Hybrid Power Dispatch Using Prospective and Real-Time Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Hybrid power systems face complexity in managing multiple types of power assets, leading to sub-optimal operation and increased computational costs due to the difficulty in accounting for type-specific aspects like maintenance and degradation, and existing optimization techniques fail to address these issues effectively as the number of assets increases.
Innovation Solution
A method and system that utilize a controller to perform both prospective and on-line optimizations, accounting for load and power availability forecasts, and asset-specific characteristics, to determine active power commands that optimize the total operating cost of the hybrid power system, thereby reducing computational complexity and improving operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional optimization techniques are applied to hybrid power systems, then the system can operate with basic control, but the computational cost increases exponentially as the number of power assets increases
Solution Approach 1:
The patent segments the hybrid power system into multiple hierarchical levels: a central coordinator and distributed controllers for each power asset group. This segmentation divides the monolithic optimization problem into smaller, manageable sub-problems that can be solved independently at each level, reducing computational complexity while maintaining optimization capability.
Solution Approach 2:
The patent introduces a temporal dimension by implementing a rolling horizon optimization approach. Instead of solving a single large optimization problem, the system solves a series of smaller optimization problems over successive time windows, effectively transforming a computationally intractable problem into a sequence of manageable tasks.
2Ease of manufacture
If rule-based algorithms are used for power distribution decisions, then the control logic is simple to implement, but the system misses edge cases and operates sub-optimally
Solution Approach 1:
The patent implements a feedback mechanism where distributed controllers continuously monitor system state and communicate with the central coordinator. The coordinator adjusts optimization parameters and sends updated control signals based on system performance and changing conditions, enabling the system to adapt to edge cases and optimize operation dynamically rather than following fixed rules.
3Object-generated harmful factors
If optimization techniques are applied to account for type-specific aspects of power assets, then the operational cost can be reduced, but the computational complexity increases rapidly
Solution Approach 1:
The patent applies local quality by allowing each distributed controller to handle type-specific characteristics of its local power assets (such as maintenance schedules, degradation rates, and operational constraints) while the central coordinator manages overall system optimization. This distributes the computational burden and allows type-specific optimizations without requiring the central system to process all detailed information centrally.
Data Source
AI summary
Systems and methods for operating a hybrid power system are disclosed. A controller may perform operations, including: obtaining a load forecast; obtaining a power availability forecast and an energy cost forecast for each power asset group; performing at least one prospective optimization to determine scheduled active power commands for the groups that optimize a total operating cost; tracking an on-line load; tracking an on-line power availability and an on-line energy cost for the groups; performing at least one on-line optimization to determine on-line active power commands for the groups that (i) account for variance between the load forecast and the on-line load, (ii) account for variance between the power availability forecast and the on-line power availability, and (iii) optimize the total operating cost; and operating the groups based on the scheduled active power commands and the on-line active power commands.