Solar Farm Development Research Articles

Predicting soil cone index and assessing suitability for wind and solar farm development in using machine learning techniques

This study proposes a novel approach that combines machine learning models to predict soil compaction using the soil cone index values. The methodology incorporates support vector regression (SVR) to gather input data on key soil parameters, and the output data from SVR are used as inputs for additional machine learning techniques such as Gradient Boosting, Decision Tree, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference System. Evaluation of Artificial Intelligent techniques shows that the XGBoost model outperforms others, exhibiting high accuracy and reliability with low mean square error and high correlation coefficient. The effectiveness of the XGBoost model has implications for soil management, agricultural productivity, and land suitability evaluations, particularly for renewable energy projects. By integrating advanced AI techniques, stakeholders can make informed decisions about land use planning, sustainable farming practices, and the feasibility of renewable energy installations. Overall, this research contributes to soil science by demonstrating the potential of AI techniques, specifically the XGBoost model, in accurately predicting soil compaction and supporting optimal soil management practices.

Mapping potential conflicts between photovoltaic installations and biodiversity conservation

European countries are expanding utility-scale solar farms to reduce carbon emissions and increase energy independence. However, the expansion of these facilities raises concerns about competition for land for other uses, including biodiversity conservation. Thus, quantitative assessment of the friction between renewable energy development and conservation potential is an important step towards the sustainable development of the energy sector in this region. Here, grounded on land sparing/land sharing concepts, we discuss a prioritization framework based on solar potential and human footprint (used as a proxy for wilderness) to identify zones where the development of solar farms may cause a negative impact on biodiversity (sparing areas), and areas where they may have beneficial outcomes (sharing areas). We apply this framework to the Iberian Peninsula, where the land conflict may become particularly significant in the near future, given the high potential for photovoltaic production and expansion of photovoltaic installations, and the vast areas of well-preserved habitats still remaining. We detected around 18,000 km2 of sparing areas, of which half are not in protected areas, and >41,000 km2 of sharing areas. Much of these sharing areas are found near urban areas, which is where energy is most needed for supplying homes, transport, and machinery. Through strategic planning, the implementation of solar farms in land sharing areas has the potential to yield dual benefits, for both local biodiversity and food production, driving economic growth. By doing so, these initiatives can also safeguard important wilderness areas, which play a pivotal role in conserving biodiversity.

Evaluating soil erosion and runoff dynamics in a humid subtropic, low stream order, southern plains watershed from cultivation and solar farm development

Much work has been done to understand and improve soil and water conservation where agriculture has driven land use intensification. Less is known about soil- and water-related impacts from intensification driven by solar farming, especially at watershed-scales. Here we employed Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to model Pond Creek, a rural watershed in Texas, USA. Land use is primarily crop cultivation and secondarily pasture for cattle grazing. Presently, several industrial-scale projects are planned to convert ≈15–30% of Pond Creek from agriculture to solar farms. The model was parameterized using public data sources and information from local stakeholders, then calibrated to several historical precipitation events. Experiments were conducted by varying precipitation depth, duration, and land uses: native vegetation pre-cultivation (control), cultivation (current), current conditions with 15% solar farm conversion (solar), and current conditions with 30% solar farm conversion (solar x2). Shifting to solar farming led to significant increases in cumulative sediment load (+12%–30%), with no significant differences in peak discharge rate changes (+0.38%–4%). Comparison to soil loss tolerance values showed current and solar treatment erosion rates exceeded tolerance values between 0.17 and 2.29 tons per hectare and all treatments were significantly different than the native treatment. We discuss high leverage strategies applicable to solar farm development sites as well as watersheds where they reside. Accelerating demand for land for renewable energy such as solar farming warrants greater attention from the soil and water conservation community to anticipate and mitigate impacts across landscapes.

ASSESSMENT OF CROPLAND TRANSITION TO SOLAR FARMS AND OTHER LAND USE/COVER USING RS, GIS AND ANN-CA

Abstract. The balance between food security and energy security is a national issue of extreme importance. A more stable supply of electricity could be achieved as solar farms expand but at the expense of losing some of the prime agricultural lands which endangers availability of sufficient agricultural produce. This study aims to use ANN-Cellular Automata (CA) via the Geographic Information System (GIS) platform and remote sensing (RS) data to assess the impact of cropland transition to solar farms and other land use/land cover (LULC). Several remotely sensed data were processed including MODIS land cover data (MCD12Q1), VIIRS nighttime lights (VNL v2.1), Advanced Himawari Imager Shortwave Radiation (AHI-SWR) product, and population density (LandScan) as inputs to the Cellular Automata-Artificial Neural Network (CA-ANN) model to simulate LULC changes in Tarlac Province, Philippines via the Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS. For years 2019, 2023 and 2027 with 2015 as the base year, results showed an increasing trend for savannas and grassland with ΔLULC values of +11.4% to +15.1% and +0.2% to 3.5%, respectively. Meanwhile, a decreasing trend is observed for built-up/water, forest, and cropland with ΔLULC values of −3.0% to −6.3%, −8.5% to −21.1%, and −3.9% to −4.2%, respectively. Results also showed a conversion of a 100-ha area of croplands to solar farm from year 2019 to 2023 which translates to an estimated monetary loss from agricultural produce due to solar farm conversion amounting to Php 7,584,720.00 (~USD 138,000) which is equivalent to the total average annual income of about 67 families in Tarlac. Lastly, the simulated 2027 LULC map showed pixels with unrealistic conversions from solar farm (year 2023) to cropland (year 2027). To improve the model, it is recommended to add more spatial data to effectively capture factors that may contribute to the expansion of solar farms in the future. Moreover, high resolution LULC maps (vector maps if available) can be used instead of a course resolution satellite-derived raster data. Nonetheless, this study has demonstrated the use of RS, GIS and machine learning techniques to model cropland conversion to solar farms and other LULC classes. Results from this study can provide scientific data to policy makers, solar industry players and other relevant stakeholders in doing technoeconomic assessment of solar farm development and expansion considering its effect on energy security and food security towards national sustainable development.

GIS-based weighted overlay model for wind and solar farm locating in Sri Lanka.

With regard to wind and solar energy, research has shown a clear discrepancy between the amount of energy currently being used with the existing potential. The present research has attempted to develop a geographic information systems (GIS) based weighted overlay model to identify potential locations for the development of wind and solar farms in Sri Lanka, which are rapidly advancing renewable energy resources globally. Four major land-use types were considered for the model development as least productive land areas to establish wind and solar farms, namely, barren lands, sand areas, open forests, and scrublands. Nine and seven criteria were identified for the wind and solar farm siting respectively, and the relative weights for each criterion were obtained through focused group discussions. The weighted overlay model was used to locate suitable areas and higher the values generally indicate that a location is more suitable. The outcome of the weighted overlay model shows that for the wind farm locating, 141km2 of highly suitable lands available accounts for 3% of the total land area of Sri Lanka. Furthermore, the model results indicated a total area of 99km2 as highly suitable for solar power generation. Central, Sabaragamuwa, Uva, and Northern provinces were found to be the most suitable for wind farm locating and Eastern, Southern, and Northern provinces we identified as highly suitable for the solar farm locating. The model validation performed by overlaying the results with the existing solar and wind farm map of Sri Lanka indicated that the highly suitable, moderately suitable, and marginally suitable lands identified by the weighted overlay lied within 1km radius of the existing wind and solar power plants. Thus, it can be concluded that GIS-based weighted overlay model can serve as a primary screening tool to shortlist the optimum land areas to locate future solar and wind farms in any selected geographical location.

A MULTI-CRITERIA GIS BASED ANALYTICAL HIERARCHICAL APPROACH FOR SOLAR PHOTOVOLTAIC FARM SITE SELECTION IN THE KOLKATA METROPOLITAN AREA, INDIA

Abstract. A smart solar city ensures smart energy resilient future, low emissions and sustainable development of cities through networked infrastructure. This study aims to determine suitable locations for solar farm development in Kolkata Metropolitan Area (KMA), India, which may help the decision makers to plan resource allocation for smart renewable energy development efficiently. Numerous criteria were considered while simulating the model, including solar irradiation, distance from power substation, distance from major roads, land cover distribution, mainly canals, vacant land, urban green zones, and agricultural land. Built-up residential spaces and protected zones were however, no considered. The analysis suggests that wetlands, canals, and a major portion of open vacant land in the outskirts of the KMA are most suitable for investment in solar photovoltaics infrastructure. The present study covers different aspects of solar city planning to determine the suitable locations, which when accompanied by careful onsite investigation and meticulous planning will help decision makers and planners to design an efficient solar energy plan for the deployment of necessary infrastructure and technologies in cities.

Reference genome of the color polymorphic desert annual plant sandblossoms, Linanthus parryae.

Sandblossoms, Linanthus parryae is a widespread annual plant species found in washes and sandy open habitats across the Mojave Desert and Eastern Sierra Nevada of California. Studies in this species have played a central role in evolutionary biology, serving as the first test cases of the shifting balance theory of evolution, models of isolation by distance, and metrics to describe the genetic structure of natural populations. Despite the importance of L. parryae in the development of landscape genetics and phylogeography, there are no genomic resources available for the species. Through the California Conservation Genomics Project, we assembled the first genome in the genus Linanthus. Using PacBio HiFi long reads and Hi-C chromatin conformation capture, we assembled 123 scaffolds spanning 1.51 Gb of the 1.96 Gb estimated genome, with a contig N50 of 18.7 Mb and a scaffold N50 of 124.8 Mb. This assembly, with a BUSCO completeness score of 88.7%, will allow us to revisit foundational ideas central to our understanding of how evolutionary forces operate in a geographic landscape. In addition, it will be a new resource to uncover adaptations to arid environments in the fragile desert habitat threatened by urban and solar farm development, climate change, and off-road vehicles.

Location and Technical Requirements for Photovoltaic Power Stations in Poland

The objective of Poland’s energy policy is to guarantee energy security while enhancing economic competitiveness and energy efficiency, thus minimizing the power sector’s environmental impact and optimizing the use of energy resources in the country. Poland is not the only European country to rely on coal for power generation. Historical factors and large coal deposits act as natural barriers to increasing the share of renewable energy in the Polish power sector. Yet, today, environmental concerns and climate change are prompting many countries to move away from fossil fuels. Renewable energy sources, such as solar and wind energy, are an alternative to traditional energy generated from fossil fuels. However, investors developing solar and wind farms in Poland encounter numerous problems at each stage of the project. These difficulties are associated mainly with the location, technical requirements, infrastructure and formal and legal documents. This study aimed to identify the key factors that influence the development of photovoltaic power stations in Poland, with special emphasis on the choice of location and technical aspects of the investment process. The demand for clean energy and the renewable energy prospects for Poland are discussed based on the example of solar farms. Sixty-seven prospective farm locations were analyzed, and the results of the analysis were used to identify the main barriers and opportunities for renewable energy development in Poland. The option of connecting solar farms to the existing power grid was also examined. This study demonstrates that the development of solar farms in Poland is inhibited mainly by technical barriers, in particular the lack of options for connecting farms to the power grid, as well as the absence of support mechanisms and dedicated legislative solutions, rather than environmental obstacles.

Evaluating Soil Erosion and Runoff Dynamics in a Humid Subtopic, Low Stream Order, Southern Plains Watershed From Cultivation and Solar Farm Development

Evaluating Soil Erosion and Runoff Dynamics in a Humid Subtopic, Low Stream Order, Southern Plains Watershed From Cultivation and Solar Farm Development

ACQUIRING ELEMENTS OF SOLAR FARM DEVELOPMENT’S APPROVAL CONSIDERATION IN JOHOR

In order to fulfil the increasing energy demand, Malaysia aims to reduce carbon emission by 45 percent by 2030, and becomes fully carbon neutral by 2050. However, promoting this energy has inevitably forced this new industry to face some drawbacks particularly related to land matters, especially solar farm development, which is still new in the country and does not have any proper guidance. As the control of land planning and development is under the responsibility of the State Authority as enshrined in Article 74 of the Federal Constitution, the implementation is different in each state due to different land policy known as the State Land Rules. Thus, selected respondents have been interviewed, and the findings have been acquired regarding the elements of solar farm development’s approval consideration from the perspectives of land administrator, planner, and developer. This leads to a direction to standardize a legal framework of the land approval consideration for solar farm development especially in Johor.

Application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in Drone Thermal Imaging for Solar Farm Monitoring

The impact of multimedia in day-to-day life and its applications will be increased greatly with the proposed model (MSVPC)–5G Multicast SDN network eminence video transmission obtained using PSO and cross layer progress in wireless nodes. The drone inspection and analysis in a solar farm requires a very high number of transmissions of various videos, data, animations, along with all sets of audio, text and visuals. Thus, it is necessary to regulate the transmissions of various videos due to a huge amount of bandwidth requirement for videos. A software-defined network (SDN) enables forwarder selection through particle swarm optimization (PSO) mode for streaming video packets through multicast routing transmissions. Transmission delay and packet errors are the main factors in selecting a forwarder. The nodes that transfer the videos with the shortest delay and the lowest errors have been calculated and sent to the destination through the forwarder. This method involves streaming to be increased with the highest throughput and less delay. Here, the achieved throughput is shown as 0.0699412 bits per second for 160 s of simulation time. Also, the achieved packet delivery ratio is 81.9005 percentage for 150 nodes on the network. All these metrics can be changed according to the network design and can have new results. Thus, the application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in drone thermal imaging helps in monitoring solar farms more effectively, and may lead to the development of certain algorithms in prescriptive analytics which recommends the best practices for solar farm development.

Supporting the sustainable development of large-scale solar farms with a package of clean policies

Supporting the sustainable development of large-scale solar farms with a package of clean policies

Optimizing photovoltaic power plant site selection using analytical hierarchy process and density-based clustering – Policy implications for transmission network expansion, Ghana

Despite the enormous solar energy resource in Ghana, the country is yet to fully take advantage of this resource to meet the country’s growing energy demand. Some studies have partly associated this to the lack of appropriate data on the country’s renewable energy potentials. This study proposed a new approach in selecting appropriate sites for the development of solar farms by combining the Analytical Hierarchical Process and Density-Based Clustering using a Geographic Information System. The study identifies suitable clusters and ranks them for the development of solar photovoltaic farms. Wa in the Upper West region, which is macro cluster 1 in this study, is ranked as the top-most priority. It has approximately 264 km2 and a mean annual Global Horizontal Irradiance (GHI) of 1998.55 kWh/m2*year. Macro cluster 3 comes as the second priority, with a mean annual GHI of 1985.69 kWh/m2 per year and a total suitable area of 73.75 km2. Macro cluster 2 comes third with a mean annual GHI of 2054.86 kWh/m2 per year and an area of 123.25 km2. The methodology used in this study is suitable for developing countries interested in incorporating renewable energy into their national energy mix whiles simultaneously planning for transmission network expansion.

A Regional GIS-Assisted Multi-Criteria Evaluation of Site-Suitability for the Development of Solar Farms

One of the fastest-growing renewable energy sources is solar energy. A strategic step for a well-performing solar project is site identification. The evaluation of site-suitability is a complex task, where multiple qualitative and quantitative criteria, inherent to the territory, are involved. In this study, a GIS-based multi-criteria decision-making (MCDM) methodology for site-suitability evaluation in the development of solar farms (DSF) is presented. Two scenarios, the ranking method (RM) and the Analytical Hierarchy Process (AHP), each representing a different weighting approach, were tested. A case study was performed for the Desert of Chihuahua, Mexico, a region with the potential to provide a significant portion of the country’s energy demand. The RM was more stringent and identified less area with high suitability (1237 km2) compared to the AHP (4983 km2). Given its flexibility in assigning weights, the AHP is considered to have greater potential in identifying site-suitability levels. The final suitability maps of the AHP showed the northern part of the study region to have high suitability for the DSF. Thus, sites in this area could be used for the construction of solar energy projects in the future. This methodology provides a useful tool for land-use planning based on its suitability level.

Geotechnical and Structural stochastic analysis of piled solar farm foundations

Development of large scale solar farms supported by large numbers of short piles has created new challenges for engineers to address. Solar arrays are highly flexible structures and the piles can be designed to move to enable more cost effective design. The structural reliability of the above-ground pile can be assessed and probabilities of failure for different section sizes calculated. Economic analysis incorporating capital cost and whole-of-life maintenance cost can be performed to work out whether adopting smaller section sizes provide the best cost outcome.Assessment of pile movements using Monte-Carlo calculations, unsaturated soil mechanics and updating material parameters with suction have been performed. The results show that soil movements are typically larger than pile movements and that soil can slip past the pile with no pile movement when the limiting conditions occur. The results also highlight that the largest soil and pile movements occur infrequently as a result of extreme wetting or drying conditions.Structural reliability analyses showed that correlating wind speed and direction results in a lower probability of failure than if wind load is considered to be uncorrelated with wind direction. The outcomes of the assessment were sensitive to the adopted probabilistic model for pile durability.The main limitation of the analyses is that there is limited information in the literature relating to the types of probability distributions and their input parameters. This adds uncertainty to the stochastic analysis.

Territorial planning for photovoltaic power plants using an outranking approach and GIS

In 2015 the Paris Agreement set the goals for reducing greenhouse gas emissions. To achieve these objectives, it is necessary to boost electricity generation coming from renewable energy sources, particularly solar energy. Finding areas that are suitable for solar farm development is crucial for the economic feasibility of these projects and the sustainable use of land. This research investigates how to identify optimal sites for the development of solar plants. A GIS-based approach combined with a Multi-Criteria Evaluation methodology is used to create a map which shows a ranking of areas with high potential for solar farm development. The GIS-based methodology is employed in the treatment of the legal and environmental criteria and sub-criteria to delimit the suitable locations. These locations are evaluated and ranked by order using the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations). The importance of each criteria and sub-criteria is determined by an Analytic Hierarchy Process (AHP). In the Valencian Community the most discriminating criterion, when selecting the best locations, is the intensity of solar radiation, while temperature is the least discriminant. In terms of areas, the most suitable are located in the province of Alicante, while the least suitable are in the north of the Castellón province. It is also observed that the most appropriate locations are those that have greater solar radiation, and/or are at a lower altitude.

Planning for Future Solar Farm Development in North Carolina: A Geographic Food Energy-Water Approach

North Carolina (NC) is a large producer of utility-scale solar power and most of the photovoltaics are located on former agricultural lands (solar farms). In 2017 4% of NC power was generated by solar, but plans are in place for higher percentages, which could have consequences for food production and water quality. Geospatial data sets were used to characterize NC watersheds by solar farm development potential. Twenty-seven watersheds were identified as good candidates due to abundance of agricultural lands, presence of high-power transmission lines, and existence of agriculturally impaired waterbodies. These watersheds were somewhat distributed throughout the state, but especially focused in Union County, and 33% contain at least one solar farm. An energy generation model found that for the 27 targeted watersheds, only 2.6% of agricultural land would need to be converted to increase solar's energy portfolio to 5%; and 21.7% would need to be converted to increase it to 12.5%.

Analyzing territory for the sustainable development of solar photovoltaic power using GIS databases

Solar energy generated by grid-connected photovoltaic (GCPV) systems is considered an important alternative electric energy source because of its clean energy production system, easy installation, and low operating and maintenance costs. This has led to it becoming more popular compared with other resources. However, finding optimal sites for the construction of solar farms is a complex task with many factors to be taken into account (environmental, social, legal and political, technical-economic, etc.), which classic site selection models do not address efficiently. There are few studies on the criteria that should be used when identifying sites for solar energy installations (large grid-connected photovoltaic systems which have more than 100 kWp of installed capacity). It is therefore essential to change the way site selection processes are approached and to seek new methodologies for location analysis. A geographic information system (GIS) is a tool which can provide an effective solution to this problem. Here, we combine legal, political, and environmental criteria, which include solar radiation intensity, local physical terrain, environment, and climate, as well as location criteria such as the distance from roads and the nearest power substations. Additionally, we use GIS data (time series of solar radiation, digital elevation models (DEM), land cover, and temperature) as further input parameters. Each individual site is assessed using a unique and cohesive approach to select the most appropriate locations for solar farm development in the Valencian Community, a Spanish region in the east of Spain.

Innovation and the Power of Ignorance – The Case of Renewable Energy

Recognizing the limits of our knowledge and being dissatisfied with incomplete understanding is a major driver of scientific progress. Humanity’s experience over the last 300 years is ample demonstration of this. If we look carefully enough at the data, it is clear that we are progressing not only in terms of scientific discovery but in applications of science that are highly beneficial to our quality of life, such as medicine, agriculture, finance, transport and energy. Economic growth, in terms both of financial gain and improvement in real living standards, depends on creative thinkers who can mine and exploit such new knowledge, both as entrepreneurs and as policymakers. Public policymakers may err in the way new technology is applied, however, because they are not well-equipped to take into account the psychology of human reactions to innovation and change. Entrepreneurs with their own resources can exploit market forces to check whether an innovation is able to address the real needs and desires of the population, and if not, to change the application so that it does respond to true demand. The human thirst for knowledge seems only to accelerate rather than be satisfied by advances in knowledge and technology, so the opportunity for applying new science to human problems has a bright future. I n Brunei, for example, one area could be the development of solar farms and other forms of renewable energy, for which we examine the pros and cons in this paper.

Solar Farm Suitability Using Geographic Information System Fuzzy Sets and Analytic Hierarchy Processes: Case Study of Ulleung Island, Korea

Solar farm suitability in remote areas will involve a multi-criteria evaluation (MCE) process, particularly well suited for the geographic information system (GIS) environment. Photovoltaic (PV) solar farm criteria were evaluated for an island-based case region having complex topographic and regulatory criteria, along with high demand for low-carbon local electricity production: Ulleung Island, Korea. Constraint variables that identified areas forbidden to PV farm development were consolidated into a single binary constraint layer (e.g., environmental regulation, ecological protection, future land use). Six factor variables were selected as influential on-site suitability within the geospatial database to seek out increased annual average power performance and reduced potential investment costs, forming new criteria layers for site suitability: solar irradiation, sunshine hours, average temperature in summer, proximity to transmission line, proximity to roads, and slope. Each factor variable was normalized via a fuzzy membership function (FMF) and parameter setting based on the local characteristics and criteria for a fixed axis PV system. Representative weighting of the relative importance for each factor variable was assigned via pairwise comparison completed by experts. A suitability index (SI) with six factor variables was derived using a weighted fuzzy summation method. Sensitivity analysis was conducted to assess four different SI based on the development scenarios (i.e., the combination of factors being considered). From the resulting map, three highly suitable regions were suggested and validated by comparison with satellite images to confirm the candidate sites for solar farm development. The GIS-MCE method proposed can also be applicable widely to other PV solar farm site selection projects with appropriate adaption for local variables.