Agentic Reconfigurable Battery Scheduling for Multi-Energy Markets

This project sits within Sheffield Hallam University's College of Business, Technology and Engineering, aiming to be a leader in applied research transforming energy systems. It focuses on reconfigurable battery systems (RBS) integrated with agentic AI that can autonomously adapt and optimise in real time across electricity, heat, transport, and hydrogen markets. The goal is to develop algorithms enabling batteries to respond rapidly to market signals, reconfigure internal topologies for revenue, health, emission, and resilience balance, and operate autonomously in complex microgrid environment. You will develop AI frameworks including multi-agent reinforcement learning, autonomous optimisation agents, and model-based reasoning systems, integrated with detailed battery hardware models. You'll design strategies for batteries to intelligently participate in markets such as frequency response, peak-shaving, EV charging optimisation, heat-pump support, and hydrogen production. Simulation environments and digital twins will be built to test performance under realistic operational and market conditions. Your research will create advanced autonomous control algorithms allowing reconfigurable batteries to maximise revenues while supporting grid stability and decarbonisation. The work will provide new insights into smart grid architectures and multi-energy market participation, advancing technologies that increase resilience and flexibility for sustainable energy systems. With renewables increasing, intelligent and adaptable energy storage is vital. This research addresses critical challenges in integrating heterogeneous energy vectors and market participation by pioneering a new intelligent battery scheduling paradigm. The project aligns with efforts to support remote communities, microgrids, and grid-constrained areas in transitioning to low-carbon energy systems globally.

Applicants should have at least a 1st or 2:1 Honours degree in Electrical and Electronic Engineering, Power Systems or related disciplines. Strong analytical, programming, AI, optimisation, and power systems experience required. IELTS 7 with minimum 6.5 in all bands for non-native English speakers. Applications encouraged from underrepresented groups.

Submit via Sheffield Hallam University's online application form. Upload the following:
(1) a personal statement (max 2 pages) detailing your interest and relevant experience;
(2) two letters of reference (at least one academic, both dated within the last 2 years);
(3) copy of your highest degree certificate;
(4) non-UK applicants must also submit IELTS results (taken within the last 2 years) and a copy of their passport. If applying to multiple GTA projects, list all in your application and submit a tailored personal statement for each. Contact: Dr Augustine Ikpehai at a.ikpehai@shu.ac.uk.