Autonomous Thermal Load Regulation via Fuzzy Logic Coefficients
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Solution Overview
Problem
Current methods for managing electricity consumption peaks, such as importing electricity or using additional power sources, increase pollution and risk accidents, while load shedding methods like temporarily shutting down thermal heating installations lead to the 'rebound effect' and network imbalances.
Innovation Solution
A method where each consuming entity, equipped with a thermal installation, adjusts its power consumption based on a power regulation coefficient calculated from the difference between its actual and setpoint power consumption and temperature, allowing for self-adaptive and autonomous power reduction without disconnecting the installation, using fuzzy logic to determine the coefficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If load shedding is implemented to reduce electricity consumption during peak periods, then overall power consumption is reduced, but thermal comfort of consuming entities deteriorates and rebound effect occurs
Solution Approach 1:
The invention changes the control parameter from binary on/off switching to continuous power modulation. Each thermal installation adjusts its power consumption continuously based on a regulation coefficient, allowing fine-grained control that maintains thermal comfort while reducing overall consumption. This avoids the abrupt temperature changes that cause the rebound effect.
Solution Approach 2:
The system implements feedback control by continuously monitoring the regulated temperature of each consuming entity and adjusting the power consumption accordingly. The regulation coefficient is determined based on the temperature difference between measured and reference temperatures, ensuring that thermal comfort is maintained while achieving power reduction goals.
2Ease of operation
If centralized control systems are used to manage power consumption, then coordination is improved, but system complexity and data processing requirements increase
Solution Approach 1:
Each consuming entity autonomously determines its own power consumption adjustment based on locally available information (temperature measurements and power consumption data). The entities self-regulate their power usage without requiring centralized control or complex data processing, simplifying the overall system architecture while maintaining coordination through the common objective of peak load reduction.
Solution Approach 2:
The invention divides the power consumption management task into independent segments at each consuming entity. Instead of centralized control processing data from all entities, each entity independently calculates its regulation coefficient based on local measurements, reducing communication overhead and system complexity.
3Productivity
If thermal installations are disconnected during peak consumption, then immediate power reduction is achieved, but network stability deteriorates due to unforeseen imbalances
Solution Approach 1:
The system implements dynamic power adjustment where each thermal installation continuously modulates its power consumption based on real-time conditions. This dynamic control allows the network to maintain stability by avoiding abrupt disconnections and rebounds, while still achieving significant power reduction during peak periods through coordinated gradual adjustments across multiple entities.
Data Source
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AI summary
A set of consuming entities (E1,...,Ei,...EN) each equipped with a thermal installation (Ch1,...,ChN) consuming electricity to regulate a temperature of the entity is connected to the electrical network.The following steps are implemented in parallel by each consuming entity for a given instant: a) determination of a first data representing a power difference between the electrical power consumed by all consuming entities and a setpoint electrical power; b) determination of a second data representing a temperature difference between a measurement of the regulated temperature of said entity and a reference temperature specific to said entity; c) determination of a power regulation coefficient for said entity from the first data of power difference and the second data of temperature difference; d) command to reduce the power consumed by said entity according to the determined regulation coefficient.