Team Name:
Hack-a-Vision
Team Members:
Hack-a-Vision
This project Genie Parcel aims to create a dynamic site selection model for large-scale solar projects in Australia. The solution allows users to adjust various analysis parameters to identify optimal locations for solar installations, considering factors like energy output, environmental impact, and grid integration.
Australia’s renewable energy sector requires efficient site selection for large-scale solar projects. Two key challenges include:
Our solution provides a dynamic, user-friendly web application for solar site selection. By leveraging geospatial data and allowing user input, the app will generate suitability scores for potential sites. The process involves:
Identifying Suitability Criteria
Defining criteria such as sunlight availability, proximity to infrastructure, and environmental impact.
Utilising Open Data Platforms
Selecting relevant data sources (e.g., land use, solar radiation) aligned with the identified criteria.
Python-Based Suitability Model
Developing a Python script to calculate suitability scores based on selected data and criteria.
Web Application Development
Building an interactive web app where users can adjust criteria and view dynamic site suitability scores.
Our solution uses a variety of tools to manage data, build the web app, and collaborate effectively:
This tech stack ensures efficient data handling, user-friendly web app development, and smooth team collaboration.
This project will provide:
During the development process, we encountered challenges integrating various datasets and refining the suitability model to ensure accurate results. Balancing multiple site selection criteria dynamically based on user input required in-depth testing and optimisation.
Begin by framing the challenge: selecting solar project sites that balance efficiency, environmental impact, and grid integration. Highlight the complexity and importance of using data to solve this problem.
The goal was to make traditional suitability analysis for renewable projects more accessible to decision makers in the energy industry who are not GIS Experts. The goal was to create an analysis model that produces the core criteria of the suitability model, and then allows the user to dynamically adjust the weights of each criteria to match their own needs.
Our example uss the implementation of large-scale solar farms as a use case, and the Area of Study was the Bendigo Statistical Area 4 (SA4), however this method is scalable to a nationwide analysis.
Describe the datasets you’ve used, such as solar radiation, land use, proximity to infrastructure, and environmental factors. Explain how each dataset contributes to the overall analysis. Mention the sources (government, open data platforms) and how you ensured data accuracy.
The model can be divided into 2 sections: exclusion layers, which are areas that prohibit renewable energy projects from being undertaken altogether, and suitability layers, where parcels are given a score based on how favourable they are for a certain criteria.
To demonstrate the methodology, three criteria were used to generate the suitability score, however the aim of the model is to be scalable to any criteria necessary.
All geospatial data management and analysis was performed in QGIS, and some processes were automated through PyQGIS, a Python module for QGIS analysis.
The final output dataset reflects the suitability of properties in a region, based on the criteria met. As such, all analysis was related back to a property level.
After the exclusion layers were filtered to their relevant features (such as filtering relevant Planning Zones), they were merged and dissolved into a single exclusion layer polygon.
Properties which lay within the exclusion area were removed from the study using the Difference tool. The output of this tool is a Property dataset clipped to only the areas lying outside the exclusion zone. This means accounts for properties which had a only portion of their area lying within the exclusion zone. If a property had a significant enough area outside the exclusion area, they may still be considered. Resultant parcels which had less than 50% of their area remaining outside the exclusion area, or were less than 4,000 sqm, where removed from consideration as well.
For each suitability criteria, the value for that criteria was obtained for each property. The methodology for each is outlined below. Once obtained, each criteria was standardised to a value between 0-100.
Land Area was obtained simply by calculating the geometric area of each property polygon, using the Field Calculator in QGIS.
The input for solar radiation was a raster dataset. Property polygons were converted to points using the Centroid tool. Using these points, representing the centre location of each parcel, the solar radiation raster was sampled at each of these points, using the Sample Raster Values tool. This tool gets the corresponding raster value at each point location, and adds it as a new field.
The input for transmission powerlines was a line vector dataset. To get the distance to the powerline for each property, the most efficient solution is to generate a Proximity Raster. To do this, the Powerline Vector dataset was converted to raster, and the Proximity (raster distance) tool was used to create a distance from powerline raster surface covering the whole area of study.
The Sample Raster Values tool was used again, to collect the distance value at each property point.
The goal of this project was to allow the user to dynamically adjust the results of suitability analysis without having to create a new in-depth model each time.
Once the values for each suitability criteria are collected, they must be combined into a single overall suitability score. This is where the user can define how they want to prioritise the criteria in the final suitability score.
When combining multiple criteria into a single value, certain criteria can be given greater emphasis over others, by weighting the final score towards that variable. On the user interface, they can use sliders to determine how they want the final score to be calculated, for example they can give 50% weight to Solar Radiation, 30% to Land Size and 20% to Proximity to transmission Grid. These inputs will affect the suitability score calculation, and the prioritisation of properties on the map.
A Python script is being developed, with the aim of being as flexible as possible. While only a limited number of criteria was used in this case study, future users may wish to incorporate more detailed exclusion layers, or also consider factors such as terrain into the suitability score. A reusable and extendable python script will allow users to run and re-run the analysis depending on their parameters.
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