Central plant control system, method, and controller with multi-level granular and non-granular asset allocation
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Solution Overview
Problem
Large and complex building systems face challenges in determining optimal resource distribution due to the intractability of modeling entire systems at their bare assets, often requiring excessive processing capabilities that are not readily available.
Innovation Solution
A method is implemented that identifies granular assets, groups them into general assets, performs non-granular and granular optimizations to determine resource allocation, and uses these allocations to operate the building system efficiently, with a controller that manages central plant equipment to optimize resource distribution across subplants, storage, and sinks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire building system is modeled at its bare assets to determine optimal resource distribution, then the optimization precision is improved, but the device complexity and processing requirements become intractable
Solution Approach 1:
The patent segments the building system into hierarchical levels: granular assets (individual devices), general assets (groups of granular assets), and sub-general assets (intermediate grouping levels). This segmentation allows optimization to be performed at multiple levels rather than attempting to optimize all bare assets simultaneously, making the problem tractable while maintaining sufficient precision through the granular level optimization.
2Measurement precision
If the entire building system is modeled at its bare assets to determine optimal resource distribution, then the resource allocation accuracy is improved, but the processing capabilities required become excessive and not readily available
Solution Approach 1:
The optimization process is segmented into hierarchical levels where computationally intensive granular optimization is performed only on grouped assets rather than all individual bare assets. This reduces the overall processing power requirements while maintaining allocation accuracy through the detailed granular level analysis of representative asset groups.
Solution Approach 2:
Similar granular assets are merged into general assets to reduce the number of optimization variables. By combining assets with similar characteristics, the system reduces computational complexity and processing power requirements while still capturing the essential behavior of individual assets through the granular optimization of representative groups.
3Device complexity
If granular assets are grouped into general assets for optimization, then the device complexity is reduced, but the measurement precision of resource allocation may be lost
Solution Approach 1:
The patent implements multi-level segmentation with granular, general, and sub-general asset levels. This allows the system to perform coarse optimization at the general asset level for complexity reduction, then refine the solution through granular optimization of specific asset groups, thereby maintaining precision while reducing overall complexity.
Solution Approach 2:
The patent adds a hierarchical dimension to the optimization problem by introducing multiple levels of asset grouping. This dimensional change allows the system to balance complexity and precision by operating at different hierarchical levels, where higher levels provide complexity reduction and lower levels maintain precision through detailed optimization.
Data Source
AI summary
Granular assets of a building are identified. Each granular asset represents one or more devices of that operate to produce, consume, or store resources in the building. General assets are defined by grouping the granular assets into groups. A non-granular optimization is performed to determine a non-granular allocation of the resources among the general assets. Non-granular allocation defines an amount of each of the resources consumed, produced, or stored by each of the general assets at each time step in a time period. A granular optimization is performed for each general asset to determine a granular allocation of the resources among the granular assets. The granular allocation defines an amount of each resource consumed, produced, or stored by the granular assets at each time step in the time period. The equipment of the building are operated to consume, produce, or store the amount of each resource defined by granular allocation.