Solar Installations Research Articles

Solar Radiation Acquisition and Data Analysis Using Arduino

The rapid increase in energy demand and environmental concerns has driven the adoption of renewable energy sources. Solar energy, being abundant and sustainable, has gained significant attention. Efficiently harnessing and monitoring solar energy systems is crucial for optimizing performance. This research focuses on developing a solar monitoring system using Arduino, a versatile and cost-effective microcontroller platform. The system aims to measure solar irradiance, temperature, and voltage in real-time, providing comprehensive data acquisition and analysis capabilities. This project aims to contribute to the advancement of solar energy technology by offering an effective and affordable solution for monitoring and optimizing solar installations.

The enduring nature of cranberry production in a changing climate: The interplay of extreme weather, knowledge networks, and adaptation

Anthropogenic climate change is causing critical issues in agriculture, including cranberry production; however, in a previous study the majority of Massachusetts cranberry growers were less likely to see global warming as a threat than the general US population, and more still reported to be little worried about such warming. This research aims to determine the influence of weather and professional information disseminated to cranberry growers on their climate change adaptation. The authors used a mixed-methods, interdisciplinary approach, including content analysis of around 300 issues of UMass Extension’s Cranberry Station monthly newsletter—a widely trusted source of information in the cranberry grower “network of knowledge”—along with historical weather data from 1974 to 2020, and interview and survey data. Despite infrequent usage of direct communication on climate change and adaptation, UMass Extension’s communication on weather challenges in general showed a small but significant increase when monthly temperature anomalies increased. Meanwhile, anomalous monthly precipitation was negatively associated with total chemical mentions (linked to chemical use behavior). Climate impacts such as increased weeds, water issues (e.g., heavy rainfall, water scarcity), and heat waves ruining harvests were important to growers, leading to the adoption of both conventional (e.g., increased strategic flooding, barge sanding due to lack of ice) and emerging (e.g., smart irrigation, solar panel installations for added income) adaptive strategies. Growers demonstrate climate and technological optimism, believing that other growers are hit worse by climate change than themselves and that cranberry plant resilience, better weather forecasting, and improved irrigation technology will allow them to handle future weather challenges. The prioritization of immediate needs over the more abstract, long-term challenge of climate change by growers and the supporting system underscores the imperative to explore the socio-environmental dynamics that shape their climate responses in cranberry production.

QUANTUM-ENHANCED SOLAR SITE SELECTION: A NOVEL MCDM APPROACH

In the pursuit of optimizing renewable energy sources, the selection of solar plant installation sites presents a complex decision-making challenge that involves multiple criteria. This research introduces a groundbreaking algorithm, leveraging quantum computing techniques to enhance Multi-Criteria Decision Making (MCDM) for solar plant site selection. The proposed algorithm harnesses the superposition and entanglement properties of quantum bits to evaluate extensive datasets and criteria with unprecedented speed and accuracy. By integrating quantum versions of established MCDM methods such as the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the algorithm provides a sophisticated tool for decision-makers. The research demonstrates the algorithm’s superiority over classical methods through rigorous simulation and validation processes. The findings suggest that quantum-enhanced MCDM can significantly streamline the solar plant site selection, paving the way for a more efficient deployment of solar energy infrastructure and contributing to a sustainable energy future.

Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for the efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel installation (e.g., height disparities, different angles), and uncertainties (e.g., AV and environmental modeling) may degrade the performance of traditional controllers. In this study, a biologically inspired method based on Brain Emotional Learning (BEL) is developed to tackle the aforementioned challenges. The developed controller is implemented numerically using MATLAB-SIMULINK. The paper concludes with a comparative analysis of the AVs’ performance using both PID and developed controllers across various scenarios, highlighting the efficacy and advantages of the intelligent control approach for AVs deployed in solar panel cleaning systems within agricultural solar farms. Simulation results demonstrate the superior performance of the ML-based controller, showcasing significant improvements over the PID controller.

SOLAR AND WIND LEADS THE CHARGE: ANALYZING INDIA'S DYNAMIC GROWTH IN RENEWABLE ENERGY CAPACITY FROM 2016 TO 2023

With a focus on significant advancements in solar, wind, and hydropower capacity, this report offers a thorough analysis of the growth trajectory of India's renewable energy industry from 2016 to 2023. The analysis highlights the exponential increase in solar installations, especially in regions like Rajasthan, which will account for over half of the nation's renewable energy mix by 2023. Solar energy emerged as the dominating force in this regard. Even if solar energy was rising faster, wind energy continued to expand steadily, highlighting its crucial place in the renewable portfolio. The report also explores the hydropower industry, highlighting the minor differences in growth patterns between small- and large-scale projects. The study's thorough data analysis shows a significant increase in the production of renewable energy, particularly in 2023, demonstrating the industry's strong reaction to India's challenging clean energy targets. The study of solar capacity at the state level reveals notable regional differences that may be ascribed to policy, geographic, and infrastructure variations. These findings indicate potential areas for focused policy interventions. The results support a multifaceted strategy for developing renewable energy, highlighting the significance of balanced growth from many sources. To maintain the sector's pace, recommendations include improving legislative frameworks, making infrastructural investments, and encouraging innovation. To support strategic choices and propel India's transition to a sustainable energy future, this research offers insightful information to stakeholders, investors, and policymakers.

Enhancing photovoltaic module sustainability: Defect analysis on partially repaired modules from Spanish PV plants

Between 2010 and 2022, there has been a significant increase in photovoltaic (PV) solar energy installations, driven by factors such as cost reductions, technological advances, policy support and innovative financing approaches. However, this rapid growth has created a significant challenge in the form of increased PV waste generation, coinciding with global commitments to achieve carbon neutrality by 2050. The potential for reuse of decommissioned PV modules represents an environmentally sustainable way forward that requires a comprehensive assessment of technical, economic, environmental and regulatory considerations. This study focuses on evaluating the impact of defects in partially repaired PV modules from Spanish installations on their power output and efficiency, with the aim of developing strategies to increase reuse and reduce environmental impact. The research focuses on 23 crystalline silicon modules and uses established techniques to evaluate their operational condition, detect defects and assess potential safety risks. Among the defects observed, moisture-induced degradation (MID) emerges as the most prevalent, followed by cracked cells and disconnected areas in cells across different technologies. Notably, despite the presence of defects, approximately 87 % of these modules have a power loss of less than 20 %.

Public perceptions on net zero energy houses in Japan

AbstractFor Japan, which has not operated nearly all of its nuclear power plants since 2011 and is dependent on thermal power generation, the introduction of renewable energy into homes is extremely important for the future formation of a sustainable society. However, the introduction of net zero energy house (ZEH) in detached houses, which account for 55% of all dwellings in Japan, has not progressed. To promote the introduction of ZEH, this study clarified the awareness of owners of detached houses regarding ZEH. We analyzed factors that influence such perception of solar photovoltaics (PV) technology using a 1000-sample online survey questionnaire. The survey was conducted in late January 2020 and included questions examining the public perception of solar installation and factors that were found to be important in previous research. We found that Japanese respondents who live in detached houses generally lack an understanding of renewables and that the level of interest in installing solar PV for the ZEH is low. We also found that awareness of renewables, such as knowing new energy policy and searching information on solar PV, is the critical factor of installing renewables. At the same time, most socio-demographic and neighborhood variables seem not to influence installing solar PV or other technologies for ZEH. This research will contribute to the Japanese government’s goal of strengthening education on renewable energy to promote ZEH.

Energy-saving strategies to convert existing campus to smart campus: a conservative method analysis

Energy consumption has wide applications in the construction industry. The pattern of energy demand depends on the nature of energy usage in accordance with time. Prominence is given to meet the demand without forfeiture of energy. This paper focuses on the importance of developing a smart campus and energy management. In the present study, a prominent engineering institute located in Karnataka, India, is considered for analysis. The approaches considered here are installing solar roof panels and replacing existing electrical fixtures with light-emitting diode (LED) fixtures. The cost and energy consumption analysis before and after the application of these methods will give a strong image of how energy and money can be saved and are useful for society. The initial investment may slightly be high but the payback period is quite early. Results of the study indicate that the initial investment required for solar panel installation and replacement of existing electrical fixtures with LED fixtures for interiors and corridors at the institution has proved worthwhile by various investment criteria used in the project. The study implicates a positive step towards energy-saving strategies, a revolutionary change in the energy sector and the development of the country.

Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability.

A Paper on Floating Solar Power Plants: A Review

Combining marine and renewable energy technology is an eco-friendly technique to produce electricity. Solar panels are made to float on the surface of water, like in a dam or reservoir. Underwater lines deliver electricity to a transmission tower. A floating solar power plant comprises essential elements: the solar module, buoyancy system, and corrosion-resistant components. These anti-corrosion elements encompass vertical and horizontal frames, an inspection footrest, and a module mounting assembly. In addition to passing the drinking water test, the solar module should be lead-free, dust-proof, humidity-resistant, and resistant to the impacts of transient water jets. Polyethylene, used to build the buoyancy body, has a 2.5 times weight capacity. K-water's floating structure is created using magnesium alloy coating product. In comparison to ground mounted PV (GPV), FPV has a number of benefits that are addressed in this review. The influence of FSPP on water quality and aquatic life is where there is a significant study void. This review paper looks at the most recent FSPP research and analyzes its advantages, disadvantages, and prospects. In terms of different water bodies that can be employed, system effectiveness, worldwide potential, and possibility for integrating FSPP with other technologies, the evaluation sheds further light on FSPP. Key Words: Renewable energy, floating solar power Plant, floating Solar System, floating solar PV installations in the world. Conventional solar power plant (CSPP).

Innovative decision-making for solar photovoltaic system installation in residential energy hubs: A probabilistic dynamic planning approach

This paper introduces an innovative probabilistic dynamic planning model tailored to optimize photovoltaic (PV) system installation within Residential Energy Hubs (REHs). The REH amalgamates diverse components, including PV systems, Combined Heat and Power (CHP) units, and gas boilers, to holistically fulfill residential thermal and electrical energy demands. The model's primary objective is to achieve an optimal equilibrium between minimizing initial investment costs associated with PV systems and managing ongoing energy expenses within the REH framework, encompassing potential PV energy losses over the planning horizon. To augment the flexibility and benefits of PV systems for residential consumers, the model integrates several pioneering features. Particularly notable is the introduction of a heating energy storage system, which complements the REH's thermal requirements and adeptly reacts to fluctuations in gas and electricity prices, thereby maximizing PV system benefits and ensuring homeowners' cost efficiency. Moreover, the model addresses the inherent uncertainty tied to PV generation by employing a two-point estimation method, offering a more realistic depiction of system performance and potential outcomes. To resolve the two-objective problem, an initial conversion into a single-objective problem via a weighted summation method precedes the application of an optimization algorithm inspired by symbiotic organisms. The paper demonstrates the practical application and efficacy of the proposed model through extensive simulations spanning a 10-year planning horizon. The results underscore the model's effectiveness and versatility in guiding residential consumers to make informed and economically viable decisions regarding PV system installation within the broader spectrum of their energy requirements. This research represents a significant stride towards empowering homeowners with invaluable tools for embracing sustainable energy solutions.

Energy Production Optimization by Positioning of Solar Panels and Surveillance System with Cleaning of Panels

The pursuit of efficient and sustainable energy generation has led to the widespread adoption of solar panels. However, the optimal performance of these panels can be hindered by factors such as dirt accumulation, suboptimal orientation, and security concerns. To address these challenges, we propose an integrated Solar Panel Tracking, Cleaning, and Surveillance System (SPTCSS). Our system employs advanced tracking technology to optimize solar panel orientation, ensuring maximum exposure to sunlight throughout the day. Additionally, it incorporates automated cleaning mechanisms, utilizing water and brushes to remove dust, debris, and other contaminants, thereby maintaining peak efficiency. Furthermore, the surveillance aspect of the system provides real-time monitoring and security features, safeguarding against theft, vandalism, and unauthorized access. Through a network of sensors and cameras, any anomalies or suspicious activities can be promptly detected and addressed. By integrating tracking, cleaning, and surveillance functionalities into a single system, our solution offers a comprehensive approach to enhancing the performance, longevity, and security of solar panel installations. This not only maximizes energy generation but also minimizes maintenance requirements and mitigates risks, ultimately contributing to a more sustainable and reliable renewable energy infrastructure

DOI awards $19M for innovative solar panel installation over canals

DOI awards $19M for innovative solar panel installation over canals

Quantum-Enhanced SOLAR Site Selection: A Novel MCDM Approach

In the pursuit of optimizing renewable energy sources, the selection of solar plant installation sites presents a complex decision-making challenge that involves multiple criteria. This research introduces a groundbreaking algorithm, leveraging quantum computing techniques to enhance Multi-Criteria Decision Making (MCDM) for solar plant site selection. The proposed algorithm harnesses the superposition and entanglement properties of quantum bits to evaluate extensive datasets and criteria with unprecedented speed and accuracy. By integrating quantum versions of established MCDM methods such as the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the algorithm provides a sophisticated tool for decision-makers. The research demonstrates the algorithm’s superiority over classical methods through rigorous simulation and validation processes. The findings suggest that quantum-enhanced MCDM can significantly streamline the solar plant site selection, paving the way for a more efficient deployment of solar energy infrastructure and contributing to a sustainable energy future.

EUROPEAN CLIMATE PROTECTION STRATEGY UNDER CONSIDERATION OF BUILT HERITAGE PRESERVATION

Climate change affects all areas of life. The European Union strives for climate-neutrality of their members until 2050 with the project European Green Deal. By this, the emissions of greenhouse gases should be reduced to zero by 2050 with simultaneous economic development decoupled from resource use. 600 Billion Euros the European Union will spend to lower net greenhouse gas emissions until 2030 by at least 55%, compared to 1990 levels. Climate change affects historical structures, thus by variations in groundwater levels, hydrological cycle, or intense precipitation. However, also climate protection activities such as renewable energy installations may result in negative effects. Solar panels or wind turbines do not emit carbon dioxide and other greenhouse gases but, installed nearby historical buildings, affect the historical appearance of structures. That may result in conflicts between ecological and heritage conservation interests. The authors inform about the European Green Deal and analyze effects of the planned measures on built heritage with a focus on solar and wind energy installations. They also discuss requirements from heritage preservation regulations. The manuscript analyzes how renewable energy installations may affect built heritage in its historical appearance. For that, the authors use available information about the European Green Deal, discuss current research literature and relevant German court cases. It is concluded that renewable energy installations may negatively affect the appearance of built heritage. Interests of climate protection and built preservation need to be balanced to ensure that future generations even can experience and enjoy the cultural heritage.

EUROPEAN CLIMATE PROTECTION STRATEGY UNDER CONSIDERATION OF BUILT HERITAGE PRESERVATION

Climate change affects all areas of life. The European Union strives for climate-neutrality of their members until 2050 with the project European Green Deal. By this, the emissions of greenhouse gases should be reduced to zero by 2050 with simultaneous economic development decoupled from resource use. 600 Billion Euros the European Union will spend to lower net greenhouse gas emissions until 2030 by at least 55%, compared to 1990 levels. Climate change affects historical structures, thus by variations in groundwater levels, hydrological cycle, or intense precipitation. However, also climate protection activities such as renewable energy installations may result in negative effects. Solar panels or wind turbines do not emit carbon dioxide and other greenhouse gases but, installed nearby historical buildings, affect the historical appearance of structures. That may result in conflicts between ecological and heritage conservation interests. The authors inform about the European Green Deal and analyze effects of the planned measures on built heritage with a focus on solar and wind energy installations. They also discuss requirements from heritage preservation regulations. The manuscript analyzes how renewable energy installations may affect built heritage in its historical appearance. For that, the authors use available information about the European Green Deal, discuss current research literature and relevant German court cases. It is concluded that renewable energy installations may negatively affect the appearance of built heritage. Interests of climate protection and built preservation need to be balanced to ensure that future generations even can experience and enjoy the cultural heritage.

The Decision to Install a Rooftop Photovoltaic System by a Small Business: A Case Study

The potential of rooftop solar power has been identified as a main driver of clean energy adoption in an urban environment. While residential solar projects have lower capacity than commercial systems, residential solar represents most of the installation base for rooftop solar projects. Rooftop solar adoption in the commercial market lags behind residential solar installation. To better understand why this is the case, we conduct a case study of a small manufacturing firm. Based on the firm’s energy demand and the physical attributes of its location, we study a 25-kilowatt solar array using the National Renewable Energy Laboratory’s (NREL) System Advisor Model (SAM). Our empirical study evaluates the economic prospects of a rooftop solar installation project for the firm under study. This analysis sheds light on the financial ramifications of the adoption of solar panels by small, commercial firms in New York state.

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.

ASSESSING THE EFFECTS OF SOLAR PANELS ON BIODIVERSITY IN THE PUGAL REGION: A COMPREHENSIVE ANALYSIS

The Pugal region, characterized by its arid landscape, is experiencing a surge in solar panel installations. While solar energy is a renewable source, its impact on local biodiversity is unclear. This study investigates the effects of solar panels on plant and animal species in the Pugal region, exploring changes in habitat quality, temperature, soil moisture, and microclimate. We conducted field surveys, species identification, and data analysis to understand the impact of solar panels on biodiversity.Rajasthan is one of the leading states in the country in solar energy production, with an estimated output of 142 Gw. Owing to the state's rising electricity demand, which is increasing by 8-10 per cent every year, the government's focus is to obtain 43 per cent of power consumption from solar energy by 2030.

Using geodetic data to optimize the distribution of solar and wind energy installations

Using geodetic data to optimize the distribution of solar and wind energy installations