The climate crisis caused by greenhouse gas emissions is the most important problem that humanity is currently facing on a global scale. One of the largest contributors to global greenhouse gas emissions is transport, which in Spain is the sector with the highest contribution: 27.7% of total emissions in terms of equivalent CO2 in 2020. Therefore, transport must be decarbonised, making a transition to electric vehicles (EVs). In Spain, road transport is the main transport mode, representing more than 80% of total mobility. It is necessary to deploy an appropriate public charging infrastructure, which has been estimated for 2030 at around 250,000 public charging points, while in 2019 there were slightly less than 8,000 charging points.
Urban traffic is a typical case of a complex system consisting of many interacting components that give rise to emergent properties at the global level, which are difficult to predict in advance except by simulation. One emergent property of urban traffic are traffic jams. Such traffic patterns determine air pollution levels, because vehicles using fossil fuels are involved in them. We have recently demonstrated through simulations [1] that the layout of urban charging stations (CSs) can change the traffic patterns in a city. But the vast majority of previous developments to determine the layout of CSs in a city are based on the analysis of previous data, such as traffic patterns or electric grid usage. They have the drawback that they do not take into account the effects of the location of CSs on urban traffic and electric grid usage.
Contribution of SANEVEC: We will build a framework for the optimal determination of the location of EVs charging stations in a city based on simulation, which will be able to reproduce the feedback effects between all the variables involved: locations of the stations, traffic patterns, characteristics and operation of the electricity grid, charging fees and air quality in the city. It will also integrate artificial intelligence methods to find high quality station location solutions from the simulations and to predict the air quality in the city. The aim will be to obtain environmental and social benefits, associated with reducing environmental pollution and carbon footprint in the city and improving the smooth flow of urban traffic and electric vehicle charging operations.
The project will employ a combination of disruptive digital technologies: simulation techniques to form the basis of a digital tool with a holistic approach to plan the operation of the complex urban system and forecast its evolution and consequences, using agent-based models and cellular automata; artificial intelligence to optimise the location of charging stations (genetic algorithm) and to make multi-day forecasts of air quality in the city (deep learning). In addition, we will use high-performance computing techniques to ensure that the software execution has adequate computational performance.
Expected environmental benefits of SANEVEC:
- Reduction of air pollution in urban areas, because of the reduction of traffic congestion and therefore in the number of polluting fossil fuel vehicles involved in traffic jams.
- Reduction in the carbon footprint of cities due to the greenhouse gases.
- Contribution to increase the proportion of EVs versus vehicles powered by fossil fuels, because of the improved user experience of EVs by reducing waiting times.
- Reduction of noise pollution in urban areas, due to reduced congestion and quieter EVs.
Expected social benefits of SANEVEC:
- It will help the process of traffic electrification by optimising EVs charging operations. This will foster economic activity in the new sectors associated with electrification, contributing to economic growth and creating new skilled jobs for the population.
- It will reduce transport times in the city, due to the reduction of traffic congestion.
- It will contribute to increasing the quality of life of citizens, due to the combination of the aforementioned benefits.
Contribution of SANEVEC to solve problems in the areas of ecological and digital transition, through the expected environmental and social benefits: Regarding ecologic transition, SANEVEC will contribute to decarbonisation, energy efficiency, deployment of renewable energies, and electrification of the economy. Regarding digital transition, SANEVEC will have a positive impact in sustainable mobility and in the urban agenda.
Starting hypothesis:
- Analysis of previous data, such as traffic patterns or electric grid usage, is not the best methodology to design the deployment of the network of electric vehicle charging stations, because their ubication and characteristics affect urban traffic and electric grid usage and can modify them.
- Simulation using a microscopic model is a better methodology than previous data analysis to design the deployment of the network of electric vehicle charging stations, because it can take into account its effect on urban traffic and electric grid usage.
- Urban traffic patterns (for example traffic jams), which are influenced and modified by the network of EV charging stations, have a direct influence in air-quality measures and carbon footprint of the city.
General objective:
The purpose of this project is to research, design, and implement a computer simulation model to predict the effects of the layout of an urban network of electric vehicle charging stations on the traffic congestion, air-quality, carbon footprint and electric grid usage of a real city. We will build a complete simulation framework, starting by previous work carried out by members of the team that designed and implemented a preliminary simulator (SIMTRAVEL), that allowed us to validate the concept idea over a synthetic test city [1]. This powerful and flexible analysis tool will be made available to municipalities, governments, firms, research groups and universities.
Specific objectives:
- To develop a methodology to extract the geographic information from a real city using a geographic information system (GIS), and to inject it as an input of the city modeling module of a simulator.
- To develop a simulator to predict the effects of a particular deployment of a network of charging stations on the traffic congestion, air-quality, carbon footprint and electric grid usage of a real city, derived from the SIMTRAVEL simulator that has already been developed for a synthetic city.
- To develop a methodology to study the optimal deployment of the network of charging stations in a real city by simulations, using a genetic algorithm and the simulator developed in objective 2.
- To develop methods and tools for the validation of the simulation results of the simulator developed in objective 2 and the analysis of its performance and scalability.
- To test and validate the produced simulation framework, by developing an exemplary application to the city of Seville (Spain).
Methodology:
The project plan consists of 5 technical work packages (WP1-WP5) for building and validating the framework, plus one more (WP0) for project management and another (WP6) for dissemination and exploitation. The framework will consist of the following modules, programmed in C++:
[1] A. García-Suárez, J.L. Guisado-Lizar, F. Diaz-del-Rio, F. Jiménez-Morales. A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations. Sustainability, Vol. 13, Issue 10, Article Id. 5421, 2021. DOI