the case farms.

Parameter	Spring Calving Operation (EUR/kWh)		Winter Calving Operation (EUR/kWh)
	−30%	+30%	−30%	+30%
Solar irradiance	0.130	0.070	0.142	0.077
PV array cost	0.085	0.097	0.094	0.104
Battery storage cost	0.078	0.104	0.082	0.117
Grant funding	0.110	0.072	0.119	0.079

and

Table 7. Payback period (PBP) sensitivity analysis for various parameters for the case farms.

Parameter	Spring Calving Operation (Years)		Winter Calving Operation (Years)
	−30%	+30%	−30%	+30%
Solar irradiance	3.30	3.30	3.83	3.83
PV array cost	2.90	3.60	3.50	4.25
Battery storage cost	2.86	3.65	3.40	4.40
Grant funding	4.50	2.25	5.40	2.50
Grid electricity cost	4.90	2.49	5.20	3.10
Clean export tariff	3.40	3.25	4.25	3.50

.

For LCOE, variations in solar irradiance, PV array cost, battery cost, and grant funding were considered. Figure 8 presents the sensitivity results for the spring- and winter-calving operations. The baseline LCOEs were EUR 0.091/kWh and EUR 0.099/kWh, respectively. LCOE was most sensitive to solar irradiance and grant funding, showing inverse relationships. A 30% increase in solar irradiance or grant support reduced LCOE significantly, while reductions in these parameters caused notable cost increases. Solar PV array and battery costs also affected LCOE but to a lesser extent. The winter-calving operation was more sensitive to battery cost due to its greater utilisation during low-solar-irradiance periods. As evident in Table 6, the spring-calving operation exhibited a narrower sensitivity range (EUR 0.070–0.130/kWh) compared with the winter-calving operation (EUR 0.077–0.142/kWh), indicating stronger economic resilience to these parameter changes.

For the PBP, the effects of solar irradiance, solar PV and battery costs, grant funding, grid electricity price, and CET rate were assessed. Figure 9 illustrates the sensitivity of PBP results to changes in these economic factors for both types of calving operations. The baseline PBPs were 3.25 years for the spring-calving operation and 3.83 years for the winter-calving operation. As shown in Table 7, for the spring-calving operation, grid electricity cost and grant funding are the dominant factors, with PBPs varying from 2.40 to 4.90 years depending on variations in these parameters. CET variations had only minor influence. For the winter-calving operation, grant funding remained the most sensitive factor, followed by grid electricity cost and CET rate. Solar PV and battery costs showed moderate effects in both cases. Solar irradiance had little influence on PBP since it is primarily governed by cost and revenue assumptions under fixed-energy-yield conditions.

The spring-calving operation demonstrated shorter PBPs and narrower sensitivity ranges, reflecting stronger investment stability and lower financial risk. These findings emphasise the critical role of grant schemes, electricity tariffs, and CET policies in ensuring the economic viability of solar PV systems for Irish dairy farms.

4. Conclusions and Future Directions

This study conducted a comprehensive techno-economic and environmental feasibility assessment of grid-connected solar PV systems with battery storage for dairy farms, focusing on the comparison between spring- and winter-calving operations. The economic results show that both types of dairy operations benefit from solar energy integration, although the spring-calving operation performs more favourably due to a better match between solar energy generation and electricity demand. Under current Irish policy conditions, the spring-calving operation achieved a PBP of 3.25 years and an LCOE of EUR 0.091/kWh, compared with 3.83 years and EUR 0.099/kWh for the winter-calving operation. Sensitivity analysis identified solar irradiance, grant funding, and grid electricity price as the most influential factors affecting financial performance. The spring-calving operation showed more stable performance across parameter variations, suggesting lower investment risk. Environmentally, the proposed systems achieved significant emission reductions of 77% and 61% for the spring- and winter-calving operation, respectively.

Although this study was conducted within the Irish policy and climatic context, the proposed framework can be adapted to other regions and extended to hybrid systems combining solar PV with other renewable resources such as wind and biogas. Several limitations should be acknowledged. First, the analysis employed monthly-averaged energy data rather than high-resolution (hourly or sub-hourly) consumption and generation profiles. Second, the battery operation model used simplified energy-balance equations rather than detailed state-of-charge tracking. Third, this study excluded tax benefits due to variations in individual farm tax circumstances and relief eligibility; incorporating after-tax effects could further improve economic performance estimates. Fourth, the analysis assumed stable operational patterns without accounting for potential changes in farm management practices, herd size variations, or technology upgrades over the 25-year system lifetime. Finally, the economic parameters adopted in this study do not include NPV analysis; rather, these concentrated on two other key economic parameters (LCOE and PBP).

Despite these limitations, this study provides a valuable contribution and provides a framework that could be adapted to overcome these limitations in future research. For example, future research could incorporate the following aspects: (1) dynamic demand modelling that may reflect daily peak usage patterns, (2) the economic parameter of NPV analysis, and (3) an assessment of the tax benefit associated with operating this proposed model. The findings of this study confirm that solar PV systems offer a practical and sustainable pathway for improving energy self-sufficiency, profitability, and environmental performance in dairy farming. It is expected that these insights will encourage greater adoption of renewable energy technologies across the agricultural sector, thereby contributing to many sustainable development goals, most specifically number seven, affordable and clean energy.