Large Scale Solar Research Articles

Long-Term Solar Power Generation Forecasting at Eastern West Large Scale Solar (LSS) Farm using Random Forest Regression (RFR) Method

Precise forecasting of power generation and demand is essential for effective resource allocation and energy trading in contemporary energy systems. Power forecasting accuracy has increased dramatically since Random Forest Regression (RFR) techniques were used. The study's primary objective is to forecast electricity generation in Malaysia's Eastern West region, with a concentration on solar energy. The research process entails gathering and examining pertinent factors, weather information, and historical power data. To evaluate the accuracy and predictive potential of RFR models, a specific power grid is used for training, validation, and testing. One of the anticipated results is the creation of an accurate model for power generation predictions, which will help to optimise energy operations and smoothly incorporate renewable sources. The paper examines the advantages, disadvantages, and best practices related to RFR-based power forecasting. The dataset, which spans the years 2019 to 2023, includes 30-minute interval records for the following variables: average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed. Using the RandomForestRegressor class from the scikit-learn library, the RFR model is implemented. In order to assess the model's overall fit, average deviation, and sensitivity to outliers, measures such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) are used on the test set. The temperature, irradiance, and AC power output of PV modules are found to be strongly correlated.

Good fences make good neighbors: Stakeholder perspectives on the local benefits and burdens of large-scale solar energy development in the United States

In order to meet decarbonization goals, the number of large-scale solar (LSS) facilities in the US is expected to increase considerably. The advantages of LSS over fossil-fueled power generation are numerous and well documented. However, residents living nearby proposed and existing LSS sites have voiced a number of concerns about LSS, including its possible impacts to farmland and agricultural production, biodiversity, stormwater runoff, home and property values, as well as concerns about solar panels' toxicity and safety. While rapid expansion of LSS currently relies on officials permitting and residents being willing to host these systems, the appetite for LSS in some communities may be waning. Here we examine the perceived benefits and burdens of recent LSS developments, conducting 54 interviews with a broad set of stakeholders including residents, officials and developers at seven LSS sites across the US. We focus on identifying residents' most common concerns regarding LSS systems across states, site types, landscapes and ownership structures. We find concerns are associated with either LSS development processes or impacts, and center on the type and amount of information provided, the community's influence over project design, the efficacy of community subscription efforts, as well as projects' economic, environmental, and visual and landscape impacts. Importantly, we also investigate strategies that have been employed to improve perceptions and project outcomes, which include increasing in-person engagement, more explicit discussion of project tradeoffs, third-party intermediaries acting as community champions, and explicit requirements for meaningful local economic benefits.

Investigation of power quality issues in 14-bus electrical network with high penetration of renewable generation

Power quality issues arise in electrical networks when variable renewable energy (VRE) is integrated into them due to their random and intermittent nature which depends on weather conditions and other factors. The variation of solar irradiance throughout the day affects the energy produced by solar panels and the integration of solar power into electrical networks will result in changes and fluctuations in the voltage profile of buses. Reactive power compensation is required to improve the bus voltage levels of the electrical network to be within the required limits and the optimal allocation of reactive power compensation devices in the network is a complex problem to be investigated for the optimum injection of reactive power to obtain better voltage profiles for the entire network. This research investigated the penetration of variable solar energy into an electrical network in terms of voltage and reactive power flow. A variety of literature was reviewed in the scope of reactive power management in power systems and a gap in addressing the optimal allocation of compensation devices in the IEEE-14 bus was addressed based on the proposed methods followed by discussions of the results in terms of voltage profiles and reactive power flow in the buses. The objective is to produce an output power of higher quality and reliability for the loads so that intermittent sources of renewable energy can be more competent with energy sources such as fossil fuels that do not depend on weather conditions. Integration of methods using compensation optimisation (optimal allocation of capacitors) and volt-var regulation (smart inverter) to improve the voltage profile that was dropped and the fluctuations after penetration of solar power were carried out. A solar bus with variable energy generation was connected to the IEEE-14 bus to study the voltage variations. This was executed by the power flow calculation module to determine the voltages and reactive power in the buses of the network. With the optimum allocation of the capacitors, the voltage levels in all weak buses of the IEEE-14 bus were increased to be between 0.95 p.u. and 1.05 p.u. which was the voltage specifications of the Malaysian Grid Code Requirements. The voltage for every weak bus in the IEEE-14 bus showed a rise of 5.7% from 7 a.m. to 12 p.m. With that, the volt-var function was used for reactive power regulation at the point of common coupling (PCC) and a reduction of voltage deviation of 2.828 to 1.3% in the IEEE-14 bus was observed. The average voltage profile of all buses managed to attain a value of 98.99% from 95.673% (with solar power) with the optimal allocation of capacitors and volt-var regulation. The beneficiaries of this project will be the Sustainable Energy Development Authority (SEDA) which administers the Net Energy Metering (NEM) scheme and Tenaga Nasional Berhad (TNB) which is the Malaysian multinational electrical company focused primarily on the generation, transmission, and distribution of electricity in Peninsular Malaysia. The Energy Commission and Ministry of Energy and Natural Resources are also beneficiaries as they carried out a competitive bidding programme for large-scale solar (LSS) known as the LSS@MEnTARI or LSSPV4 to attain bids for the development of around 1000 MW AC of LSS power plants to be operational in Malaysia by 2022. This work will also be beneficial in future research in planning reactive power compensation devices in networks of multiple VRE sources, communication, and coordinated control of smart inverters, and incorporation of these devices for smart grid applications.

Examining Large-Scale Solar (LSS) Photovoltaic (PV) Operating Utilities by using Environmental Impact Screening (EIS)

For Malaysia’s future energy needs, solar photovoltaic (PV) technology and farming is becoming a more costeffective option and technology in achieving Malaysia’s goal of having 31% renewable energy in its power mix by year 2025. Malaysia’s global climate commitment is to reduce its economy-wide carbon intensity (as a percentage of GDP) by 45% by 2030 compared to 2005 levels. Realizing the government’s vision is critical to assisting the country in meeting its Nationally Determined Contributions (NDC) targets (SEDA, 2022). Hence, this paper highlights the evaluation of an existing solar farm located at Seberang Perai Pulau Pinang, Malaysia as a case study to examine the potential environmental impact arising from of large-scale solar (LSS) PV development utilising the Analytic Hierarchy Process (AHP) under the Multi-Criteria Decision Making (MCDM) technique. The approach for this study was conducting in depth interviews with 16 people, including academics, authorities, and experts in the field. Large projects need to be evaluated to make sure that any potential problems are found and resolved early in the planning and design phases. This study considers the effects on the environment and chemicals (EC), as well as occupational safety (OSH) and economic impact (EI). AHP is then used to assess the data and utilized to produce the final component rankings. By showing the outcomes, the bar chart will aid in improved decision-making. The bar chart’s ability to display the results will help in the decision-making. From the results, LSS PV farm were conducive to achieving strong sustainability however in term of environmental degradation, mitigation action must be implemented on site to tackle the arising issues.

Forecasting meteorological impacts on the environmental sustainability of a large-scale solar plant via artificial intelligence-based life cycle assessment

Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12–68 times, 13–73 times, and 18–98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.

Climate change impact on solar system in Malaysia: Techno-economic analysis

Malaysia is actively addressing environmental responsibilities by pledging a 45 % reduction in greenhouse gas emissions by 2030, aided by a large-scale solar photovoltaic initiative. Nonetheless, the unprecedented meteorological events due to climate change such as extreme temperatures and precipitation may reduce the large-scale solar performance and lead to infrastructure damage. The primary objective of this study is to conduct a screening-level analysis from both technical and economic perspectives, in order to assess the effects of extreme temperature and precipitation on large scale solar in Malaysia. This study hypothesized that extreme temperature and precipitation reduce large scale solar performance and increase infrastructure damage. The results demonstrate that both extreme temperature and precipitation increase the power and efficiency loss over time. In general, infrastructure damages caused by extreme temperature and precipitation can result in degradation, a shortened lifespan, and ultimately a collapse. In this study, the costs of physical damage were estimated using the modified levelized cost of energy, and it was found that the cost of physical damage in extreme temperature conditions was approximately 30 % greater than the cost in extreme precipitation conditions, which was nine times greater than in normal condition. Compared to a no-adaptation scenario, it is possible to reduce the additional costs due to extreme temperature and precipitation by up to 53 % and 49 %, respectively. Enhancements, additional analysis, and validation of these findings could aid in the readiness for climate change mitigation efforts, not only for solar power systems but also for other types of energy systems.

Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant

Detecting and monitoring faults in solar photovoltaic (PV) systems is crucial to ensure optimal efficiency and prevent safety and fire hazards. However, the conventional operation and maintenance (O&M) of solar PV systems do not utilize machine learning for fault detection and classification. This poses challenges for plant operators, especially those managing large-scale solar (LSS) PV plants, who typically rely on manual approaches to screen large amounts of electrical data and inspect numerous string panels. Consequently, the cost of O&M is high. This study aims to use advanced machine-learning techniques to detect anomalies in a Large-Scale Photovoltaic (LSSPV) plant. The study collects data from the plant which located in the central of Peninsular Malaysia and employs K-Means for clustering and Long-Short Term Memory (LSTM) for anomaly detection in the predicted electrical current of string modules. The model is developed using Jupyter Notebook from the Python Package Index. To validate the accuracy of the proposed model, the study compares LSTM with Artificial Neural Network (ANN) using relative error as the evaluation metric. The results indicate that the LSTM method identifies anomalies in the predicted output current of the string modules more accurately and with lower relative error than the conventional ANN technique. The proposed model could help plant operators perform predictive maintenance of LSSPV plants at a minimal cost and time.

Agrivoltaic approach in improving soil resistivity in large scale solar farms for energy sustainability

Recently, approaches have been established to improve the grounding system performance, especially with the increasing numbers of large-scale solar (LSS) PV farms. Soil resistivity is one of the contributing factors to the inefficiency of power generation as it induces high impedance on electricity flow from the source to highly sensitive PV equipment and devices such as inverters. Some critical issues on nutrient leaching derived from ammonium sulphate fertilizer direct to ground soil are the focal point of this work with the fact that soil resistivity value is inversely proportional to increasing soil salt content. Therefore, a low grounding system could be obtained with a high Electrical Conductivity (EC) and high soil moisture content. In this work, a conceptual setup for an agrivoltaic condition with 500 units of herbal crops has been cultivated directly underneath 1 PV string in Puchong Solar Farm, Selangor, Malaysia. The amount of salt or nutrient under this condition was measured leaching approximately 40 mL per polybag as means of ground treatment to improve soil resistivity value, thus indirectly reducing the risk of electrical damage. The agrivoltaic approach has shown that it can improve the performance of a grounding system in an LSS PV farm with a significant reduction of 4.45 Ω earth resistance on average.

Univariate and multivariate short-term solar power forecasting of 25MWac Pasir Gudang utility-scale photovoltaic system using LSTM approach

The generation of solar photovoltaic (PV) systems in Malaysia has great potential due to the abundance of sunlight and high irradiation level. Malaysia’s unique location near the equator makes solar energy the most attractive option for future energy sources. However, the erratic weather leads to variability of power generation, especially during high feed-in of solar energy that will eventually affect grid system stability. Hereof, solar power forecasting is critical, especially in operating utility-scale photovoltaic systems or large-scale solar (LSS) plants. This paper presents an approach to forecasting solar power generation for 10-min to 180-min ahead based on univariate and multivariate using Long -Short-Term Memory (LSTM) technique. The model performances are evaluated based on a real dataset from the 25MWac Pasir Gudang LSS plant from July 2021 to May 2022. The result shows that LSTM with a univariate model outperformed the multivariate model for the short-term forecasting (10-min to 50-min ahead) by 2.09% of RMSE. However, multivariate outperformed univariate for the longer forecasting horizon at 180-min ahead by 34.87% of RMSE. The forecasting output for the multivariate model that only uses historical meteorological data is less reliable than the multivariate model that uses historical meteorological and AC power output data. This research finding is envisaged to provide benefits to the grid system operation, planning, maintenance, and scheduling, thereby improving the reliability of the LSS plant.

An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting

The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm.

Rallying the anti-crowd: Organized opposition, democratic deficit, and a potential social gap in large-scale solar energy

Declining costs and high public support for solar energy in national surveys predict accelerated development of large-scale solar (LSS) systems across the United States. Both scholarly work and the popular press suggest LSS projects increasingly face local resistance at the project level. This gap between the number of LSS projects proposed and those completed can be thought of as a social gap, just as Bell, Gray and Haggett first described with respect to wind energy development in 2005. Here we use those scholars' framework to investigate a potential LSS social gap in four communities in Michigan. Using 34 qualitative semi-structured interviews with residents, local officials and LSS developers, we describe how a social gap may develop because of organized opposition groups generating a democratic deficit. The undue influence and organization of this vocal minority, along with a lack of meaningful engagement with residents by local officials and developers, led to distrust in relationships and the decision-making practices necessary to address residents' qualifications. Those qualifications centered on LSS's aesthetic impact, inadequate compensation, improper use of agricultural land, water safety, project size, and the transparency of decision-making processes and residents' ability to influence those processes. Our results suggest that addressing such a social gap requires increased communication and earlier engagement by local officials and developers with residents, and better alignment of agricultural values and production with LSS development. Finally, better organization of—and by—project supporters may increase opportunities to influence and take ownership of LSS projects.

DETERMINATION OF SPECIAL PERMIT RATE FOR LARGESCALE SOLAR DEVELOPMENT IN JOHOR BASED ON PLANNER PERSPECTIVE

Over the past decade, the Malaysian government has put its effort into various programmes to promote solar energy in the country. As for now, Large Scale Solar (LSS) is the biggest programme conducted by Sustainable Energy Development Authority (SEDA) and permitted by Energy Commission (EC). As the large-scale solar does not have any proper guideline, especially in development approval consideration, Johor land administrators have put telecommunication tower development as a benchmark in approving large-scale solar developments that use the Special Permit method. However, the Special Permit fee appears unfitting for the development. Thus, this paper aims to determine the Special Permit rate for large-scale solar development which are profitable to the stakeholders. Its drawback is particularly related to solar radiance, which is the key criterion of site selection to develop the most profitablesite. Selected respondents have been interviewed and the findings have led to a direction to standardise a legal framework for large-scale solar, especially in Johor.

Energy storage sizing and enhanced dispatch strategy with temperature and safety considerations: A techno‐economic analysis

AbstractMalaysia signed Paris agreement in 2015 as global response to climate change and committed to lower the greenhouse gases emissions by 45% by 2030. Under this national initiative, Malaysia government planned a series of large scale solar (LSS) projects and regulated by Energy Commission. It is aimed to achieve installed capacity of 1 GW by 2020. One of the main drawbacks for using solar energy is the intermittent issue in solar power generation. This will affect the power quality and reliability of the power grid especially in the LSS systems. This article aims to identify and optimize the most technically and economically viable energy storage system (ESS) for selected locations among the ongoing mega projects. The location selection for this study is based on the largest solar capacity planned for Peninsular Malaysia. This project involved development of hybrid scheme including Flywheel and nine different types of battery at six locations in Malaysia using Energy Commission Malaysia's data. Comprehensive power systems have been developed with different storage options applied to selected locations. This is followed by extensive techno‐economic evaluation using net present cost, levelized cost of electricity including costs of ESS, operation & maintenance, initial capital investment, replacement cost, revenue from ESS, annual throughput, and so on. Comparative studies under two different dispatch strategies with local contextualized data for LSS projects in Malaysia have been carried out. The optimal ESS type both technical and economically has been identified. In addition to this, performance of battery ESS with and without temperature consideration has been evaluated. Last, data from a net metering scheme is used to analyze the ESS types that exhibit best performance. Safety concerns related to these ESS types have been provided. Last, this article also presents the adaptability and replicability of the proposed methodology in countries that planned for LSS with local contextualized data.

Current Status, Scenario, and Prospective of Renewable Energy in Algeria: A Review

Energy demand has been overgrowing in developing countries. Moreover, the fluctuation of fuel prices is a primary concern faced by many countries that highly rely on conventional power generation to meet the load demand. Hence, the need to use alternative resources, such as renewable energy, is crucial in order to mitigate fossil fuel dependency, while ensuring reductions in carbon dioxide emissions. Algeria—being the largest county in Africa—has experienced a rapid growth in energy demand over the past decade due to the significant increase in residential, commercial, and industry sectors. Currently, the hydrocarbon-rich nation is highly dependent on fossil fuels for electricity generation, with renewable energy only having a small contribution to the country’s energy mix. However, the country has massive potential for renewable energy generation, such as solar, wind, biomass, geothermal, and hydropower. Therefore, the government aims to diversify away from fossil fuels and promote renewable energy generation through policies and renewable energy-related programs. The country’s Renewable Energy and Energy Efficiency Development Plan focuses on large scale solar, wind generation as well as geothermal and biomass technologies. This paper provides an update on the current energy position and renewable energy status in Algeria. Moreover, this paper discusses renewable energy (RE) policies and programs that aim to increase the country’s renewable energy generation and its implementation status.

OPTIMUM TILT ANGLE FOR MAXIMIZING LARGE SCALE SOLAR ELECTRICAL ENERGY OUTPUT

Many large-scale solar (LSS) plants that are being installed today have solar photovoltaic (PV) panels mounted on fixed structures, which limits its electrical energy production. Tracking system can be installed so that the PV panels could change its tilt angle automatically in accordance with the sun’s movement. However, it will increase the construction, operation and maintenance cost significantly. Another option is to manually adjust the tilt angle on periodically basis, but the time period and the optimum tilt angle need to be systematically determined. This paper investigates the impact of using monthly and seasonal optimum tilt angle, βopt on electrical energy production of LSS plant. The proposed strategy can be implemented by using tiltable solar panel mounting structures which is far cheaper than the tracking system. For the study, 1 MW LSS system model is used. Twelve cities around the globe with latitude angle ranging from 0º to 55º are strategically selected. The electrical energy output from the 1 MW LSS plant is simulated by using PV mathematical model that is developed in Matlab software. The overall results show that by adjusting the tilt angle of the PV modules into its optimum angle on monthly or seasonal basis, it would increase the generated energy output between 1.91% and 7.24% for monthly adjustments and between 1.59% and 6.06% for seasonal adjustments.

Performance comparison of fixed and single axis tracker photovoltaic system in large scale solar power plants in Malaysia

<span>Malaysia is rapidly expanding the generation capacity of solar power through large scale solar (LSS) projects with the aim to achieve 20% renewable energy mix by 2025. This has motivated many solar industry players to explore the usage of solar PV with single axis tracker (SAT) system. However, many are still hesitant due to the lack of understanding on the comparative performance between fixed mounted solar PV with solar PV with SAT system. This paper aims to provide a comparative analysis on the performance of both systems. Simulation using PVSyst 6.83 was performed in five potential LSS sites spread across Peninsular Malaysia in Perlis, Kelantan, Pahang, Selangor and Johor with the same installed capacity of 10.32MWp. The energy yield and capacity factor for 21 years were simulated. On the average, it was found that SAT outperforms fixed mounted solar PV system by 15.08% based on their performance on their first year operation. </span>

Design, optimization and safety assessment of energy storage: A case study of large‐scale solar in Malaysia

AbstractMalaysia signed the Paris Agreement in 2015 and committed to reduce the greenhouse gases emission up to 45% by 2030. Various large‐scale solar (LSS) projects are in operation and planned for the next decade to meet the national target of 20% renewable energy among energy mix by 2025. Major issues faced in LSS integration are the fluctuations in supply due to intermittency and inability to meet the demand during night hours. An optimized large energy storage system could overcome these challenges. In this project, a power system which includes a large‐scale energy storage system is developed based on the maturity of technology, levelized cost of electricity and efficiency and so on, to meet the demands of electricity generation in Malaysia. In addition, a financial analysis of the proposed storage system is carried out by comparing with a baseline study without energy storage. To develop a more realistic solution, the environmental aspect including safety and location dependency are taken into consideration. The proposed model for the LSS can be replicated in nations with similar geographical conditions and energy target.

Psychosocial Implications of Large-Scale Implementations of Solar Power in Malaysia

The present study aimed to investigate the psychosocial impacts of large-scale solar (LSS) power projects. There were 225 participants (n = 109 women, n = 3 did not indicate gender) participated in our study by completing a series of questionnaires. We found that participants who lived farther from the LSS power project location and those who viewed the project as being impactful were optimistic about the benefits LSS power projects could bring. Our participants also demonstrated support for renewable energy development in Malaysia. These findings may provide important implications for the implementation and execution of LSS power projects and policies.

An outlook on large-scale solar power production in Peninsular Malaysia for scenario year 2030

Traditional power generation mix lacks renewable energy (RE) sources to cover fast depletion of fossil fuel. Malaysia is picking up on solar energy with the aim of enhancing the national power generation mix by reducing the dependency on fossil fuel and thus mitigate the greenhouse gas (GHG) emissions. However, integration of large amount of solar power may pose a challenge to power system planning and operation. This paper therefore, attempts to present an outlook on large-scale solar (LSS) in Peninsular Malaysia for scenario year 2030, with objective to serve a guideline for future power planning by adopting the optimum penetration of LSS. Firstly, total optimal potential areas (OPA) for LSS power production in Peninsular Malaysia are determined based on several important geometry factors. Next, its corresponding technical potentials include energy generation potential (EGP), installation capacity (IC) and annual carbon dioxide emission reduction (CO2 ER) are reported. Three hypothetical studies of solar penetration: 5%, 10% and 15% are demonstrated and subsequently compare with national electricity consumption forecast 2030. Peninsular Malaysia has enormous potential for LSS power production as reflected from total OPA of 10,092 km2. With only 10% of solar penetration (206,691 GWh/yr), it is sufficient to cover national energy demand, forecast to be 134,642 GWh/yr. This positive finding is very encouraging to reveal the significant potential of LSS in national energy mix. It will give a much-needed boost to the country RE sector and a robust growth is envisaged.

Assessment of Malaysia's Large-Scale Solar Projects: Power System Analysis for Solar PV Grid Integration.

Malaysia targets to become the second‐largest producer of solar photovoltaic (PV) in the world by increasing the current output from 12% to 20% in 2020. The government also expects to achieve 45% reduction of greenhouse gas emission by 2030 through renewable energy mainly by solar PV. Large‐scale solar (LSS) aims to produce 2.5 GW, which contributes to 10% of the nation's electricity demands. The LSS system is held back by the grid‐scale integration, transmission, and distribution infrastructure. Thus, power system analysis is crucial to achieve optimization in LSS to power grid integration. This paper investigates various power system analysis models and recommends an optimized configuration based on Malaysia's LSS scenario. In stage 1, an optimal PV sizing is carried out based on real data of LSS installation in different locations. In stage 2, power analysis is carried out using to analyze the potential difference variation when connected to a nine‐bus power system. The potential variation at each bus of the system is assessed and hence provides a feasibility statement on the most effective configurations for LSS–grid integration. This paper serves as the reference model for LSS–grid integration in Malaysia and is expected to be replicated in the other countries with similar conditions.