The high popularity of electric vehicles (EVs), especially plug-in electric (PEVs) and plug-in hybrid electric (PHEVs) vehicles, of recent years is a result of the heightened concern for climate change and the advancements in battery technology. In The Netherlands the number of EVs has more than doubled in little under 1.5 years. This trend is expected to continue world-wide as battery technology drives EV prices down and consumer comfort up, increasing the attractiveness of electric driving. For instance, the highly anticipated Tesla Model 3, due fall 2017, was reserved almost 400,000 times within 2 weeks world-wide of which an estimated 20,000 can be attributed to The Netherlands.
EVs and green, decentralised energy generation can become important keys to unlocking future sustainable energy systems. However, their introduction leads to several new problems. Power systems driven by renewable generation face a number of problems, owing to their intermittent nature. Problems can include, amongst others, voltage fluctuations, surges, and frequency variability, resulting in low reliability and quality.
The increase of distributed generation (DG) can lead to situations where the market price no longer follows the market demand. On the demand side, “uncontrolled charging” may lead to demand peaks, making the technology hard to match with a variable power source such as wind or the sun. Furthermore, it increases the already existing peak load. As such, problems are caused on a decentralised level, while the centralised market model tries to solve them from a central point-of-view.
To analyse the electrical patterns that emerge in a smart grid environment, an agent-based model is developed, using smart metering data from a Dutch neighbourhood. By modelling a large number of individual agents subject to business logic, macro behaviour can be analysed. The model contains household agents and both public charging stations and household-owned plug-in electric vehicles. The emergent electricity patterns are used to analyse the imbalances occurring at decentralised grid level and the potential benefits of local trading can be analysed. As a proof-of-concept for blockchain-driven energy markets, the model’s output is connected to a blockchain network.