Optimal Ramp Rate Control of a Solar PV and Wind Power Plant with Predictive Control of Battery Energy Storage

High penetration of renewable energy sources (RES) in the electric network necessitates the integration of energy storage systems (ESS) to decrease variability and uncertainty in power generation while ensuring network and grid stability. The interaction between the renewable energy sources and the energy storage devices requires sophisticated controls and intelligent energy management systems with predictive functionalities. Deploying predictive controls as part of the energy management system can further optimize the combined operation of RES and ESS to minimize generation costs and maximize renewable energy utilization and/or offer optimal dispatch services. The authors present an optimization algorithm designed to efficiently manage the dispatch of ESS power over a 24-hour period. This algorithm aims to control the power ramp rate of a combined solar photovoltaic (PV) and wind power plant, thereby reducing output variability. The effectiveness of this optimization algorithm is demonstrated through its application to a combined 1MW solar PV and 1MW wind power plant. This demonstration incorporates wind and solar PV power forecasts for a winter day, alongside a range of energy storage capacities and their associated technical constraints.