Peer-to-Peer Smart Sockets for TCL Peak Load Smoothing
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Solution Overview
Problem
Existing large-scale Demand Side Management (DSM) solutions for thermostatically controlled loads (TCLs) require costly replacements of appliances with 'smart' versions, centralized control architectures, and centralized data collection, compromising user privacy and system integration, and are vulnerable to cyber attacks.
Innovation Solution
A decentralized, peer-to-peer network of smart sockets with autonomous processing and communication capabilities, estimating future consumption profiles and minimizing peaks through local decision-making without a central coordinator, using dynamic modeling and distributed computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control architecture is used for DSM, then coordination of TCLs can be achieved, but system complexity and vulnerability to cyber attacks increase
Solution Approach 1:
The patent divides the centralized control system into multiple autonomous edge computing units distributed across different locations. Each unit independently processes data from nearby TCLs, eliminating the single-point failure vulnerability of centralized architecture while reducing overall system complexity through modular design.
Solution Approach 2:
Each TCL is equipped with embedded intelligence and autonomous decision-making capabilities, allowing it to self-regulate its operation based on local conditions and aggregate signals from edge units, rather than requiring constant centralized control. This reduces the complexity burden on the control architecture.
2Extent of automation
If TCLs are replaced with smart versions, then autonomous processing capabilities are gained, but manufacturing costs increase
Solution Approach 1:
The patent introduces edge computing units as intermediary devices that provide advanced processing capabilities externally. These units act as mediators between TCLs and the cloud, enabling autonomous processing without requiring expensive smart modifications to each individual appliance. The edge units can be deployed incrementally and serve multiple TCLs simultaneously.
Solution Approach 2:
The patent combines the processing capabilities of multiple TCLs by aggregating their data at edge computing units. This merging approach allows the system to achieve collective autonomous processing power equivalent to smart appliances, but at lower individual cost since the intelligence is shared across the network rather than embedded in each device.
3Productivity
If centralized data collection is implemented, then consumption profiles can be optimized, but user privacy is compromised
Solution Approach 1:
The patent implements data processing with local quality by keeping detailed consumption data processing at the edge computing units closest to the users. Only aggregated, anonymized statistics are transmitted to central servers, while local edge units retain and process detailed data for immediate optimization. This ensures privacy protection while maintaining optimization effectiveness.
Solution Approach 2:
The patent transforms the data privacy problem by adding a spatial dimension to data handling. Instead of a single centralized collection point, the system creates multiple hierarchical levels of data processing (device level, edge level, cloud level), each with different privacy requirements. This dimensional approach allows detailed data to remain local while still enabling system-wide optimization through aggregated insights.
Data Source
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AI summary
A method is described that will reduce the peaks in electricity consumption for a plurality of electrical appliances (2) comprising a first set of n Thermostatically Controlled Load (TCL) type electrical appliances. The method comprises: associating smart socket (1) not part of the electrical appliance, with each electrical appliance. The smart socket consists of the following: a controller (4); a unit for connection (6) to a network (100); a device that measures the power (7) absorbed by electrical appliance (2) associated therewith; a switch (3) to turn the power supplied to the associated electrical appliance on or off. The method consists of the following steps: connecting the smart sockets associated with the plurality of electrical appliances through a "peer to peer" network (100); locally estimating, for each smart socket, a future electricity consumption, for the electrical appliance associated with the smart socket, in a sliding time window; sending said estimate in the "peer to peer" network for each smart socket; estimating a future average consumption in said sliding time window of the plurality of electrical appliances on the basis of said local estimates; the future average consumption is calculated with cooperation between the various smart socket controllers. Furthermore, for each smart socket associated with a thermostatically controlled load TCL electrical appliance, on the basis of said estimated average future consumption, the future sequence of periods to turn the switch of the smart socket on and off is planned to reduce variations in the total electrical power profile absorbed by the plurality of electrical appliances.