Distributed Energy Trading Supported by Data Analytics

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As the majority of greenhouse gas emissions are linked to energy production and use, increasing green renewable resources is at the core of combating climate change. Thus, nations are setting aggressive targets: for example, Canada aims for 90% and Denmark for 70 % renewables by 2030. At the same time, the global energy market is expected to exceed 500 billion in 2027. Distributed energy systems have been recognized as a way of achieving sustainability; however, the immense volatility of renewable energy generation such as solar or wind makes their integration challenging. Distributed energy trading can assist with balancing supply and demand while promoting renewable generation. Blockchain-based solutions have been proposed, but they still face challenges such as transaction throughput and privacy in presence of transparency. Moreover, these techniques typically do not take advantage of data analytics to bring better decisions.Consequently, this project will design and develop a state-of-the-art distributed energy trading system for peer-to-peer transactions in Smart Grid supported by data analytics. This will improve the quality of trading decisions, facilitate the expansion of renewables, and contribute to environmental sustainability. The two main components are Distributed Trading Platform for enabling transactions between consumers, producers, and prosumers, and Load/Generation analytics for facilitating smart trading through data-driven decisions.This project will help London Hydro to provide new services to its customers, improve demand-response programs, and promote renewables. Moreover, this project will help Canada to reach its aggressive energy targets and deliver environmental benefits in terms of reduced carbon footprint and sustainability. Training in energy systems, machine learning, and artificial intelligence will build Canada's strengths in these fast-growing fields.