Central Plant Controller Asset Allocation Override
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Solution Overview
Problem
Central plants face challenges in optimally allocating energy loads across subplants due to real-time pricing fluctuations, making it difficult to minimize operational costs and resource consumption.
Innovation Solution
A controller with a processing circuit that performs optimization subject to override constraints to determine resource production amounts, allowing for user input to override specific resource production by subplants, and presenting the impact of these overrides on cost and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the central plant performs optimization to minimize operational costs and resource consumption, then economic efficiency is improved, but flexibility to accommodate user preferences and constraints deteriorates
Solution Approach 1:
The system dynamically adjusts the optimization process by allowing users to override specific constraints and parameters. The asset allocator can re-run optimizations with different constraint settings based on user input, enabling the system to adapt between fully automated cost-minimization and user-preference-based operation modes.
Solution Approach 2:
The system presents cost impact information to users based on their override preferences, allowing them to make informed decisions. This feedback loop enables users to understand the trade-offs between their preferences and operational costs, creating a responsive system that adapts to user needs while maintaining efficiency awareness.
2Adaptability or versatility
If the system allows user overrides of optimization outputs, then flexibility and user control are improved, but system complexity and computational burden increase
Solution Approach 1:
The system segments the override functionality into specific, targeted constraints rather than requiring complete re-optimization of all parameters. Users can override individual asset constraints or time-period constraints independently, reducing the computational complexity compared to full system re-optimization.
Solution Approach 2:
The system performs partial re-optimizations only for the affected portions of the system when overrides are applied, rather than re-optimizing the entire asset allocation. This approach reduces computational burden while still providing user control over specific parameters.
3Productivity
If the central plant optimizes resource allocation without constraints, then economic costs are minimized, but reliability of meeting specific user requirements deteriorates
Solution Approach 1:
The system preemptively addresses potential user requirement violations by allowing users to set override constraints before optimization execution. These pre-set constraints ensure that specific user requirements are met while the optimization minimizes costs within those boundaries, preventing reliability issues before they occur.
Solution Approach 2:
Users can pre-configure their preferences and constraints before the optimization process runs. The system then incorporates these pre-set user requirements into the optimization constraints, ensuring that cost minimization does not compromise the reliability of meeting specific user needs.
Data Source
AI summary
A controller for building equipment that operate to produce or consume resources for a building or campus. The controller performs an optimization of an objective function subject to an override constraint to determine values for a plurality of decision variables indicating amounts of resources to be produced or consumed by the building equipment. The override constraint defines one or more of the values for a subset of the plurality of decision variables by specifying an override amount of a first resource of the resources to be produced or consumed by a first subset of the building equipment and the optimization determines a remainder of the values for a remainder of the plurality of decision variables. The controller controls the building equipment to produce or consume the amounts of the resources determined by performing the optimization subject to the override constraint.