Smart Decision Support System for Urban Energy and Transportation (JPI Urban)

During this reporting period we further developed the app that encourages users to reduce their carbon footprint from driving and energy consumption. The app monitors users' greenhouse gas emissions from driving and energy consumption in real time. It suggests how the carbon footprint can be cut by installing a solar pv installation, replacing the vehicle with an electric one, and by cycling or walking instead of driving. The carbon and monetary savings from these efficient alternatives can be compared to actual performance in real time. The app was developed in interaction with the Norwegian energy company NTE as well as the Austrian and Dutch partners in the project. This year we tested the app with the gamification methods of our partners' at NTNU improved the user experience. The app was tested with users at NTNU and with Austrian partners. However, due to changes in the underlying software platform Android OS new issues appeared and we were not able to launch a fullscale test.

We believe the interaction with a Norwegian electricity supplier had an effect on the app and services the company developed. The company did not directly implement our solution, but their service does resemble the work in DESENT. Other than that the project seems to have opened a new understanding in the partners' perceptions. We expect further work to be done in this field of combining energy and transportation services, including the collection of data on blockchain.

This proposal aims at the development, application and dissemination of an integrated decision support system for energy use of buildings and transport, ensuring maximum efficiency with respect to supply and distributed energy generation. The specific goal is to develop a smart energy control concept of household/vehicle energy use through the implementation of advanced ICT technology. Using multi-agent simulation, household preferences, energy use and responses to policies/services will be incorporated into an integrated activity-travel demand model to predict the energy demand at a high level of temporal and spatial resolution. This system will be complemented with a model of distributed energy generation, which represents the possible interactions among local energy suppliers, grid operators and customers. The system design and implementation will be accomplished through co-creation in several demo cities. Experiences will provide the basis to evaluate whether such innovative high-resolution demand predictions provide improved insights into demand flexibility that can be used to balance services in the power grid. This is particularly important in the view of increased penetration of variable renewable generation, electrical mobility and distributed energy generation schemes in cities.