Power Management Control System Using Segmented Sub-Controllers
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Solution Overview
Problem
Complex vehicular power systems face challenges in predicting power demand and optimizing load and source management, leading to suboptimal configurations and potential overload conditions due to the simplicity of existing load shedding methods.
Innovation Solution
A power management control system utilizing a dual decomposition method with a scalable approach that treats total energy usage as sub-problems, solved individually via a shortest path algorithm and coordinated using an algebraic update rule to achieve optimal or near-optimal source and load allocation, incorporating cost-utility functions to optimize operation across multiple units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If load shedding is used to avoid overload conditions, then system reliability is improved, but power system efficiency deteriorates due to suboptimal load configuration
Solution Approach 1:
The patent segments the power system into multiple independent controllable units (generators and loads) with individual controllers. Each unit can be independently managed and optimized, allowing the system to avoid complete load shedding by selectively controlling individual units based on real-time conditions, thus maintaining efficiency while ensuring reliability.
Solution Approach 2:
The system dynamically adjusts load configurations in real-time based on changing power demands and generator capabilities. The controller continuously monitors system state and reconfigures the network topology and load distribution, preventing overload conditions without resorting to static load shedding, thereby maintaining optimal efficiency while ensuring reliability.
2Loss of energy
If complex optimization algorithms are used to achieve optimal load allocation, then power system efficiency is improved, but computational complexity increases
Solution Approach 1:
The complex optimization problem is segmented into smaller sub-problems, with each controllable unit having its own controller that solves local optimization tasks. This distributed approach reduces computational complexity at each node while achieving global optimization through coordinated control, avoiding the need for a single complex centralized algorithm.
Solution Approach 2:
Each controllable unit in the power system is equipped with its own controller that autonomously performs optimization calculations based on local measurements and system-wide signals. This self-service capability distributes the computational burden across multiple simple units rather than requiring one complex centralized computer, reducing overall computational complexity while maintaining efficiency.
3Stability of the object's composition
If centralized control is used to manage power distribution, then system coordination is improved, but computational speed deteriorates due to single-point processing bottleneck
Solution Approach 1:
The centralized control function is segmented and distributed to multiple independent controllers, one at each controllable unit. This segmentation allows parallel processing of control decisions across the network, eliminating the single-point processing bottleneck while maintaining system coordination through standardized communication protocols and shared optimization objectives.
Solution Approach 2:
While control functions are distributed, the system merges coordination through a standardized communication framework where all controllers share common optimization goals and exchange necessary information. This merging of coordination mechanisms at the protocol level enables parallel execution at the controller level, achieving both fast computational speed and strong system coordination.
4Adaptability or versatility
If more controllable units are added to the power system, then system versatility is improved, but system complexity increases
Solution Approach 1:
The system is segmented into modular controllable units that can be independently added or removed. Each unit is a self-contained module with its own controller, allowing the system to scale in versatility by simply adding more identical or similar modules without proportionally increasing overall system complexity, as each module manages itself autonomously.
Solution Approach 2:
The controllers are designed with universal functionality to handle diverse controllable units (different types of generators, loads, and storage devices). This multi-functionality allows the system to accommodate various unit types without requiring specialized control logic for each, thereby increasing versatility while keeping system complexity manageable through standardized control architecture.
Data Source
AI summary
A system to control a power distribution system includes a system controller configured to determine an allocation of power during a first time period for each of a plurality of subsystems. The system also includes a subsystem controller communicatively coupled to the system controller. The subsystem controller is associated with a device and configured to receive power allocation data indicating the allocation of power for the device from the system controller. The subsystem controller is further configured to receive operation request data indicating a request to operate the device and produce a model operation of the device for a second time period based on the power allocation data, the operation request data, and a cost-utility function associated with the device. The subsystem controller is also configured to communicate, to the system controller, projected power demand data associated with the modeled operation of the device during the second time period.