Microgrid Control Using ML Feedback for Facility Energy Coordination
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Solution Overview
Problem
Facilities face challenges in optimizing energy consumption and production across diverse components, leading to inefficiencies and increased costs, due to the complexity of managing multiple energy sources and varying operational conditions, which existing technologies struggle to address effectively in real-time.
Innovation Solution
A centralized controller system utilizing machine learning models, such as reinforcement learning, to coordinate energy consumption and production across facilities by analyzing signals from various components, optimizing short, medium, and long-term performance goals, and adjusting control operations to minimize costs and maximize efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a centralized controller system with machine learning models is implemented to coordinate energy consumption and production, then energy efficiency and cost optimization are improved, but device complexity increases
Solution Approach 1:
A centralized controller acts as an intermediary between multiple energy sources (solar arrays, batteries, generators) and energy consumers (compressors, cooling systems, vehicles). The controller receives signals from various components, processes them through machine learning models, and coordinates energy flow to optimize efficiency while managing the complexity of integrating diverse energy assets.
Solution Approach 2:
The system continuously monitors energy consumption and production signals from facility components, feeds this data into machine learning models, and uses the model outputs to adjust control operations in real-time. This closed-loop feedback enables the system to adapt to changing conditions and optimize energy efficiency dynamically.
2Productivity
If real-time coordination of multiple energy sources and components is implemented, then energy performance optimization is improved, but computational complexity increases
Solution Approach 1:
Machine learning models are trained in advance on historical energy data to learn optimal coordination strategies. During real-time operation, the pre-trained models rapidly process incoming signals and generate control decisions, avoiding the need for complex real-time optimization calculations while still achieving optimal energy performance.
Solution Approach 2:
The machine learning models autonomously process energy signals and generate control operations without requiring manual intervention or complex real-time computation. The system self-adjusts energy coordination based on learned patterns, reducing computational burden while maintaining optimization performance.
3Reliability
If diverse energy sources and components are integrated into a microgrid, then energy flexibility and reliability are improved, but system complexity increases
Solution Approach 1:
The centralized controller is designed to universally interface with multiple types of energy sources (solar arrays, batteries, generators) and consumers (compressors, cooling systems, electric vehicles) through standardized signal protocols. This multi-functional capability allows diverse components to be integrated into a cohesive microgrid system while managing complexity through unified control architecture.
Data Source
AI summary
A disclosed system for dynamically controlling energy performance operations in a facility includes: energy sources, energy sources controllers, and a centralized controller. The centralized controller can: receive signals indicating performance of the energy sources in real-time, retrieve at least one model that is iteratively trained using machine learning techniques and training data including (i) at least a portion of the received signals and (ii) decisions made by the centralized controller, provide at least a portion of the received signals as input to the model, and receive, as output from the model, control operations for one or more of the energy sources for a predetermined period of time, and return the control operations to respective controllers of the one or more energy sources for execution during the predetermined period of time.