To trade or not to trade: Simultaneously optimising battery storage for arbitrage and ancillary services

This work presents a novel methodology for determining the value battery storage system provides while participating in competitive frequency response market, considering uncertainties. Battery systems are an attractive choice power services low-carbon electricity grids and their optimal operation commonly studied matter. However, non-deterministic nature of markets is often overlooked. Here, we consider these market uncertainties device providing Great Britain’s Firm Frequency Response (FFR) arbitrage services. We use machine learning classifier to determine set all possible FFR outcomes associated probabilities. These then propagated through linear optimisation model generate scenarios, from which most likely can be ascertained. Several different classifiers bidding strategies compared, suitable maximise revenue whilst minimising probability worse-case scenario identified. It found that mean expected income overestimated by ?28% when not considered. Providing over tight band still provide significant does impede on storage’s ability real time.