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:

Implementation Approach: Monolithic

Test Parameters of Test System Model :

Further details see D4-3 Description of optimization strategies.pdf

Outputs/Measured Parameters:

  • 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

Temporal Resolution:

  • The sampling time is 1h

Related System Configuration

FlexOffice system configuration

Related Test Case

KIT.TC1 FlexOffice Thermal MPC

Related Use Case

UC11 – Minimize heating, cooling and electrical energy consumption via Model Predictive Control

KIT.TC1.TS1 FlexOffice Thermal MPC test system model implemntation - short description

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

Source Code of the Implemented Result Object

Please contact:

Name: Sami Ghazouani and Mickael Rousset / EDF

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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.