This paper focuses on the implications of the smart grids that are based on the bidirectional exchange of information and energy flow in the electricity networks. By optimising, saving energy, and delivering power precisely where it is needed, smart grids represent the leading concept constituting the Internet of Energy (IoE). The power networks of the future are likely to be automated and based on algorithms run by the artificial intelligence (AI). We tackle some of the implications of smart grids in the IoE with a special focus on electric transport. Electric vehicles might represent one of the biggest challenges for the smart grids of the future. Our results show that selfoptimising consumers might combine the generation of energy with its trading via peer-to-peer (P2P) networks and using it for charging the electric vehicles for achieving a better balance and energy market equilibrium. Models are demonstrated and some useful results are derived and explained in detail. Our results might be of some special interest for policymakers and stakeholders dealing with autonomic power systems and energy efficiency and security. Electricity is supplied via the electricity grids. An intelligent (or "smart") power grid uses bidirectional power and information flows to create an automated power grid. The term "smart grid" is typically used to describe an electricity system that supports four basic operations that include electricity generation, electricity transmission, electricity distribution, and electricity control (see Strielkowski 2017). Smart grid technologies include integrated communication, acquisition and measurement technologies, advanced components, advanced control methods, and improved interfaces and decision support (Kabalci 2016). With the cost and benefit analysis in smart grid, we have found that smart grid can really make people live a better, healthier, and higher-quality life. In contrast, today's power system raises big questions about its ability to continue providing citizens and businesses with relatively clean, reliable and affordable energy services. The technological limitations in the measurement no longer lead to the averaged peak prices being passed on equally to all consumers. The rapidly declining costs indicate a major shift from the central network topology to a highly distributed one, where power is generated and consumed directly at the grid boundaries (Lakhov et al. 2018). An intelligent energy management system keeps the grid stable by balancing the power generated from all sources with the power consumed. An IoE enables consumers and prosumers to independently coordinate supply and demand and is equipped with intelligent forecasting systems that use weather forecasts, expected traffic flows and other information to predict future energy needs (Evglevskaya et al. 2018). Some examples of these advanced apps include measurement data management, network analysis, substation management, distributed energy resource management systems (DERMS), and the low voltage outage management system.