Different countries have ambitious goals for energy and climate change adaptation and mitigation, aiming for 50 % more efficient energy use and 100 % renewable energy generation. Thermal Energy Storage (TES) is a key enabling technology for a realisation of a carbon neutral energy system. District Heating (DH) is a mature technology for the heating of the built environment, especially in large cities. Large-scale Thermal Energy Storage (LTES) systems are necessary to further decarbonise the DH systems and to enable a more flexible operation. LTES are needed, in order to further reduce the specific costs of the storage technology and to have storage capacities that are better suited to the sizes of larger DH systems. Present experience with TES for integration in DH is in the utilisation of Pit Thermal Energy Storage (PTES) systems up to 200,000 m3 and of Tank Thermal Energy Storages (TTES) systems up to 50,000 m3. Also the subject of this task are the TES technologies Aquifer Thermal Energy Storages (ATES) and Borehole Thermal Energy Storages (BTES).
Large-scale thermal energy storages offer more flexibility in DH Systems (also adding operational flexibility to power plants and industrial processes), they enable a higher share of renewables and waste heat, they can provide peak shaving functionality for electricity grids through Power-to-Heat (P2H) thus enabling sector coupling of the power and heating sector.
The market for large thermal energy storages is growing, with new plants built and planned in Denmark and Germany, mostly PTES with volumes in the range of 400,000 to 500,000 m³ (in Denmark). In order to facilitate and accelerate the market uptake of these large storages, better materials and knowledge is needed to improve the service lifetime of storages, better tools are needed for designing, planning and integrating the storages and more knowledge of the potential and integration possibilities of these storages is needed for decision makers.
Aim and Objectives of this Task
The Task aims at determining the aspects that are important in planning, design, decision-making and realising very large thermal energy storages for integration into district heating systems and for industrial processes, given the boundary conditions for different locations and different system configurations.
The key objectives of the Task are:
- Definition of a number of representative application scenarios, the connected boundary conditions and Key Performance Indicators
- Improve LTES materials and materials performance measurement methods
- Prepare guidelines for obtaining proper water quality
- Compare the performance and accuracy of simulation models for LTES
- Derive validation tests for LTES simulation models
- Generate information packages for decision makers and actively disseminate the information
For these purposes, four subtasks work on the content subjects:
- Subtask A: Application Scenarios, Assessment of Concepts, Integration Aspects
- Subtask B: Components and Materials Database
- Subtask C: Round robin simulation
- Subtask D: Knowledge base for decision makers
Subtask A: Application Scenarios, Assessment of Concepts, Integration Aspects
Subtask B: Components and Materials Database
The goal of Subtask B is to define common test procedures for hygrothermal and mechanical tests for materials, to define the water quality and to create a database for TES materials that go beyond the standard values. The activities are grouped as follows:
- Hygrothermal and mechanical tests
- Water condition and corrosion protection
- Materials Database for LTES
Ongoing work: The activities were structured, and system boundaries were defined. The activities of the subtask will be focus on the PTES and the outputs can be applied for other type of long-term thermal energy storage methods. In the coming Expert Meeting (2 and 3 of Nov. 2021), the specific actions for activities 1 and 2 will be decided. University of Birmingham has already a data base and it is possible that the results of the data obtain in activities 1 and 2 can be stored in the existing data base.
Subtask C: Round robin simulation
Subtask D: Knowledge base for decision makers