Development of a Renewable Energy Forecasting Strategy Based on Numerical Weather Prediction for the Cold Ironing System at the Port of Ancona, Italy

Since renewable energy sources have an intermittent nature, forecasting strategies are increasingly important.In parallel, ports are characterized by large energy demands, especially from berthed ships.Cold ironing systems have already been proven to reduce their environmental impact by connecting ships to the electricity grid and allowing them to switch off their auxiliary engines in port.In this work, a local energy production, consisting of photovoltaic, wind turbines, and an energy storage system, is proposed to cover the energy demand of ships.In addition, an energy forecasting strategy is presented, where the solar and wind energy potential is provided by the Weather and Research Forecasting (WRF) mesoscale model.By forecasting the energy production for the following day, the storage system can be charged from the grid at night, namely in offpeak periods, reducing the pressure on the grid in on-peak periods.The methodology is tested on the port of Ancona (Italy).Results show that energy production can directly cover 54% of energy demand, and up to 70% by adding the storage system.The forecasting strategy reduces the energy withdrawn during the daytime by 24.9% and increases that during the nighttime by 18.9%, proving the effectiveness of the proposed strategy.