Geospatial Data for Selecting Optimal Renewable Energy Sites

As the world continues to move towards renewable energy sources, solar and wind power plants have become increasingly important. The trend is also dictated by the increasing demand for electricity while reducing carbon emissions. 

However, choosing the right location for these power plants can be a complex process that involves a range of factors, from available land and solar irradiation to wind speeds and topography to climate change impact. To make informed decisions, developers and operators of solar and wind power plants use geospatial data to analyze potential sites and determine their viability.

Geospatial data refers to any data that is associated with a specific location on the earth’s surface. This data can come from a variety of sources, including satellite imagery, LiDAR (Light Detection and Ranging), GPS (Global Positioning System), and geographic information systems (GIS). By analyzing this data, developers can identify suitable locations for solar panels and wind turbines as well as potential obstacles and risks that could impact the success of the project.

In this article, we’ll explore how geospatial data is used in site selection for solar and wind power plants. We’ll discuss the various factors that need to be considered when selecting a site, as well as the tools and techniques that developers can use to analyze and interpret geospatial data.

Factors to Consider in Site Selection

Before we dive into the details of using geospatial data for site selection, it’s important to understand the various factors that need to be considered when choosing a site for a solar or wind power plant. Some of the key factors include:

Solar Irradiation. Solar irradiation refers to the amount of sunlight that reaches a given location on the earth’s surface. In order for a solar power plant to be successful, it needs to be located in an area that receives sufficient sunlight. Developers can use historical data on solar irradiation to identify areas with high levels of solar energy potential.

Wind Speeds. Wind speed is a critical factor in the performance of wind turbines. The higher the wind speed, the more electricity a turbine can generate. Developers can use historical data on wind speeds to identify areas with high wind energy potential.

Weather Impacts. Extreme weather events such as extreme heat, floods, hurricanes etc can cause damage to renewable energy infrastructure. Therefore, it is important to consider the frequency and severity of extreme weather events when selecting a site for a renewable energy plant. 

Third-Party Objects Nearby. In order to minimise security issues of the investment, renewable power plants should be located at a decent distance to buildings, construction and other areas and activities, where unauthorised visits or dangerous encroachments may occur to the project site.

Land Use. Solar and wind power plants require large amounts of land in order to be effective. Developers need to identify sites that are suitable for power plants and that will not conflict with other land uses. Geospatial data can be used to analyze land use patterns and identify areas with low levels of development, for example, brownfield sites or abandoned industrial sites. By repurposing these sites for renewable energy projects, developers can also help to revitalize these areas and reduce the need for new land development.

Topography. The topography of a site can impact the performance of solar and wind power plants. Areas with steep slopes or rugged terrain may be more difficult to develop. Flat, open areas with few obstructions are ideal for solar power plants, as they allow for maximum exposure to sunlight. Wind speed, in turn, is affected by local terrain effects, such as hills, valleys, and ridges.

Environmental Factors. Developers need to consider a range of environmental factors, such as the presence of wildlife, protected habitats, and archaeological sites. These factors can impact the viability of a site and may require additional permits and approvals.

Accessibility. The location of a site can impact its accessibility and ease of construction. Developers need to identify sites that are accessible by road and that have suitable infrastructure for transporting equipment and materials.

Proximity to Transmission Lines. Renewable energy projects need to be connected to the electrical grid to be able to deliver electricity to customers. Therefore, developers need to identify sites that are close to existing transmission lines or that can be easily connected to the grid. By taking transmission infrastructure into account, developers can ensure that their renewable energy projects are feasible and cost-effective.

Using Geospatial Data for Site Selection

Once developers have identified the key factors that need to be considered in site selection, they can begin to use geospatial data to analyze potential sites. This data can include information on the availability of natural resources, existing infrastructure, land use regulations, and environmental conditions. Geospatial data can also be used to identify potential environmental impacts of a proposed development, assess the financial viability of a project, and analyze the projects overall impact on the local economy. By using geospatial data, developers are able to make informed decisions about where and how to build renewable energy projects. There are a variety of tools and techniques that can be used to interpret geospatial data and identify suitable sites for solar and wind power plants.

Satellite Imagery. One of the most common sources of geospatial data for site selection is satellite imagery. Satellites can capture high-resolution images of the earth’s surface, which can be used to identify potential sites for solar panels and wind turbines. Developers can use satellite imagery to analyze land cover, topography, and other factors that may impact site selection.

LiDAR. LiDAR is a remote sensing technology that uses lasers to create a detailed 3D model of the earth’s surface. LiDAR data can be used to create highly accurate digital elevation models, which can be used to identify suitable locations for renewable power plants. By analyzing the terrain, developers can identify areas with high wind speeds and low turbulence, which are ideal for wind turbines as well as the amount of sunlight that reaches the site for optimal site selection for solar panels.

Geographic Information Systems. GIS is a powerful tool for analyzing geospatial data. Developers can use GIS to create maps that show key factors such as solar irradiance, wind speed and direction, land use, and transmission lines. By overlaying these maps, developers can identify areas that are suitable for solar and wind power plants.

Machine Learning. Machine learning algorithms can be trained to identify patterns in geospatial data that may not be immediately apparent. For example, machine learning can be used to identify areas with high solar irradiance or areas with high wind speeds. By analyzing large amounts of geospatial data, machine learning algorithms can identify potential sites for renewable energy systems.

Solar and Wind Resource Assessment Tools. Solar and wind resource assessment tools are software programs that use historical data on solar irradiation and wind speed to estimate the potential solar and wind energy output of a given site. These tools can be used to identify areas with high levels of solar and energy potential and can help developers estimate the financial viability of a solar and wind power plant.

Impact of Renewables on the Local Environment 

Geospatial data can be used to analyze the potential environmental impacts of renewable energy systems. For example, the construction of wind turbines can have an impact on bird migration patterns, while solar power plants can have an impact on wildlife habitats. Geospatial data can be used to identify areas that are important for bird migration and wildlife, respectively, and avoid placing wind turbines and solar plants in those areas. 

Challenges and Limitations of Geospatial Data for Site Selection

While geospatial data is a powerful tool for site selection for solar and wind power plants, there are also challenges and limitations that developers need to be aware of.

Data Availability. One of the biggest challenges of using geospatial data for site selection is data availability. Some areas may not have sufficient data available, or the data may not be of sufficient quality to make accurate assessments of solar and wind energy potential.

Data Interpretation. Interpreting geospatial data can be complex and requires specialized knowledge and skills. Developers may need to work with experts in remote sensing, GIS, and machine learning to analyze the data accurately.

Data Processing. Geospatial data can be complex and require specialized software and expertise to process and analyze. This can be a barrier for smaller developers who may not have the resources or expertise to use geospatial data for site selection.

Regulatory Framework. Site selection for renewable energy systems is often subject to regulatory requirements. Developers may need to navigate complex regulatory frameworks, which can impact site selection.

Conclusion

Geospatial data is a powerful tool for site selection in the renewable energy sector. By analyzing factors such as solar irradiance, wind speed and direction, terrain, and land use, developers can identify suitable locations for solar and wind power plants. However, there are also challenges and limitations, such as data availability, quality, and processing, that need to be considered.

As renewable energy continues to grow in importance, the use of geospatial data will become even more critical for ensuring that renewable energy systems are placed in the most suitable locations. By carefully analyzing geospatial data and considering all relevant factors, developers can make informed decisions about site selection for solar and wind power plants, leading to more successful and sustainable renewable energy projects. 

Niccolo Teodori
Niccolo Teodori

Spottitt Chief Growth Officer

Our latest news:

Satellites Drones Geospatial Data Collection Comparative Analysis

Deciding Between Satellites and Drones for Geospatial Data Collection: A Comparative Analysis

In recent times, the landscape of asset monitoring has witnessed a transformative shift with the emergence of advanced technologies, particularly satellite and drone systems.

The utilization of Earth observation satellites for monitoring purposes began gaining momentum in the late 20th century. Free government programs, such as those initiated by NASA and the European Space Agency (ESA), played a pivotal role in providing open access to satellite imagery. These programs not only facilitated scientific research but also allowed industries to leverage satellite data for monitoring critical infrastructure and environmental changes.

spottitt transgrid transport deloitte

Spottitt to Collaborate with Transgrid and Transport for NSW

Spottitt has been selected by Transport for NSW and Transgrid to apply satellite-based and AI-powered technology to automatically detect changing conditions in the NSW road and rail network and to provide real-time insights for Transgrid’s easement route maintenance and planning, respectively.

These projects will be conducted within the GRAVITY Challenge 06 program led by Deloitte Australia, an initiative that brings together start-ups, scale-ups, entrepreneurs and universities to address real industrial and environmental problems using space data. The Collaborate Phase will continue until mid-March 2024.

climate change EU UK Ireland satellite data

Weathering the Storm: Climate Change Threats to Power Grid Infrastructure

As our global climate continues to undergo profound transformations, the challenges posed by climate change are increasingly felt across various sectors of society, including critical infrastructure.

Climate change, characterized by rising temperatures, increased occurrences of extreme weather events, and shifting precipitation patterns, have exposed vulnerabilities within power networks. Often designed under the assumptions of historical climate patterns, now they are increasingly susceptible to the new normal of extreme weather, prolonged heat waves, and more severe freezing.

InSAR analysis Infrastructure Monitoring Sentinel Imagery

InSAR Analysis and Corner Reflector Experiments for Infrastructure Stability Monitoring Using Sentinel-1 Imagery

National Grid Energy Transmissions (NGET), which owns and maintains the high-voltage electricity transmission network in England and Wales, conducts invasive analysis annually to monitor the towers most at risk of movement. Moreover, the NGET inspection teams perform annual line walking activities and monthly substation inspections during which they visually assess the presence of asset motion. These interventions are crucial to avoid issues which may cause expensive assets replacements or reconstruction. It costs NGET over £6 million per year to monitor only 1% of their most at risk assets.

WorldPipelines

Utilizing Satellite Data to Mitigate Pipeline Failures and Risks

The operation of oil and gas transmission pipelines entails inherent risks associated with the potential for unintentional product releases. Oil and gas product releases have traditionally been treated as safety issues due to the risk of explosions and asphyxiation, but increasingly, the environmental impact of unintentional product releases is fast becoming the key risk to be reduced and avoided via Leak Detection and Repair (LDAR) programs.

Spottitt MF object detection

Behind the Spottitt MF Curtains: Role of Machine Learning in Analyzing Satellite Imagery

Spottitt is known for utilising satellite data to offer infrastructure owners valuable insights into the diverse external risks affecting their assets. But how precisely do we derive these insights from satellite imagery? The answer is – machine learning, and today we’re inviting you behind the curtains of our product to unveil the whole process.