Behind-the-Meter Energy Control With Predictive Load Forecasting
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Solution Overview
Problem
Coordinating behind-the-meter (BTM) distributed energy resources at scale in field settings is challenging due to limitations in sensing, communications, and modeling, particularly in agricultural environments with variable weather conditions and diverse load profiles, which affects energy efficiency and costs.
Innovation Solution
A cloud-based BTM system that uses sensor networks and model predictive control to forecast loads and solar power, minimizing electricity costs by optimizing the operation of equipment like fans in dairy farms, using variable frequency drive components to switch between grid, stored, and alternate energy sources based on real-time environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If sensor networks and model predictive control are used to optimize equipment operation, then electricity costs are reduced, but device complexity increases
Solution Approach 1:
A cloud-based management system acts as an intermediary between sensors, predictive models, and equipment controllers. The system receives sensor data, processes it through predictive models to determine optimal operational parameters, and sends control signals back to equipment, thereby reducing electricity costs while centralizing complexity in a manageable platform
Solution Approach 2:
The system performs preliminary actions by using predictive models to forecast future energy consumption and operational needs before they occur. This allows the system to pre-determine optimal operational parameters and schedule equipment operation in advance, reducing peak demand and electricity costs while maintaining simplicity through automated decision-making
2Productivity
If real-time environmental monitoring and predictive control are implemented, then energy efficiency improves, but measurement and communication requirements increase
Solution Approach 1:
The management system serves multiple functions: it collects data from diverse sensor networks, processes information through predictive models, communicates with various equipment types, and performs optimization calculations. This multi-functional approach consolidates complex sensing and communication requirements into a single universal platform that can handle diverse inputs and outputs
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor real-time environmental conditions and equipment operation, the predictive model processes this data to assess energy efficiency, and control signals are sent back to adjust operational parameters. This feedback mechanism improves energy efficiency while managing measurement complexity through automated closed-loop control
3Use of energy by moving object
If variable frequency drive components are used to control equipment operation, then power consumption is optimized, but device complexity increases
Solution Approach 1:
The system replaces traditional mechanical control methods with electronic variable frequency drives that are digitally controlled by the cloud-based management system. This substitution allows for precise optimization of power consumption through software-based control algorithms while the complexity is managed through centralized digital control rather than distributed mechanical systems
Data Source
AI summary
A method and a system for managing power resources. One or more measurements received from one or more sensors communicatively coupled to at least one processor are processed. The sensors monitor and measure at least one of: one or more operational parameters associated with operation of at least one equipment, one or more external parameters associated with an environment of the equipment, and one or more power parameters associated with a power consumption by the equipment. Based on the processed one or more measurements, one or more future operational parameters associated with an operation of the are determined. The operation of the equipment is controlled using the determined future operational parameters.