Sustainability and Grid Reliability of Renewable Energy Expansion Projects in Saudi Arabia by 2030

Abstract

The penetration of renewable energy, especially solar and wind, is increasing globally to promote a sustainable environment. However, in the Middle East, this momentum is slower compared to other regions, primarily due to abundant local fossil fuel reserves and historically low energy prices. This trend is shifting, with several countries, including the Kingdom of Saudi Arabia (KSA), setting ambitious goals. Specifically, KSA’s Vision 2030 aims to generate 50% of its energy from renewable sources by 2030. Due to favorable conditions for solar and wind, various mega-projects have either been completed or are underway in KSA. This study analyzes the potential and reliability impact of these projects on the power system through a three-step process. In the first step, all major projects are identified, and data related to these projects, such as global horizontal irradiance, wind speed, temperature, and other relevant parameters, are collected. In the second step, these data are used to estimate the solar and wind potential at various sites, along with annual averages and seasonal averages for different extreme seasons, such as winter and summer. Finally, in the third step, a reliability assessment of power generation is conducted to evaluate the adequacy of renewable projects within the national power grid. This study addresses a gap in the literature by providing a region-specific reliability analysis using actual project data from KSA, which remains underexplored in existing research. Sequential Monte Carlo simulations are employed, and various reliability indices, including Loss of Load Expectation (LOLE), Loss of Energy Expectation (LOEE), Loss of Load Frequency (LOLF), Energy Not Supplied per Interruption (ENSINT), and Demand Not Supplied per Interruption (DNSINT) are analyzed. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability, with improvements observed across all reliability indices, demonstrating the viability of meeting Vision 2030 targets.

1. Introduction

1.1. Background and Motivation

The global energy market is undergoing a significant transformation driven by an urgent need to address climate change and secure a sustainable environment for future generations. Growing awareness of the environmental and social impacts of fossil fuel consumption, particularly the emission of greenhouse gases, has mobilized the international community toward a cleaner energy future [1]. This shift is not merely about environmental responsibility but also reflects a recognition of the economic potential that renewable energy presents. Nations and industries are increasingly viewing the transition to renewable energy as an opportunity to foster economic growth, create jobs, and reduce dependence on depleting fossil fuels. This momentum has led to widespread adoption of solar, wind, hydro, and other renewable energy technologies, backed by supportive policies and investments [2]. Furthermore, this energy transition aligns with the evolving global demand for resilient, decentralized energy systems, which can better withstand extreme weather events and reduce energy inequality [3]. Thus, the movement toward renewables represents a dual approach, addressing climate goals while capitalizing on sustainable economic opportunities.

1.2. Current Status and Literature Review

Globally, various efforts are underway to support the clean energy transition across different regions. For example, the global expansion of wind energy is reported in [4] with installed capacity nearly tripling since 2014, surpassing 1 TW in 2023. This paper reviews recent developments in wind power generation, discussing key factors influencing its success, such as technology advancements, resource availability, and social acceptance, and highlights five critical topics for the future of onshore and offshore wind energy. Similarly, it is reported in [5] that China can reach 15 PWh/year of PV and wind power by 2060 through optimized plant deployment, grid upgrades, and energy storage. Similarly, a recent comprehensive review [6] highlights the growing role of emerging technologies, in the U.S., such as floating PV, solar-plus-storage, and hybrid power plants, and projects a 77% reduction in solar technology costs by 2031 using Wright’s law. The European Union’s push for low-carbon economies requires all member states to transition to renewable energy sources. A cluster analysis of RES diversification in these countries from 2010 to 2019 identified subgroups with similar energy profiles and tracked their progress, offering insights for tailored national energy strategies and collective policy development [7]. The authors in [8] highlight the role of government policies, including binding climate targets and adaptive planning, in accelerating the region’s clean energy transition in Southeast Asia. The research in [9] evaluates the economic feasibility of Canada’s provincial energy transitions to carbon neutrality by 2060, using energy simulation and costing models. It finds that most provinces will benefit from fossil fuel savings, with those relying on fossil fuels for electricity generation seeing the greatest benefits from the transition. Finally, in [10], the public support for offshore wind energy development in Poland is analyzed, finding 68% support with a 5% annual increase. Economic benefits and regional development, particularly in the labor market, were key factors, while concerns about landscape changes were minimal. An optimal integrated renewable energy system for five major Australian cities in proposed in [11], analyzing ten building types in different climatic zones. The results provide viable scenarios for hybrid wind-solar systems, offering guidelines for cost-effective and low-carbon power system designs for these cities and others with similar characteristics.

Despite the global momentum toward renewable energy, the Middle East has had slower adoption of renewable energy sources, largely due to abundant local fossil fuel reserves and historically low energy prices [12]. However, some nations, including the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA), have recently implemented ambitious initiatives aimed at advancing their energy sectors and increasing energy efficiency. For instance, Saudi Arabia’s Vision 2030 initiative [13] represents a transformative national agenda focused on diversifying the energy mix and reducing reliance on oil. A major part of this vision includes developing large-scale renewable energy projects, with Saudi Arabia committing to generating 50% of its energy from renewables by 2030. Neom, a futuristic smart city under development, is one such example, aiming to run entirely on renewable energy, with plans to utilize solar, wind, and green hydrogen technologies [14]. Saudi Arabia has set ambitious goals, initially targeting 9.5 GW off renewable energy by 2023, later expanding this to 58 GW by 2030 [15].

Saudi Arabia has substantial potential for both solar and wind energy, owing to its vast deserts and coastal areas that provide ideal conditions for harnessing renewable resources. With some of the highest solar irradiance levels globally, Saudi Arabia is well-positioned to tap into solar energy on a large scale [16], while areas near the Red Sea offer strong, consistent winds suitable for wind power generation [17]. In recent years, the KSA has accelerated its renewable energy projects under Vision 2030. Major initiatives include the Sakaka Solar Power Plant, Saudi Arabia’s first large-scale solar project, which became operational in 2019 and has a capacity of 300 megawatts [18]. Additionally, the Dumat Al Jandal Wind Farm, the Kingdom’s first utility-scale wind project, is now one of the largest in the region with a capacity of 400 megawatts [19]. Further projects, such as the planned Neom green hydrogen plant and the Red Sea Project’s renewable energy installations, are underway to diversify Saudi Arabia’s energy mix [20].

1.3. Research Gaps and Contributions

It can be observed that various studies have highlighted the importance of region-specific analysis in the development and implementation of energy systems. These studies emphasize that factors such as local climate, economic conditions, resource availability, and population density significantly influence energy demand and the feasibility of renewable energy technologies. Tailoring solutions to these regional characteristics not only enhances performance and cost-efficiency but also ensures a more sustainable and reliable energy transition. Additionally, considering local regulations, infrastructure, and social dynamics is crucial for the successful adoption and integration of energy innovations. In addition, energy potential modeling and analysis are crucial for effectively deploying renewable resources, ensuring that projects are not only economically viable but also reliable in meeting energy demand [21]. By assessing local energy potential, through solar irradiance, wind patterns, and seasonal changes, planners can identify optimal locations and forecast long-term output, reducing uncertainties associated with renewable resources. In addition, adequacy assessment plays a key role in verifying that renewable projects can reliably meet demand, using established indices that quantify system performance under variability [22]. Key indices such as Loss of Load Expectation (LOLE), Loss of Energy Expectation (LOEE), Loss of Load Frequency (LOLF), Energy Not Supplied per Interruption (ENSINT), and Demand Not Supplied per Interruption (DNSINT) provide valuable insights into the stability and resilience of renewable projects. These metrics allow decision-makers to evaluate the likelihood of energy shortages, frequency of load interruptions, and the overall impact of supply disruptions, which is essential for maintaining grid reliability.

Therefore, this study focuses on enhancing renewable energy planning and implementation in Saudi Arabia in alignment with Vision 2030 sustainability objectives. The research features several distinctive contributions aimed at strengthening the reliability and adequacy of renewable energy projects across the country. First, it involves gathering, reviewing, and analyzing data related to renewable energy initiatives in Saudi Arabia, with an emphasis on weather data from multiple locations. This initial phase, conducted using Excel and MATLAB R2023b, includes collecting and processing data on Global Horizontal Irradiance (GHI), wind speed, temperature, and other relevant parameters to estimate the solar and wind potential for various sites. The second phase centers on a comprehensive reliability assessment for power generation, aimed at evaluating the generation adequacy of renewable projects within the national power grid. Employing sequential Monte Carlo simulations, this analysis calculates key indices such as LOLE, LOEE, LOLF, and ENSINT. By analyzing the impact of renewable energy projects on grid stability and assessing their ability to meet demand, this research provides crucial insights to support Saudi Arabia’s transition toward sustainable energy and inform strategic planning for future renewable deployments. The major contributions of this study can be summarized as follows:

• The study focuses on enhancing renewable energy planning in Saudi Arabia in line with sustainability objectives, including gathering and processing weather data (e.g., GHI, wind speed, and temperature) to estimate solar and wind potential across various sites.
• The research evaluates the generation adequacy of renewable projects within the national grid, employing sequential Monte Carlo simulations to calculate key reliability indices (LOLE, LOEE, LOLF, and ENSINT).
• The study analyzes the impact of renewable energy projects on grid stability, providing insights that support Saudi Arabia’s transition to sustainable energy and inform future renewable deployments and strategic planning.

The remainder of the paper is organized as follows: Section 2 provides an overview of renewable energy projects in the Kingdom of Saudi Arabia (KSA), covering both completed and planned initiatives. Section 3 outlines the renewable energy estimation process, including the assessment of average annual potential of wind and solar energy. Section 4 presents the reliability analysis, while Section 5 concludes the paper and discusses directions for future research.

2. Overview of Renewable Energy Projects in KSA

The shift to renewable energy is crucial to meet the rising energy needs driven by population growth and industrialization in developing nations, while reducing environmental impact. Investing in renewables not only helps combat climate change but also boosts economic growth, creates jobs, and reduces dependency on depleting fossil fuels [23]. This transition is both a necessary environmental response and a strategic social and economic transformation, supported by advances in technology, government policies, incentives, and increased awareness of fossil fuel impacts.

It is worth mentioning that when the share of renewable energy in total generation exceeds 30%, it can significantly impact the dynamic behavior of the power system, necessitating the reconfiguration of emergency automation schemes. Advanced control strategies, such as the rotor inertial power source (RIPS) control for self-standby PV systems [24] and synthetic inertia algorithms supported by hydrogen-based energy storage systems [25], have been proposed to enhance frequency stability and system resilience during emergency events. While these strategies provide valuable insights into managing high renewable penetration, a detailed assessment of emergency stability and automation reconfiguration for the KSA power system is not in the scope of this study.

Saudi Arabia, with its vast potential for wind and solar power and exceptionally high solar radiation levels, averaging 2200 kWh/m² annually, ranks among the top ten countries for photovoltaic potential, as depicted in Figure 1 [26]. Saudi Arabia is actively exploring various solar energy applications, such as water desalination, irrigation, and solar cooling, showcasing a strategic commitment to diversifying its energy mix and reducing dependence on fossil fuels. Policy frameworks and strategic investments, including the establishment of the King Abdullah City for Atomic and Renewable Energy (KACARE), are steering the country toward its renewable energy targets [27]. However, challenges remain, such as technological constraints and high soiling levels, which underscore the need for substantial investment in research and infrastructure to fully realize the potential of solar energy in the region.

Similarly, KSA is among the top fifteen countries with the highest wind potential, as depicted in Figure 2 [26], owing to its vast desert landscapes, long coastlines, and favorable meteorological conditions, particularly in regions such as the Red Sea coast and parts of the central and eastern provinces. These areas offer high average wind speeds that make them well-suited for large-scale wind farm deployment. However, the development of wind power in the Kingdom still faces significant challenges, notably the variability and intermittency of wind speeds, which can lead to fluctuations in power output and pose integration difficulties for the national grid. These fluctuations require advanced forecasting techniques, energy storage systems, and grid flexibility measures to ensure consistency, reliability, and alignment with national energy transition goals under Vision 2030.

2.1. Completed and Planned Projects

Saudi Arabia is positioning itself as a leader in renewable energy through its National Renewable Energy Program (NERP), a Vision 2030 initiative focused on boosting renewable energy production and enhancing energy security. This program aims to cut carbon emissions by 278 million tons of CO₂ and reduce fossil fuel dependency, while fostering local economic growth, research, and job creation. Several significant projects have been launched or are in development across various regions of Saudi Arabia. A list of all major projects is presented in

Table 1. List of all major renewable energy projects in KSA.

Project Number	Project Name	Energy Type	Capacity (MW)
1	Sakaka	PV	300
2	Rafha	PV	20
3	Quriyat	PV	200
4	Tabarjal	PV	400
5	Waad AlShimal	WIND	500
6	Doumat AlJandal	WIND	400
7	Mahd AlDahab	PV	20
8	alHinakiah One	PV	1100
9	alHinakiah Two	PV	400
10	AlKahfah	PV	1400
11	AlMasa	PV	1000
12	Shoiba One	PV	600
13	Shoiba Two	PV	2060
14	Jeddah	PV	300
15	Rabigh One	PV	300
16	Rabigh Two	PV	300
17	Saad One	PV	300
18	Saad Two	PV	1125
19	AlGhat	WIND	600
20	Sudir	PV	1500
21	AlRass One	PV	700
22	AlRass Two	PV	2000
23	Yanbu	WIND	700
24	Madina	PV	50
25	WadiAlDawasir	PV	120
26	AlSadawi	PV	2000
27	Layla	PV	91
28	Red Sea	PV	1200

and a brief description of selected major projects is presented in the following sub-sections. The dataset includes 19 renewable energy projects in Saudi Arabia, comprising 16 solar photovoltaic (PV) and 3 wind projects. The solar projects vary in capacity from 20 MW to 2060 MW, with major developments like Shoiba Two (2060 MW), AlKahfah (1400 MW), and AlHinakiah One (1100 MW) significantly contributing to solar capacity. The wind projects, Waad AlShimal (500 MW), Doumat AlJandal (400 MW), and AlGhat (600 MW), add up to 1500 MW.

2.1.1. Sakaka PV Project

Located in AlJouf, this 300 MW project is the first utility-scale renewable energy initiative under the REPDO. Developed by ACWA Power at a cost of $302 million, it employs approximately 1.2 million Jinko JKM 320P polycrystalline panels (They are manufactured in company in Shanghai, China), generating around 930 gigawatt-hours of electricity annually and making a substantial contribution to carbon emissions reduction [28].

2.1.2. Doumat AlJandal Wind Farm Project

Situated near Domat Al Jandal city, this wind farm is one of the largest in the Middle East, featuring 99 V150 model wind turbines with a rotor diameter of 150 m and a total capacity of approximately 400 MW. These advanced turbines enable cost-effective energy production, achieving a low Levelized Cost of Electricity (LCOE) of about $0.019 per kWh [29].

2.1.3. Mahd AlDahab PV Project

As part of the REPDO Phase 3 plan, this project is expected to generate approximately 20 megawatts of power upon its completion in 2025. The Rabigh project, a collaboration between Logi and the Saudi Green Technologies Company, aims for a total capacity of 300 MW and includes a 25-year power purchase agreement. Additionally, the Saad PV project, owned and operated by Jinko Power Technologies, is a 300 MW initiative within the second phase of the REPDO plan, with plans for an additional 1125 MW in the subsequent phase [30].

2.1.4. Alkahfah PV Project

Located in Hail Province, this project aims to generate 1400 MW of clean energy across an area of over 82 km². The Wadi Aldawaser project, part of the REPDO Phase 3 plan, is a 120 MW photovoltaic facility anticipated to become operational by 2025. The Al Madinah project, developed by Desert Technologies, is a 50 MW PV initiative scheduled to begin construction in 2024. Additionally, the Jeddah (Alnoor) PV project, developed by Masdar, EDF Renewables, and Nesma Company, represents a promising 300 MW photovoltaic plant with a 25-year power purchase agreement [31].

2.1.5. Shoiba 1 and Shoiba 2 PV Projects

The Alfaisaliah PV project, encompassing the Shoiba 1 and Shoiba 2 phases, is being developed in two stages with capacities of 600 MW and 2030 MW, respectively. This project will utilize n-type bifacial monocrystalline silicon modules with peak powers of 605, 610, and 615 Wp, leveraging silicon mono technology for high output relative to the space occupied. A single axis tracking system will be employed across approximately 55 km for both phases, estimated to incorporate over 5 million solar panels. Shoiba 1 is projected to commence production in 2025, while phase 2 is currently in the planning stages [32].

2.1.6. AlRass PV Project

Designed and constructed by ACWA Power, this project is another major initiative under Phase 3 of the REPDO plan, expected to generate over 700 MW of electricity using bifacial modules with tracking technology. The Sudair IPP PV project is among the largest photovoltaic plants globally, boasting a generation capacity of 1500 MW. With an investment of $906 million, the project aims to reduce emissions by nearly 2.9 million tons of carbon dioxide [33].

3. Renewable Energy Estimation

This section discusses the development of models for renewable energy estimation, focusing on solar and wind energy projects in Saudi Arabia. It analyzes weather data (solar radiation, wind speed, and temperature) to evaluate project productivity while addressing challenges related to data variation affecting energy output through statistical methods. The methodology comprises four stages: data collection, data verification, data processing, and conversion modeling. Data are collected from various entities, including the Saudi Investment Fund, ACWA Power, and the Saudi Electricity Company, with weather data sourced from King Abdullah City for Atomic and Renewable Energy (K.A.CARE).

After collecting the primary data, they are processed to identify and remove erroneous data and outliers, utilizing different tools (Microsoft Excel Office 2024 and IBM SPSS Statistics 29.0) for statistical analysis. The final stages involve modeling the data and converting them to power outputs using the MATLAB R2023b toolbox, accounting for factors such as wind speed, solar radiation, and temperature through appropriate mathematical equations. Figure 3 illustrates the main activities undertaken to achieve the objectives.

3.1. Average Annual Potential

This study utilized weather data from K.A.CARE, measured at various locations across Saudi Arabia. The dataset includes solar irradiance, wind speed, and temperature, with hourly measurements spanning one to three years. After collection, the data were verified and filtered to eliminate outliers, and average annual values were calculated, accounting for leap years. This organized dataset facilitates comparisons of the potential productivity of various sites designated for renewable energy projects.

3.1.1. Solar Radiation Level

In this study, readings from 15 sites were gathered based on their proximity to the announced projects. The impact of seasonal variations in solar radiation throughout the year was analyzed. Figure 4 illustrates solar irradiance at four locations: Riyadh, Al-Qassim, Yanbu, and Hafr Al-Batin. These figures demonstrate that solar radiation fluctuates throughout the day, peaking around midday. Additionally, the maximum solar radiation levels vary by season, with winter showing the lowest levels and summer experiencing a significant increase in solar radiation during the day, as expected.

3.1.2. Wind Speed Levels

Figure 5 presents the wind speed levels for four selected locations, highlighting the crucial role of wind speed in energy generation from wind. The results indicate notable seasonal variations, with wind speeds generally peaking in summer. Wind speeds tend to rise gradually throughout the day, reaching their highest levels around midday due to solar heating effects. A detailed examination of the figures reveals that wind speeds tend to increase after the late morning hours, reaching their peak by the end of the day. Annually, summer months exhibit the highest wind speeds, particularly in coastal regions like Yanbu and nearby locations such as Hafr Al-Batin, which experience notably stronger winds. In contrast, Al-Qassim and Riyadh demonstrate medium to low wind speeds, highlighting the geographical influence on wind energy potential in these areas.

3.2. Energy Production Potential

3.2.1. Solar Potential

A MATLAB code was developed to assess the performance of solar energy systems by calculating power output based on temperature and solar radiation percentages for each selected location. The program integrates data from the specified panel model in

Table 2. Technical parameters of the PV cell module used in this study [34].

Parameter	Sub-Parameter	Value
Model	-	JKM320P
Technology	Type	Poly Crystalline
	Module Peak Power	320 Wp
	Open Circuit Voltage, Voc, V	46.4 V
Standard Test Conditions, STC	Short Circuit current, ISC, A	9.05 A
	Maximum Voltage, Vmp, V	37.40 V
	Maximum Current, Imp, A	8.56 A
	Fill Factor	76.20%
Module Efficiency		16.50%
Temperature coefficient of Pmax		−0.40%/°C

, applying appropriate solar power models to analyze performance based on the defined number of units. By simulating the energy produced according to the station’s production capacity, the program compares the generated energy with solar radiation data over a span of 8784 h, providing insights into system performance and efficiency.

The following figures (Figure 6) present simulations for four stations within Saudi Arabia’s solar energy projects: Al-Rass, Al-Madina, Sudair, and Al-Shuaiba One (Table 1). They illustrate hourly variations in energy output, highlighting differences in energy production based on the percentage of solar radiation at each location. These simulations offer valuable insights into how solar radiation fluctuations impact hourly energy generation across different regions.

3.2.2. Wind Potential

A MATLAB code was developed to estimate wind energy production by adjusting wind speed for turbine height and using unit-specific data to simulate energy output based on turbine speed. The code accounts for conditions such as cut-in and cut-out speeds: if wind speed is below the cut-in, the output is zero; if it is between the cut-in and rated speed, output increases linearly; if it is above the rated speed but below cut-out, output remains constant; and if it is above cut-out, the output returns to zero. Details of the wind turbine model used for the analysis is presented in

Table 3. Technical parameters of the wind turbine used in this study [35].

Parameter	Value
Manufacturer	Vestas (Aarhus, Denmark)
Type	Vestas V150-4.2 MW 50 Hz
Mode	P01
Rated Power	4200 kW
Turbine serial no.	228051
Hub-height above ground	130 m
Rotor diameter	150 m
Distance: middle of tower to middle of blade flange	4.5 m
Gearbox type	ZF, EH1052A
Generator type	Vestas, 3 Phase IG, VND DASG 560/6M
Rotor blades	Vestas Wind Systems A/S, Vestas 73.65 m
Blade additional components	Serrated trailing edges
Power control (pitch/stall)	Pitch

.

Figure 7 illustrates the direct relationship between wind speed (blue) and power generated (red) by a single turbine over the year. The turbine’s operation is directly impacted by fluctuations in wind speed, which remains within the cut-in and cut-out thresholds. These figures emphasize how the power output adjusts in response to seasonal and daily variations in wind speed.

4. Reliability Analysis

This section evaluates the adequacy of renewable energy generation in Saudi Arabia using the IEEE Roy Billinton Test System (RBTS). It assesses 28 renewable projects across different locations through Monte Carlo simulations over 10,000 simulated years to replicate operational conditions. The Monte Carlo method was chosen for its robustness in handling the stochastic nature of renewable energy generation and demand profiles. It allows for repeated random sampling to simulate a wide range of possible system states over time, which is particularly useful when evaluating reliability metrics such as LOLE, LOEE, and LOLF under high variability and uncertainty. While alternative methods such as analytical techniques or point estimation methods exist, they often rely on assumptions of linearity or limited variability, which may not capture the full spectrum of intermittent renewable behavior. Moreover, Monte Carlo simulation is widely adopted in power system adequacy studies, including those based on the IEEE RBTS framework, due to its flexibility and ability to model complex systems with multiple interdependent random variables [36,37]. Key reliability indices analyzed include LOLE, LOEE, LOLF, ENSINT, and DNSINT. These metrics, informed by renewable data from previous section, reveal how renewable sources impact system reliability, aiding utility companies and strategists in aligning projects with stability and reliability goals.

4.1. Test System

The IEEE RBTS Bus 6, developed at the University of Saskatchewan [38] and illustrated in Figure 8, is designed to support detailed reliability studies. This system enables the testing of new reliability techniques and methods by providing a model for analyzing composite power system adequacy [39]. Engineers and researchers use the RBTS to simulate diverse scenarios, gaining insights into system performance and reliability under varying conditions. By understanding how changes in one part of the system affect overall adequacy, the RBTS serves as a valuable tool for refining planning and operational strategies in real-world power systems.

Figure 8 represents the test system while Figure 9 illustrates the annual load pattern of the RBTS, spanning 8760 h, with electrical load values between 50 MW and 185 MW. The load profile shows seasonal variations, with higher loads likely occurring during summer or winter peaks due to shifts in consumer demand. Weekly cycles are evident, reflecting higher weekday loads linked to increased business and industrial activity. Recognizing these fluctuations aids in capacity planning, reliability assessment, and demand response strategies.

4.2. Simulation Process

The primary procedures for simulating generation, load, and risk models using the sequential Monte Carlo Simulation method include the following:

4.2.1. Load Model

The load model estimates peak loads on an hourly, daily, and yearly basis through simulations. The process starts with data collection and standardization from historical and reliable sources to prepare for the simulations, where hourly load is calculated using the following relation:

Hourly Load = Week Peak Power × Day Peak Power × Hour Peak Power

Next, adjustments are made to account for the renewable energy integrated into the system:

Net Hourly Load = Hourly Load − Renewable Generation

This approach helps to simulate a realistic load profile by incorporating the impact of renewable generation, providing a more accurate assessment of the demand the system must support.

4.2.2. Generation Model

The power generation setup includes units with varying capacities, Mean Time to Failure (MTTF), and Mean Time to Repair (MTTR). To simulate each unit’s uptime, random values are applied to represent failure and repair durations, following a distribution that characterizes these events. This approach models the reliability and downtime of the units, providing insights into operational dependability.

The times for failure and repair events are calculated as follows:

$$\mathbf{T}\mathbf{T}\mathbf{F}=-{\mathrm{M}\mathrm{T}\mathrm{T}\mathrm{F}}_{\mathrm{i}}\times \mathrm{l}\mathrm{n}({\mathrm{U}}_1)$$

(1)

$$(\mathbf{T}\mathbf{T}\mathbf{R})=-{\mathrm{M}\mathrm{T}\mathrm{T}\mathrm{R}}_{\mathrm{i}}\times \mathrm{l}\mathrm{n}({\mathrm{U}}_2)$$

(2)

where TTF is time to failure and TTR is time to repair. Finally, ${\mathrm{U}}_1$ and ${\mathrm{U}}_2$ are random values between 0 and 1 generated for each failure and repair event.

4.2.3. Risk Indices

The risk assessment model assesses measures of reliability, including LOLE, LOEE, LOLF, ENSINT, and DNSINT [40].

$$\mathbf{L}\mathbf{o}\mathbf{s}\mathbf{s} \mathbf{o}\mathbf{f} \mathbf{L}\mathbf{o}\mathbf{a}\mathbf{d} \mathbf{E}\mathbf{x}\mathbf{p}\mathbf{e}\mathbf{c}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}: \mathrm{L}\mathrm{O}\mathrm{L}\mathrm{E}={\displaystyle \frac{\sum \mathrm{L}\mathrm{o}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d} \mathrm{i}\mathrm{n} \mathrm{H}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}}{\mathrm{N}}}$$

(3)

$$\mathbf{L}\mathbf{o}\mathbf{s}\mathbf{s} \mathbf{o}\mathbf{f} \mathbf{E}\mathbf{n}\mathbf{e}\mathbf{r}\mathbf{g}\mathbf{y} \mathbf{E}\mathbf{x}\mathbf{p}\mathbf{e}\mathbf{c}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}: \mathrm{L}\mathrm{O}\mathrm{E}\mathrm{E}={\displaystyle \frac{\sum \mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y} \mathrm{N}\mathrm{o}\mathrm{t} \mathrm{S}\mathrm{e}\mathrm{r}\mathrm{v}\mathrm{e}\mathrm{d}}{\mathrm{N}}}$$

(4)

$$\mathbf{L}\mathbf{o}\mathbf{s}\mathbf{s} \mathbf{o}\mathbf{f} \mathbf{L}\mathbf{o}\mathbf{a}\mathbf{d} \mathbf{F}\mathbf{r}\mathbf{e}\mathbf{q}\mathbf{u}\mathbf{e}\mathbf{n}\mathbf{c}\mathbf{y}: \mathrm{L}\mathrm{O}\mathrm{L}\mathrm{F}={\displaystyle \frac{\sum \mathrm{L}\mathrm{o}\mathrm{s}\mathrm{s} \mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d} \mathrm{F}\mathrm{r}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}}{\mathrm{N}}}$$

(5)

$$\mathbf{Expected} \mathbf{Energy} \mathbf{not} \mathbf{Supplied}: \mathrm{E}\mathrm{N}\mathrm{S}\mathrm{I}\mathrm{N}\mathrm{T}={\displaystyle \frac{\sum \mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y} \mathrm{N}\mathrm{o}\mathrm{t} \mathrm{S}\mathrm{e}\mathrm{r}\mathrm{v}\mathrm{e}\mathrm{d}}{\sum \mathrm{L}\mathrm{o}\mathrm{s}\mathrm{s} \mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d} \mathrm{F}\mathrm{r}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}}}$$

(6)

$$\mathbf{D}\mathbf{e}\mathbf{m}\mathbf{a}\mathbf{n}\mathbf{d} \mathbf{n}\mathbf{o}\mathbf{t} \mathbf{S}\mathbf{u}\mathbf{p}\mathbf{p}\mathbf{l}\mathbf{i}\mathbf{e}\mathbf{d}: \mathrm{D}\mathrm{N}\mathrm{S}\mathrm{I}\mathrm{N}\mathrm{T}={\displaystyle \frac{\sum \mathrm{D}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{d} \mathrm{n}\mathrm{o}\mathrm{t} \mathrm{S}\mathrm{u}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{d}}{\sum \mathrm{L}\mathrm{o}\mathrm{s}\mathrm{s} \mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d} \mathrm{F}\mathrm{r}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}}}$$

(7)

4.3. Results and Analysis

A total of 28 projects were simulated and partially injected into the RBTS, showing reliability improvements for each project when benchmarked against the RBTS standard operating conditions, as shown in

Table 4. Reliability indices of PV projects analyzed in this study.

NAME	CI (MW)	D	DNSINST	ENSINT	LOEE	LOLE	LOLF
RBTS		4.994149	4.914966	44.88368	9.205642	1.0243	0.2051
Sakaka	30	2.986718	4.761892	25.88211	4.676897	0.5397	0.1807
	60	2.669365	4.649016	24.24778	3.894194	0.4287	0.1606
	90	2.349966	5.388132	22.71643	3.323414	0.3438	0.1463
Rafha	30	3.142857	4.842287	28.63115	5.230911	0.5742	0.1827
	60	2.642556	4.655558	24.48623	4.062266	0.4384	0.1659
	90	2.356098	5.548341	22.55982	3.237334	0.3381	0.1435
Quriyat	30	3.17982	4.975205	30.81557	5.466683	0.5641	0.1774
	60	2.589841	4.532415	22.27057	3.507614	0.4079	0.1575
	90	2.345748	5.364778	21.5679	3.069113	0.3338	0.1423
Tabarjal	30	3.125984	4.805532	28.85852	5.131045	0.5558	0.1778
	60	2.65656	4.674117	24.14912	4.141575	0.4556	0.1715
	90	2.36633	5.493511	21.86075	3.246321	0.3514	0.1485
Mahd AlDahab	30	2.677807	4.366655	23.19977	4.442755	0.5128	0.1915
	60	2.099757	5.066566	17.75568	2.919033	0.3452	0.1644
	90	1.987325	5.207146	17.38938	2.606669	0.2979	0.1499
Alhinakiah one	30	2.753143	4.643417	24.82771	4.344849	0.4818	0.175
	60	2.184211	4.939707	19.28224	3.22399	0.3652	0.1672
	90	2.032258	5.348638	17.37403	2.423678	0.2835	0.1395
Alhinakiah two	30	2.678845	4.541725	23.54033	3.994794	0.4546	0.1697
	60	2.1	4.809712	17.69739	2.654609	0.315	0.315
	90	2.081789	5.620253	19.87212	3.109987	0.3258	0.1565
Alkahfah	30	2.80402	4.757255	25.32033	4.534871	0.5022	0.1791
	60	2.378116	5.260928	24.12934	3.969277	0.3912	0.1645
	90	1.983075	5.265626	15.77222	2.236501	0.2812	0.1418
AlMasa	30	2.787826	4.544256	25.02822	4.317369	0.4809	0.1725
	60	2.300306	5.046317	21.12569	3.454051	0.3761	0.1635
	90	2.087066	5.382204	18.83279	2.984997	0.3308	0.1585
Shoiba One	30	2.734428	4.56952	24.19266	4.272424	0.4829	0.1766
	60	2.184262	5.016889	19.0928	2.911652	0.3331	0.1525
	90	2.066781	6.362751	18.56957	2.168926	0.2414	0.1168
Shoiba Two	30	2.653017	4.432701	23.13387	4.293647	0.4924	0.1856
	60	2.29914	5.120657	21.84279	3.300446	0.3474	0.1511
	90	1.934265	6.029225	16.57603	2.017302	0.2354	0.1217
Jeddah	30	2.731303	4.315463	24.69448	4.622807	0.5113	0.1872
	60	2.298935	5.051233	21.09275	3.168131	0.3453	0.1502
	90	1.987988	6.079867	17.45155	2.324547	0.2648	0.1332
Rabigh One	30	2.417309	4.461878	19.38621	3.270453	0.4078	0.1687
	60	2.212195	4.894609	19.71965	3.234022	0.3628	0.164
	90	2.081954	6.514252	19.24481	2.324773	0.2515	0.1208
Rabigh Two	30	2.648633	4.580811	23.716	4.16453	0.4651	0.1756
	60	2.2	4.929288	19.52724	2.968141	0.3344	0.152
	90	2.02717	6.311944	17.94559	2.377791	0.2686	0.1325
Saad One	30	2.941514	4.93582	29.17826	5.088689	0.513	0.1744
	60	2.458333	4.757371	21.43469	3.446698	0.3953	0.1608
	90	2.29255	5.66847	21.66061	3.168947	0.3354	0.1463
Saad Two	30	2.798216	4.817981	23.76363	4.263196	0.502	0.1794
	60	2.448916	4.577374	22.39531	3.616843	0.3955	0.1615
	90	2.305939	5.646492	20.79966	3.01179	0.3339	0.1448
Sudir	30	2.77814	4.514425	24.34175	4.476448	0.5109	0.1839
	60	2.395657	5.037301	22.62988	3.752034	0.3972	0.1658
	90	2.171657	5.521199	20.31033	3.052642	0.3264	0.1503
AlRass One	30	2.80436	4.788836	25.01335	4.589951	0.5146	0.1835
	60	2.345761	4.809997	21.00356	3.492892	0.3901	0.1663
	90	2.156499	5.583292	19.88464	2.998604	0.3252	0.1508
AlRass Two	30	2.71646	4.687851	22.85204	4.053952	0.4819	0.1774
	60	2.324807	4.576877	20.74374	3.487023	0.3908	0.1681
	90	2.119497	5.572055	19.54363	2.796694	0.3033	0.1431
Madina	30	2.768091	4.40172	25.41738	4.46075	0.4858	0.1755
	60	2.111041	5.131926	17.52175	2.777197	0.3346	0.1585
	90	2.049413	5.500514	18.71461	3.029895	0.3318	0.1619
WadiAlDawasir	30	2.624086	4.364111	23.32585	4.145004	0.4663	0.1777
	60	2.289651	5.028973	21.69476	3.542754	0.3739	0.1633
	90	2.057692	5.54631	18.56898	2.413967	0.2675	0.13
AlSadawi	30	2.922142	4.575217	26.44041	4.788358	0.5292	0.1811
	60	2.491599	4.931046	23.15658	3.721263	0.4004	0.1607
	90	2.289347	5.890661	21.25047	3.091944	0.3331	0.1455
Layla	30	2.672113	4.331021	22.61298	4.013803	0.4743	0.1775
	60	2.309669	5.05217	20.75151	3.069149	0.3416	0.1479
	90	2.197115	5.605279	20.58378	2.996998	0.3199	0.1456
Red Sea	30	2.618255	4.619257	22.89541	3.988381	0.4561	0.1742
	60	2.239708	5.212128	20.12781	3.031249	0.3373	0.1506
	90	2.062758	6.488719	18.96678	2.296877	0.2498	0.1211

. For instance, in the Sakaka project, an increase in injected capacity from 30 MW to 90 MW consistently lowers reliability indicators, such as the duration of load shedding, demand not supplied per interruption, energy not supplied per interruption, LOEE, LOLE, and LOLF. Notably, the LOLE decreases from 0.5397 at 30 MW to 0.3438 at 90 MW, indicating improved reliability with higher capacity.

Similarly, analyzing projects such as Rafha, Quriyat, and Tabarjal reveals that higher injected capacities generally result in improved reliability metrics, with decreased LOEE, LOLE, and LOLF values. For instance, the Rafha project’s LOEE decreases from 5.230911 at 30 MW to 3.237334 at 90 MW. These outcomes demonstrate that increasing the grid-injected capacity of renewable energy typically enhances the power system’s dependability by reducing the frequency and duration of load shedding while improving energy supply adequacy. This comprehensive analysis highlights the positive impact of higher renewable capacities on overall system reliability and performance.

The data in Table 4 show that as power levels increase, most regions experience improved system reliability, with reductions in interruption duration (D), ENSINT, and LOEE. Higher power levels lead to more stable performance, though the LOLF varies across regions. Regions like Sakaka and Rafha exhibit stable metrics, while areas like Madina and Sudir show more variability, indicating potential infrastructure or energy management challenges.

Similarly, analysis of different wind farms is carried out and results are tabulated in

Table 5. Reliability indices of wind projects analyzed in this study.

Project	CI (MW)	D	DNSINST	ENSINT	LOEE	LOLE	LOLF
RBTS		4.994149	4.914966	44.88368	9.205642	1.0243	0.2051
Waad AlShimal	30	3.795455	5.026138	34.21653	4.064923	0.4509	0.1188
	60	2.941292	5.277538	26.3325	2.691181	0.3006	0.1022
	90	2.658889	5.984268	26.14721	2.353249	0.2393	0.09
Doumat AlJandal	30	3.84089	4.959299	35.20673	4.270576	0.4659	0.1213
	60	3.124869	5.448135	28.86731	2.751054	0.2978	0.0953
	90	2.518214	5.705097	21.76991	1.852619	0.2143	0.0851
Yanbu	30	4.432872	5.235267	40.57095	4.98617	0.5448	0.1229
	60	4.207765	5.424261	36.77935	3.505072	0.401	0.0953
	90	3.99453	5.999263	36.80344	3.363834	0.3651	0.0914
AlGhat	30	3.630905	4.800456	32.55675	4.066338	0.4535	0.1249
	60	2.794199	5.503536	24.80155	1.795632	0.2023	0.0724
	90	2.367742	5.471581	20.08779	1.245443	0.1468	0.062

. This positive change was observed in projects such as Rafha, Quriyat, Tabarjal, and Mahdaldahab, where higher capacities resulted in decreased energy deficits and fewer instances of load shedding. The Rafha and Quriyat projects notably improved their LOEE. Moreover, the expansion of capabilities implies that a higher adoption of renewable energy could mitigate the likelihood of energy deficits during disruptions. Other projects like Alhinakiah One, Almasa, and Shoiba demonstrated a reduction in both LOLE and LOLF, suggesting that integrating renewable energy leads to fewer and shorter durations of power outages. Furthermore, wind projects such as Waad AlShimal, Doumat AlJandal, and Yanbu also exhibited this trend. In the Doumat AlJandal project, increasing the capacity from 30 MW to 90 MW resulted in improvements across all reliability aspects, highlighting the positive impact of wind energy on system reliability.

The results in Table 5 indicate a general trend of improved reliability as capacity increases from 30 MW to 90 MW across most regions. Indicators such as interruption duration (D), DNSINST, ENSINT, LOEE, LOLE, and LOLF all show noticeable decreases with higher capacity. Regions like AlGhat and Doumat AlJandal demonstrate strong performance improvements, especially in ENSINT and LOEE. However, Yanbu exhibits less sensitivity to increased capacity, with only modest reductions in reliability indices, suggesting room for targeted improvements.

5. Discussion and Future Research Directions

The findings of this study provide valuable insights into the performance and reliability of power systems with increasing levels of renewable energy integration, particularly in desert regions. However, the current methodology relies on modeled data and historical weather datasets. Incorporating real-time grid data in future work would improve model accuracy and enhance the applicability of the results to real-world operations. Moreover, the absence of a cost–benefit or economic analysis limits the ability to evaluate trade-offs between reliability, investment, and operational costs. Including such an analysis would support more robust decision-making aligned with both technical and financial objectives.

While the models developed in this study offer a solid foundation, they do not account for climate-related risks such as sandstorms, extreme heat, and dust accumulation on PV modules. These environmental stressors can significantly reduce generation efficiency and accelerate component degradation. Future work should include detailed climate-resilient modeling frameworks that capture these impacts and inform maintenance strategies and system resilience planning.

Finally, based on the findings presented in this thesis, several areas for future investigation are suggested. First, conducting a detailed reliability assessment of power systems aiming to meet 50% of local demand through renewable sources—aligned with Saudi Arabia’s Vision 2030—would be instrumental in identifying infrastructural and operational challenges. Additionally, the IEEE RTS-79 system, with its load capacity of up to 2800 MW, offers a robust platform for integrating and simulating various levels of renewable energy. Future studies should leverage this flexibility to evaluate system performance under different penetration levels and environmental scenarios.

6. Conclusions

This study demonstrates that integrating renewable energy capacities into Saudi Arabia’s power grid can significantly enhance system reliability, particularly when supported by robust planning and effective implementation strategies. By conducting a comprehensive reliability assessment using sequential Monte Carlo simulations and real project data, the research provides a quantitative foundation for evaluating renewable energy’s contribution to grid adequacy, an area that remains underexplored in the context of the Gulf region. The findings contribute to the existing literature by filling a critical gap concerning region-specific modeling of renewable integration, which is vital for informing national energy policies aligned with Vision 2030 goals. The results offer a practical framework for utility companies and system operators to maximize the benefits of renewable resources while safeguarding key reliability metrics. Notably, increasing renewable injections from 30 MW to 90 MW resulted in substantial improvements across all indices—Loss of Load Expectation (LOLE), Loss of Energy Expectation (LOEE), Loss of Load Frequency (LOLF), Energy Not Supplied per Interruption (ENSINT), and Demand Not Supplied per Interruption (DNSINT). These improvements validate the adequacy of renewables in meeting growing demand without compromising system reliability.

While site-specific outcomes are promising, the research also identifies a need to further examine the aggregated performance of the national grid when diverse renewable projects are integrated simultaneously. Such scenarios may introduce new operational uncertainties that require advanced modeling and real-time validation. Future work should therefore expand the geographic and temporal scope of simulations, incorporate real-time grid data, and assess techno-economic trade-offs under evolving climate risks such as extreme heat and sandstorms.