Virtual Mass Emulator for Smart Grid Demand-Response Simulation
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Solution Overview
Problem
Current systems for simulating demand-response programs in smart grids lack integrated emulation capabilities for heterogeneous environments with both real and virtual devices, fail to provide privacy protection, and lack control mechanisms to accurately determine device responses, leading to inefficient testing and implementation of demand-response management strategies.
Innovation Solution
A system and method for emulating smart grid devices in a virtual environment, allowing for simulation of demand-response programs with virtual and hybrid real-virtual environments, enabling serial or parallel simulations, and optimizing accuracy by incorporating data from real devices into the feedback loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional simulation systems are used to test demand-response programs, then implementation time is reduced, but the accuracy and reliability of simulation results deteriorates due to lack of integrated emulation capabilities for heterogeneous environments
Solution Approach 1:
The patent creates virtual copies of smart grid devices that replicate the behavior and characteristics of physical devices. These virtual devices are emulated software representations that can be deployed in simulation environments to test demand-response programs, allowing multiple scenarios to be tested without physical hardware while maintaining realistic device responses and interactions.
Solution Approach 2:
The patent introduces a virtualization layer that acts as an intermediary between physical smart grid devices and simulation systems. This layer enables bidirectional communication where virtual devices can receive control signals and report status information, allowing accurate simulation of demand-response programs while isolating the simulation environment from physical infrastructure.
2Ease of operation
If brute force mechanisms are used to determine device responses, then control capability is simplified, but measurement precision deteriorates due to inability to accurately determine how many devices responded
Solution Approach 1:
The patent implements feedback mechanisms where virtual smart grid devices report their status and response information back to the simulation system. This feedback loop provides detailed information about which devices responded to control signals, their individual states, and aggregate system responses, enabling precise measurement of device responses while maintaining ease of control through standardized communication protocols.
3Ease of operation
If centralized architecture is used for demand-response control, then control capability is improved, but device complexity increases due to need for collecting information from various sources and providing control signals to multiple units
Solution Approach 1:
The patent creates universal virtual device profiles that can represent multiple types of smart grid devices with different functions and characteristics. These standardized virtual templates enable a single centralized control system to manage diverse device types without requiring complex device-specific logic, as the virtualization layer handles device-specific behaviors while maintaining uniform communication interfaces.
Data Source
AI summary
The present invention is directed to a system and method for emulating smart grid devices in a smart grid for demand-response program analysis and optimization. Smart grid devices may be emulated in a virtual environment on a server, and can also be emulated individually on smart grid devices themselves. Demand-response programs can be simulated in a virtual environment with virtual emulated smart grid devices, or they can be simulated in a hybrid real-virtual environment with both real smart grid devices and virtual emulated smart grid devices. Demand-response programs can be simulated serially or in parallel. Additionally, such hybrid demand-response program simulations can be enhanced and optimized by including data obtained from the real smart grid devices into the simulation feed-back loop.