Community Energy Management Unit for Peak Demand Control
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Solution Overview
Problem
The 'rebound' effect in smart grid energy management systems, where scheduled energy usage peaks occur after off-peak pricing periods, negating the benefits of off-peak pricing models and potentially worsening load peaks, is not effectively addressed by existing complex power scheduling protocols.
Innovation Solution
A community energy management system that includes an Energy Management Unit (EMU) capable of monitoring and controlling smart grid-enabled appliances, allowing for interoperability across different manufacturers, scheduling appliance usage outside direct smart grid control, and using algorithms like first-in, first-out to manage energy consumption and delay appliance operation during peak demand periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If smart grid systems use off-peak pricing models to reduce peak demand, then energy cost efficiency is improved, but a rebound effect creates new peaks after off-peak periods that worsen overall load management
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating appliances and storing energy in batteries during off-peak hours before the anticipated rebound period. This allows the system to meet future demand without creating peak loads, as the appliances can operate from stored energy rather than drawing power during the rebound period.
Solution Approach 2:
The system continuously monitors grid conditions, pricing signals, and appliance states to dynamically adjust scheduling decisions. By incorporating real-time feedback from smart meters and grid operators, the system can respond to changing conditions and prevent rebound effects by adjusting appliance operation based on actual grid state rather than fixed schedules.
2Object-generated harmful factors
If complex power scheduling protocols are implemented to manage appliance operation timing, then peak demand control is improved, but system complexity increases and compatibility across different manufacturers is reduced
Solution Approach 1:
The system segments the scheduling function into independent, standardized modules that can be implemented by different manufacturers. By dividing the complex scheduling protocol into discrete, interoperable components with defined interfaces, the system achieves sophisticated demand management while maintaining compatibility across diverse appliances and manufacturers.
Solution Approach 2:
The patent implements a universal scheduling framework that can accommodate multiple appliance types and control strategies through a common interface. This multi-functional approach allows a single standardized protocol to manage diverse loads (HVAC, water heating, laundry, etc.) without requiring manufacturer-specific implementations, thereby reducing overall system complexity.
3Loss of energy
If appliance operation is delayed until off-peak periods to reduce costs, then energy pricing efficiency is improved, but user convenience and control are reduced
Solution Approach 1:
The system provides dynamic control where users can adjust appliance scheduling in real-time based on their needs and preferences. Rather than fixed delayed operation, users can modify schedules, set priorities, and override decisions as conditions change, maintaining convenience while achieving energy efficiency through flexible, adaptive control interfaces.
Solution Approach 2:
The system enables appliances to autonomously manage their own operation scheduling based on user-defined preferences and grid conditions. Appliances self-adjust their timing and operation without requiring direct user intervention, achieving energy optimization while preserving user convenience through automated decision-making that respects user priorities.
Data Source
AI summary
A community based energy management method which avoids energy peaks oftentimes associated with the restart of appliances after the delayed start of an appliance due to energy use restrictions is lifted by a utility company. The method includes the application of a queue model, such as a first-in, first-out model for the restart time of any delayed electrical device.