IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Battery Scheduling

One of the most important challenges to tackle climate change is the decarbonisation of energy production with the use of renewable energy sources such as wind and solar. A challenge here is that renewable energy cannot be produced on demand but the production depends literally on when the wind blows and when the sun shines, which is usually not when demand for electricity is highest. Storing energy is costly and normally associated with loss of energy. Thus, with having more and more renewable energy in the grid, it becomes increasingly important to forecast accurately both the energy demand and the energy production from renewables, to be able to produce power from on-demand-sources (e.g., gas plants) if needed, and to optimally schedule energy storage solutions such as batteries. In particular, a nowadays common setup is a rooftop solar installation and a battery. Here, we need to forecast the electricity demand, the renewable energy production, and the wholesale electricity price, to be able to then optimally schedule the charging and discharging of the battery, to charge the battery with overproduction of solar energy, and use power from the battery instead of power from the grid when energy prices are highest. Researchers from the IEEE Computational Intelligence Society (IEEE-CIS) want to improve solutions to this complex problem of predict+optimize, in this particular application of battery scheduling in the context of renewable energy. IEEE-CIS works across a variety of Artificial Intelligence and machine learning areas, including deep neural networks, fuzzy systems, evolutionary computation, and swarm intelligence. Today they’re partnering with Monash University (Melbourne, Australia), seeking the best solutions for battery scheduling, and now you are invited to join the challenge. Monash University is committed to achieve Net Zero emissions by 2030, within the Monash Net Zero Initiative. As part of this initiative, Monash has set up a microgrid with rooftop solar installations and a battery for energy storage. The challenge will use data from the Monash microgrid and you will develop an optimal schedule for the Monash battery. From a machine learning point of view, the provided data poses an interesting time series prediction problem, with multiple seasonality, use of external data sources (weather, electricity price), and the opportunity for cross-learning across time series on two different prediction problems (energy demand and solar production). Then, from an optimization point of view, uncertainty in the inputs needs to be addressed together with a couple of constraints, to achieve a good solution. If successful, you will not only help making renewable energy more reliable and affordable, thus playing your part in the fight against climate change, but the proposed technical challenge may be applicable in many other similar fields facing problems of optimal decision-making under uncertain predictions. Please report any issues or feedback to [e-mail]