Facility Power Monitoring Control for Peak Demand Spikes
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Solution Overview
Problem
Power systems face challenges in efficiently managing peak power demand, leading to high costs and potential system overload, as existing technologies lack effective methods to predict and control transient spikes in power usage, resulting in demand rate charges for facilities.
Innovation Solution
A system comprising sensors and a monitoring and control element that generates performance data, identifies analogous facilities, and sends operational adjustment commands to manage power usage by projecting future demand based on historical data and external factors, thereby controlling power consumption to avoid exceeding predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power systems are sized to handle peak demand, then adequate power generation capacity is provided, but system cost increases
Solution Approach 1:
The system performs preliminary actions by predicting future power demand using historical data and analog facility performance before the peak demand occurs. This allows proactive operational adjustments to be made in advance, such as pre-cooling facilities or pre-heating water, to reduce the need for oversized peak capacity infrastructure.
Solution Approach 2:
The system continuously monitors actual power consumption and compares it with predicted demand, using this feedback to dynamically adjust facility operations. This closed-loop control enables the system to respond to actual conditions in real-time, optimizing power usage patterns to avoid peak demand charges without requiring oversized infrastructure.
2Productivity
If demand rates are implemented to encourage customers to avoid peak power usage, then power demand is smoothed, but demand rate charges still occur when thresholds are exceeded
Solution Approach 1:
The system performs preliminary actions by predicting future power demand using historical data and analog facility performance before the peak demand occurs. This allows proactive operational adjustments to be made in advance, such as pre-cooling facilities or pre-heating water, to reduce the need for oversized peak capacity infrastructure.
Solution Approach 2:
The system continuously monitors actual power consumption and compares it with predicted demand, using this feedback to dynamically adjust facility operations. This closed-loop control enables the system to respond to actual conditions in real-time, optimizing power usage patterns to avoid peak demand charges without requiring oversized infrastructure.
3Use of energy by moving object
If facilities switch to alternative fuel sources during high demand periods, then power consumption is reduced, but operational complexity and cost increase
Solution Approach 1:
The system enables facilities to self-regulate their power consumption by automatically adjusting operational parameters based on real-time monitoring and predictive analytics. This self-service approach eliminates the need for manual intervention or complex operational procedures to switch between energy sources, as the system autonomously optimizes power usage patterns.
Solution Approach 2:
The system replaces mechanical or manual switching between energy sources with an intelligent software-based control system that uses data analytics and automated decision-making. This substitution eliminates the operational complexity of physical fuel source switching while achieving the same goal of reducing peak power consumption.
4Productivity
If real-time monitoring and control systems are implemented, then power usage is optimized, but system complexity increases
Solution Approach 1:
The monitoring and control system is designed to be universal and multi-functional, serving multiple facilities and purposes through a single integrated platform. By consolidating monitoring, prediction, and control functions into one system that can be applied across diverse facility types, the per-facility complexity is reduced while maintaining comprehensive optimization capabilities.
Solution Approach 2:
The system uses analog facilities as templates or copies to predict and optimize target facility performance. By leveraging data and operational patterns from similar facilities, the system reduces the complexity of building comprehensive monitoring and control systems from scratch for each individual facility, as proven effective patterns can be replicated and adapted.
Data Source
AI summary
A system includes a first facility element having a sensor and configured to generate recent performance data associated with a system of a facility, and a monitoring and control element in communication with the first facility element, where the monitoring and control element is configured to identify one or more analogous facility elements analogous to the first facility element, receive the recent performance data for the first facility element, generate projected performance data for the facility element according to historical performance data associated with the facility element and the one or more analogous facility elements, compare the projected performance data to a performance threshold, and override a setting or operating parameter of the first facility element according to a relationship of the projected performance data to the performance threshold and by sending one or more operational adjustment commands to at least one second facility element.