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SHIFT is building an AI-driven energy management system for hydrogen microgrids and we need talented students to help make it happen. It’s a complex, multidisciplinary challenge, and every team member plays a role in moving it forward.
Currently, SHIFT has four team clusters: Business and Economics, Energy Management, Simulations, and AI Control Systems.
The Business and Economics cluster bridges the gap between engineering and adoption. We analyze operational cost reductions, develop business cases, and navigate the legislative frameworks surrounding energy systems and AI in critical infrastructure. We also handle PR and partnerships to bring SHIFT’s work to a wider audience.
Do you have a background in data analysis or economics? Then this is the right place for you.
As the simulations team, our role is to develop a digital twin of the infrastructure energy system that accurately represents the interaction between solar PV, batteries, hydrogen technologies, and grid operations. We are responsible for building and integrating component-level models in Simulink, ensuring that each subsystem behaves realistically under different operating conditions. The team designs and runs simulation scenarios — such as varying energy demand, renewable generation changes, and grid disturbances — to evaluate performance. We also support the development and testing of the AI-driven energy management system by providing a reliable virtual environment for validation.
The Energy Management cluster is responsible for collecting and processing data from across the system to evaluate its overall performance and efficiency. This includes analyzing energy flows, as well as key parameters such as CAPEX, OPEX, efficiencies, inputs, and outputs. The cluster also examines the performance of subsystems, including electrolyzers, hydrogen fuel cells, and other infrastructure components. By transforming raw data into actionable insights, we assess whether the system operates optimally and identify opportunities for improvement. In close collaboration with other clusters, we continuously share data and findings to support informed decision-making across the entire system.
At Team SHIFT, we are bridging the gap between classical control theory and modern data science. Our cluster focuses on the integration of traditional control systems with data-driven decision-making to tackle complex, real-world energy challenges.
Our primary initiative is the development of an intelligent Energy Management System (EMS) designed for hospital environments. In a healthcare setting, power reliability is not just a metric — it is a critical requirement. We are engineering a system that allows hospitals to achieve grid independence, ensuring seamless operation during outages while maximizing efficiency.
Our team focuses on three technical domains:
Forecasting — We utilize novel AI techniques to generate high-accuracy supply and demand predictions. This involves modeling renewable energy availability and the volatile energy loads of a 24/7 medical facility.
Optimization — We develop algorithms to optimize key parameters, specifically targeting cost reduction and load balancing without compromising the rigorous safety standards required by medical infrastructure.
Advanced Control — While we utilize standard optimization today, our ultimate objective is the implementation of Reinforcement Learning (RL). By employing RL, we aim to create a system capable of autonomous, real-time adaptation to shifting grid conditions and hardware states.
The AI & Control Systems cluster at Team SHIFT is more than just a software team — we are architects of resilient infrastructure.