Autonomous Load Enablement Decision Logic for Peak Demand Control
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Solution Overview
Problem
Current methods for managing energy consuming loads in energy supply systems often require negotiation among loads and centralized control, which can be complex and inefficient in reducing peak energy demand.
Innovation Solution
A method and apparatus that allow each energy consuming load to be independently managed using shared information about other loads, making enablement state decisions independently without negotiation, to control peak energy demand within a group of loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stress or pressure
If centralized control and negotiation among loads are used to manage energy demand, then peak energy demand can be reduced, but system complexity and operational difficulty increase
Solution Approach 1:
Each load is equipped with a microprocessor that autonomously makes enablement state decisions based on locally stored shared information about other loads. The load independently calculates its contribution to target system equilibrium and determines whether to remain enabled or transition to disabled state without requiring centralized control or negotiation with other loads, thereby reducing system complexity while still achieving peak demand reduction
Solution Approach 2:
The centralized control function is segmented and distributed to individual loads. Each load's microprocessor contains the logic and shared information needed to make independent decisions, transforming a centralized control system into multiple autonomous decision-making units that collectively achieve the same peak demand reduction goal
2Stress or pressure
If centralized control is implemented to manage energy demands, then peak energy demand can be controlled, but the system becomes less efficient and more difficult to operate
Solution Approach 1:
The load autonomously monitors its own operational parameters and shared information about other loads stored in its microprocessor, automatically making enablement state decisions without requiring external control signals or negotiation protocols, thereby simplifying operation while achieving peak demand control
Solution Approach 2:
The load periodically evaluates its enablement state based on shared information and target system equilibrium calculations, making discrete decisions at intervals rather than requiring continuous centralized control signals, which improves operational efficiency by reducing communication overhead
Data Source
AI summary
A method for managing an energy consuming load in a group of energy consuming loads and a method for managing the group of energy consuming loads. The method includes generating sets of load state data from the loads, making enablement state decisions for one or more loads independently of the other loads using the sets of load state data, and implementing the enablement state decisions. An apparatus for managing an energy consuming load in a group of energy consuming loads, including a transmitter for transmitting a set of load state data generated from the load, a receiver for receiving sets of load state data from other loads, a processor for processing the sets of load state data to make an enablement state decision for the load, and a controller for implementing the enablement state decision.