KIT.TC1.TS1 FlexOffice Thermal MPC
KIT.TC1.TS1 FlexOffice Thermal MPC - title
KIT.TC1.TS1 FlexOffice Thermal MPC Test System Model
KIT.TC1.TS1 FlexOffice Thermal MPC - input
Test System Model Overview
Author / organization: Alexander Engelmann / KIT
Component Models:
Test Parameters: Building dimensions, Material parameters etc. for deriving the matrices ?,? and ?, the machine-readable names and
Outputs/Measured Parameters: The outputs of the models are the simulated wall, indoor and concrete core temperatures, i.e. the states ?(?).
KIT.TC1.TS1 FlexOffice Thermal MPC - input
Input
Related System Configuration
FlexOffice / Factory (KIT)Related Test Case
KIT.TC1 FlexOffice Thermal MPC
Related Use Case
UC11 – Using heat Storage to Minimize Heat Use and Provide Electrical Flexibility
KIT.TC1.TS1 FlexOffice Thermal MPC - description
Short Description
This test case is used to verify that self-consumption of renewable energy sources in a coupled heat and power network improved using distributed power-to-heat appliances compared to a base scenario without power-to-heat. This means that energy flows flowing out of the network are reduced. At the same time energy imports should not increase. Relevant effort variables of both networks, i.e., bus voltages, supply temperatures and differential pressures, must stay within the allowable range. Also, the loading of the transformer is not allowed to reach critical levels. Otherwise the test fails.
Electric boilers are used as power-to-heat appliances. They consist of an electric heater, for the conversation of power to heat, and a thermal energy storage, for the (short-term) storage of generated heat. To plan the operation of the storage unit and the electric heater, a model predictive controller is used. The aim of the controller is to minimize negative residual load of the electric network. To enable this planning, negative residual load in the electric as well as heat demand in the heat network need to be known/predicted. This test assumes perfect knowledge of these time-series data and, thus, does not focus on the quality of predictions.
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KIT.TC1.TS1 FlexOffice Thermal MPC - details
Test System Model Details
Title of Test | KIT.TC1.TS1 FlexOffice Thermal MPC | ||
Author / Organization | Alexander Engelmann / KIT | ||
Reference to Test Case | KIT.TC1 FlexOffice Thermal MPC | ||
Test Rationale | In this test aims at evaluating the capabilities of a MPC to optimize the system behaviour with respect to the previously defines KPIs and the OF. Specifically, this is lowering the fluctuation in the energy demand and to keep the thermal comfort in the building within an acceptable range. The simulation results are subject to a given set of inputs/disturbances defined in this test specification. |
Specific Test System | The system under test is the thermal domain of the FlexOffice system configuration as shown in Figure E.2. The thermal behaviour is described on a floor level with influences shown in Figure E.3. | ||
Test and Output Parameters | Test Parameters: As described in deliverable D4.1 (Appendix D, page 77), the thermal building model has the form
Furthermore, the inputs are
The disturbances are
Methods for data synthesis are described in D3.4. Outputs / Measured Parameters: The outputs of the models are the simulated wall, indoor and concrete core temperatures, i.e. the states ?(?). | ||
Test Design | The disturbance ?(?) for testing represent a typical week of operation such that the test outcome reflects indoor and wall temperatures of a typical winter week. They are shown in Figure E.4. The test is executed as follows: First one has to set up a model representing the relation between room and wall temperatures. Then, a simulation is started using the given disturbances and fixed inputs described above with different or no controllers. |
Component Models | thermal building model (see deliverable D4.1, Appendix D.1, page 77) | ||
Initial System State | All temperatures are initialized with 21°C. | ||
Temporal Resolution | The sampling time is 1h. | ||
Evolution of System State and Test Signals | Figure E.5 shows typical open-loop trajectories for the given
| ||
Source of Uncertainty Stopping Criteria | There are uncertainties in the weather forecast and also in the model parameters (material data). - | ||
Storage of Data | MATLAB matrices/vectors which are converted into the SmiLES data format. |