Decentralized Energy Allocation Control for Autonomous Node Correction
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Solution Overview
Problem
Decentralized power systems face challenges in managing energy storage resources effectively, particularly in microgrids with intermittent renewable energy sources, as they require flexible and scalable control to balance power generation and load demands without frequent recalculation or communication between nodes.
Innovation Solution
A controller system that includes a processor and memory, capable of receiving forecast and measured trajectories, determining error signals, and providing instructions for energy transfer between power nodes and energy storage devices, allowing for autonomous correction of deviations from forecasted states and allocation of energy based on priority functions, enabling decentralized operation and efficient use of distributed energy resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decentralized control is implemented for distributed energy resources, then scalability and flexibility are improved, but system complexity and difficulty of coordination increase
Solution Approach 1:
The system divides the decentralized energy management into independent functional modules: local controllers at each node handle autonomous decision-making, while a coordination layer manages inter-node communication. This segmentation allows each component to operate independently, improving scalability without proportionally increasing overall system complexity.
Solution Approach 2:
A standardized communication protocol and data exchange format act as intermediaries between distributed energy resources and the control system. This intermediary layer simplifies coordination by providing uniform interfaces, reducing the complexity of direct peer-to-peer communication between numerous decentralized nodes.
2Stability of the object's composition
If energy storage is used to power balance the system, then system stability is improved, but management complexity and control difficulty increase
Solution Approach 1:
The energy storage system incorporates autonomous control algorithms that automatically adjust charging and discharging operations based on real-time system conditions. The storage devices perform self-balancing operations without requiring complex external management, reducing control difficulty while maintaining system stability.
Solution Approach 2:
Real-time monitoring and feedback mechanisms track the state of charge, power flow, and system demand, automatically adjusting energy storage operations. This closed-loop feedback simplifies management by providing automated responses to system imbalances, reducing the need for complex manual coordination.
3Measurement precision
If frequent recalculation and communication between nodes is implemented, then optimization accuracy is improved, but communication overhead and system latency increase
Solution Approach 1:
The system implements periodic recalculation at predetermined intervals rather than continuous computation, combined with event-triggered communication that only activates when threshold changes are detected. This approach maintains optimization accuracy by updating at sufficient frequency while dramatically reducing communication overhead during stable operating conditions.
Solution Approach 2:
The system performs preliminary optimization calculations using forecasted data and historical patterns to pre-determine likely operational decisions. This preliminary action reduces the need for frequent real-time recalculation and communication, as many decisions can be made in advance based on predicted conditions.
4Reliability
If autonomous correction capability is added to nodes, then system reliability is improved, but local control complexity and processing requirements increase
Solution Approach 1:
Nodes are pre-configured with correction algorithms, lookup tables, and decision logic during system initialization or deployment. This preliminary configuration allows nodes to perform autonomous corrections using pre-computed strategies, reducing the need for complex real-time processing while maintaining high reliability.
Solution Approach 2:
The system uses simplified local controllers with limited processing capabilities that make basic autonomous corrections, rather than requiring expensive, high-performance computing hardware at each node. The controllers perform adequate corrections locally and defer complex optimization to centralized or periodic updates.
Data Source
AI summary
Controllers and control methods for a power system are disclosed. The controllers can be configured to perform operations for correcting a state of a first node of the power system. The operations can include obtaining rules for correcting the state of the first node, each rule specifying a corrective action for the first node. The operations can further include obtaining instructions for the first node, the instructions at least partially specifying a configuration of the first node. The operations can further include generating a forecast for the state of the first node based on the instructions and monitoring the state of the first node. Based on the forecast state and the monitored state of the first node, the controller can select one of the rules and apply the corrective action specified by the selected rule to correct the state of the first node.