Methods for simulation, planning, and operation of Aquifer Thermal Energy Storage under deep uncertainty

The building sector currently accounts for approximately one-third of the global demand for energy, and one-fifth of all energy-related greenhouse gas emissions (GHG). The development and adoption of energy-efficient technologies in this sector is therefore a key element towards efforts for the mitigation of climate change. In particular,...

Aquifer Thermal Energy Storage (ATES) smart grids: Large-scale seasonal energy storage as a distributed energy management solution

Aquifer Thermal Energy Storage (ATES) is a building technology used to seasonally store thermal energy in the subsurface, which can reduce the energy use of larger buildings by more than half. The spatial layout of ATES systems is a key aspect for the technology, as thermal interactions between neighboring systems can degrade system performance....

Methods for planning of ATES systems

Aquifer Thermal Energy Storage (ATES) systems contribute to reducing fossil energy consumption by providing sustainable space heating and cooling for buildings by seasonal storage of heat. ATES is important for the energy transition in many urban areas in North America, Europe and Asia. Despite the modest current ATES adoption level of about...

Assessing the sustainable application of Aquifer Thermal Energy Storage

Aquifer Thermal Energy Storage (ATES) can yield significant reductions in the energy use and greenhouse gas (GHG) emissions of larger buildings, and the use of these systems has been rapidly growing in Europe – especially in the Netherlands, where over 3000 systems are currently active in urban areas. However, the successful management of this...

Building climate energy management in smart thermal grids via aquifer thermal energy storage systems

This paper proposes a building energy management framework, described by mixed logical dynamical systems due to operating constraints and logic rules, together with an aquifer thermal energy storage (ATES) model. We develop a deterministic model predictive control strategy to meet building thermal energy demand. At each sampling a mixed...