2 VOLUME THERMAL STORAGE
2 Volume Thermal Storage -title
2 Volume Thermal Storage Component Model
2 Volume Thermal Storage - Model overview
Model Overview
Author / organization: Matthildi Apostolou / EDF
Domain: Thermal storage
Intended application: Optimisation processes
Modelling of spatial aspects: Discretized (single device)
Model dynamics: Quasi-static
Model of computation: To be used by an optimization algorithm
Functional representation: Implicit
2 Volume Thermal Storage - Input and output
Input and Output
Input variables :
- Real Pdemand: thermal demand (kW)
- Real Tdemand,in: input temperature of the demand (degC)
- Real Tdemand,out: output temperature of the demand (degC)
- Real Psource,max: maximum power of the heat source (kW)
- Real Tsource,in: input temperature of the source (degC))
- Real Tsoure,out: output temperature of the source (degC)
Output variables:
- Real Vtot: total volume (m3)
- Real Vup: Upper layer’s volume at every time step (m3)
- Real Vdown: Lower layer’s volume at every time step (m3)
- Real Vload: Volume of water corresponding in the exchange during the loading phase(m3)
- Real Vunload: Volume of water corresponding in the exchange during the unloading phase (m3)
- Real Tup: Upper layer’s temperature (degC)
- Real Tdown: Lower layer’s temperature (degC)
- Real MC: heat mass of the cold flow (corresponding to the loading phase) (kW/K)
- Real MH: heat mass of the hot flow (corresponding to the unloading phase) (kW/K)
2 Volume Thermal Storage - related documents
Related Documents
2 Volume Thermal Storage - description
Short Description
A simple model of a thermal storage tank with two layers (hot and cold volume). The temperature of each layer is stable (not varying during the study period), whereas the volume of each layer is varying in order to meet the charging/ discharging requirements.
This system is intended to be used for optimisation processes. Temporal resolution depends on the component: for daily storage resolution can go up to an hour/minute range; for seasonal storage representative days have to be used.
The model is an equivalent circuit model including lookup tables. Equations describing the state of the battery are time-continuous, while the sensors measuring the real-time status are discrete event-based.
In test configuration, the production unit and the storage tank are the two elements to be sized in order to satisfy the demand. The total power produced by the production unit is therefore equal to the total energy demand. The total volume of the storage tank is obtained accordingly, in order to store the energy when available and discharge it when needed. The initial state of the storage must be equal to its final state for the optimisation’s time horizon, in order to ensure that no extra energy is supplied to the system. The optimal volume implies that the storage tank is emptied and fully charged at least once during the time horizon given.
Present use / development status
The model allows, by considering the energy conservation in each layer, the sizing of the storage tank (in terms of total volume and temperature level of each zone) and the optimisation of its operation (charging/discharging). The model is therefore useful for the sizing of this component at the design phase of the optimisation of multi-energy systems.
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2VolumeThermalStorage
Model Details
Domain |
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Intended application (including scale and resolution) | Intended to be used for optimisation processes. Temporal resolution depends on the component: for daily storage resolution can go up to an hour/minute range; for seasonal storage representative days have to be used.
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Modelling of spatial aspects |
For each of the two layers of the storage tank, mass and temperature are considered homogeneous. | ||
Model dynamics |
Thermal equilibrium is assumed at every time step considered. | ||
Model of computation |
Equations describing the state of the battery are time-continuous, while the sensors measuring the real-time status are discrete event-based. | ||
Functional representation |
The model is an equivalent circuit model including lookup tables. |
Input variables (name, type, unit, description) |
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Output variables (name, type, unit, description) |
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Parameters (name, type, unit, description) |
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Internal variables (name, type, unit, description) |
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Internal constants (name, type, unit, description) | - | ||
Model equations | Governing equations | ||
1. Vtot = Vup + Vdown 3. Vup[t+1] = Vup[t] + Vload[t] - Vunload[t] | |||
Constitutive equations | |||
10. C = C_{0} | |||
Boundary conditions |
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Optional: graphical representation (schematic diagram, state transition diagram, etc.) |
Model Validation | |||
Narrative | Given a specific heat demand profile, this test allows the sizing of a storage tank when coupled with a production unit. Temperatures are fixed for both sides in order to allow the simulation models that do not account for the temperature level match to run the optimisation. | ||
Test system configuration |
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Inputs and parameters |
Pdemand(t) and Psource,max(t) are provided as time series in the attached data file (Storage_input.csv in data file EDF_ 2VolumeThermalStorage_data.zip). | ||
Control function | -
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Initial system state | The initial state of the system is an output of the optimization. | ||
Temporal resolution | 24 periods are investigated, considering a 1-hour step in order to represent a day | ||
Evolution of system state | In this configuration, the production unit and the storage tank are the two elements to be sized in order to satisfy the demand. The total power produced by the production unit is therefore equal to the total energy demand. | ||
Results |
The results are provided as time series in the attached data file (Storage_output.csv in data file EDF_2VolumeThermalStorage _data.zip). | ||
Model harmonization | |||
Narrative | same as model validation setup | ||
Test system configuration | same as model validation setup | ||
Inputs and parameters | same as model validation setup | ||
Control function (optional) | same as model validation setup | ||
Initial system state | same as model validation setup | ||
Temporal resolution | same as model validation setup | ||
Evolution of system state | same as model validation setup | ||
Results | Real EnergyCharg = 135.03 kWh: total energy stored within the 24 hours of optimisation time (integral of Pcharging) | ||