Adaptive Load Control Profiles for Peak Demand Events
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Solution Overview
Problem
Traditional load control methods in utility companies fail to ensure all loads contribute equally to peak load reduction during load management events, leading to 'free-riding' where some residences receive incentives without reducing peak loads due to coinciding natural off-cycles.
Innovation Solution
An adaptive load control system that monitors and adjusts the duty cycles of individual loads based on their actual runtime and external factors like weather, ensuring that loads contribute to peak load reduction by increasing off-time during scheduled events when they would normally be off.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a uniform duty cycle is applied to all loads during load control events, then the control system is simple to implement, but some loads may not contribute to peak load reduction due to coinciding natural off-cycles
Solution Approach 1:
The patent applies local quality by customizing duty cycles for individual loads based on their specific operational characteristics. Each load receives a tailored control profile that reflects its natural runtime patterns, ensuring that every load contributes effectively to peak load reduction. This is achieved by monitoring individual load behavior and generating personalized control instructions rather than applying a uniform duty cycle to all loads.
Solution Approach 2:
The patent implements dynamics by making duty cycles adaptive rather than static. The system continuously monitors load operational patterns and adjusts duty cycles dynamically based on observed behavior and forecasted conditions. This allows the control system to respond to changing load characteristics and external factors, optimizing peak load reduction effectiveness in real-time.
2Productivity
If individualized duty cycles are assigned to each load based on runtime monitoring, then peak load reduction effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling loads to effectively control themselves through automated monitoring and adaptive duty cycle adjustment. The system monitors each load's natural operational patterns and automatically generates customized control profiles without requiring manual intervention. This reduces the perceived complexity for users while achieving effective peak load reduction through intelligent, autonomous load management.
Solution Approach 2:
The patent implements feedback by continuously monitoring load operational patterns and using this information to adjust duty cycles. The system observes actual load behavior, compares it with control objectives, and refines individualized duty cycles accordingly. This closed-loop approach ensures effective peak load reduction while managing system complexity through automated adaptive control rather than complex manual configuration.
3Ease of operation
If load control events are scheduled without considering external factors, then scheduling is straightforward, but load contribution accuracy decreases
Solution Approach 1:
The patent applies preliminary action by obtaining forecasted external factor data before scheduling load control events. The system proactively gathers weather forecasts and other relevant external condition information in advance, allowing it to predict load behavior patterns and optimize duty cycle assignments before the control event begins. This提前 preparation improves load contribution accuracy without significantly complicating the scheduling process.
Data Source
AI summary
An adaptive load control system includes: a load monitoring and control device configured to monitor operating conditions of a load connected to an electrical power grid, and transmit load operating condition data; and a head-end load controller configured to: receive the load operating condition data; obtain data from externals sources about external factors that influence the load operating conditions; and generate load control profiles based on the data from external sources that control a duty cycle for the load during a scheduled load control event. The load control profiles include instructions to increase the off time of the duty cycle for the load when the scheduled load control event overlaps a normal off time of the load, and one of the load control profiles is selected based on a comparison of the data from external sources and a forecast of external conditions during the scheduled load control event.