Swarm Optimization for Power Grid Load Scheduling
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Solution Overview
Problem
Existing systems for managing electric power consumption in domestic and industrial scenarios lack the ability to automatically optimize consumption and prevent overloads efficiently, particularly requiring high computational resources and being unsuitable for dynamic scenarios.
Innovation Solution
A system utilizing a swarm optimization approach to adjust the start times of power profiles of devices, employing particles following different random walk strategies based on Lévy and uniform probability distributions to generate a time schedule that keeps total power consumption under a maximum threshold, thereby preventing overloads without requiring high computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a brute-force approach is used to optimize power consumption scheduling, then optimization accuracy is improved, but computational resource requirements and processing time increase significantly
Solution Approach 1:
The patent transforms the continuous power consumption optimization problem into a discrete scheduling problem by parameterizing device start times and power profiles. This allows the use of combinatorial optimization algorithms that are computationally efficient while maintaining adequate optimization accuracy, resolving the contradiction between precision and computational complexity.
Solution Approach 2:
The patent segments the power consumption management into discrete time slots and discrete device scheduling decisions. By dividing the continuous optimization space into discrete segments, the system can apply efficient discrete optimization algorithms rather than computationally intensive continuous optimization methods.
2Measurement precision
If a brute-force approach is used to optimize power consumption scheduling, then optimization accuracy is improved, but processing time increases making it unsuitable for dynamic scenarios
Solution Approach 1:
The patent performs preliminary segmentation and parameterization of power profiles and scheduling options before the actual optimization process. This pre-processing step structures the problem in advance, enabling faster real-time optimization decisions without sacrificing accuracy, thus reducing processing time for dynamic scenarios.
Solution Approach 2:
The patent implements a dynamic scheduling approach where power profiles and start times can be adjusted in real-time based on changing conditions. The discrete time slot structure allows for rapid recalculation and adaptation to dynamic scenarios, making the system responsive while maintaining optimization accuracy.
3Loss of information
If existing monitoring systems are used, then power consumption data collection is achieved, but automatic optimization and overload prevention capabilities are lacking
Solution Approach 1:
The patent implements a feedback mechanism where power consumption data collected from devices is continuously monitored and fed into the optimization algorithm. The system automatically adjusts scheduling decisions based on this feedback, enabling both data collection and automatic optimization without requiring separate manual intervention systems.
Solution Approach 2:
The system enables devices to self-optimize their power consumption schedules automatically based on the optimization algorithm's decisions. Each device receives scheduling instructions and autonomously adjusts its operation timing, providing self-service optimization without requiring external control for each individual device.
Data Source
AI summary
A system for managing devices supplied through a power grid is proposed. Each device is configured to consume over time electric power according to at least one respective power profile when operating. The system comprises at least one unit interfaced with the devices for exchanging data. The at least one unit collects power profile data indicative of the power profiles of the devices, and generates a time schedule of the device operations by distributing over time start times of the power profiles in such a way that at any time the total power consumption of the devices is kept under a maximum power threshold of the power grid.