Agentic Reconfigurable Battery Scheduling for Multi-Energy Markets
Investigate autonomous AI-driven battery systems that optimize multi-energy market participation. Develop novel algorithms integrating AI with advanced battery models to enhance grid flexibility and decarbonization. Create simulation tools to test real-world operational performance.
AI-generated overview
Project Description
Project Overview
This project explores reconfigurable battery systems (RBS) integrated with agentic artificial intelligence capable of autonomous reasoning, planning, and multi-step actions. The aim is to develop AI-enabled batteries that respond in real time to dynamic multi-energy market signals by reconfiguring internal topology for revenue, battery health, emission reduction, and grid resilience. RBS will participate intelligently across electricity, heat, transport, and hydrogen markets, enhancing flexibility and resilience in evolving renewable-based energy systems.
What You Will Do
You will develop advanced AI frameworks including multi-agent reinforcement learning, autonomous optimization agents, and large model-based reasoning integrated with accurate reconfigurable battery models. You will design and test strategies for market participation such as frequency response, peak shaving, EV charging optimization, heat pump support, and hydrogen production. Building simulation environments or digital twins will enable realistic performance evaluation under operational and market conditions.
Expected Outcomes
Deliver innovative algorithms that enable RBS to autonomously optimize across multiple energy vectors and market environments. Demonstrate improved grid stability, revenue maximization, battery longevity, and decarbonization impacts. Provide a foundation for next-generation smart grid architectures featuring intelligent, self-adaptive energy storage solutions.
Why This Matters
As renewable energy expands, intelligent storage systems are critical to grid flexibility and low-carbon transitions. This research addresses limitations of traditional battery scheduling by creating AI-driven adaptable batteries that integrate diverse energy vectors. Results will directly impact microgrids, remote communities, and grid-constrained regions, offering practical solutions aligned with future multi-energy market developments and carbon reduction goals.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Dr. Augustine Ikpehai is a researcher specializing in intelligent energy systems, focusing on integrating advanced AI frameworks with battery technologies to enhance energy storage and smart grid resilience. His work bridges electrical engineering, optimization, and AI to deliver practical low-carbon energy solutions. He is actively involved in multidisciplinary projects addressing future energy market challenges.