Smart Grid ADL Scheduling via Historical Data Analysis
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Solution Overview
Problem
Current smart home systems lack effective scheduling control to optimize power usage and minimize costs, especially during peak and off-peak pricing periods, due to limited integration of data from smart meters and the smart grid for activities of daily life.
Innovation Solution
A system and method that utilize historical data from smart meters and smart grid networks to categorize activities of daily living (ADLs) into flexible and non-flexible types, determining variable start times for flexible ADLs and fixed start times for non-flexible ADLs, and creating schedules to optimize energy consumption based on historical data and energy tariff structures, while considering penalties for scheduling outside designated time windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If smart home systems operate appliances without intelligent scheduling, then appliances can run whenever needed, but power usage costs increase during peak pricing periods
Solution Approach 1:
The system performs preliminary actions by pre-scheduling flexible ADLs during off-peak periods based on historical data and forecasted pricing. The control center analyzes historical consumption patterns and pricing structures in advance to determine optimal start times for flexible activities, executing energy-intensive tasks before peak pricing periods begin.
Solution Approach 2:
The system implements dynamic scheduling by continuously adjusting ADL start times based on real-time pricing signals and grid conditions. The control center modifies the duty cycle of smart appliances dynamically, shifting flexible ADLs between different time windows according to current pricing structures and user preferences, rather than using fixed schedules.
2Use of energy by stationary object
If flexible ADLs are scheduled during off-peak periods, then power usage cost decreases, but scheduling complexity increases
Solution Approach 1:
The system segments ADLs into three distinct categories: flexible, semi-flexible, and non-flexible activities. This segmentation allows the control center to apply different scheduling strategies to each category, simplifying the overall scheduling complexity by treating different types of activities differently rather than applying a single complex algorithm to all activities.
Solution Approach 2:
The control center acts as an intermediary between the user, smart appliances, and the utility company. It receives pricing signals and grid status information from the utility, processes this information along with historical data and user preferences, and generates optimized schedules for smart appliances, thereby managing scheduling complexity centrally rather than at each appliance level.
3Productivity
If smart meters collect and transmit detailed consumption data, then energy optimization improves, but data privacy concerns increase
Solution Approach 1:
The system implements local quality by processing and analyzing detailed consumption data locally at the control center level rather than transmitting all raw data to the utility company. The control center maintains local historical databases of consumption patterns and performs optimization calculations locally, sharing only aggregated or anonymized information with the utility, thereby preserving energy optimization capabilities while protecting user privacy.
Data Source
AI summary
The present invention is a system and method for optimizing power on a smart grid network. The system includes one or more units, such as a smart home, each having a control center with a historical database. The control center is in digital communication with a network component of a smart grid. Historical data including a soft window period and a hard window period representing acceptable start times for one or more flexible ADLs are stored in the historical database. The network component receives historical data for the flexible ADLs from multiple control centers. The network component, which creates a schedule based at least in part on the historical data and transfers energy to the control center according to the schedule. While scheduling, the network component manages peak load, energy tariffs, penalties, forecasting of non-ADL demand, and forecasting of supply over the scheduling horizon among other things.