• Participating Institution: Liaocheng University
• Advisors: Xiao Yan, He Zhenfang
• Team Members: Guo Tonghe, Tang Xianrui, Ma Wanli, Miao Xinyuan

From left: Tang Xianrui, Guo Tonghe, Miao Xinyuan

ⅠProject Introduction

In recent years, with the rapid development of the new energy vehicle market, the limitations of traditional charging stations have become increasingly apparent, making it difficult to meet the environmental requirements of users for green travel. In contrast, integrated photovoltaic-storage-charging stations utilize solar power generation and energy storage technology to achieve energy self-sufficiency and renewable utilization, significantly reducing reliance on traditional power grids.

Geographic Information System (GIS) technology, with its strengths in cartographic representation, spatial analysis, and integration of spatiotemporal big data, has been widely applied across various industries. By integrating spatiotemporal big data and coupling algorithmic models, GIS technology plays a crucial role in the layout, planning, and management of integrated photovoltaic-storage-charging stations. This GIS-integrated management model not only provides strong support for the sustainable development of the photovoltaic-storage-charging industry but also promotes the adoption and application of green energy.

Futian District, located in the central part of Shenzhen, is one of the city's political, cultural, and commercial centers. In terms of new energy vehicle charging infrastructure, Futian District is a pioneer known as the "City of Supercharging," with the highest average density of charging piles in the city. It is the first district in Shenzhen to achieve more supercharging stations than gas stations, making it a new showcase for "smart photovoltaics + advanced energy storage + supercharging" in Shenzhen.

Based on this context, this study takes Futian District, Shenzhen, as a case area. Starting from the layout optimization of photovoltaic-storage-charging stations, it aims to identify the optimal site selection points through spatiotemporal data analysis, striving to fill the gap in the emerging field of "photovoltaic-storage-charging" integrated with GIS.

1. Analysis Process

1.1 Methods and Workflow

This study primarily employed GIS, Analytic Hierarchy Process (AHP), K-means clustering, NSGA-II algorithm, and TOPSIS algorithm to optimize the site selection of photovoltaic-storage-charging stations in Futian District, Shenzhen. 

1.2 Key Results

1.2.1 Pre-Optimization Service Area Analysis

Using a topological network construction tool with road network data from the study area, existing photovoltaic-storage-charging stations were input as service points to generate and analyze service areas.

Figure 1: Pre-Optimization Service Area

1.2.2 Determining New Station Locations

A site suitability model was constructed to identify highly suitable points. These points were processed using Python code, ultimately applying K-means clustering, NSGA-II, and TOPSIS algorithms to obtain general candidate sites. A solar radiation analysis model was built to determine solar radiation conditions from 12:00 to 14:00 on the winter solstice. Existing station data was used to validate this model's accuracy.

The results of the solar radiation analysis were overlaid with the general candidate sites to derive the final dataset for new photovoltaic-storage-charging station locations.

Figure 2: Shadow Areas and Model Validation Based on Solar Radiation (12:00-14:00, Winter Solstice)

Figure 3: Site Selection Results

1.2.3 Post-Optimization Service Area Analysis

Using the topological network tool again, both existing and newly planned stations were input as service points to generate and analyze the updated service area.

Figure 4: Post-Optimization Service Area

Ⅱ Project Highlights and Improvement Directions

1. Highlights

• Focus on the emerging field of "PV-Storage-Charging," demonstrating strong foresight. As an emerging area, the integration of PV-storage-charging stations with GIS is still in its exploratory stages. This project features a scientific and comprehensive design that addresses contemporary environmental needs, helping to fill gaps in the "PV-Storage-Charging + GIS" field while improving solar energy utilization and promoting sustainable development.
• Integration of multiple analytical methods and platforms for more precise site selection. This work combines AHP, K-means clustering, NSGA-II, and TOPSIS algorithms, utilizing various software platforms like SuperMap iDesktopX 11i, Visual Studio Code, yaaph, and Excel. This enables data interoperability and leads to more accurate research conclusions.
• In-depth analysis of spatiotemporal big data using Python code. The project implemented automated modeling and utilized Python code to process the modeling results, deeply analyzing candidate points, demand points, and final site selection data. This enhances the project's logical rigor and scientific basis, resulting in more rational site selection for PV-storage-charging stations.

2. Improvement Directions

• Incorporate more suitability analysis indicators. To further enhance the scientific rigor and practicality of the site selection plan, future suitability analyses could include additional indicators such as temperature, direct sunlight hours, and government funding, considering multi-dimensional needs to improve the accuracy of candidate point selection.
• Increase the objectivity of weights derived from AHP. In future applications of AHP, collecting opinions from multiple experts or using surveys could help reduce subjective bias and yield more reasonable weight assignments.
• Introduce AI technology. The development of artificial intelligence presents new opportunities for data analysis. Future work could incorporate AI technologies, such as machine learning algorithms, to automatically identify and predict key factors influencing site selection.

Ⅲ Advisors' Comments

This project integrates GIS technology with the layout optimization of integrated PV-storage-charging stations, providing a new research approach for planning new energy charging infrastructure. By employing various algorithms and models like NSGA-II and TOPSIS, combined with solar radiation analysis, it conducts site selection planning for PV-storage-charging stations in Futian District, Shenzhen. The research results not only offer a theoretical basis for optimizing the layout of these stations but also lay a solid foundation for promoting the adoption and application of green energy. This work holds significant scientific value and practical importance for the sustainable development of new energy infrastructure.