Back to Projects

Team Name:

Hack-a-Vision


Team Members:


Evidence of Work

Genie parcel: Solar Site Selection with Dynamic Analysis

Project Info

Hack-a-Vision thumbnail

Team Name


Hack-a-Vision


Team Members


Krishna , Shawn , Mingzhe , Onur , Laila , Jack Batson , Nikhita Sreya Rajaram and 2 other members with unpublished profiles.

Project Description


Logo

Summary

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.

Problem Statement

Australia’s renewable energy sector requires efficient site selection for large-scale solar projects. Two key challenges include:

  1. Optimally positioning and site selection solar projects to maximise efficiency, minimise environmental impact, and ensure seamless grid integration.
  2. Providing user-adjustable parameters to customise the site suitability analysis according to specific needs, enhancing flexibility in decision-making.

Solution Overview

Solution

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:

  1. Identifying Suitability Criteria

    Defining criteria such as sunlight availability, proximity to infrastructure, and environmental impact.

  2. Utilising Open Data Platforms

    Selecting relevant data sources (e.g., land use, solar radiation) aligned with the identified criteria.

  3. Python-Based Suitability Model

    Developing a Python script to calculate suitability scores based on selected data and criteria.

  4. Web Application Development

    Building an interactive web app where users can adjust criteria and view dynamic site suitability scores.

Technology Stack

tech stack

Our solution uses a variety of tools to manage data, build the web app, and collaborate effectively:

1. Data Management

  • Excel: For organising and managing datasets.
  • Python: For processing and analysing data.
  • QGIS: For visualising and working with geospatial data.

2. Web App Development

  • Angular: To build the front-end interface of the web app.
  • Ionic: To build the responsive designed web app that can be used on both mobile and desktop, such as PWA
  • Leaflet: For creating interactive maps and displaying geospatial data.

3. DevOps:

  • GitHub Repository: To be used as version control of the project source codes;
  • GitHub Action: To build the docker image and push to the Docker Hub;
  • Docker Hub: To host the Docker image;
  • AWS EC2: The server where the web app will run;
  • Jenkins: CI/CD tool to automatically pull the latest Docker image and deploy it;
  • Cloudflare: DNS service, and can speed up access for end users.

4. Workflow Diagrams

  • draw.io: For creating diagrams to visualise the app’s architecture and workflows.

5. Collaboration Tools

  • Slack & Teams: For communication and team collaboration.
  • Google Drive: For sharing files and storing project resources.
  • Notion: For task management and documentation.

This tech stack ensures efficient data handling, user-friendly web app development, and smooth team collaboration.

Impact and Benefits

Impact

This project will provide:

  • Simplified Suitability Analysis: Non-GIS users can easily analyse solar site suitability through an intuitive interface.
  • Comprehensive Data for Developers: Renewable energy developers can access critical datasets to make informed site selection decisions.

Challenges and Learnings

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.

Future Improvements

  • Enhanced Visualisation: Future versions could provide a map-based visualisation of how adjustments in criteria impact site suitability.
  • Additional Criteria: Expanding the dataset to include more factors like economic and social impacts for a more holistic analysis.

Resource

Github
Google Drive
Figma

Social Media

image.png


#spatial vision #geospatial analysis #gis development #web app development #solar sites #dynamic analysis #renewable energy site #environment

Data Story


Introduction to the Problem

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.

story board

The Data Sources

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.

Exclusion Layers

Suitability Layers

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.

The Data Workflow

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.

Exclusion Layers

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.

Suitability Layers

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

Land Area was obtained simply by calculating the geometric area of each property polygon, using the Field Calculator in QGIS.

Solar Radiation

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.

Proximity to Transmission Grid

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.

Dynamic Analysis

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.

AI or Algorithm Application

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.


Evidence of Work

Video

Homepage

Project Image

Team DataSets

Administrative boundaries

Data Set

Indigenous Protected Areas (IPA) - Dedicated

Data Set

Transmission Substations

Data Set

Electricity Transmission Lines

Data Set

Australia's Transport Network

Data Set

SA2 Population projections 2022 to 2032

Data Set

ABS 2021 Census G34 Number of motor vehicles by dwellings by 2021 SA2

Data Set

Major Power Stations

Data Set

Renewable Energy Site Selection Model

Data Set

Challenge Entries

Renewable Energy Site Selection Model

How can we optimally position renewable energy projects across Australia to maximize efficiency, minimize environmental impact and integrate seamlessly into the existing grid?

Go to Challenge | 7 teams have entered this challenge.