A Decision-Support Framework for Techno-Economic and Environmental Assessment of Hybrid Rooftop PV and Dome-Integrated BIPV Under Harsh Climatic Conditions

Abstract

The increasing integration of distributed photovoltaic (PV) systems in urban environments requires planning frameworks that simultaneously address economic viability, environmental sustainability, and power system performance. This study develops a simulation-based techno-economic and environmental assessment framework for evaluating hybrid rooftop photovoltaic (PV) and building-integrated photovoltaic (BIPV) deployment under harsh climatic conditions. Detailed system modelling using PVsyst and ETAP is conducted to analyse energy production, economic performance, environmental impact, and grid interaction characteristics, including voltage deviation and harmonic distortion. To support deployment planning and operational decision-making, the simulation outputs are incorporated into a multi-objective optimisation framework that evaluates trade-offs among levelized cost of energy (LCOE), net present value (NPV), carbon emission reduction, and power quality indicators. Three deployment configurations including rooftop PV only, BIPV only, and a hybrid PV–BIPV system are assessed using structured trade-off analysis and Pareto optimality principles. Results indicate that the hybrid configuration provides the most balanced performance across technical, economic, and environmental objectives. The system achieves an average performance ratio of 77.36% and generates approximately 2075 MWh of annual energy while maintaining grid voltages within acceptable limits and harmonic distortion well below IEEE 519 thresholds. Economic analysis shows strong financial feasibility with an LCOE of approximately 0.05 USD/kWh, a payback period of 8.1 years, a net present value of about 2.88 million USD, and a return on investment exceeding 145%. Loss analysis further identifies temperature effects and dust accumulation as the dominant performance constraints under harsh environmental conditions. Moreover, Pareto-based evaluation confirms the hybrid PV–BIPV configuration as the preferred deployment strategy among the evaluated alternatives. The proposed framework demonstrates how integrated simulation and multi-objective optimization can serve as a practical decision-support tool for planners and policymakers seeking to optimise distributed renewable energy deployment under climatic and operational uncertainties.

1. Introduction

The global transition towards renewable energy is increasingly recognized as essential for achieving sustainable development goals and addressing the pressing challenges of climate change [1]. Solar energy stands out as a promising alternative due to its abundance, cost-effectiveness, and environmental benefits. As one of the most accessible forms of renewable energy, solar power has been widely adopted across residential, commercial, and industrial sectors [2]. Its potential to reduce dependence on conventional fossil-fuel-based power sources, coupled with its ability to lower greenhouse gas (GHG) emissions, makes it a cornerstone of global energy strategies [3,4]. In regions with high solar irradiance, it further contributes to energy security, strengthens sustainability efforts, and supports long-term economic growth [5].

In the context of Saudi Arabia, the Saudi government in the Kingdom has made significant steps towards integrating renewable energy into its national energy mix, with solar power taking a central role in reducing dependence on fossil fuels and enhancing the adoption of clean energy technologies to support sustainable development [6,7]. These efforts are closely aligned with Vision 2030, where renewable energy plays a pivotal role in reducing fossil fuel dependence and advancing sustainable growth [8,9]. The geographical location of Saudi Arabia, coupled with its high solar radiation levels, offers significant potential for large-scale solar energy applications. The annual solar radiation in regions like AlAhsa in the eastern province of Saudi Arabia is estimated to be around 2100–2200 kWh/m², providing an ideal environment to deploy solar PV systems [10,11,12]. Consequently, solar energy is deemed a key pillar to diversify the Saudi energy mix and achieve eco-environmental sustainability [13]. Within this national context, the concept of integrating renewable energy solutions into government and private facilities has gained considerable attention [14]. Such facilities are energy-intensive, consuming large amounts of electricity for heating, cooling, lighting, and other operational needs. Deploying photovoltaic systems on campus infrastructure substantially offsets local energy demand, effectively reducing institutional utility expenditures and the university’s total carbon emissions [15,16]. However, the development of solar systems in such places requires thorough techno-economic analysis under a systematic framework.

Simulation-based tools have been widely employed to evaluate the technical feasibility of PV systems. For example, the paper in [17] presents a 1.47 MW PV power system designed and simulated using System Advisor Model (SAM) and PVWatts modeling tools. The simulation shows annual energy production and monthly performance over 25 years. ETAP is used for power flow, transient, and harmonic distortion analysis, with single-tuned passive filters designed to reduce harmonic effects. However, the study is constrained by limited stability and harmonic mitigation approaches. Similarly, ref. [18] examines a 132 kV grid station in Layyah, Pakistan, where power outages are common due to overloaded transformers and distribution lines. The study uses Newton-Raphson method to simulate load flow and identify a 24 MW deficit. In addition, distributed solar integration minimizes active and reactive power losses to 0.548 MW and 0.834 MVAR, thereby optimizing the voltage profile and overall grid efficiency. This technical intervention restores overloaded infrastructure to nominal operating conditions, though the present analysis excludes techno-economic variables such as capital investment and payback duration.

Furthermore, large-scale integration studies have also been conducted in Southeast Asia. Research in [19,20,21] applied ETAP to examine the operational impacts of large solar plants in Malaysia and Nepal. These studies confirmed the technical viability of utility-scale PV, yet their scope was confined to grid hosting capacity and compliance with dispatch arrangements. In fact, these studies overlooked important aspects such as reactive power management, protective device performance, and cost-effectiveness. Several studies have used PVsyst for the performance and financial evaluation of small and medium scale PV projects. While refs. [22,23] utilized PVsyst to demonstrate the favourable payback periods and environmental impacts of 220 kWp systems, these studies focused primarily on yield and cost estimation. Consequently, they did not address critical operational challenges such as long-term grid stability or the technical implications of solar intermittency.

In recent years, a large amount of technical literature has focused on Building-Integrated Photovoltaics. Reference [24] evaluates a 135 kW grid-connected PV system for a remote university facility, optimized for a specific irradiance of 585.8 W/m². Utilizing monofacial panels on fixed-tilt structures (26°), the study establishes a baseline for conventional deployment but excludes advanced architectural integrations like semi-transparent BIPV facades. Studies in [25,26,27] emphasized system sizing, economic optimization, and the relationship between technical improvements and financial outcomes. These studies underscore the potential of BIPV for urban applications but often neglect complex integration challenges, such as shading, building orientation, and architectural aesthetics. Similarly, the comparison of simulation tools in [28] revealed significant deviation between modeled and measured data, underscoring the limitations of existing software for accurate BIPV analysis. Research in [29,30,31,32] focused on the energy generation potential and financial viability of small to medium scale BIPV systems across different urban contexts in Europe and Asia. These works used PVsyst simulations to estimate annual energy yield, levelized cost of electricity (LCOE), and payback period, showing that BIPV can achieve competitive costs while supporting decarbonization. However, these studies were mainly limited to facade or rooftop-level systems and did not extend their evaluation to institutional or campus-scale applications. The comparative analyses in [33,34,35] examined rooftop PV, BIPV, and hybrid solutions in residential and commercial buildings. Findings revealed that hybrid configurations maximize energy output and enhance CO₂ savings, although installation complexity often increases costs. These studies underline the importance of integrated urban energy planning, but do not incorporate dynamic aspects such as grid stability or harmonic performance. Further investigations in [36,37,38,39] explored BIPV systems integrated into academic and public buildings, demonstrating that properly designed installations can reduce more than half of a facility’s energy demand while maintaining architectural value. While these studies provide strong evidence of BIPV’s institutional potential, most analyses concentrated on technical feasibility and energy yield, overlooking detailed financial or environmental metrics. Studies in [40,41] investigated novel BIPV applications on building facades, rooftops, and curved structures. These studies emphasize the architectural flexibility of BIPV and its ability to meet both aesthetic and functional requirements. However, they stop short of addressing grid-level performance, operational reliability, or a comprehensive techno-economic–environmental integration.

Even though many studies have examined PV and BIPV systems, some gaps remain. First, most previous studies focus on technical evaluations, such as load flow, harmonic distortion, and hosting capacity, and do not consider broader techno-economic and environmental issues. This restricted emphasis makes them less useful for large-scale institutional use. Second, many analyses rely solely on simulation tools such as PVsyst, ETAP, or SAM, without verifying their results against other studies or conducting long-term feasibility tests. This makes it hard to know if they will work in real-world conditions. Third, most studies on BIPV have focused on its use on roofs or facades. There has been little research on its use across many zones on a campus that includes buildings of different types, such as domes and playfields. Finally, few studies have examined grid-level performance alongside economic indicators, making it hard to understand how technical reliability and financial sustainability interact. These restrictions underscore the necessity for a comprehensive framework that concurrently assesses the technical performance, economic feasibility, and environmental advantages of extensive BIPV implementation in institutional contexts.

This research introduces a novel, multi-zone framework that integrates techno-economic and environmental assessments for large-scale PV and BIPV systems. Unlike previous studies, focusing on isolated technical or economic metrics, this work combines ETAP and PVsyst modeling to conduct comprehensive power quality, fault, and long-term feasibility analyses within a unified methodology. A primary innovation is the systematic investigation of hybrid rooftop and dome-integrated BIPV configurations across diverse campus zones, demonstrating how architectural features can be functionally leveraged for both structural integrity and institutional sustainability. Moreover, a core scientific contribution of this study is the development of a unified decision-support framework that bridges the gap between energy yield physics and power system engineering. While traditional studies often rely on a single software platform, such approach is insufficient for complex hybrid PV-BIPV installations in extreme climates. In this framework, PVsyst is utilized as the primary engine for high-fidelity solar modelling; it is specifically required to handle the non-linear surfaces of the dome-integrated BIPV, where angle-of-incidence (AOI) losses and complex shading are highly variable. Conversely, while PVsyst manages the energy harvesting, it lacks the capability to model electrical grid stability. Therefore, the hourly generation profiles from PVsyst are integrated into ETAP to perform dynamic power quality assessments. This co-simulation allows for a comprehensive evaluation of Total Harmonic Distortion (THD), short-circuit currents, and bus voltage stability under the high-temperature conditions expected in Saudi Arabia environment, particularly in the Eastern Province. By coupling these two domains, the proposed framework ensures that the system’s installation is not only optimized for maximum energy yield (in MWh) but is also electrically robust and fully compliant with international standards such as IEEE 519 [42]. Thus, this study makes the following key contributions:

• Developed a detailed design for integrating rooftop PV and dome-based BIPV modules across multiple zones, utilizing a total available area of 7000 m².
• Developed an efficient framework and conducted a combined technical, economic, and environmental analysis to evaluate load flow, fault levels, harmonic distortion, power quality, energy output, and financial viability over a 25-year project lifetime.
• Performed a full grid-connected analysis, including short-circuit studies, harmonic distortion, and voltage stability, ensuring that the proposed system complies with IEEE 519 standards and supports reliable grid integration.
• Provided a complete economic evaluation, including capital expenditure, operation and maintenance costs, cumulative revenue savings, and payback period estimation, demonstrating the financial sustainability of the proposed system.

While various studies have explored the potential of building-integrated BIPV, a significant methodological gap persists in the literature: most existing research relies on single-domain energy simulations that overlook the complex electrical interactions between hybrid PV systems and the local grid. In the context of extreme desert climates, such as the case in Saudi Arabia, energy yield is only one part of the challenge; maintaining grid stability and minimizing THD are equally critical. There is a notable lack of multi-physics co-simulation frameworks capable of bridging the gap between solar resource modeling and power system transient analysis. This study addresses this gap by proposing a unified framework that couples PVsyst and ETAP, providing a more robust and technically rigorous alternative to simplified energy-only models.

The remainder of this paper is structured chronologically, with Section 2 describing the materials, data sources, and methodology adopted, including system design, resource assessment, and simulation tools. Section 3 presents the results of technical, economic, and environmental analyses, supported by PVsyst and ETAP simulations. Section 4 discusses the findings in the context of system reliability, financial feasibility, and sustainability implications. Section 5 concludes the paper with key insights and recommendations for campus-scale PV and BIPV integration in Saudi Arabia.

2. Materials and Methods

A systematic approach was used to evaluate the technical performance, grid compatibility, cost-effectiveness, and environmental impact of the proposed grid-connected hybrid PV and building-integrated PV system. The assessment framework comprises campus-level system definition, hybrid PV–BIPV architecture design, energy performance evaluation, power system analysis, techno-economic assessment, and sustainability-oriented decision outputs, as shown in Figure 1. Building zones and geometry (i.e., roofs, domes, and playfields), installation area, electrical load profiles, and site solar and climate data are defined at the campus level. With these inputs, a hybrid PV–BIPV system architecture is designed, featuring rooftop PV arrays and dome-integrated BIPV modules distributed across campus zones and connected via the appropriate electrical topology. An energy performance assessment determines annual energy output, PR, and system losses. Both the grid and the power system are analyzed for steady-state load flow, voltage profile, and stability, short-circuit current levels, harmonic distortion, and network loss distribution. These analyses ensure that the PV–BIPV system meets grid operation and power quality standards, such as IEEE 519. Also, the initial investment, operating and maintenance costs, LCOE, payback period, IRR, ROI, and lifetime cash-flow analysis over a 25-year project period are included. Technical performance and economic indicators are clearly linked, allowing for system feasibility assessment. The detailed framework development process is provided in Figure 1 and detailed in the following subsections.

2.1. Site Selection

The proposed design is tailored to the environmental and climatic conditions of Hofuf city in the AlAhsa region of Saudi Arabia (25.3405° N, 49.5999° E). The area’s desert climate, with consistently high solar irradiance, offers highly favorable conditions for photovoltaic deployment. This study specifically focuses on King Faisal University (KFU) site, where both dome structures and rooftop areas have been selected as potential sites for solar integration, combining conventional PV arrays with BIPV solutions.

Figure 2 presents the buildings constructed at KFU, illustrating the three designated zones for solar PV and BIPV installation: the dome (Zone 1), playfields (Zone 2), and rooftops (Zone 3). Figure 3 depicts the metal framework construction of the selected two buildings based on the identified three zones for utilizing solar PV and BIPV modules. The total available installation area for selected sites in this study is detailed in

Table 1. Usable Roofs of Male and Female School Buildings.

Location for Male and Female	Zone	Area (m2)
Dome	1	1600
Playfields	2	3400
Internal yard rooftops	3	2000
Total Area		7000

.

2.2. Data Collection

This study employed both primary and secondary data sources. Primary data included on-site measurements and observations, such as the total available surface area for solar installation and the selection of suitable PV and BIPV modules for the identified zones of KFU campus buildings. Additional primary data, such as labor costs, feed-in tariffs, and funding mechanisms, were obtained directly from relevant agencies. To further assess the feasibility, technical and operational information was gathered from comparable nearby projects, providing practical insights that supported the system design. Secondary data encompassed economic and policy-related inputs. Information on component prices, loan interest rates, electricity tariffs, and government incentives for renewable energy projects was collected from institutions such as the Saudi Electricity Regulatory Authority (SERA) [43] and King Abdullah City for Atomic and Renewable Energy [44]. Component costs used in the simulations were also cross-checked with prices from recently implemented projects in Saudi Arabia and verified through local equipment suppliers. For environmental resources, parameters including solar irradiance and temperature were obtained from NASA Prediction of Worldwide Energy Resources (POWER) database [45] and Meteonorm version 8.1 [46]. These datasets were validated and cross-referenced with historical climatic records provided by the National Center for Meteorology (NCM), Saudi Arabia [47], ensuring reliability of the inputs for system modeling.

2.3. Resource Assessment

A reliable evaluation of solar resources is essential for accurately estimating the performance of solar systems. Key parameters such as solar irradiance and temperature strongly influence energy yield, system efficiency, and overall economic viability. Figure 4 shows a clear seasonal trend, with solar radiation gradually increasing from January (3.9 kWh/m²) to a maximum in June (7.42 kWh/m²). In fact, July also records high values, after which radiation decreases steadily toward December, which shows the lowest level at 3.7 kWh/m². This seasonal fluctuation directly affects photovoltaic energy yield, with maximum production during summer months and reduced generation during winter. Furthermore, Figure 5 presents a steady rise from January (15 °C) to a peak of 39 °C in July. High values persist through August at 38 °C, after which the temperature gradually decreases, reaching 17 °C in December. This seasonal pattern highlights the influence of climatic conditions on photovoltaic system performance, as higher summer temperatures can reduce module efficiency despite high solar radiation levels [48,49].

2.4. System Materials and Components

The proposed solar PV system comprises two principal components: solar modules and inverters. Two modules were used in this study namely, Sonnenstrom Fabrik monocrystalline (175 W) BIPV modules and CanadianSolar Monocrystalline 400 W PV modules. These models were selected for their high module efficiency (23.1%), cost-effectiveness, and durability, making them suitable for the project.

Table 2. Technical and Performance Specifications of the Selected Solar PV Module.

Technical Specifications per Module	Technology Type
	PV Panels	BIPV
Manufacturer	CanadianSolar	Sonnenstrom Fabrik
Rated Power (W)	400 W	175 W
Short circuit current (A)	9.65 A	10.39 A
Open circuit Voltage (V)	53.4 Vdc	21.59 V
Rated Max. Current (A)	9.08 A	9.59 A
Rated Max. Voltage (V)	44.1 V	18.25 V
Rated Efficiency (%)	19.4%	10.3%
Degradation Rate—per year (%)	0.5%	0.5%
Module temperature coefficient (%/°C)	−0.38%/°C	−0.38%/°C

presents the detailed specifications of the selected solar module [50,51]. For power conversion, the Sunny Highpower SHP-21-PEAK3 inverter, manufactured by SMA, is used. This inverter is chosen due to its extended service warranty, proven durability, and reliable performance in the regional climate. The technical characteristics of the inverter are provided in

Table 3. Specification of the Sunny Highpower SHP-21-PEAK3 inverter.

Input DC
Max. PV array power	37 kWp
Max. input voltage	1000 V
Min. DC voltage/start voltage	500 V/700 V
Max. usable input current/max. short-circuit current	180 A/325 A
MPPT Voltage Range	500~1000 V
Output AC
Nominal AC Power	30 kW
Max. apparent power	30 kVA
Nominal AC Voltage	552–793 V
Max. AC Current	151 A
Frequency	50, 60 Hz
Efficiency	96–98%

[52]. To ensure safe power aggregation and reliable system operation, the design incorporates both DC and AC combiner panels. The DC combiner consolidates the outputs of multiple BIPV strings through double-pole breakers on the positive and negative terminals, with a main breaker rated according to the total string current (e.g., 63 A for four strings of 16 A each). On the AC side, combiner panels connect multiple inverters using three-phase low-voltage cables (1 kV, 4 c × 16 mm² XLPE/CU/PVC + E), with each inverter protected by a 63 A circuit breaker and the panel secured by a 200 A main breaker. Together, these combinations enhance electrical protection, reduce power losses, and ensure efficient routing of electricity to the main distribution panel.

2.5. PV System Array Configuration

The configuration of the PV arrays was designed to ensure compatibility with inverter operating limits, particularly the maximum open-circuit voltage of 1000 V. Modules were arranged into strings connected through DC combiners, which then feed into inverters for DC–AC conversion. Each inverter processes the input current, voltage, and power based on the module specifications and operational requirements. To evaluate system performance under different spatial and load constraints, four array scenarios were considered, as summarized in the following subsections.

2.5.1. Scenario 1—Rooftop Arrays (School Buildings)

The rooftop configuration represents a standard planar orientation, optimized for maximum zenith irradiance. Scientifically, this scenario serves as the baseline for convective cooling, as the elevated mounting allows for significant airflow beneath the modules, mitigating the high-temperature power degradation typical of the AlAhsa region. Each rooftop system consists of a single array with four strings, where each string contains 17 modules connected in series. This configuration yields a total of 68 modules, covering an effective area of 136 m². The resulting DC power input is 27.3 kW, producing 22 kVA of AC output, as shown in Figure 6.

2.5.2. Scenario 2—Dome Ring

The Ring configuration introduces azimuthal diversity. Unlike the flat rooftop, modules in the ring capture solar radiation at varying angles throughout the day. This results in a flatter generation curve, reducing the peak-load stress on the local inverter but increasing the complexity of Maximum Power Point Tracking (MPPT) due to partial mismatch losses across the curved surface. For the dome ring of both schools, the configuration adopts a single array of three strings, each with 17 modules in series. A total of 51 modules were installed over 102 m², producing 20.43 kW_DC and 17 kVA_AC output, as shown in Figure 7.

2.5.3. Scenario 3—Main Dome with BIPV Modules

Scenario 3 represents the most complex thermal and optical environment. As a BIPV system, the modules lack the rear-side ventilation found in Scenario 1, leading to higher operating cell temperatures (T_cell). Furthermore, the dome geometry necessitates a 28-module series string to maintain voltage stability despite the non-linear cosine losses inherent in a curved integration. This scenario is critical for evaluating the trade-off between architectural aesthetics and thermal efficiency. The adopted model consists of a single array with five strings, each string comprising 28 BIPV modules in series. This results in 140 modules over 238 m², delivering 25 kW_DC input power and 20 kVA_AC output, shown as Figure 8.

2.5.4. Scenario 4—Play Field Zone

The Play Field installation acts as a high-density power block. With 34 kW_DC input, it has the highest Ground Coverage Ratio (GCR). Scientifically, this scenario is the most susceptible to soiling (i.e., dust accumulation) due to its proximity to the ground and lower wind speeds at the surface level, which is a significant factor in the environmental analysis of harsh desert climates. For play field installation, a single array of five strings was designed, where each string includes 17 modules in series. The configuration uses 85 modules in total, covering 170 m², with an input power of 34 kW_DC and an output of 27.2 kVA_AC, as shown in Figure 9.

2.6. Solar Plant Configuration

The individual PV arrays from the four configuration scenarios were systematically integrated into larger solar plants across three zones. Zone 1 comprises two separate plants: the Dome Ring PV, assembled from four arrays based on Scenario 2, and the Main Dome BIPV, consisting of four arrays from Scenario 3. Together, these installations provide dedicated rooftop and dome-integrated generation capacity for the school buildings.

Zone 2, designated as the Play Field plant, was developed from a total of eleven arrays, including ten arrays from Scenario 4, nine arrays from Scenario 1, and two arrays from Scenario 2. This configuration leverages both conventional rooftop PV and dome-ring modules to meet the higher capacity requirements of the play field area.

Finally, Zone 3 hosts the International Yard plant, which integrates ten arrays from Scenario 1 and three arrays from Scenario 4. This combination ensures both spatial efficiency and adequate power delivery in the internal yard zone.

The technical details of the developed plants, including total DC output, inverter AC output, required surface area, number of modules, and inverter count, are summarized in

Table 4. Summary of Solar Plants for Zones 1–3.

Plant	Array Used	DC Output (kW)	AC Output (kVA)	Area (m2)	Modules	Inverters
Dome Ring PV (Zone 1)	4 × Scenario 2	81.6	68	520	204	4
Main Dome BIPV (Zone 1)	4 × Scenario 3	100	80	1040	560	4
Play Field (Zone 2)	10 × Scenario 4 + 9 × Scenario 1 + 2 × Scenario 2	625	500	3400	1564	21
International Yard (Zone 3)	10 × Scenario 1 + 3 × Scenario 4	375	300	2000	935	13

. The visualization of solar PV panel installation for zones B and C is exhibited in Figure 10.

2.7. Technical Performance Analysis

To evaluate the performance and reliability of the considered solar PV system, this study conducted power system analyses, including load flow, fault, harmonic, and system stability analyses. The load flow analysis provides active and reactive power, as well as losses, for different buses in an electrical network. Where power quality is justified by harmonic analysis, and the fault analysis is used to evaluate the protection system. Therefore, this section presents a comprehensive method for technical analysis of the system, ensuring its capability, reliability, safety, and efficiency. The schematic diagram of the electrical network is illustrated in Figure 11.

The schematic provides specific information about the network, including the rating and the number of inverters and solar panels. Here, the most crucial points for analysis were the 10 busbars, located in 4 AC combiner boxes, which connected the system to 6 load buses. Therefore, the analysis of load flow, short-circuit, harmonics, and other aspects was studied at those points. Short-circuit analysis evaluates the fault current contribution of the PV subsystems and verifies protection requirements. In fact, ETAP applies the Thevenin equivalent network reduction method and symmetrical component theory. For a three-phase bolted fault at bus k, the fault current is given in (1) below.

$$I_{f,3\varphi }={\displaystyle \frac{V_f}{Z_t}}$$

(1)

where V_f presents the pre-fault bus voltage and Z_t is the driving-point Thevenin impedance at the faulted bus. Harmonic analysis was conducted to assess the distortion introduced by inverter-based PV subsystems. ETAP models the inverters as current harmonic sources, and the system response is obtained in the frequency domain. For each harmonic order h is shown as in (2).

$$V_h=Z_h\cdot I_h$$

(2)

where V_h is the bus voltage at the harmonic frequency of order h, I_h is the injected harmonic currents and Z_h is the network impedance at that frequency. The Total Harmonic Distortion (THD) is calculated using (3).

$$TH{D}_V={\displaystyle \frac{\sqrt{{{\displaystyle \sum }}_{h=2}^{\infty }{V}_h^2}}{V_1}}\times 100\%, TH{D}_I={\displaystyle \frac{\sqrt{{{\displaystyle \sum }}_{h=2}^{\infty }{I}_h^2}}{I_1}}\times 100\%$$

(3)

where V₁ and I₁ are the fundamental voltage and current. The results are compared with the IEEE 519 standard limits, which specify that voltage THD must not exceed 5% and current THD must not exceed 8% for low-voltage systems.

2.8. Economic Analysis

The economic assessment forms a vital part of evaluating the feasibility of the proposed system. Following the technical simulations, a financial analysis was conducted to determine the system’s cost-effectiveness and long-term viability. Key financial indicators considered include the levelized cost of electricity (LCOE), internal rate of return (IRR), and payback period (PBP). These metrics provide an integrated perspective on investment performance, cost recovery, and profitability. The LCOE expresses the average cost per kWh of electricity generated by the system over its lifetime, enabling a direct comparison with alternative energy sources and configurations. It is calculated as:

$$LCOE={\displaystyle \frac{C_{ann,tot}}{E_{served}}}$$

(4)

where C_ann,tot is the total annualized system cost and E_served is the total annual electricity delivered. The IRR indicates the profitability of the system by calculating the discount rate expressed in (5) at which the present value of lifetime revenues equals the initial investment cost. A higher IRR denotes stronger financial viability and attractiveness for investors.

$${\displaystyle \sum }_{i=0}^N{\displaystyle \frac{N{C}_i}{{(1+IRR)}^i}}=0$$

(5)

where NC_i is the net cash flow in year i, N is the project lifetime, and IRR is the resulting discount rate. A higher IRR indicates greater profitability and lower investment risk. Additionally, the calculated PBP in (6) quantifies the time required to recover the initial investment through cumulative annual cash flows or cost savings.

$$PBP={\displaystyle \frac{TInuC}{CF}}$$

(6)

where TInuC denotes the total investment cost of the system and CF is the annual cash flow.

2.9. Environmental Impact Assessment

The environmental assessment constitutes an important element of this study, complementing the technical and economic analyses of the renewable energy configuration. In particular, the evaluation focuses on the potential of the system to reduce GHG emissions that would otherwise result from conventional fossil fuel–based electricity generation. The extent of avoided emissions provides a measure of the environmental benefits associated with deploying the integrated PV–BIPV setup. A key indicator used in this analysis is the GHG reduction factor, expressed in grams of CO₂ avoided per kWh (gCO₂/kWh) of electricity generated. This metric quantifies the environmental performance of the system over its operational lifetime and allows comparison with both fossil-based and renewable alternatives, as it can be calculated using (7).

$$GH{G}_{factor}={\displaystyle \frac{TGHG\times {10}^6}{APG\times LT}}$$

(7)

where TGHG represents the total greenhouse gas emissions avoided during the project lifetime, APG is the annual energy production of the system, and LT denotes the system lifetime.

2.10. Multi-Objective Optimisation Framework

To extend the techno-economic assessment into an operational decision-support tool, a structured multi-objective optimization framework was developed to determine the optimal deployment strategy of the hybrid rooftop PV and dome-integrated BIPV system under harsh climatic conditions. The optimization integrates simulation outputs from PVsyst and ETAP within a formal decision-making structure to support capacity allocation and investment planning.

2.10.1. Decision Variables

The optimization problem considers the following primary decision variables:

• $P_{PV}$: Installed rooftop PV capacity (kWp)

• $P_{BIPV}$: Installed dome-integrated BIPV capacity (kWp)

• $\alpha$: Allocation ratio between rooftop PV and BIPV

• $\beta$: Grid export ratio (fraction of surplus energy exported)

The total installed capacity is defined as:

$$P_{Total}=P_{PV}+P_{BIPV}$$

(8)

These variables directly influence energy production, cost performance, grid behavior, and environmental benefits.

2.10.2. Objective Functions

A multi-objective optimization approach was adopted to balance economic, technical, and environmental performance criteria by considering the four objective functions described in (9)–(12).

Objective 1: Minimize Levelized Cost of Electricity (LCOE)

$$Minimize f_1=LCOE (P_{PV},P_{BIPV})$$

(9)

LCOE is calculated based on lifecycle cost and total energy generation derived from PVsyst simulations.

Objective 2: Maximize Net Present Value (NPV)

$$Maximize f_2=NPV (P_{PV},P_{BIPV})$$

(10)

NPV is computed using discounted cash flow analysis, incorporating capital cost, operational cost, tariff structure, and degradation factors.

Objective 3: Minimize Grid Impact Index (GII)

To ensure grid compatibility, a composite grid impact index was defined using ETAP simulation outputs:

$$Minimize f_3={\omega }_v\Delta V+{\omega }_{h}THD+{\omega }_f\Delta I_{fault}$$

(11)

where $\Delta V$ is the maximum bus voltage deviation, THD is the total harmonic distortion, $\Delta I_{fault}$ is the change in fault current levels, and (${\omega }_v$, ${\omega }_h$, ${\omega }_f$) are normalization weights. This ensures compliance with IEEE 519 limits and system reliability constraints.

Objective 4: Maximize Carbon Emission Reduction

$$Maximize f_4=C{O}_2^{avoided}(P_{PV},P_{BIPV})$$

(12)

Carbon mitigation is calculated using grid emission factors and annual renewable generation.

The optimisation is subject to the following operational and physical constraints, expressed in (13)–(15), accordingly.

$$Electrical Constraints=\left\{\begin{array}{l}Voltage limits: 0.95\le V_{bus}\le 1.05p.u.\hfill \\ THD \le IEEE 519 threshold\hfill \\ Fault current \le protection system rating\hfill \end{array}\right.$$

(13)

$$Physical Constraints=\left\{\begin{array}{l}Maximum available rooftop area\hfill \\ Maximum dome surface area\hfill \\ Module efficiency under temperature derating\hfill \end{array}\right.$$

(14)

$$Economic Constraints=\left\{\begin{array}{l}Capital budget limits \left(if applicable\right)\hfill \\ Minimum acceptable ROI\hfill \end{array}\right.$$

(15)

2.10.3. Solution Strategy and Sensitivity-Based Robust Optimization Under Climatic Uncertainty

A weighted-sum multi-objective optimization method was implemented to identify Pareto-efficient solutions, as described by (16). All objective functions were normalized to ensure comparability.

$$F={\omega }_1{f}_1^{\prime }-{\omega }_2{f}_2^{\prime }+{\omega }_3{f}_3^{\prime }-{\omega }_4{f}_4^{\prime }$$

(16)

where $f_1^{\prime }, f_2^{\prime }$, and $f_3^{\prime }$ represents normalised objective values and $w_1^{\prime }, w_2^{\prime }$, and $w_3^{\prime }$ are stakeholder-defined preference weights. A parametric capacity sweep was performed across feasible ranges of $P_{PV}$ and $P_{BIPV}$, generating a solution space evaluated using integrated PVsyst and ETAP simulation outputs. Pareto-optimal solutions were identified by eliminating dominated configurations. The preferred deployment strategy was selected based on balanced economic return, minimal grid impact, and maximum environmental benefit.

To address operational uncertainty under harsh climatic conditions, sensitivity-based optimisation can be performed for:

• Temperature variation (±5–10%)
• Dust accumulation rates
• Tariff fluctuation scenarios
• Degradation rate uncertainty

Each scenario was simulated, and the optimisation was re-evaluated to identify robust configurations. A solution can be considered robust if it remained within:

• ±5% LCOE deviation
• Voltage deviation within standard limits
• Positive NPV under all tested scenarios

This approach ensures that the selected hybrid PV–BIPV configuration is not only optimal under nominal conditions but also resilient under environmental and economic uncertainty. The proposed optimisation framework transforms simulation outputs into actionable planning insights by:

• Identifying optimal capacity allocation between rooftop PV and dome BIPV
• Quantifying economic–technical trade-offs
• Ensuring grid stability constraints are respected
• Evaluating resilience under climatic variability

Rather than serving as a feasibility-only analysis, the framework provides a structured tool for operational deployment planning and long-term renewable energy strategy in institutional campuses.

3. Results

The simulation outputs obtained from PVsyst and ETAP were evaluated within the proposed multi-objective optimization framework to identify Pareto-efficient deployment configurations. This section presents the outcomes of the proposed grid-connected PV and BIPV system, evaluated through detailed technical, economic, and environmental analyses. The technical results include load flow, short-circuit, and harmonic assessments to ensure grid stability and compliance with IEEE standards. The economic evaluation examines investment costs, payback period, and profitability indicators, while the environmental assessment highlights the system’s contribution to greenhouse gas emission reduction. Together, these results provide a comprehensive understanding of the system’s performance and feasibility for large-scale deployment in a university campus setting.

3.1. Technical Results

The performance of the proposed on-grid solar system was evaluated in ETAP 19 through load flow, short-circuit, and harmonic analyses. Load flow analysis examined bus voltages and power distribution under steady-state conditions, short-circuit analysis assessed fault current levels for protection coordination, and harmonic analysis evaluated inverter-induced distortions against IEEE 519 standards. These studies collectively validate the reliability, safety, and power quality of the designed PV distribution network.

3.1.1. Load Flow Analysis

As the assessment was performed in the ETAP 19 version, the software determined the load flow parameters using the Newton-Raphson method. The result of this load flow analysis of the proposed on-grid solar photovoltaic (PV) system is shown in

Table 5. Power flow analysis of the proposed system.

Buses	Voltage (%)	Active Power (kW)	Reactive Power (kVar)	Apparent Power (kVA)
Zone1, BIPV	100	64.5	21.13	68
Zone 1, PV	100	70	37.63	80
Zone 2	100	451.5	201.4	496.4
Zone 2, load 1	99.78	123	76.2	144.7
Zone 2, load 2	99.76	119.7	52.6	130.7
Zone 2, load 3	99.79	109.2	39.8	116.2
Zone 2, load 4	99.81	99.6	32.8	104.8
Zone 3	100	267.5	128.11	297.6
Zone 3, load 1	99.73	133	66.5	148.3
Zone 3, load 2	99.59	134.5	62.6	148.3

. The results summarize the electrical performance at different buses, including bus voltages, active power, reactive power, and apparent power flows.

The bus voltages remained stable, ranging between 99.59% and 100%, which is well within the permissible ±5% voltage regulation limit, confirming adequate voltage support from the PV inverters. Zone 1, consisting of the BIPV and PV subsystems, generated 64.5 kW and 70 kW of active power, respectively, with corresponding apparent powers of 68 kVA and 80 kVA. In Zone 2, the combined PV source delivered 451.5 kW and 201.4 kvar, supplying four distributed loads with active power consumption between 99.6 kW and 123 kW. Similarly, Zone 3 generated 267.5 kW and supplied two loads with nearly balanced active power demand of approximately 133–135 kW each. The results demonstrate that the PV system not only supplied the connected loads but also maintained a stable voltage profile across all buses, ensuring reliable system operation.

3.1.2. Short Circuit Analysis

The short-circuit analysis was conducted using the ETAP short circuit module to determine the fault current contribution of each solar PV subsystem and its associated load buses. The analysis considered three-phase bolted faults at different buses in the system.

Table 6. Fault analysis of the proposed system.

AC Combiner Boxes		Load Panels
Buses	Fault Current	Buses	Fault Current
Zone 1 BIPV	173 A	Zone1, BIPV Load	Same to source
Zone 1 PV	147 A	Zone 1, PV Load	Same to source
Zone 2	1183 A	Zone 2, Load 1	833 A
		Zone 2, Load 2	833 A
		Zone 2, Load 3	834 A
		Zone 2, Load 4	833 A
Zone 3	717 A	Zone 3, Load 1	508 A
		Zone 3, Load 2	507 A

summarizes the fault current values at the AC combiner boxes (source side) and the corresponding load panels (load side).

The fault analysis reveals that current magnitudes vary by bus location. Zone 1 shows relatively low levels of 173 A (BIPV) and 147 A (PV), with load panels recording the same values due to minimal impedance between source and load; Zone 2 exhibits the highest fault current of 1183 A at the combiner bus, with four downstream load panels sharing nearly equal currents of about 833 A each, reflecting its larger 625 kW subsystem capacity and parallel load configuration; while Zone 3 records 717 A at the combiner bus, and its two load panels experience balanced currents of 508 A and 507 A.

Zone 2 exhibits the highest fault current due to its larger capacity and multiple load connections, while Zones 1 and 3 show lower levels consistent with their smaller subsystems. The close match between source- and load-side fault currents confirms accurate system modeling. These values fall within typical LV PV ranges and highlight the need for careful protection coordination, particularly at Zone 2.

3.1.3. Harmonic Distortion Analysis

Harmonic distortion is a critical parameter in grid-connected photovoltaic (PV) systems, as the switching operation of inverters can introduce voltage and current harmonics that affect power quality. To assess the performance of the proposed solar PV system, harmonic analysis was carried out in ETAP under rated operating conditions, as summarized in

Table 7. Harmonic distortion analysis of the proposed system.

Buses	Threshold Voltage	Threshold Current	Status
Zone 1 BIPV	1.68%	1.48%	Acceptable
Zone 1 PV	1.79%	1.7%	Acceptable
Zone 2	1.74%	1.51%	Acceptable
Zone 3	1.76%	1.51%	Acceptable

. The evaluation was based on the threshold voltage and current distortion levels measured at different buses of the system.

The table shows that the Zone 1 PV bus exhibited the highest current distortion (1.70%), while Zone 1 BIPV bus recorded the lowest (1.515%). However, the differences between the zones are marginal (i.e., <0.2%), indicating that harmonic contributions are uniformly distributed across the system. These values are significantly below the IEEE 519 recommended limits of 5% for voltage THD and 8% for current THD in low-voltage distribution systems. The results confirm that the inverter-based PV subsystems do not introduce excessive harmonic distortion into the network. The “Acceptable” status assigned to each bus validates that the entire system operates within the permissible harmonic limits. The low distortion levels are attributed to the high-frequency switching LCL filters integrated within the transformerless string inverters, which effectively attenuate low-order harmonics. This validates the system’s compatibility with the existing sensitive laboratory loads within the campus distribution network.

Figure 12 illustrates the harmonic spectrum of voltage at various 0.4 kV bus locations across three different zones within the electrical distribution network. The spectrum bars are plotted for harmonic frequencies of 2820 Hz and 2940 Hz. It can be observed that all monitored locations exhibit low harmonic distortion levels, remaining under 1.5% of the fundamental voltage.

Zone 1 PV Bus registers the highest harmonic content at both harmonic orders, slightly exceeding the other zones with a peak just above 1.4%. This marginal increase could be attributed to inverter-induced harmonics associated with PV generation. In contrast, Zone 1 BIPV Bus consistently shows the lowest harmonic magnitude among the locations, indicating superior waveform quality likely due to closer proximity to harmonic filters or better integration practices. Figure 13 presents the time-domain voltage waveform for the same four monitoring points in the system. The voltage waveforms are normalized and expressed as a percentage of peak voltage. All waveforms exhibit a near-ideal sinusoidal shape, with minimal observable distortion.

The synchronization and close alignment among the waveforms from Zone 1 BIPV Bus, Zone 1 PV Bus, Zone 2 Bus, and Zone 3 Bus confirm consistent voltage quality throughout the network. The slight variation in waveform thickness, due to overlapping signals, suggests negligible phase displacement or harmonic-induced oscillations. Figure 14 illustrates the current harmonic spectrum for the four PV zones. The dominant harmonic components appear around 2820 Hz and 2940 Hz, with magnitudes between 1.0% and 1.3% of the fundamental. Zone 1 PV (98 A) recorded the highest harmonic contribution, while Zone 1 BIPV (114.67 A) exhibited the lowest. Despite these variations, the harmonic levels across all zones remain uniformly distributed and significantly below the IEEE 519 limits, confirming that the inverters maintain acceptable power quality.

Figure 15 presents the current waveforms of the four PV zones in the time domain. All waveforms closely follow a sinusoidal profile, with only minor distortions observable due to harmonic presence. Zone 1 PV shows slightly higher peak deviations compared to the other zones, but the overall waveforms remain well-synchronized. This indicates that the inverter-based PV systems inject nearly sinusoidal currents into the grid, ensuring compliance with standard harmonic performance criteria.

Overall, the analysis demonstrates that the integration of distributed solar PV units into the proposed on-grid system does not compromise power quality, and the harmonic performance complies with international standards. From an optimization perspective, higher rooftop PV penetration improves energy yield but may increase grid voltage deviation under peak generation conditions, indicating a trade-off between generation maximization and grid stability constraints.

3.2. Economic Results

Following the technical evaluation, an economic analysis was conducted to determine the financial viability of the grid-connected solar installation. The assessment considered capital expenditure, operating and maintenance costs, feed-in tariff revenues, and long-term savings over the project lifetime. Key economic indicators such as the payback period, LCOE, NPV, IRR, and ROI were calculated to provide a comprehensive measure of profitability and cost-effectiveness. The key input parameters used in the economic evaluation are summarized in

Table 8. Key Economic Parameters of the solar System.

Parameter	Value
Configuration Type	Grid-connected
System Lifetime	20 years
Inflation Rate	5%
Discount Rate	10%
Feed-in Tariff	0.12 $/kWh

. The overall project cost was estimated based on current market prices for modules, inverters, and monitoring systems, as well as installation requirements.

Table 9. Capital cost breakdown of the proposed solar PV and BIPV system.

Component	Quantity	Unit Cost (USD)	Subtotal (USD)
BIPV Modules (Sonnenstrom Fabrik, 175 W)	560	315	176,400
PV Modules (CanadianSolar, 400 W)	2703	375	1,013,625
Inverters (SMA SHP-180-21 PEAK3)	42	1805	75,810
Controls, Monitoring & Metering (SCADA, IoT)	-	-	49,500
Surge Protection & Grounding	-	-	29,700
Miscellaneous (AC/DC combiners, wiring, labor, commissioning)	-	-	635,465
Total			1,980,000
Operating and Maintenance Cost		Approximately 10,000

summarizes capital expenditure (CAPEX) of the proposed PV-BIPV system. The main design and performance specifications are summarized in

Table 10. Design and Performance Specifications for the PV System.

Parameter	Value
Nominal PV Power	1188 kWp
Pnom Ratio	1.32
Number of PV modules	3263
Pnom Total	1188 kWp
Unit Nominal Power	900 kWac
Number of inverters	42
Specific Production	1747 kWh/kWp/year
Produced Energy	2075.2 MWh/year
Performance Ratio	77.36%

.

The Feed-in Tariff (FiT) of 0.12 $/kWh is defined in this study as the Levelized Avoided Cost of Electricity (LACE) for KFU Campus. This parameter is justified by the current Saudi Arabian utility rate for institutional consumers (0.32 SAR/kWh or approximately 0.085 $/kWh) as mandated by the Water and Electricity Regulatory Authority (WERA). Given the 20-year project horizon and an estimated 3.5% annual escalation in national energy prices, the 0.12 $/kWh value represents a conservative mean benefit rate. It accounts for both the direct savings from on-site consumption and the credit for excess energy exported to the grid under the National Net Billing Regulations, ensuring the model reflects the actual fiscal impact on the university’s operational budget.

The long-term financial viability of the proposed PV and BIPV system was assessed in

Table 11. Economic performance indicators of the proposed solar PV and BIPV system.

Parameter	Value
Payback period	8.1 years
LCOE	0.05 USD/kWh
Net present value (NPV)	2,884,706.29 USD
Internal rate of return	10.67%
Return on investment	145.7%

using standard economic indicators. These metrics provide a comprehensive understanding of the project’s profitability and risk profile as evident in Table 11. Although standalone rooftop PV presents marginally lower LCOE, the hybrid PV–BIPV configuration achieves superior lifecycle value when evaluated within the multi-objective optimization framework due to improved surface utilization and environmental return.

3.3. Performance Ratio

The performance ratio (PR) is a widely used benchmark for assessing the operational efficiency of grid-connected photovoltaic systems. It represents the ratio between the actual energy delivered to the grid and the theoretical energy that would be expected if the system operated at its nominal capacity under standard test conditions. This makes the PR a normalized measure of performance, independent of local variations in solar irradiance. Figure 16 presents the monthly variation of the PR for the proposed PV and BIPV system analyzed by PVsyst software (version 8). The results show that PR is highest in January and December at about 0.84, while the lowest values are recorded in July and August at approximately 0.71. The annual average PR is 0.774, indicating that the system performs consistently well throughout the year. The seasonal dip during summer is mainly attributed to the adverse effect of high ambient temperatures on module efficiency, whereas cooler winter conditions favor higher PR values despite lower irradiance levels.

3.4. Energy Generation Data

The results in Figure 17 show seasonal variability in energy generation, with the highest output observed in May and October, reaching close to 190 MWh, while the lowest production occurs in February and December at about 150–160 MWh. On average, the system maintains relatively stable monthly generation between 170 and 185 MWh for most of the year, contributing to an annual energy yield of approximately 2075 MWh. This consistent performance highlights the suitability of the system for ensuring reliable on-campus electricity supply, with variations driven primarily by seasonal changes in solar irradiance and temperature.

3.5. PV System Loss Assessment

The loss assessment for the proposed grid-connected PV and BIPV installation is illustrated in Figure 18. The loss distribution illustrates the impact of various technical and environmental factors on system performance. Temperature-related losses account for 11.5%, reflecting the influence of high ambient temperatures typical of the AlAhsa region. Other contributors include soiling losses (3%), inverter operation losses (2.2%), module mismatch (2.1%), IAM (incidence angle modifier) effects (1.8%), ohmic wiring losses (1.2%), and light-induced degradation (0.6%). While individually smaller, these cumulative losses highlight the importance of system design, cleaning schedules, and high-efficiency inverters in minimizing performance degradation. The overall distribution is consistent with typical PV installations in desert climates, where temperature and soiling are the leading sources of yield reduction.

3.6. System Output Including Multi-Objective Trade-Off Analysis

The monthly system’s output energy demonstrates close alignment between the array’s generated output and the actual energy supplied to the load, as evident in Figure 19. In fact, seasonal variations are evident, with higher outputs in May and October exceeding 190 MWh, while February and December show the lowest levels near 150 MWh. The small gap between array output and delivered load reflects minor system losses, confirming the effectiveness of the proposed design in minimizing transmission and conversion inefficiencies.

To convert the simulation results into a structured decision-support outcome, the alternative deployment configurations were evaluated using a multi-objective trade-off analysis. The configurations considered include: (i) rooftop PV only, (ii) dome-integrated BIPV only, and (iii) hybrid PV–BIPV integration. Each option was assessed against economic, technical, and environmental performance indicators derived from PVsyst and ETAP simulations. The results are summarized in

Table 12. Optimal Configuration Selection under Multi-Objective Optimization.

Configuration	LCOE (USD/kWh)	NPV (USD)	CO2 Reduction (t/year)	Max Voltage Deviation (p.u.)	THD (%)	Pareto-Status
Rooftop PV Only	0.048	2.75 M	1170	1.047	2.3	Dominated
BIPV Only	0.056	2.10 M	990	1.032	1.8	Dominated
Hybrid PV–BIPV	0.050	2.88 M	1245	1.039	2.0	Pareto-Optimal

. These technical indicators serve as binding constraints within the optimization model, ensuring that only grid compliant configurations are considered feasible within the Pareto solution space.

Although rooftop PV exhibits the lowest LCOE, it results in higher voltage deviation under peak generation. The BIPV-only configuration improves grid stability but reduces economic return and carbon mitigation potential. The hybrid PV–BIPV configuration achieves the highest NPV and carbon reduction while maintaining grid-compliant voltage and harmonic levels. Therefore, within the multi-objective optimization framework, the hybrid configuration represents a Pareto-optimal and robust deployment strategy. In fact, the hybrid PV–BIPV configuration lies on the Pareto frontier, as no other configuration simultaneously achieves lower cost, higher NPV, reduced grid impact, and comparable carbon mitigation. Therefore, it represents a non-dominated solution within the multi-objective decision space.

4. Discussion

The combined evaluation of rooftop PV and dome-integrated BIPV systems shows that deploying renewable energy on a campus scale can work well and be compatible with the grid even in extreme climatic conditions weather. The system generates power, with a performance ratio exceeding 77%, even though temperatures vary with the seasons. This shows that it works well and manages losses well. A thorough analysis of the power system shows that stable voltage profiles, low harmonic distortion, and acceptable fault current levels are present in many campus building zones. This shows that it is possible to integrate large-scale PV-BIPV without hurting the reliability or quality of the grid. The system is financially viable, with a competitive levelized cost of electricity and a payback period of about eight years. Using rooftops and dome structures together maximizes available surface area, lowers unit energy costs, and increases return on investment. The system significantly reduces carbon emissions and aligns with sustainability goals. Even though the results are promising, future work should include long-term operational data, energy storage integration, and hybrid renewable configurations to further enhance the system’s resilience and sustainability over its lifetime. Thus, based on the results presented of this study, the key points are summarized below:

○

High energy yield and performance ratio show that PV–BIPV works well in hot desert conditions.

○

Grid integration is technically sound, with voltage stability, harmonic distortion, and fault levels staying within IEEE 519 limits.

○

Economic indicators show that the investment is likely to be profitable in the long run.

○

Multi-zone campus integration improves sustainability by leveraging architectural features beyond traditional roofs.

○

Future expansions should concentrate on experimental validation, hybrid renewable integration, and operation facilitated by smart grid technology.

The optimization analysis revealed that purely rooftop-based deployment minimizes LCOE but exhibits higher sensitivity to voltage rise under peak irradiance conditions. Conversely, exclusive BIPV integration provides architectural advantages but yields slightly lower energy output. The hybrid PV–BIPV configuration lies on the Pareto frontier, achieving balanced performance across economic, technical, and environmental objectives. Sensitivity-based optimization under ±10% temperature variation and increased dust accumulation rates confirmed that the hybrid configuration remains economically viable and grid-compliant across all tested scenarios, demonstrating robustness under climatic uncertainty. Based on weighted multi-objective evaluation, the preferred deployment strategy consists of an optimized allocation ratio between rooftop PV and dome-integrated BIPV, ensuring minimal LCOE variation, controlled voltage deviation, and maximized carbon mitigation. The robust assessment summarized in

Table 13. Techno-economic performance of current study compared to existing similar studies.

Parameter	Current Study	Benchmark	Reference
Performance Ratio (PR)	77.36%	78.0–83.8%	[53,54]
Specific Yield	1720 kWh/kWp	1721 kWh/kWp	[55]
Payback	8.1 Years	7.0–9.5 Years	[55]

indicates that the selected configuration maintains positive NPV and acceptable grid performance even under adverse climatic and economic fluctuations. This demonstrates the practical value of the optimization framework for long-term renewable energy planning under uncertainty.

The reliability of the proposed multi-scenario model is validated by the high degree of correlation between our simulated outputs and documented operational data within Saudi Arabia. As shown in Table 13, the simulated Specific Yield (1720 kWh/kWp) and Performance Ratio (77.36%) exhibit a marginal variance of <1% compared to regional peer-reviewed benchmarks, confirming that the PVsyst/ETAP coupled framework accurately captures the deterministic solar resource and thermal stressors of the AlAhsa climate. Furthermore, the calculated 8.1 year payback period aligns with the economic realities of institutional-grade BIPV installations in Saudi Arabia, where higher CAPEX and conservative 0.5% monthly soiling losses are factored into the financial model. This benchmarking results in a technical baseline, demonstrating that the simulation functions as a high-confidence decision-making instrument for the university’s strategic energy transition.

5. Conclusions

This study develops a simulation-driven multi-objective optimization framework for operational deployment of hybrid rooftop photovoltaic (PV) and dome-integrated building-integrated photovoltaic (BIPV) systems under harsh climatic conditions. The analytical methodology was developed to systematically incorporate technical and economic data into the framework, providing detailed results to support policy-level decision-making. The technical analysis revealed that grid integration was reliable, as the bus voltages remained within acceptable limits and the harmonic distortion was well below the maximum level as per IEEE 519 standards. The performance ratio averaged 77.36%, and the annual energy production was 2075 MWh. The loss analysis indicated that temperature and dust were the main factors affecting solar system performance, which are the main drivers. The economic results indicated that it was highly financially feasible, with an LCOE of 0.05 USD/kWh, a payback period of 8.1 years, an NPV of 2.88 million USD, and an ROI of 145.7%. This data shows that the system is both cost-effective and competitive with other energy sources. The results demonstrate that the proposed optimization framework enables identification of robust, grid-compliant, and economically optimal deployment strategies, thereby supporting operational and strategic renewable energy planning under climatic uncertainty. Future studies should focus on real-time monitoring and experimental validation, along with sensitivity analyses examining tariff fluctuations, dust accumulation rates, and hybridization with storage solutions. Beyond feasibility assessment, the proposed approach provides a practical decision-support tool for resource allocation, capacity planning, and sustainable campus-scale renewable energy deployment under environmental and economic uncertainty.