Predictive Control of Energy Storage Systems - Battery CAES
With the increased penetration of renewable energy sources and the goal of reducing the reliance on fossil fuel dependence for power generation the goal for most countries.
Centre for Advanced and Sustainable Energy (CASE) - Invest NI
About the Project
With the increased penetration of renewable energy sources and the goal of reducing the reliance on fossil fuel dependence for power generation the goal for most countries. To maintain the ageing electrical grid system within Northern Ireland a new approach is required to integrate these renewable resources.
The goals of this project were to develop a hybrid CAES/Battery storage system and control system architecture that could make use of predictive analysis in order to optimise bidding into various energy market services. This predictive control algorithm determined the best bidding strategy available for the day ahead when bidding into these energy market services.
Projected key outcomes
- work with 3 partner companies to acheive aims and objectives
- adapt an existing optimisation algorithm developed for Arbitrage energy trading by Edward Barbour based on work conducted within the British energy market
- adapt this previous optimisation algorithm work to incorporate the requirements of the Irish SEM/I-SEM market structure
- demonstrate the effectiveness of this adapted optimisation algorithm for use with an energy storage system
- deploy this algorithm as part of a larger cloud based control strategy for an energy storage system