Data analysis of battery storage systems

Battery energy storage systems can assist distribution network operators (DNOs) to face the challenges raised by the substantial increase in distributed renewable generation. A challenge is that these resources are intermittent and often 'invisible'to the DNO. If not monitored, the aggregate size of small embedded generation resources can cause thermal wearing of distribution assets and voltage excursions, especially in sunny/windy periods with insufficient local demand. Several developers of energy storage solutions, with technologies such as lithium-ion (Li-ion) batteries, offer their products to address peak shaving, frequency and voltage control needs within the network. Once deployed within the energy network batteries experience capacity degradation with usage, these companies will need to incorporate methods from prognostics and health management (PHM) in order to better manage their products. The main deliverable of this project is validation of data analysis, based on relevance vector machine, to predict the remaining useful life of Li-ion batteries. The accuracy of the predictions for different batteries is all within 10 cycles (within 8.5% relative error). These results confirm the importance of PHM methods within a distribution system operator model, where lifecycle management of critical sub-systems and systems will become increasingly important to network operators.