KIT.TC1.TS1 FlexOffice Thermal MPC implementation
KIT.TC1.TS1 FlexOffice Thermal MPC test system model implemntation
KIT.TC1.TS1 FlexOffice Thermal MPC Test System Model Implemntation
KIT.TC1.TS1 FlexOffice Thermal MPC test system model implemntation - overview
Test System Model Implementation Overview
Author / organization: Alexander Engelmann / KIT
Implemented Component Models / Implementation Tool:
- Electrical distribution grid
- NHN Battery /
- NHN Charging post and electric vehicle
- Photovoltaic panel model
Implementation Approach: Monolithic
Test Parameters of Test System Model :
Further details see D4-3 Description of optimization strategies.pdf
- The outputs of the models are the simulated wall, indoor and concrete core temperatures, i.e. the states ?(?)
Initial State of Test System Model:
- All temperatures are initialized with 21°C
- The sampling time is 1h
Related System Configuration
Related Test Case
Related Use Case
KIT.TC1.TS1 FlexOffice Thermal MPC test system model implemntation - short description
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.
KIT.TC1.TS1 FlexOffice Thermal MPC test system model implemntation - source code
KIT.TC1.TS1 FlexOffice Thermal MPC test system model implemntation - evaluation
Evaluation of System State and Test Signal
Figure E.5 shows typical open-loop trajectories for the given disturbances and fixed input from D4.1.
Figure E.6 shows typical closed-loop trajectories where a tracking MPC scheme is used to compute optimal inputs. One can see that the KPI of maximizing the comfort (i.e., keeping the indoor Temperatures at 21°C) is satisfied to a very high degree. Small peaks deviating from 21°C come from the absence of cooling capabilities in winter time. A variety of MPC formulations, e.g., minimizing fluctuation, are possible. The predefined KPIs and the objective function values for the different controller formulations are compared in a second step.