Demand Response Asset Configuration for Grid Frequency Stability
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Solution Overview
Problem
Existing demand response systems struggle to effectively configure a diverse pool of assets to respond to grid frequency fluctuations while ensuring stability and responsiveness, particularly in managing power consumption or supply at the portfolio level without relying on centralized control.
Innovation Solution
A computer-implemented method using a trained neural network model to map asset configurations to performance indicators, optimizing asset configurations through a search process to meet specific demand response requirements, allowing real-time control at the asset level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If demand response programs require customer premises equipment to be modified or replaced, then energy consumption can be reduced, but implementation cost and complexity increase
Solution Approach 1:
The system enables self-service demand response by allowing customers to autonomously adjust their energy consumption through automated control of connected devices. The customer premises equipment automatically responds to demand response events without requiring manual intervention or complex modifications, reducing both implementation complexity and energy consumption through autonomous operation.
Solution Approach 2:
The system provides universal demand response capabilities that work across multiple types of customer premises equipment and energy consumption scenarios. By creating a multi-functional platform that can interface with various devices and implement different demand response strategies, the system reduces implementation complexity while achieving energy reduction across diverse applications.
2Device complexity
If demand response programs are implemented without customer incentives, then program simplicity is maintained, but customer participation and energy reduction effectiveness decrease
Solution Approach 1:
The system implements feedback mechanisms that provide customers with information about their energy consumption patterns, demand response participation, and cost savings. This feedback loop motivates customer participation and enables the system to achieve effective energy reduction while maintaining program simplicity through automated tracking and reporting rather than complex incentive structures.
Solution Approach 2:
The system acts as an intermediary between utility companies and customers, facilitating demand response participation by automatically managing communication, control, and tracking. This intermediary role enables effective energy reduction through automated coordination while keeping the program simple for customers by handling complexity in the background.
3Use of energy by moving object
If real-time monitoring and control of customer premises equipment is implemented, then energy optimization is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system replaces complex mechanical monitoring and control mechanisms with automated software-based solutions. By using computational algorithms and digital communication protocols instead of physical monitoring systems, the platform achieves real-time energy optimization while reducing overall system complexity through virtualization and automation of monitoring functions.
Data Source
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Figure 2A~2B
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AI summary
A method of controlling assets connected to an electricity distribution grid is disclosed. For each of a plurality of assets to be configured, a trained neural network model is provided, which takes an asset configuration as input, the asset configuration specifying a response of the asset to variations in one or more operating conditions detected at the asset, and outputs one or more performance indicators relating to the operation of the asset when operated using the asset configuration. The outputs of the neural network models are provided as inputs to an optimization function. A search process optimizes the optimization function by varying asset configurations for the assets. The final asset configurations are then used to control the assets to provide a coordinated demand response service.