ML-Enhanced DER Management for Peak Demand Load Capacity
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Solution Overview
Problem
The electrical grid faces challenges in balancing supply and demand due to the rapid transition to clean energy sources, leading to issues like peak demand management, energy price increases, and the need for dispatchable reserve capacity, which existing strategies such as classical energy sources and battery storage are costly, environmentally impactful, and pose safety risks.
Innovation Solution
A distributed energy resources management system (DERMS) using machine-learning-based algorithms for thermostat control, load-shifting, and load-shedding, integrated with cloud-based solutions, to manage peak demand by predicting energy needs based on historical and real-time data, and dynamically adjusting participation of distributed energy resources like solar, batteries, and electric vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery storage is used to balance the grid during peak demand, then energy supply reliability is improved, but cost and safety risks increase
Solution Approach 1:
The patent introduces a DERMS platform as an intermediary that coordinates distributed energy resources (thermostats, water heaters, EVs) to provide grid balancing services. This mediator approach replaces the need for centralized battery storage, eliminating safety risks associated with large battery installations while maintaining energy supply reliability through distributed resource coordination.
2Reliability
If battery storage is deployed to manage peak demand, then energy supply reliability is improved, but cost increases
Solution Approach 1:
The patent enables distributed energy resources to self-manage and self-coordinate through the DERMS platform. Thermostats, water heaters, and EVs automatically adjust their operation based on grid conditions and incentive signals, eliminating the need for expensive centralized battery storage infrastructure while maintaining energy supply reliability.
Solution Approach 2:
Instead of deploying physical battery storage infrastructure, the patent creates a virtual copy of battery functionality through software-based coordination of distributed resources. The DERMS platform replicates the energy buffering and dispatch functions of physical batteries using intelligent control algorithms applied to existing distributed assets.
3Reliability
If machine-learning-based DERMS is implemented, then grid stability is enhanced, but device complexity increases
Solution Approach 1:
The patent creates a universal DERMS platform that performs multiple functions: load forecasting, real-time coordination, incentive optimization, and grid stability management. This multi-functional system consolidates what would otherwise require multiple separate complex systems, achieving grid stability enhancement while managing overall system complexity through functional integration.
Data Source
AI summary
Techniques for providing a machine learning-enhanced distributed energy resource management system are provided. In one technique, a machine-learning (ML) model is trained based on a training dataset that comprises historical demand response (DR) event data and historical weather data. The trained ML model is used to predict a load capacity to be made available for an upcoming DR event based, at least in part, on current DR event data and weather data. The predicted load capacity made available for an upcoming DR event is determined to be not sufficient to balance energy supply and demand during the upcoming DR event. Responsive to this determination, one or more load capacity increasing actions are automatically performed. Examples of such actions include increasing a level of participation of a set of dynamically-enrolled customers and causing a request for additional participation in load-shedding to be sent to one or more customers.