Predictive Energy Management System for Grid Reliability
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Solution Overview
Problem
Current systems for managing and reducing electricity demand on the power grid lack efficient mechanisms for customers to control and optimize their energy usage in response to price signals, leading to suboptimal economic and reliability outcomes.
Innovation Solution
The development of a system that uses predictive modeling and mathematical algorithms to enable customers to manage their energy assets, including generation, storage, and load control, by providing them with information and schedules to optimize their energy use and earn revenue by curtailing consumption in response to price signals, thereby converting demand into a controllable resource for the grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RTO uses command and control measures to physically change energy flow to preserve reliability, then grid reliability is maintained, but economic efficiency deteriorates because load control is not incentivized through price signals
Solution Approach 1:
The system dynamically adjusts energy management strategies based on real-time price signals from the RTO. Customers can flexibly shift their energy consumption patterns in response to varying prices, transforming static demand into dynamic, responsive load control that achieves both reliability and economic efficiency
Solution Approach 2:
The system implements a feedback loop where customers receive price signals from the RTO, analyze their energy consumption patterns using predictive models, and adjust their energy usage accordingly. This closed-loop system enables economically efficient load control while maintaining grid reliability through continuous monitoring and adjustment
2Productivity
If RTO accepts customer offers to curtail electricity use at prices lower than competing generation offers, then economic efficiency is improved by reducing electricity costs, but grid reliability may deteriorate if curtailment exceeds acceptable levels
Solution Approach 1:
The system enables partial curtailment of energy consumption rather than complete elimination. Customers can reduce their energy usage to optimal levels that provide economic benefits while maintaining sufficient consumption to ensure grid reliability, avoiding excessive curtailment that would compromise system stability
Solution Approach 2:
The system changes the parameter of energy consumption from a fixed value to a variable that responds to price signals. By adjusting consumption levels dynamically based on economic incentives, the system achieves cost reduction while maintaining reliability through controlled parameter variation
3Device complexity
If customers lack access to predictive modeling and optimization algorithms, then system complexity is reduced, but energy management effectiveness deteriorates leading to suboptimal economic outcomes
Solution Approach 1:
The system introduces an intermediary energy management platform that provides predictive modeling and optimization algorithms to customers. This intermediary layer enables customers to access sophisticated analytical tools without requiring them to develop complex systems themselves, thereby improving energy management effectiveness while maintaining reasonable system complexity
Solution Approach 2:
The system enables customers to independently manage their energy consumption by providing them with predictive models and optimization tools. Customers can autonomously analyze their energy patterns, receive recommendations, and implement adjustments without requiring complex external intervention, improving effectiveness while keeping the system accessible
Data Source
AI summary
Embodiments of the present invention assist customers in managing the four types of energy assets, that is, generation, storage, usage, and controllable load assets. Embodiments of the present invention for the first time develop and predict a customer baseline (“CBL”) usage of electricity, using a predictive model based on simulation of energy assets, based on business as usual (“BAU”) of the customer's facility. The customer is provided with options for operating schedules based on algorithms, which allow the customer to maximize the economic return on its generation assets, its storage assets, and its load control assets. Embodiments of the invention enable the grid to verify that the customer has taken action to control load in response to price. This embodiment of the invention calculates the amount of energy that the customer would have consumed, absent any reduction of use made in response to price. Specifically, the embodiment models the usage of all the customer's electricity consuming devices, based on the customer's usual conditions. This model of the expected consumption can then be compared to actual actions taken by the customer, and the resulting consumption levels, to verify that the customer has reduced consumption and is entitled to payment for the energy that was not consumed.