AI for Energy: A Blockchain-based Trading Market

With the emergence of distributed energy generation through residential and commercial solar PV applications, new energy markets are created where consumers and producers are no longer separated, giving rise to the concept of energy prosumers. In a distributed database architecture that utilizes cryptographic hashing and consensus-based verification, blockchain technology offers utilities, consumers and prosumers with a novel, secure, and cost-effective energy-trading solution that automates direct energy transactions. A blockchain-based energy trading simulation environment integrated with a Geographic Information System (GIS) is proposed in this study for Qatar's Education City Community Housing (ECCH). A comprehensive amount of daily energy activity data is collected and analyzed as part of the approach for recreating spatiotemporal characteristics of trading in a small marketplace. Through this type of simulation, stakeholders can better understand the dynamics of a real trading market, and thus make better decisions for developing a decentralized energy market. Using GIS information and an agent-based design, the results indicate that the characteristics of transactions executed in a local housing market can be easily tailored by adjusting parameters. It is possible to improve model performance by employing high-performance computing to conduct large-scale simulations with many agents to provide more realistic outcomes. The model offers a scalable environment for analyzing an energy blockchain from the perspective of Qatari society, finance, and technology.