No abstract

The integration of distributed energy resources and the transition to smart cities are shifting the urban energy sector to a decentralized operating system. Blockchain-based microgrids, where small-scale operators trade electricity among each others, have gained remarkable attention recently. However, most of the proposed schemes study smart grids in prosperous cities. In this study, the performance of a solar-based power trading scheme is investigated in a shortage-prone context, Beirut City. Thus, we resort to a game-theoretic approach to model power trading as a repeated game between buildings at the urban scale. Results show that solar energy can cover up to 25% of the city electricity needs, depending on the rooftops area coverage. On the other hand, we found that deploying a peer-to-peer trading scheme has marginal impact since the energy demand in the city exceeds the supply and most buildings would prioritize self-consumption.

In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings’ number of floors and construction periods’ dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow.