Investigating demand-side response with aquifer thermal energy storage (ATES) in the U.K.

Understanding sector coupling of thermal and electrical power is increasingly important as the energy sector transitions towards widespread electrification. In scenarios with high-levels of intermittent renewable energy sources in the national energy generation mix, demand-side management offers opportunities to maximise the use of this generation while producing operational cost benefits to time-shifting grid loads. Included within this dynamic response to grid state are energy system components with power-to-heat and thermal energy storage functionality, as in the case of aquifer thermal energy storage (ATES). The control of these integrated energy systems requires simulation through reliable modelling frameworks.

The work herein explores the impact of electricity price signals on an energy system with low-temperature ATES in the United Kingdom. Using a commercial-scale greenhouse simulation as the basis of heating and cooling loads, a fixed-order control approach was applied to the energy system, using price signals as a key variable for operational decision-making. This used a receding horizon approach to energy system scheduling and applied machine learning models to forecast day-ahead wholesale electricity prices. A Python-FEFLOW co-simulation model was developed to investigate the impact of demand-side response on the energy system components, using key indicators of technical and economic performance.