No abstract

Profound changes driven by decarbonization, decentralization, and digitalization are disrupting the energy industry, bringing new challenges to its key stakeholders. In the attempt to address the climate change issue, increasing penetration of renewables and mobility electrification augment the complexity of the electric grid, thus calling for new management approaches to govern energy exchanges while ensuring reliable and secure operations. The emerging blockchain technology is regarded as one of the most promising solutions to respond to the matter in a decentralized, efficient, fast, and secure way. In this work, we propose an Ethereum-based charging management framework for electric vehicles (EVs), tightly interlinked with physical and software infrastructure and implemented in a real-world demonstration site. With a specifically designed solidity-based smart contract governing the charging process, the proposed framework enables secure and reliable accounting of energy exchanges in a network of trustless peers, thus facilitating the EVs’ deployment and encouraging the adoption of blockchain technology for everyday tasks such as EV charging through private and semi-private charging infrastructure. The results of a multi-actor implementation case study in Switzerland demonstrate the feasibility of the proposed blockchain framework and highlight its potential to reduce costs in a typical EV charging business model. Moreover, the study shows that the suggested framework can speed up the charging and billing processes for EV users, simplify the access to energy markets for charging station owners, and facilitate the interaction between the two through specifically designed mobile and web applications. The implementation presented in this paper can be used as a guideline for future blockchain applications for EV charging and other smart grid projects.

The eVIP (Energy Visualisation Integration and Prediction) project aims to predict the load curve of electric vehicles in a semi-private context related to hotels and restaurants. Using the gradient boosted tree algorithm, it is possible to predict the consumption of a hotel with an accuracy of approximately 83.8% with nonintrusive devices. By using this prediction and the data collected when an electric vehicle is being charged at the hotel's charging station, the peak consumption of the hotel can be optimized. We have also opened the way for V4G (Vehicle for Grid) to allow bi-directional energy flows in this semi-private micro-grid and propose flexibility services to Distribution System Operators (DSO).