Method and system for controlling a current-fed heating and/or cooling device
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing integration of fluctuating distributed energy resources poses challenges in balancing supply and demand, particularly in maintaining indoor temperature limits while optimizing energy consumption by HVAC systems for grid stability and flexibility.
Innovation Solution
A method using a statistical model to estimate future zone temperatures and control HVAC systems by selecting optimal model parameter sets to adjust energy consumption, incorporating historical data and weather forecasts, while maintaining temperature constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If HVAC systems operate continuously to maintain indoor temperature within limits, then temperature stability is improved, but energy consumption increases and grid flexibility is reduced
Solution Approach 1:
The system performs preliminary cooling or heating of the building before periods of high electricity demand or high prices. By pre-conditioning the building using stored thermal energy in the structure, the HVAC system can be operated at reduced capacity during peak periods while still maintaining comfortable indoor temperatures, thus reducing energy consumption during expensive periods without sacrificing temperature stability.
Solution Approach 2:
The system dynamically adjusts operational parameters such as setpoint temperatures, operating schedules, and capacity levels based on forecasted electricity prices, demand conditions, and weather predictions. This allows the HVAC system to optimize between temperature stability and energy consumption by changing operational parameters in response to varying grid conditions.
2Adaptability or versatility
If HVAC systems are shut down during high demand periods to reduce energy consumption, then energy flexibility is improved, but indoor temperature control deteriorates
Solution Approach 1:
The system stores thermal energy in the building structure (walls, floors, furniture) before periods when the HVAC system needs to be reduced or shut down. This thermal mass acts as a cushion that maintains indoor temperature comfort even when the HVAC system is not operating, allowing the building to withstand extended periods without active cooling or heating while maintaining acceptable temperature ranges.
Solution Approach 2:
The system uses historical operational data and thermal models to create predictive models of building thermal behavior. These models simulate future temperature responses to different HVAC operating strategies, allowing the system to identify optimal shutdown schedules and duration that maintain temperature control within acceptable ranges while maximizing energy flexibility and cost savings.
3Use of energy by moving object
If complex simulations are performed to optimize HVAC operation, then energy optimization is improved, but computational complexity increases
Solution Approach 1:
The system pre-calculates optimal operational schedules using forecasted electricity prices, weather predictions, and building thermal models before the periods they apply to. By performing these computationally intensive simulations in advance rather than in real-time, the system can explore multiple scenarios and identify optimal strategies without requiring complex real-time computational resources, reducing online computational complexity while maintaining optimization quality.
Solution Approach 2:
The system simplifies the optimization problem by reducing the number of variables and constraints considered in real-time control. Instead of optimizing all possible parameters simultaneously, the system focuses on key parameters such as setpoint temperatures and operating schedules, using pre-computed results from more comprehensive offline simulations. This hierarchical approach maintains energy optimization while reducing computational complexity for real-time operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient exploitation of HVAC flexibility to stabilize energy grids by adjusting energy demand in response to supply fluctuations, ensuring precise temperature control and reducing computational complexity.
Implementation Method 1
The statistical model can describe the thermal behavior of the air-conditioned zone using local historical data; in particular, a hysteresis similar to a low-pass thermal behavior can be modeled as a characteristic.
Implementation Method 2
Transform Electrical Energy to Thermal Energy
Implementation Method 3
Transform Electrical Energy to Thermal Energy
Implementation Method 4
The advantage achieved by the invention is based on the exploitation of flexibility potential for the power supply of heating and cooling devices while maintaining a required zone temperature.
Data Source
Figure 1~2
AI summary
A method for controlling an electrically powered heating and/or cooling device is proposed, comprising the steps of: a.) providing a statistical model for at least one zone air-conditioned by the device for calculating a future zone temperature; b.) training the statistical model using historical data; c.) determining at least two time profiles (C-ZT, D-ZT) for the zone temperature for the air-conditioned zone within a defined time interval by means of a simulation with the statistical model, wherein a respective model parameter set is used for each of the profiles, wherein of at least two recorded time profiles (C-ZT, D-ZT) at least one time profile (C-ZT) is recorded with the currently used model parameter set as a base scenario and at least one further time profile (D-ZT) is recorded with changes compared to the base scenario; d.) Calculation of the energy requirement for each determined curve, using the model parameter set used in the simulation; e.) Filtering the curves as a first sub-step of a selection algorithm, by means of a threshold value for the average daily temperature deviation, whereby the curves which exceed this threshold are not taken into account any further, whereby in a second sub-step of the selection algorithm, a model parameter set is selected and the model parameter set corresponds to one of the remaining curves with the lowest or highest energy requirement; f.) Control of the device, whereby the previously selected model parameter set is applied to the device.Furthermore, the invention relates to a system for controlling an electrically powered heating and/or cooling device (15a) for the air conditioning of at least one air-conditioned zone, comprising at least a first and a second entity (10, 11), wherein the first entity (10) is designed to carry out steps a, b, c, d and e of the method according to claim 1 and the second entity (11) is designed to carry out step f of the method according to claim 1, wherein the two entities are designed to communicate by means of a common interface, via which the entity (11) first requests a model parameter set from the entity (10), the entity (10) carries out steps a to e and returns the selected model parameter set as a response to the entity (11), which finally carries out step f of the method.