Energy Controller Predictive Optimization
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Solution Overview
Problem
Existing energy system architectures, particularly those using logic-based controllers, are inadequate in recovering stored energy and realizing the full economic benefits of energy storage systems, as they lack advanced predictive capabilities to optimize energy distribution and storage.
Innovation Solution
An energy control system that includes a renewable energy resource, a stored energy resource, and an energy system controller capable of predicting energy requirements and generation over a long time horizon, generating optimally planned power profiles, and adjusting energy supply based on comparisons between predicted and actual values over a short time horizon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If logic-based control systems with predetermined thresholds are used, then the system can integrate energy storage devices and provide basic control functionality, but the system cannot optimize energy distribution to maximize economic benefits and energy recovery
Solution Approach 1:
The system performs preliminary actions by predicting energy requirements, renewable energy generation, and stored energy capacity over a long time horizon before actual energy distribution occurs. This advance planning enables optimized power profiles to be generated in advance, allowing the system to maximize economic benefits and energy recovery without requiring complex real-time control decisions
Solution Approach 2:
The system implements feedback by comparing predicted values with actual energy requirements, actual energy generation, and actual energy capacity over a short time horizon. This feedback mechanism allows the controller to adjust energy distribution in real-time based on deviations from predictions, optimizing energy utilization and economic benefits while maintaining manageable system complexity
2Productivity
If the controller monitors and compares predicted versus actual energy parameters, then the system can optimize energy supply timing and maximize storage benefits, but the system requires complex predictive algorithms and data processing
Solution Approach 1:
The controller performs preliminary predictions of energy requirements, renewable generation, and storage capacity over extended time horizons. This advance planning allows the system to identify optimal energy distribution strategies before actual energy events occur, maximizing recovery efficiency without requiring complex real-time computational algorithms
Solution Approach 2:
The system uses feedback mechanisms to compare predicted energy parameters with actual measurements over short time horizons. This feedback allows the controller to make simple adjustments based on prediction errors, maintaining high productivity through iterative optimization rather than complex predictive algorithms
Data Source
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AI summary
A system and method for controlling the distribution of energy from a plurality of energy resources to a load. The system includes an energy system controller to control the distribution of energy to an electric load provided by a plurality of energy resources. The energy resources include dispatchable sources of energy such as diesel generators and combined heat and power generators; renewable sources of energy including photo-voltaic cells, wind turbines, and geothermal sources; and storage resources such as electrochemical batteries or pumped hydro reserves.