Solar Electricity Generation Research Articles

Solar photovoltaic adoption among landed residential property in Malaysia: Integration of sustainable energy security dimension

The Malaysian government’s efforts to promote solar photovoltaic (PV) usage among households face a challenge due to its low adoption rate. This study delves into the factors influencing the exponential adoption of solar PV electricity generation among landed residential property owners in Malaysia. The research aims to comprehensively examine the predictors influencing the adoption of solar PV systems among Malaysian households. Hence, the study employs an enhanced Theory of Planned Behavior framework, integrating sustainable energy security dimensions such as availability, affordability, efficiency, acceptability, regulation, and governance. The sample comprised 556 Malaysian residents who owned and resided in the landed properties. The home locations where at least one solar PV installation existed within a residential street. Snowball sampling was employed through referrals, leveraging social and community networks. Collected data was analyzed using the partial least squares structural equation modeling. Attitude, affordability, and acceptability emerged as pivotal factors significantly impacting the intention to use solar PV systems among Malaysian households. This research not only enriches academic discourse but also offers practical implications for policymakers, guiding the formulation of targeted strategies to promote sustainable energy practices and facilitate the widespread adoption of solar PV systems in Malaysia.

Modeling local distributed solar energy potential: a case study from Virginia, USA

ABSTRACT In this paper, we use GIS analysis to estimate potential distributed solar PV capacity and solar electricity generation in a suburban neighborhood in Virginia, United States. Using a combination of LiDAR and solar insolation data, we find that 37% of rooftop space in the study area would receive sufficient solar insolation to support a distributed solar (DPV) system. Applying conservative assumptions, we estimate that nearly 19 MW (AC) of distributed rooftop PV could realistically be installed, providing 28% of the study area’s estimated annual electricity demand. These findings provide evidence of the significant untapped potential of DPV, and support the need for streamlined permitting processes and other incentives to reduce soft costs and facilitate DPV installation. We also discuss opportunities for merging planning and engineering research to support targeted utilization of DPV in locations where it can best support distribution grid operations, such as on commercial sector buildings in particular.

Green villages by Wind + Solar + Hydrogen

This paper describes 5 steps on the move to cost-effective green villages, powered mainly by Solar, Wind and Hydrogen. In Britain, there are around 6000 villages comprising almost half the total population. These village dwellers wish to reduce their expensive energy bills, achievable by profitably going green with local Wind and Solar generation of electricity plus Hydrogen energy storage. The first step is to measure the total energy usage in the village. This defines the size of the required renewable private wire energy installations. The second step calculates savings on grid bills for electricity, natural gas and petroleum transport products, to ensure rapid payback for the renewables to be installed. A third concern is the planning permission for Wind/Solar installations that may take months to approve. Perhaps the most important requirement is to attract investment in the fourth step. Finally, the fifth step is to procure and commission the equipment needed in the village to prove that local energy can beat the present centralized grid energy systems, using Hydrogen as the vital energy storage molecule to fill gaps in Wind/Solar supply.

Potential Assessment and Techno-Economic Evaluation of Photovoltaic Energy Conversion: A Case of Addis Ababa Light Rail Transit System

Climate change resulting from the burning of fossil fuels has led to severe consequences like global warming, flooding, and melting of ice sheets. One of the significant contributors to this problem is the ever-increasing production and consumption of energy, which is still primarily fossil-based and emits billions of tons of hazardous GHG. The transportation industry is one of the biggest contributors to carbon dioxide emissions, and the rail transportation sector has a significant opportunity to reduce its carbon footprint by adopting renewable energy sources like solar power. This research aims to assess the potential of solar photovoltaic systems in powering railway transportation and to evaluate the economic viability of such a system. The study focuses on the light rail transit system in Addis Ababa, Ethiopia, and aims to determine the energy-generating capacity and economic benefits of installing solar panels on various structures like train rooftops, railway depots, passenger stations, and DC traction substations. Subsequently, the research tried to address the question of how much energy could be generated by a solar photovoltaic system installed on various structures in the railway transportation system and to what extent these energies could support the railway traction supply system. The study also aimed to determine the economic feasibility of adopting solar power in railway transportation. Therefore, the research employs a quantitative research methodology and uses a Google Earth system with Helioscopes software to evaluate the potential of PV systems along rail lines and on rooftops. The study uses a case study approach and analyses the data collected through simulations to determine the energy-generating capacity and potential economic benefits. Consequently, the research finds that the solar PV system can generate 72.6 MWh per day, with an annual power output of 10.6 GWh, which can reduce CO2 emissions by 180,000 tons while generating a total profit of 892 million Ethiopian birrs. The PV-AA-LRTS has a return on investment of 200%, with a payback time of less than 13 years, and the price of solar-generated electricity is less than $0.08/kWh. Finally, the research concludes that solar power has tremendous potential in the railway transportation sector, particularly in reducing carbon emissions and generating economic benefits. In addition, the research findings support the adoption of solar power in railway transportation systems and provide a framework for assessing the potential of renewable energy sources in powering transportation systems.

ML-Enabled Solar PV Electricity Generation Projection for a Large Academic Campus to Reduce Onsite CO2 Emissions

Mitigating CO2 emissions is essential to reduce climate change and its adverse effects on ecosystems. Photovoltaic electricity is 30 times less carbon-intensive than coal-based electricity, making solar PV an attractive option in reducing electricity demand from fossil-fuel-based sources. This study looks into utilizing solar PV electricity production on a large university campus in an effort to reduce CO2 emissions. The study involved investigating 153 buildings on the campus, spanning nine years of data, from 2015 to 2023. The study comprised four key phases. In the first phase, PVWatts gathered data to predict PV-generated energy. This was the foundation for Phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. The SHAP (SHapley Additive exPlanations) technique was incorporated into the proposed framework to enhance model explainability. Phase III involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in Phase IV to project future CO2 emissions post-solar PV installation. This comparison estimated a potential emissions reduction and assessed the university’s progress toward its net-zero emissions goals. The study’s findings suggest that solar PV implementation could reduce the campus’s CO2 footprint by approximately 18% for the studied cluster of buildings, supporting sustainability and cleaner energy use on the campus.

Uncovering Displacement between Nuclear and Renewable Electricity Generation for G7 Countries by Novel Wavelet-Based Methods

ABSTRACT Under the pressure of climate-related issues, importance of clean electricity generation (EG) has been developing in ensuring sustainable environment. Accordingly, countries have been trying to increase clean EG. Although increasing clean EG is important, it is also critical that clean EG alternatives should not cause displacement. Otherwise, there may not be certain progress in stimulating clean EG. So, this study aims to investigate displacement between nuclear and renewable EG types. In this context, the study considers four clean EG sources (i.e. nuclear, hydro, solar, and wind), analyzes G7 countries, uses high-frequency daily data between 1 January 2019 to 30 December 2023, applies novel Wavelet Local Multiple Correlation (WLMC) method as the main model, and performs Wavelet Transform Coherence (WTC) for the robustness check. The study defines that (i) hydro and wind EG have a displacement (supporting) effect on nuclear EG at short (long) term; (ii) solar EG has a supporting (displacement) effect on nuclear EG at short (long) term; (iii) the most critical EG source is nuclear for France and United States and hydro for Great Britain and Japan; (iv) the WTC method reveals the robustness. The study determines that renewable EG has a displacement (supporting) effect on nuclear EG in short (long) term. Therefore, there are certain conflict between nuclear and renewable EG in the short term, whereas they support each other in the long term. Thus, there is a need for comprehensive and balanced energy transition policy so that choices should not cause a displacement among alternatives.

The Impact of Abrupt Sunlight Reduction Scenarios on Renewable Energy Production

To combat global warming, energy systems are transitioning to generation from renewable sources, such as wind and solar, which are sensitive to climate conditions. While their output is expected to be little affected by global warming, wind, and solar electricity generation could be affected by more drastic climatic changes, such as abrupt sunlight reduction scenarios (ASRSs) caused by nuclear war (“nuclear winter”) or supervolcanic eruptions (“volcanic winter”). This paper assesses the impacts of an ASRS on global energy supply and security in a 100% renewable energy scenario. National generation mixes are determined according to roadmaps for a global transition to renewable energy, with wind and solar contributing a combined 94% of the global energy supply. Wind and solar generation are determined for a baseline climate and an ASRS following a large-scale nuclear exchange. While effects vary by country, overall wind and solar generation are expected to reduce by 59% in the first year following an ASRS, requiring over a decade for full recovery. Ensuring sufficient energy for everyone’s critical needs, including water, food, and building heating/cooling, would require international trade, resilient food production, and/or resilient energy sources, such as wood, geothermal, nuclear power, tidal power, and hydropower.

Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.

ANALIZA PRELIMINARĂ DE VALORIFICARE A SISTEMELOR FOTOVOLTAICE INTEGRATE ÎN CLĂDIRI LA ÎNTREPRINDERILE TERMOENERGETICE

The research carried out in this paper focuses on the study of the integration of photovoltaic panels into buildings, a solution known as BIPV (Building Integrated Photovoltaics), which represents a new form of solar energy use: these are building materials, but these also facilitate the generation of solar electricity on additional surfaces and allow an optimisation of the overall operation as a main source of electricity and can also be seamlessly integrated into the building envelope and its components. In the paper, the opportunities for development of building-integrated photovoltaic systems at thermal power enterprises were analysed, using concrete data of the JSC "CET-Nord" in the Republic of Moldova as an example for further research, as well as determining the potential photovoltaic capacity of administrative and production buildings of the enterprise. This will help to improve the understanding of the impact of BIPV systems on the energy transition of cities and on the notion of near-zero energy cities in Europe, by identifying possible future approaches to BIPV that can be used by energy specialists as well as architects and urban planners to assess how much of the energy used by buildings could be provided by BIPV systems when implemented as building envelope materials across the entire building envelope.

Synergistic Effect Between 0D CQDs and 2D MXene to Enhance the Photothermal Conversion of Hydrogel Evaporators for Efficient Solar Water Evaporation, Photothermal Sensing and Electricity Generation.

Solar-powered interfacial water evaporation is a promising technique for alleviating freshwater stress. However, the evaporation performance of solar evaporators is still constrained by low photothermal conversion efficiency and high water evaporation enthalpy. Herein, 0D carbon quantum dots (CQDs) are combined with 2D MXene to serve as a hybrid photothermal material to enhance the light absorption and photothermal conversion ability, meanwhile sodium carboxymethyl cellulose (CMC)/polyacrylamide (PAM) hydrogels are used as a substrate material for water transport to reduce the enthalpy of water evaporation. The synergistic effect in 0D CQDs/2D MXene hybrid photothermal materials accelerate the carrier transfer, inducing efficient localized surface plasmon resonance (LSPR) effect. This results in the enhanced photothermal conversion efficiency. The integrated hydrogel evaporators demonstrate a high evaporation rate (1.93 and 2.86kgm-2h-1 under 1 and 2 sunlights, respectively) and low evaporation enthalpy (1485Jg-1). In addition, the hydrogel evaporators are applied for photothermal sensing and temperature difference power generation (TEG). The TEG device presents an efficient output power density (230.7mWm-2) under 1 sunlight. This work provides a feasible approach for regulating and controlling the evaporation performances of hydrogel evaporators, and gives a proof-of-concept for the design of multipurpose solar evaporation systems.

A novel multi-objective decision-making model to determine optimum resource and capacity configuration for hybrid electricity generation systems: A comparative case study in Türkiye

The rapid depletion of fossil fuels, their negative environmental impacts due to harmful carbon emissions, and the drawbacks of the sole use of intermittent renewable energy resources on system reliability necessitate the use of more advanced energy system technologies. As a result, hybrid electricity generation systems offer an innovative alternative to conventional energy systems. However, the technical complexity and variability of these systems require the application of multi-objective optimization techniques to determine the optimal sizing and system design. Therefore, in this study, we suggest a multi-objective decision-making (MODM) model that involves five conflicting objectives to find the optimal resource configuration and auxiliary source capacity allocation within the framework of the multi-source electricity production method in Türkiye. The MODM model aims to maximize the utilization of resource potentials and the jobs created, while simultaneously minimizing the levelized cost of energy (LCOE), construction duration, and carbon emissions. The MODM model was solved using fmincon and fgoalattain multi-objective optimization algorithms using the Goal Attainment Method in MATLAB and the model results were subsequently compared to the real-world data. According to the model results, the optimal capacity to be allocated for the auxiliary source units is 13,749.58 MWm, while the capacity allocated according to the real-world data as of March 2024 is 2495.49 MWm. In contrast to the model's prediction of 90.8 % solar-based auxiliary unit installations, 99.8 % of auxiliary units are based on solar PV in the actual case. Furthermore, hydro or geothermal-based auxiliary sources are not allocated any capacity, which is consistent with the actual situation. The capacity allocated for the wind (primary source) + solar (auxiliary source) configuration is 76.2 %, whereas in reality, it accounts for 65.6 % of the total installations. Consequently, the model findings suggest that the maximum capacity allocation is for the wind (primary source) + solar (auxiliary source) type hybrid electricity generation system.

How does geopolitical risk affect clean energy generation? Daily evidence from five highly Russia-reliant European countries

Europe has recently experienced an energy crisis that began with the war between Russia and Ukraine and continued with reciprocal sanctions and power cuts, increasing geopolitical tensions and risks. In such an environment, European countries must decide how to replace energy imports from Russia. As the best option may be to rely on clean energy, this study explores how geopolitical risk (GPR) affects clean electricity generation (i.e., hydro, solar, wind, & nuclear) in five European countries (i.e., Bulgaria, Czechia, Germany, Romania, and Switzerland), which have a high dependency on Russia. The study therefore uses daily data between January 2, 2019 and January 29, 2024 and applies a set of nonlinear methods (i.e., wavelet coherence, quantile-on-quantile regression, Granger causality in quantiles, and quantile regression). The results show that (i) there is a strong dependence of GPR on renewable and nuclear electricity generation over different times, frequencies, and countries; (ii) GPR stimulates hydro electricity generation at higher quantiles across countries except the Czechia; (iii) GPR increases solar electricity generation at higher quantiles in all countries except the Czechia and Switzerland; (iv) GPR mainly stimulates wind electricity generation at higher quantiles, except for Bulgaria and Switzerland; (v) GPR is almost inefficient in nuclear electricity generation. Overall, the study clearly shows the supportive effect of the GPR in promoting renewables, while this is not the case for nuclear energy. Therefore, European countries can rely on renewables as the best alternative against energy blackouts in the short term, considering the varying effect based on time, frequency, quantile, country, and clean energy sources.

Is the clean energy transition making fixed-rate electricity tariffs regressive?

Wholesale electricity prices can rapidly change in real-time, yet households usually face fixed-price electricity tariffs. In markets with large amounts of solar electricity generation, households that predominantly import energy in the daytime when wholesale prices are low implicitly cross-subsidize households with energy use that is more weighted to the higher-priced evening. We map substation data on electricity use to demographic data, to identify the household characteristics associated with this cross-subsidization in a high-solar setting. We find that households in areas with low house prices and high levels of renters are the net funders of this implicit subsidy. These households currently have the lowest average energy cost for retailers to service, and could be the greatest immediate beneficiaries if real-time retail tariffs are made available, before accounting for price-responsiveness. Finally, we present evidence that cross-subsidy magnitudes have grown significantly in recent years, coincident with rapid solar generator penetration.

A novel solar-aided NOx removal system in peaking power plants

Solar–coal hybrid power generation achieves the production of efficient, clean, and low-carbon electricity. The association between solar heat and selective catalytic reduction NOx removal (SCR de-NOx) can lead to the development of a novel solar-aided NOx removal system for peaking power plants. In this study, a novel system was proposed, in which large amount of flat-plate and small amount of trough solar energies were integrated to optimise the SCR de-NOx and energy utilisation in the boiler tail. Thermodynamic, NOx removal, and economic analyses were performed. For a typical peaking unit, the proposed system produced 14.6 MWe of solar-generated electric power and exhibited a high solar-to-electricity efficiency of 30.15 %. The integrated solar heat significantly increased the SCR de-NOx temperature and NOx removal efficiency. Based on its outstanding solar conversion performance, the proposed system achieved a net annual benefit (NAB) of 24.01 million Chinese yuan (CNY) and maintained a low cost of solar-generated electricity (COEs) of 0.56 CNY/kWh. Based on cascade utilisation of solar thermal energy, the proposed system achieved high performance under all loads and significantly improved the NOx removal performance under low and medium loads, especially for peaking power plants.

Polar bear hair inspired 3D photothermal evaporator for efficient solar steam desalination and electricity generation

While solar steam generation for desalination and electricity production is a burgeoning field, traditional systems often face challenges with rapid heat loss and salt buildup. To address these issues, we introduce a biomimetic approach, inspired by the unique thermal properties of polar bear hair. Our study presents a photothermal 3D hierarchical hollow Ppy@CuO nanorod array system, featuring a dual-structured design: a cone-shaped 3D topological structure for improved light absorption and a hollow structure for enhanced thermal insulation. This innovative approach effectively maximizes solar energy capture and reduces thermal losses, particularly in marine environments. Our system exhibits outstanding performance in solar steam generation, achieving high water evaporation rates and maintaining stability and efficiency in high-salinity settings. Under one solar illumination at a 75° tilt angle, the Ppy@CuO-3 NAs demonstrated an evaporation rate of 3.3 kg m−2 h−1 and an energy efficiency of 97.5 %. Additionally, the synergistic effect of released Cu2+ and Ppy resulted in a 99.9 % antibacterial rate against E. coli and S. aureus. This significant advancement in simultaneous, effective seawater desalination and efficient evaporation-driven electricity generation marks a crucial step toward future sustainable resource utilization.

Design of a Fertilizing Robot Application with Regard to Energy Consumption

Electrically driven agricultural robots experience faster battery depletion than vehicles moving on asphalt because higher rolling and traction resistance require more energy. The problem arises in areas without access to the electrical grid, where charging electrically driven agricultural robots is not possible, resulting in interruptions to the robot's continuous operation. A combined energy production station powered by biogas, hydrogen, and solar energy with the prototype of autonomous fertilizing robot for blueberry plantations, to perform plant-based precision fertilization on depleted milled peat fields was created by the agrorobotics working group of the Estonian University of Life Sciences [1, 2]. The station includes unique components such as an automatic battery exchange system and an electric generator with a membrane motor. These, together with a solar energy and electric generator control system, as well as a battery charger, are mounted on a mobile platform (Figure 1). The objective of this article was to determine the energy requirements of an autonomous fertilizing robot during movement in the field and for carrying out technological operations. For this purpose, the mechanical power and energy required for the operation of the robot fertilizer spreader were first determined. Based on this, an accumulator with suitable power and capacity for the operation of the robot fertilizer spreader was selected. The travel distance of the robot on a single charge of the chosen accumulator was determined, as well as the traction power efficiency and specific power.

Energy yield database management system based on solar photovoltaic cell using internet of things technology

This study investigates the performance of solar cell electric power generation, focusing on data collected from Prince of Songkla University, Surat Thani Campus, analyzing the Mean Squared Error (MSE) for each phase consisting of Phase A: 0.00133, Phase B: 0.00137, and Phase C: 0.00216. Through linear regression analysis, we establish the correlation between current and electrical energy in each phase, facilitating the prediction of photovoltaic power generation system performance and timely maintenance alerts. This predictive analysis aids in mitigating greenhouse gas emissions, preventing potential damages from short circuits, and ensuring system safety during installation. Our findings, which are accessible through a user-friendly dashboard interface, address a gap in previous studies by providing empirical data specifically from Prince of Songkla University, Surat Thani Campus, and by integrating IoT technology for real-time monitoring and analysis, underscoring the novelty of our approach.

Farm electricity system simulator (FESS): A platform for simulating electricity utilisation on dairy farms

The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using 12 months of data from three representative farms using the repeat hold out method for data partitioning with 75 % of data used for training and 25 % used for validation. An optimisation algorithm was used to minimize the error during model training. Through cross-validation, FESS achieved a root mean squared error (RMSE) of 7.65 kWh, mean absolute percentage error (MAPE) of 7.10 %, mean percentage error (MPE) of −0.86 % and a relative prediction error (RPE) of 7.56 % for total daily electricity consumption. Across the three farms, the simulated outputs of FESS achieved an average R2 value of 0.72, demonstrating good agreement with observed data. FESS’s utility was demonstrated by analysing the effects of different electricity pricing structures and on-site solar photovoltaic electricity generation on total farm energy costs. We concluded that FESS simulated on-farm electricity consumption with sufficient accuracy for the intended application. FESS accurately simulated dairy farm electricity consumption across three dairy farms of different herd and parlour sizes while evaluating the effects of demand side management and renewable generation on farm electricity consumption and costs.

Ultrastrong and Reusable Solar‒Thermal‒Electric Generators by Economical Starch Vitrimers.

In frigid regions, it is imperative to possess functionality materials that are ultrastrong, reusable, and economical, providing self-generated heat and electricity. One promising solution is a solar‒thermal‒electric (STE) generator, composed of solar‒thermal conversion phase change composites (PCCs) and temperature-difference power-generation-sheets. However, the existing PCCs face challenges with conflicting requirements for solar‒thermal conversion efficiency and mechanical robustness, mainly due to monotonous functionalized aerogel framework. Herein, a novel starch vitrimer aerogel is proposed that incorporates orientational distributed carboxylated carbon nanotubes (CCNT) to create PCC. This innovative design integrates large through-holes, mechanical robustness, and superior solar‒thermal conversion. Remarkably, PCC with only 0.8 wt.% CCNT loading achieves 85.8MPa compressive strength, 102.4°C at 200mW cm-2 irradiation with an impressive 92.9% solar-thermal conversion efficiency. Noteworthy, the STE generator assembled with PCC harvests 99.1W m-2 output power density, surpassing other reported STE generators. Strikingly, even under harsh conditions of -10°C and 10mW cm‒2 irradiation, the STE generator maintains 20°C for PCC with 325mV output voltage and 45mA current, showcasing enhanced electricity generation in colder environments. This study introduces a groundbreaking STE generator, paving the way for self-sufficient heat and electricity supply in cold regions.

SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT

The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud dynamics, hinders the transition to reliable renewable energy systems. Information on future sky conditions, especially cloud coverage, holds the promise for improving PV output forecasting. Leveraging recent advances in generative artificial intelligence (AI), we introduce SkyGPT, a physics-constrained stochastic video prediction model, which predicts plausible future images of the sky using historical sky images. We show that SkyGPT can accurately capture cloud dynamics, producing highly realistic and diverse future sky images. We further demonstrate its efficacy in 15-minute-ahead probabilistic PV output forecasting using real-world power generation data from a 30-kW rooftop PV system. By coupling SkyGPT with a U-Net-based PV power prediction model, we observe superior prediction reliability and sharpness compared with several benchmark methods. The propose approach achieves a continuous ranked probability score (CRPS) of 2.81 kW, outperforming a classic convolutional neural network (CNN) baseline by 13% and the smart persistence model by 23%. The findings of this research could aid efficient and resilient management of solar electricity generation, particularly as we transition to renewable-heavy grids. The study also provides valuable insights into stochastic cloud modeling for a broad research community, encompassing fields such as solar energy meteorology and atmospheric sciences.