Large-scale Solar Farms Research Articles

Locating the suitable large-scale solar farms in China's deserts with environmental considerations

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4–84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47–110 PWh which is 1.7–3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8–62.6 gigatons annually, offsetting 73–170 % of global emissions. Covering just 4.8–11.5 % of China's desert area (8 × 104–19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

PHASOR ANALYSIS OF DECENTRALIZED GRID-CONNECTED RESIDENTIAL SOLAR FARM: ADDRESSING NIGERIA ELECTRICITY CAPACITY ISSUES

Addressing Nigeria’s energy capacity issues, this paper presents a Phasor-evaluation of a simulation comparing the feasibility of using a Photovoltaic (PV) system with backup power for home use at a large-scale solar farm with a Residential Energy Storage System (RESS) in Nigeria. The Hydro-Québec Research Institute (IREQ) designed a standard power operation system for the simulation, which is essential. The findings, displayed in phasor mode, aim to assess energy storage and solar intensity, potentially revolutionizing the energy landscape for Nigeria’s rural and urban residents.

Economic Impacts of Performance Optimization in Large-Scale Solar Farms: A Case Study Using Artificial Neural Networks in Eastern Malaysia

Economic Impacts of Performance Optimization in Large-Scale Solar Farms: A Case Study Using Artificial Neural Networks in Eastern Malaysia

Improvement of Solar Farm Performance based on Photovoltaic Modules Selection

The emissions of greenhouse gases from conventional power plants are currently a significant cause for worry. In China, about 75% of total domestic energy is dependent on coal-fire power, which emits 50% of total SO2 and has a significant impact on the human respiratory system. Therefore, solar power plants are a viable option that can mitigate this problem. Furthermore, the efficiency of solar modules exhibits a progressive upward trend, while their price per watt experiences a corresponding decline, making it a promising source for future energy. This article examines the performance and effectiveness of several photovoltaic (PV) modules in designing solar plants on a certain land area measuring 10000 m2 (100 m * 100 m). The PV plant performance was evaluated by comparing occupation ratio (OR), PV power capacity, net energy production, performance ratio (PR) via PVsyst software, and lastly financial analysis. Consequently, the PV module (PV7), characterized by its high efficiency, low temperature coefficients, and affordable price, result in a significant OR (73.81%), increased installed PV power capacity (1568kW), enhanced net energy output (2269029 kWh/year), improved yearly PR (83.4%), and lastly, the shortest payback period (around two years). Instead of optimizing shadow length in existing research, this paper aims to improve the performance of large-scale solar farm based on PV module selection which results in less computation and structure installation efforts.

A distributed economic model predictive control-based FPPT scheme for large-scale solar farm

Increasing solar-photovoltaic-power penetration necessitates the implementation of flexible power point tracking (FPPT) in solar farms. However, coordinating multiple solar photovoltaic (PV) power generation systems (SPVPGSs) poses a significant challenge due to their inherent intermittency and varying dynamic characteristics, complicating FPPT implementation. To overcome this challenge, this paper proposes a distributed economic model predictive control (DEMPC) scheme to achieve FPPT, while simultaneously enhancing overall economic performance in solar farms. By integrating solar farm control and local control into one optimal control framework, this scheme eliminates the need for power allocation, PV voltage reference calculation, and pulse width modulation. Leveraging the SPVPGS model and soft power constraint, DEMPC controllers are designed to achieve the economic targets of solar farms, which cooperate through a communication network to realize FPPT and economic optimization. Additionally, the strong nonlinearity of SPVPGS causes a non-convex mixed integer nonlinear programming (MINLP) problem, solved by a MINLP algorithm using finite converter switching states. Simulations under step-changed irradiance and power reference, as well as urgent maintenance conditions, demonstrate that the DEMPC-based FPPT scheme significantly outperforms the traditional hierarchical model predictive control-based FPPT scheme, presenting both superior dynamic response and enhanced economic performance in FPPT implementation.

Revolutionizing Energy Harvesting: Harnessing Ambient Solar Energy for Enhanced Electric Power Generation

The escalating demand for sustainable and renewable energy sources has propelled the exploration of ambient solar energy as a pivotal avenue for cleaner power generation. This review paper delves into the intricacies of solar energy harvesting technologies, providing a comprehensive analysis of principles, efficiency metrics, material advancements, and versatile applications. Focusing on the photovoltaic effect as the cornerstone of solar cells, the critical impact of semiconductor material selection and bandgap engineering on efficiency is thoroughly examined. Key performance measures such as fill factor, open-circuit voltage, and short-circuit current are emphasized for their pivotal role in evaluating system efficiency. The adaptability of solar energy solutions is exemplified through diverse applications, spanning from portable electronics to large-scale solar farms. Looking towards the future, this review envisions a promising trajectory for solar energy harvesting, driven by continual advancements in technology, material science, and efficiency measures. The insights gleaned from this comprehensive examination are poised to catalyze the development of more efficient and accessible solar energy harvesting technologies. This, in turn, promises to play a significant role in the global transition towards a greener and more sustainable energy landscape, contributing substantively to sustainability, climate mitigation, and an enhanced quality of life worldwide. Keywords: Solar energy harvesting, solar cells, material selection, panel orientation, storage techniques, system integration, case studies.

Performance Evaluation of Multiple Machine Learning Models in Predicting Power Generation for a Grid-Connected 300 MW Solar Farm

Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms.

Large-scale photovoltaic solar farms in the Sahara affect solar power generation potential globally

Globally, solar projects are being rapidly built or planned, particularly in high solar potential regions with high energy demand. However, their energy generation potential is highly related to the weather condition. Here we use state-of-the-art Earth system model simulations to investigate how large photovoltaic solar farms in the Sahara Desert could impact the global cloud cover and solar generation potential through disturbed atmospheric teleconnections. The results indicate negative impacts on solar potential in North Africa (locally), Middle East, Southern Europe, India, Eastern China, Japan, Eastern Australia, and Southwestern US, and positive impacts in Central and South America, the Caribbean, Central & Eastern US, Scandinavia and South Africa, reaching a magnitude of ±5% in remote regions seasonally. Diagnostics suggest that large-scale atmospheric circulation changes are responsible for the global impacts. International cooperation is essential to mitigate the potential risks of future large-scale solar projects in drylands, which could impact energy production.

Assessing Sustainability Impacts and Paradoxes of Large-Scale Solar Farms in Energy Infrastructure Project

This study aims to determine the sustainability impact of large-scale solar PV farms and to recommend strategies for their sustainable development. Using a comprehensive assessment framework, we analyzed environmental, economic, and social dimensions through a combination of life cycle assessment (LCA), cost-benefit analysis, and respondent interviews. Our findings reveal that while solar farms significantly reduce greenhouse gas emissions and dependence on fossil fuels, they also pose challenges such as land use conflicts, ecological disruption, and social equity issues. Specifically, the displacement of agricultural land and impacts on local biodiversity emerged as critical concerns. Additionally, although solar farms create jobs and reduce energy costs, these benefits are sometimes offset by high initial investment costs and long-term maintenance. The study underscores the importance of integrated planning and policy-making to address these challenges and enhance the sustainability of solar energy projects. These insights contribute to the broader discourse on balancing renewable energy development with sustainable land and community management practices.

The promising future of developing large-scale PV solar farms in China: A three-stage framework for site selection

Large-scale Photovoltaics (PV) play a pivotal role in climate change mitigation due to their cost-effective scaling potential of energy transition. Consequently, selecting locations for large-scale PV power plants has gained worldwide prominence in recent decades. Although massive studies have examined various siting criteria, land consolidation and cost-benefit feasibility issues are not often included due to the lack of a comprehensive and discretionary decision-making system. In this study, we introduced a three-stage framework combining DBSCAN clustering and cost-benefit analysis to identify the most efficient and cost-effective land parcels. The results indicate that while a total area of 425,191 km2 is considered developable for PV installation in China, only 23% of that area (128,588 km2) are consolidated land parcels which are suitable for developing large-scale PV power plants. Moreover, the potential of these consolidated land parcels (8,289,662 gWh/year) can supply about 90% of China's electricity demand. However, when considering the infrastructure investment per kWh, high-potential land parcels doubled the moderate-potential ones. These results emphasize the importance of large-scale PV plant siting as it impacts the efficiency of PV integration and the optimal land use. Hence, this methodology equips decision-makers with a practical and efficient tool for economically developing large-scale solar PV.

Local acceptance of solar farms: The impact of energy narratives

This research explores how a new concept, support for energy transition narratives, and other commonly applied social licence factors influence local acceptance of large-scale solar farms. Producing renewable electricity requires deploying more large-scale renewable technology infrastructure and support of host communities will be paramount. There is a dearth of studies that investigate a comprehensive set of social licence factors that influence local support of solar farms. Moreover, there is a lack of research considering how broader environmental worldview and beliefs about the energy transition influence local level support for renewable energy infrastructure. Survey data from a large, representative sample of the Australian population is analysed using a network modelling approach to identify conditional associations between variables of interest. Results reveal that general support for renewables in the energy transition had relatively strong direct and indirect associations with local acceptance in addition to social licence factors. Conversely, beliefs about the energy transition beyond the role for renewables (i.e., support for the role of natural gas, support for exporting fossil fuels and willingness to make trade-offs for a faster energy transition) had relatively weak effects on local acceptance and other social licence factors. The findings suggest that individual alignment with broader energy transition narratives explains local solar farm support beyond social licence factors. Future social licence research should consider the role of broader public narratives when investigating the factors that facilitate or impede local acceptance of renewable energy projects.

Community benefit agreements for solar energy: Examining values, preferences and perceived benefits in the United States using a discrete choice experiment

Large-scale solar energy developments bring significant environmental and community benefits, but they can also impose negative impacts, particularly on host communities. Community Benefit Agreements (CBAs) have emerged as a strategy to mitigate these adverse effects. This study examines community preferences regarding CBAs associated with large-scale solar farms. Our investigation focuses on two critical CBAs aspects: the choice between mandated or voluntary implementation and the preference for private or collective benefit distribution. Additionally, we explore how individual values shape community members' attitudes towards solar technology. Our findings reveal a clear preference within the surveyed community for privately distributing benefits over establishing a collective trust fund, and for voluntary implementation rather than government mandates. However, it is essential to recognize that the community is not monolithic and can be placed into three classes: a minority of solar-reluctant individuals (7.5 %), a significant solar-receptive group (24.2 %), and a substantial disengaged majority (68.3 %). Among these groups, the solar-receptive segment displays the most interest in private benefits; yet, they favor government-mandated agreements. Moreover, values play a significant role in shaping preferences within each group, with biospherism correlating with greater support for mandated, private benefits among respondents in the reluctant and receptive groups. Conversely, hedonistic values predict a greater inclination among the reluctant group to live further away from solar developments. Based on these findings, policies should prioritize voluntary, private benefit distribution while remaining adaptable to align with community preferences. Engaging community members in the decision-making can help tailor solutions that resonate with their values and preferences.

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.

Impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle

Amassing the available solar energy over the Sahara desert, through the installation of a large-scale solar farm, would satisfy the world’s current electricity needs. However, such land use changes may affect the global carbon cycle, possibly offsetting mitigation efforts. Here a fully coupled Earth System model EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of photovoltaic solar panels over the Sahara desert. The resulting changes to the carbon cycle were an enhancement of the carbon sink across Northern Africa, particularly around the Sahel but a simultaneous weakening of the carbon sink in the Amazon basin. This is observed through spatial pattern changes to the values of net biome production (NBP), more evident during Northern Hemisphere summer season. NBP changes are contributed by competing responses in the net primary production and heterotrophic respiration rates. These changes to carbon exchange correspond to a wetter and warmer climate occurring in Northern Africa and a drier and warmer climate in the Amazon, with stronger driving effects of precipitation. Due to these coupled responses and complex teleconnections, thorough investigation of remote impacts of solar farms are needed to avoid unintended consequences on the terrestrial carbon cycle.

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.

Reliability of photo-voltaic power plants

The quest for alternative energy sources, replacing the environmentally impacting fossil fuels, has resulted in the growing need to develop various new strategies in the field of renewable energies. A possible energy source is clean solar energy. The most common technology used is a silicon semiconductor device, which generates electrical output while interacting with light, known as Photo-Voltaic (PV) System. The reliability of PV modules and systems is critical to the commercial success of Photovoltaic systems. The technology combines multiple components, each of which could cause a system failure or energy losses. The reliability characterization measurements of a PV power system are complicated and only a few studies succeeded to design and simulate an efficient PV system. In the current study, two relatively large-scale solar farms were designed with an output of 20 MW. A comparison of the solar farms' performances was conducted, with an emphasis on the reliability indices of the systems. The obtained reliability indices are 83% and 88% for the studied solar farms.

Forecasting solar energy production: A comparative study of machine learning algorithms

The use of solar energy has been rapidly expanding as a clean and renewable energy source, with the installation of photovoltaic panels on homes, businesses, and large-scale solar farms. The increasing demand for sustainable energy sources has pushed the growth of the solar industry, as well as advancements in technology, making solar panels more efficient and cost-effective. The implementation of solar energy not only reduces our reliance on non-renewable fossil fuels but also helps to mitigate the effects of climate change by reducing carbon emissions. This paper presents a complete and comparative study of solar energy production forecasting in Morocco using six machine learning (ML) algorithms : Support Vector Regression (SVR), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Generalized Additive Model (GAM) and Extreme Gradient Boosting (XGBOOST), based on Solar Power Plant daily data installed in Benguerir city of Morocco between January and December 2022. The models were trained, tested, and then evaluated. In order to assess the models performance four metrics were used in this study, namely root mean squared error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE)and R-squared (R2). The performance of the models reveals ANN to be the most effective predictive model for energy forecasting in similar cases with the lowest value of RMSE, MSAE and the highest value of R-squared, which are accepted as one of the most important performance criteria by the ANN model. The findings of this study not only validate the effectiveness of the ANN algorithm but also offer the appropriate parameters for achieving the best results in predicting solar energy production. By identifying the optimal configuration of the ANN algorithm, we provide valuable insights that can be directly applied in real-world applications, thereby enhancing the optimization of solar energy systems and contributing to a sustainable future, particularly the integration of these results in an edge device for the predictive maintenance of photovoltaic power plants.

Assessing Australia’s future solar power ramps with climate projections

Increasing levels of photovoltaic (PV) penetration to the electricity grid brings challenges to both design and operation of the grid due to its vulnerability to climate change. A crucial aspect of PV operation is power ramps leading to variability and instability in the grid. With notable large-scale PV deployment planned, including the world’s largest planned solar energy infrastructure in Powell Creek Australia, characterising future ramps is crucial for ensuring stable power generation to support large-scale economic development. Using CORDEX-Australasia projections under RCP8.5 and RCP4.5 emission scenarios, future solar ramps across Australia have been characterised up to 2100. Results predict a reduction in ramp magnitude across Australia, with changes in frequency and period length varying with the location. This work highlights the importance of considering future changes in climate when designing large-scale solar farms to ensure the incorporation of frequency control devices and storage plans for a reliable power supply.

Power quality improvement of a distribution system integrating a large scale solar farm using hybrid modular multilevel converter with ZSV control

Power quality improvement of a distribution system integrating a large scale solar farm using hybrid modular multilevel converter with ZSV control

ENHANCING PHOTOVOLTAIC PANEL EFFICIENCY WITH AN IOT-ENABLED ROBOTIC CLEANING SYSTEM – A COMPREHENSIVE REVIEW

The maintenance and cleaning of photovoltaic panels is critical to ensure maximum energy output and prolong their lifespan. However, manual cleaning of large-scale solar farms is time-consuming, labor-intensive, and expensive. To address this challenge, this paper proposes an IoT-based robotic cleaner for efficient monitoring and cleaning of photovoltaic panels. The proposed robotic cleaner is equipped with a camera, sensors, and a cleaning mechanism. The camera captures real-time images of the solar panels, which are analyzed by an AI-based algorithm to detect and locate dirt, debris, and other obstructions. The sensors measure environmental factors such as temperature, humidity, and light intensity, which are used to optimize the cleaning schedule and ensure the safety of the cleaning operation. The cleaning mechanism of the robotic cleaner is based on a high-pressure water jet that removes dirt and debris without damaging the solar panels. The water is supplied by an onboard tank and can be heated or cooled as required. The cleaning process is automated and can be controlled remotely through a web-based interface. The proposed IoT-based robotic cleaner offers several benefits compared to manual cleaning. It reduces labor costs, minimizes the risk of injury to workers, and improves the efficiency of cleaning operations. Moreover, it ensures consistent and high-quality cleaning, which leads to increased energy output and prolongs the lifespan of the solar panels. Overall, this paper presents a novel approach to automate the cleaning of photovoltaic panels using IoT and robotics technologies. The proposed solution has the potential to revolutionize the maintenance of solar farms and contribute to the development of sustainable and clean energy systems.