Solar Energy Generation Research Articles

TOWARDS ECOLOGICAL SUSTAINABILITY: HARVEST PREDICTION IN AGRIVOLTAIC CHILI FARMING WITH CNN TRANSFER LEARNING

Agrivoltaic systems, which integrate agricultural production with solar energy generation, present a promising approach to ecological sustainability. This study focuses on predicting chili harvests within an agrivoltaic setup using Convolutional Neural Networks (CNN) with transfer learning. Accurate yield prediction is vital for optimizing both agricultural output and energy generation. The study evaluates three pre-trained CNN models—EfficientNetV2L, EfficientNetV2M, and ResNet 50—fine-tuned with specific agrivoltaic data. The experimental setup includes a solar-powered greenhouse with IoT-controlled micro-climate management to ensure optimal growing conditions. The models were selected based on their high accuracy in Keras applications, with EfficientNetV2L and ResNet 50 achieving 100% accuracy, and EfficientNetV2M reaching 96% in chili crop counting. The results show quick convergence during training and validation, indicating effective model learning. The study also includes a life cycle analysis (LCA), confirming that using photovoltaic systems as a substitute for conventional energy sources is environmentally sustainable. Overall, this research demonstrates that CNN transfer learning is highly effective for crop counting and resource management, contributing to sustainable agrivoltaic farming and highlighting the potential of advanced AI techniques in agriculture.

Impact of weather variables on roof top green energy production in a multibuilding, multi-capacity ecosystem – A regression based study of 'SAI MITRA’

The importance of green energy in the current times cannot be understated. The ill effects of traditional forms of energy generation, make solar energy one of the most environmentally friendly alternatives. SAI MITHRA – a roof top multi building, multi-capacity solar energy generation system, executed by Sri Sathya Sai Central Trust at Prasanthinilayam in South India is a prime example for promotion of green energy. Using a regression model, this study attempts to understand the impact of weather variables on solar energy production across different production capacities using high frequency daily data. In order to provide predictive insights, the impact of weather variables with t-1 and t-2 days lags on solar energy generation have also been studied. The study identifies the three important weather variables that have an impact on solar energy production – atmospheric pressure, relative humidity and dew point temperature. The insights from the paper are relevant for multi-capacity solar energy systems for improving operational efficiencies and promoting green energy ecosystems.

Advancing Grey Modeling with a Novel Time-Varying Approach for Predicting Solar Energy Generation in the United States

This paper proposes a novel time-varying discrete grey model (TVDGM(1,1)) to precisely forecast solar energy generation in the United States. First, the model utilizes the anti-forgetting curve as the weight function for the accumulation of the original sequence, which effectively ensures the prioritization of new information within the model. Second, the time response function of the model is derived through mathematical induction, which effectively addresses the common jump errors encountered when transitioning from difference equations to differential equations in traditional grey models. Research shows that compared to seven other methods, this model achieves better predictive performance, with an error rate of only 2.95%. Finally, this method is applied to forecast future solar energy generation in the United States, and the results indicate an average annual growth rate of 23.67% from 2024 to 2030. This study advances grey modeling techniques using a novel time-varying approach while providing critical technical and data support for energy planning.

Enhancing Efficiency in Hybrid Solar–Wind–Battery Systems Using an Adaptive MPPT Controller Based on Shadow Motion Prediction

Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to their inherent uncertainties, making their management more intricate than traditional power plants. This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. A hybrid network is modeled, comprising a wind turbine with a doubly-fed induction generator (DFIG), a solar power plant with photovoltaic (PV) cells, an MPPT system, a Z-source converter, and a storage system. The proposed approach employs a motion detection-based method, utilizing image-processing techniques to optimize the MPPT of PV cells based on shadow movement patterns within the solar power plant area. This method significantly reduces the time required to reach the maximum power point (MPP), lowers the computational load of the control system by predicting shadow movements, and enhances the MPPT speed while maintaining system stability. The approach, which is suitable for relatively large solar farms, is implemented without the need for any additional sensors and relies on the system’s history. The simulation results show that the proposed approach improves the MPPT system’s efficiency and reduces the pressure on the control circuits by more than 70% in a 150,000 m2 solar farm under shaded conditions.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING MONTE CARLO OPTIMIZATION TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using three types of Monte Carlo simulation techniques. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing the Monte Carlo technique to extract maximum energy profit using the hybrid microgrid. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

School Start Times for Solar Alignment: Evaluating the Benefits of Schedule Optimisation for Peak and Cost Reduction

The global push towards sustainable energy usage and the increasing adoption of renewable energy sources, such as solar power, requires innovative approaches to energy management, particularly in energy-intensive sectors such as education. This study proposes a change in school start time from 7 a.m. to 9 a.m. to align operational hours with periods of off-peak electricity demand and maximum solar availability. Four scenarios are compared: baseline (current schedule without solar), shifted schedule without solar, baseline with solar, and shifted schedule with solar integration. The analysis reveals that shifting the school’s operational hours alone leads to a peak demand reduction of 40%, mitigating strain on the grid during high-demand periods. Solar integration without schedule has a less pronounced effect on peak demand (26%). The combination of schedule shifting and solar integration delivers the most significant benefits, with the highest cost reductions (28%) and peak demand reductions (60%). This study demonstrates that synchronised solar energy generation and optimised scheduling can enhance energy efficiency and long-term financial savings, offering a practical solution for reducing operational costs and improving sustainability in schools. This study demonstrates how public institutions can contribute to the energy transition by adapting their operational schedules to align with renewable energy availability, rather than relying on conventional fixed schedules.

Comparative Study of Intelligent and Classical MPPT Techniques Applied to Solar Battery Charger

Renewable solar energy is an excellent alternative to conventional energy sources and, recently, it has shown remarkable growth on electrical power grid in various countries around the world. Maximum power point tracking (MPPT) has critical importance in solar energy generation since it increases the system's efficiency in many scenarios. Photovoltaic modules (PV) are influenced by environmental factors such as temperature and solar irradiation density, changing the amount of energy generated throughout the day. In order to extract the maximum available power from a PV, it’s necessary to use the MPPT technique, which considers the system’s nonlinearities. Maximum power tracking (MPP) is done by tuning the duty cycle of the DC-DC converter, so that the PV's terminal voltage is modulated to match the environmental conditions. This tuning, generally, is dependent on sensor readings. Normally, only the module’s voltage and current sensors are used, due to the low cost and ease of acquisition of these signals. However, intelligent techniques demand the usage of temperature and irradiation sensors, increasing the complexity of the system’s implementation. The use of intelligent control techniques applied to the energy generation system has had a notable presence in recent publications. The high efficiency obtained from its application demonstrates that the use of intelligent systems in the decision-making process is a useful tool in obtaining maximum generation power. The objective of the present study is the development and simulation via MATLAB/SIMULINK of a battery charging system with solar energy, which consists of a PV, a Buck converter, a 12V battery, and the MPPT controller. The intelligent fuzzy logic MPPT technique (FLS) and the classical incremental conductance technique (INC) will be compared, both techniques using only voltage and current measurements.

Intelligent fuzzy-PSO based grid connected solar EV charging station for electric buses to enhance stability and energy management

Abstract Modern power networks are relying more and more on Distributed Generation (DG) and solar energy-powered Electric Bus (EB) charging stations; nevertheless, controlling the charging process under situations of peak EB load and variable solar irradiation presents considerable hurdles. This study presents a coordinated control approach that maximizes energy distribution for solar (PV)-based EB charging stations by using an intelligent energy management system (IEMS). Through the analysis of load demand and meteorological data in real time, the IEMS optimizes PV generation and grid power consumption, thereby halving peak power demand, minimizing system losses, and easing distribution grid stress. In order to enable dynamic modifications to the energy flow between PV power, the grid, and battery storage, the method integrates an adaptive neuro-based fuzzy control system that anticipates solar energy generation and predicts EB load demands. Furthermore, to balance power distribution between battery and ultracapacitor systems and control energy storage in ultracapacitors, a fuzzy inference system optimized using Particle Swarm Optimization (PSO) is utilized. This prolongs battery life and guarantees effective energy management. The overall performance of the IEMS is improved by the fuzzy logic controller, which produces output signals that specify the best power distribution for any energy storage system. Digital simulations and a real-time hardware-in-loop experimental setup are used to validate the effectiveness of the proposed system, which shows notable gains in energy management, grid stability, and system efficiency. In order to improve intelligent energy management in grid-connected solar-powered systems, this study offers a viable method for incorporating renewable energy sources into EB charging stations.

Enhancing power quality in solar-wind grid-connected systems through soft computing techniques

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

Parametric analysis of self-excited aeroelastic instability of an isolated single-axis two-dimensional flat solar tracker

The escalating global energy demand is driving a transition towards sustainable energy sources, particularly solar power, which is experiencing significant growth. Single-axis, horizontally mounted photovoltaic (PV) solar trackers are emerging as one of the most cost-effective methods for solar energy generation. However, the reduction in structural stiffness has led to an increase in aeroelastic issues, some of which have resulted in catastrophic failures around the world, posing significant challenges. Recent aerodynamic studies have attributed these instabilities to stall flutter. The absence of a validated theory hampers early estimation of these aeroelastic phenomena. The primary aim in this work is to develop a semi-empirical framework to predict aeroelastic instabilities in isolated two-dimensional solar trackers, focusing on dynamic self-excited responses. This effort aims to improve the reliability and efficiency of solar tracker designs, optimizing energy utilization in the context of the evolving renewable energy landscape. The semi-empirical formulation developed here reveals the influence of geometric and mechanical characteristics on the critical wind speed at which aeroelastic instabilities set in. The effects of reduced inertia (m⁎) and mechanical damping coefficient (ξmech) of the structure on the critical wind speed at which aeroelastic instabilities arise are analyzed here. It is shown that increasing either inertia or damping has a minor impact on the critical speed when the initial values of these parameters are large, while the changes are significant when these initial values are small.

The Potential Related to Microgeneration of Renewable Energy in Urban Spaces and Its Impact on Urban Planning

This research aims to explore the potential of renewable energy sources in urban planning, focusing on microgeneration technologies, through a structured literature review. A systematic review was conducted using the PRISMA method, encompassing the identification, selection, eligibility, and analysis of studies related to renewable energy microgeneration in urban environments. The findings emphasize key areas such as policy development, energy security, and future scenario projections, with a particular focus on solar energy generation. The review highlights the importance of robust regulatory frameworks and monitoring systems for effectively managing prosumers and ensuring equitable energy distribution. Key challenges identified include the intermittency of renewable energy sources, regulatory complexities, monitoring systems, prosumer management, energy sizing risks, and the lifecycle of microgeneration technologies. The research accentuates the need for outstanding collaboration between academia, industry, and urban planners to accelerate the adoption and implementation of renewable energy solutions. The main conclusion is that such collaboration is essential for addressing challenges, driving innovation, and contributing to the development of sustainable urban energy systems.

The Development of an Advanced Facade Map: An Evolving Resource for Documenting Case Studies

This paper describes the creation and the potentials of an online tool to identify and document case studies that demonstrate perceived best practices in the design and implementation of advanced, sustainable, and climate-responsive integrated buildings facades. The project was created to catalog these projects in sufficient detail to allow users—expected to include design professionals, students, and faculty—to discover and study relevant examples, based on key project features, defined by the authors as technologically advanced and worthy of relevance: daylight and solar control, natural ventilation, noise control, embodied carbon, energy generation, and innovative insulation systems. The website documenting 44 case study buildings and this paper provides a preliminary overview about how it was made, what it is, and what some potential uses of the tool might be. This study emphasizes adaptability across climates, showcasing sustainable facades designed to balance energy efficiency with occupant comfort. This study also shows how data can be analyzed through the Map, based on four case studies. Presenting these statistics, the resource offers a foundation for exploring facade technologies that support sustainable building practices and respond effectively to climate-specific challenges. In doing so, the authors aim to inspire further exploration of innovative facade solutions within the context of sustainable building practices.

Ultrahighly-efficient and stable luminescent solar concentrators enabled by FRET-based carbon nanodots

Luminescent solar concentrators (LSCs) as an optical device to concentrate light from large areas illuminated by sunlight into small-area photovoltaic cells, can greatly reduce the cost of solar energy generation, thus showing promise in building-integrated photovoltaics. Here, we report the synthesis of a novel type of composite nanoparticle via the simple sol-gel method. Starting from hydroxyl-rich carbon nanodots (CDs), Rhodamine B (RhB) molecules were embedded into a silica matrix shell, forming a composite core-shell system, where the CDs represent the core and RhB embedded in SiO2 are the shell of the final CDs/RhB@SiO2 structure. These nanoparticles were successfully employed in high-performance LSC fabrication. The Förster resonant energy transfer (FRET) between the CDs and the dye was successfully exploited to improve the light absorption efficiency and power conversion efficiency (ηPCE) of LSC devices. The solid-state photoluminescence quantum yield of the as-synthesized composite nanoparticles can reach ∼ 57 % due to the effective suppression of the aggregation-induced quenching. These fluorescent composite nanoparticles were used for fabricating LSC devices with an ultrahigh ηPCE of ∼ 3.8 % for 5 × 5 cm2 devices, thanks to the high loading of luminophores and effective FRET.

The macro view of solar policy: The case for supporting utility-scale power

New solar energy generation is drastically needed as a source of clean electricity as the U.S. and the globe make the transition away from fossil fuels. Yet, even as solar costs have dramatically declined, solar sources still provide less than 5% of global electricity. We examine issues in solar policy leading to this low adoption rate. Examining the variables of cost, baseload power and intermittency, and land use, we evaluate the tradeoffs among policy support for utility-scale, commercial and residential solar systems. We argue that utility-scale solar power makes far more sense if there is adequate grid integration, so that such installations can be placed in locations that minimize land use tradeoffs. By focusing policy support on a few large solar installations, governments can vastly increase the solar contribution to electricity generation.

Incorporating Ecosystem Services into Solar Energy Siting to Enhance Sustainable Energy Transitions.

Solar energy is expected to play a large role in decarbonization of the energy sector globally. In the United States, solar energy is forecasted to generate roughly 45% of the electricity by 2050. Although solar energy mitigates the negative effects of climate change by providing electricity without releasing greenhouse gases, little is known about the implications of solar energy development for ecosystem services. In this study, we developed a spatially explicit, techno-ecological solar suitability model consisting of six scenarios designed to evaluate the trade-offs between ground-mounted solar energy generation and multiple ecosystem services. By incorporating solar suitability modeling with ecosystem service evaluation, we develop a method that provides a comprehensive understanding of potential techno-ecological trade-offs. To test our methodology, we used Connecticut (USA) as a study site for analyzing the potential trade-offs of future solar energy facilities, but the methods can be widely applied. Our results suggest that well-sited solar energy development can decrease sediment and nutrient export while offsetting carbon emissions from power plants. This study provides a holistic assessment of incorporating ecosystem services in future solar energy development decision-making and presents an approach for minimizing trade-offs and maximizing sustainable outcomes.

Transmission and Volatility Mechanisms of Mineral and Oil Prices in the Context of Clean Energy Transition: A Global Perspective

The prices that govern the output of producers in specific industries hold great significance, particularly in the context of the renewable energy industry, which is profoundly influenced by the dynamics of critical mineral prices. Oil prices also affect mineral transportation and cost-competitive consumer choices, altering the renewable energy sector. This study examines the global transmission of shocks and volatility spillover effects of essential mineral and oil prices in renewable energy production covering the monthly data from January 1990 to June 2023. We use partial cross-quantilogram (PCQ) and rolling window-based recursive cross-quantilogram (RCQ) methods to address the dataset's non-normal and fat-tailed nature. The PCQ analysis shows that aluminum and copper price shock transmission and volatility spillover effects boost cumulative and decomposed renewable energy generation, particularly solar and wind power, in bullish market conditions with long-term memory. Nickel prices decline in bullish markets with long memory. Interestingly, oil price shock transmission and spillover effects benefit solar and wind energy generation over long time horizons. Thus, this study emphasizes the importance of leveraging mineral prices by determining favorable values and implementing market operations to promote environmental sustainability and sustainable development in the face of oil-based industrial expansion.

Optimization of Solar Power Plant with Variation of Solar Reflector Angles and Use of Passive Cooling Integrated Internet of Things

With the growing global demand for energy, exploring alternative energy sources, particularly solar energy, in equatorial regions where abundant sunlight is essential. Solar panels, which convert sunlight into electricity, must be optimally positioned to capture the maximum amount of sunlight and operate within an ideal temperature range for efficiency. However, two key challenges must be addressed: ensuring solar panels are consistently aligned with the sun and managing heat buildup, which can reduce performance. This study proposes a specialized optimization system to enhance solar panel efficiency by addressing these issues. The system adjusts the angle of solar reflectors to maximize sunlight exposure. It incorporates passive cooling mechanisms, such as heatsinks and cooling blocks, which are attached to the back of the panels to regulate temperature. Real-time monitoring using Internet of Things (IoT) technology tracks critical parameters, including solar reflector angles, panel and ambient temperatures, light intensity, weather conditions and electrical output. A comparative analysis between standard solar panels and those equipped with this optimization system demonstrates that the latter significantly outperforms conventional setups. The system ensures maximum sunlight absorption, maintains optimal operating temperatures and boosts overall energy production. These findings underscore the potential of the proposed system to improve the reliability and efficiency of solar energy generation in equatorial regions, contributing to more sustainable energy solutions in high-sunlight environments.

The Need for Solar Power Energy Utilization in the Northern Part of Nigeria for Sustainability and Economic Growth (A case study of Nigeria)

Northern Nigeria is one of the most recognizable areas, having vast amounts of solar radiation and rapidly growing populations. Thus, there is an opportunistic aspect of the use of solar power in this region. Since this region enjoys all-year sunshine, it is one of the best places for the integration of solar energy. With huge problems of energy she presently faces such as irregular supply of electricity, people's growth and thus demand, solar power presents an immediate usable, reliable, and sustainable solution. The energy demand in northern Nigeria is expected to sharply increase from about 48 billion kWh in 2024 to 64.5 billion kWh by 2034. This escalated energy requirement directly puts a demand for an efficient and sustainable source of energy, and as such, the promise of steady and renewable energy supply through solar power makes it very appealing. The average regional solar radiation exceeds 6 kWh/m2 per day, which points towards a very vast potential for solar energy generation. But to meet the energy requirements that will arise in the future from solar, it would call for a tremendous investment in infrastructure. This is because it is estimated that approximately 246,000 km2 of solar panels might be needed by 2034 in order to be able to cover the energy needs of the country. However, this massive area is required, but the benefits of solar energy do not stop there at solving shortages of energy. Solar power would increase economic activities due to job creation, stimulation of local industries, and the end reliance on fossil fuels. It has implications on environmental sustainability as it reduces greenhouse gas emissions and promotes cleaner practices of energy consumption. Northern Nigeria will unlock a stable and reliable source of power through investment in this infrastructure. This is important for economic development, thereby eventually improving the quality of life. Indeed, the integration of solar power addresses the immediate needs of satisfying energy challenges but builds a foundation for sustainable growth and environmental stewardship over the long term. Solar power in northern Nigeria is important for ensuring future demands for energy and significant economic development while emphasizing sustainability within the environment. A lot still has to be tapped into by tapping into the solar resources that are available. Key words: Solar Energy,Sustainability,Economic Growth,Energy Demand,Renewable Resources

Assessment of Solar Energy Generation Toward Net-Zero Energy Buildings

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational costs. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system.

Rooftop solar PV in Bhutan: A systemic analysis of feed-in-tariff program

Solar photovoltaic (PV) systems are critical to the global electrification efforts, especially in the rural and remote communities of the developing countries. This study analyses the prospects of a feed-in-tariff program for solar PV systems in Bhutan. It is based on the analysis of a pilot project covering 361 households in rural areas of Bhutan. A mix of qualitative and quantitative methods is applied, which captures the multi-disciplinary variables and generates primary data from the pilot project in Bhutan. The two critical variables argued are user acceptability and financial sustainability in the long-term in the context of access to clean energy and empowerment in rural areas. From the field data assessment, it was found that the low existing energy tariff has a cybernetic effect on user acceptability and the financial sustainability of the solar PV feed-in-tariff system in Bhutan. The current tariff rate for low voltage (LV) consumers is $ 0.038/kWh whereas the solar energy generation cost ranges between $ 0.04–0.045/kWh considering the PV project life of 25 years. The findings of the study suggest that users are willing to accept the feed-in-tariff as an enabler for rural livelihood provided the tariff rate is reasonable (at least in the range $ 0.05 to 0.07/kWh) to sustain the capital investment.