Renewables for industryProject Advancing Renewables with PCM Thermal Energy Storage
This case study shares information on the advanced control and forecasting algorithm (ACFA) implemented at the Pernod Ricard Winemakers’ Rowland Flat to control the charging and discharging of thermal energy storage.
The Advanced Control Forecasting Algorithm (ACFA) provides an optimal control strategy for the operation of thermal energy storage based on forecasts of the National Energy Market (NEM) half hourly Recommended Retail Price (RRP) for energy, available renewable energy from the onsite photovoltaic (PV) installation, and thermal and electrical loads, in order to establish the best arbitrage for the storing and extraction of energy from a thermal store in an effort to minimise electricity costs. ACFA reduces the cost of electricity by:
- using electrical energy from the grid to store as thermal energy when the cost of electricity is low (or negative);
- using thermal energy from the store instead of electricity from the grid when the cost of that electricity is high;
- using on-site solar energy generation to charge the store instead of exporting it to the grid when the export price is low;
- managing peak demand.