Battery size determination for photovoltaic capacity firming using deep learning irradiance forecasts

Abstract

Large photovoltaic (PV) power plants benefit from the introduction of batteries to increase their dispatchability. Among other services, batteries enable PV plants to firm their hourly energy production and avoid in this way the economic penalties associated with deviations between the contracted commitment made by the renewable generator to the grid and the final energy delivered. Due to the increase in the cost of the plant derived from the storage integration, the size of these batteries must be minimized. This work analyses the minimum battery capacity required for such a service when using a new deep-learning irradiance forecasting methodology. The low prediction error of the developed forecasting tool supports the optimized operation of large PV plants under different European intraday electricity markets with no deviations and reduced battery sizes. Results obtained for a whole year analysis using actual data at three different locations with varying irradiance patterns prove that 1-hour capacity batteries grant PV capacity firming in most intraday continuous market structures regardless of their lead times.