Energy Manager Adaptive Demand Response Control
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Solution Overview
Problem
Existing Demand Response systems in power grids lack intelligence in controlling end-device responses, leading to inefficiencies and potential transformer overload due to randomization of device startup times, which can result in reduced service levels and shorter transformer lifespan.
Innovation Solution
An energy manager determines specific Demand Response commands based on energy profiles of local end-devices, coordinating device startup sequences to minimize inrush current and stabilize grid operations, using communication protocols to intercept and modify global DR requests into localized control messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If end-devices shut down and restart at randomized times during Demand Response, then power consumption is reduced during peak hours, but inrush current peaks occur and transformer lifespan is reduced
Solution Approach 1:
The energy manager determines and stores optimal restart times for end-devices before the Demand Response event begins, based on their energy profiles. This preliminary scheduling prevents simultaneous restarts and inrush current peaks, allowing devices to resume operation in a controlled sequence that protects transformer lifespan while still achieving peak demand reduction.
Solution Approach 2:
The system uses energy profiles that contain historical consumption data and device characteristics to continuously optimize restart scheduling. By analyzing feedback from device performance and grid conditions, the energy manager adjusts restart times to minimize inrush currents while maintaining energy reduction goals, creating a closed-loop control system that balances both objectives.
2Loss of energy
If end-devices shut down and restart at randomized times during Demand Response, then power consumption is reduced during peak hours, but service level quality is reduced
Solution Approach 1:
The energy manager determines and communicates optimal restart times to end-devices before the Demand Response event begins. This allows users to plan their device operations in advance, ensuring that critical devices restart at appropriate times for their specific needs, thereby maintaining service quality while still achieving peak demand reduction.
Solution Approach 2:
The system tailors restart scheduling to individual device characteristics and user needs by using energy profiles specific to each end-device. Critical devices receive priority restart times, while non-critical devices are scheduled for later restart, ensuring that service quality is maintained for essential loads while still achieving overall demand reduction.
3Loss of energy
If global Demand Response requests are issued to all customers, then peak power demand is reduced, but localized grid conditions and device characteristics are not considered
Solution Approach 1:
The system segments the global Demand Response request into localized control decisions by using energy profiles specific to each end-device and their respective grid conditions. The energy manager processes global DR signals at the local level, allowing customized restart scheduling that adapts to local device characteristics, grid stability requirements, and user preferences, thereby achieving both peak demand reduction and localized adaptability.
Solution Approach 2:
The system applies local quality by tailoring the Demand Response implementation to each end-device's energy profile and local grid conditions. Instead of uniform restart scheduling, the energy manager determines device-specific restart times that consider local factors such as device criticality, historical usage patterns, and local grid stability requirements, enabling adaptive response to localized conditions while contributing to overall peak demand reduction.
Data Source
AI summary
In one embodiment, an energy manager determines respective energy profiles of one or more local end-devices for which the energy manager is responsible. Through communication between a power grid controller and the energy manager, one or more aspects of power grid operations may be controlled based on the communicating and the respective energy profiles. For example, in one embodiment, upon receiving a global demand response (DR) request, the energy manager determines respective specific DR control for the one or more local end-devices based on the received DR request and the respective energy profiles, and transmits the respective specific DR control to the one or more local end-devices, accordingly. In another embodiment, power grid operations may be stabilized based on the respective energy profiles.