Case Study: Barcelona Municipal Services

How Awesense helped the city of Barcelona understand how much of the city’s electricity consumption was due to EVs and thus be able to plan where to put more EVs.

Introduction

In 2020, in collaboration with Barcelona’s City Council Commissioner for the 2030 Agenda, the Digital Future Society launched the Tech & Climate Open Innovation Challenge for smart energy management solutions. This initiative aimed to identify innovative solutions to reduce the environmental footprint of the technology sector in the context of climate emergencies. Awesense won the innovation challenge.

Scope Snapshot
Goal

Increase EV charger awareness and planning intelligence through data on a smart energy management system

The Data
  • AMI Metering
  • EV charger specs and consumption
Awesense Tools Used
  • Energy Data Model (EDM)
  • EDM data ingestion APIs
  • EDM data retrieval APIs
  • True Grid Intelligence (TGI) web app

The Challenge

Bacelona’s municipal services(B:SM) needed to understand how much of the city’s electricity consumption was due to EVs, and where EV charging was occurring. This would greatly assist in their EV charger deployment planning processes. A major hurdle however was to effectively integrate EV charging session data and other grid data for analytics. The city had over 500 charging points distributed throughout stations and car parks as well as over two years of EV charger session data.

The project started with the Digital Future Society’s Tech & Climate Open Innovation Challenge for smart energy management solutions. This initiative’s aim was to identify innovative solutions that reduce the environmental footprint of the technology sector in the context of climate emergencies. Awesense won the innovation challenge.

The Solution & Results

Awesense Platform integrated the numerous charge point and meter data from stations and car parks across the city. The data was synchronized and structured into a digital twin. Analytics were subsequently built using notebooks and visualizations on top of the Awesense APIs using tools like Jupyter, SQL & Python.

The insights from the data ingestion and analytics performed by Awesense proved invaluable. The city could identify where EV charging was taking place in their grid. The team at the City of Barcelona could also properly assess which sections of their grids were ready for more connected EV charging stations. Furthermore, they had the tools to perform continued management and optimization of these assets after deployment.

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