Central Plant Control With Maintenance Cost Segmentation
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Solution Overview
Problem
Existing energy cost optimization systems for buildings and central plants face challenges in incorporating maintenance contracts with varying maintenance costs, particularly those with piecewise-defined functions, into economic optimization algorithms, which complicates the optimization of equipment operation.
Innovation Solution
A control system that includes a controller configured to obtain a cost function defining the operation cost over an optimization period, modify it to include a maintenance cost term based on a rate variable and equipment usage variable, simulate costs at different rate variable values, and perform online optimization to generate setpoints for equipment operation, thereby optimizing maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a piecewise-defined maintenance cost function is incorporated into the economic optimization, then maintenance cost accuracy is improved, but the optimization algorithm complexity increases
Solution Approach 1:
The maintenance cost function is segmented into multiple linear segments, where each segment corresponds to a specific range of equipment usage hours. The controller divides the optimization problem into sub-problems, solving for each segment separately and selecting the optimal segment based on current equipment state. This segmentation approach maintains cost accuracy while reducing algorithmic complexity by avoiding the need to handle the entire piecewise function as a single complex entity.
Solution Approach 2:
The controller pre-calculates and stores the parameters of each linear segment (slope and intercept) before the optimization process begins. During real-time optimization, the controller only needs to determine which segment the current equipment usage falls into and apply the corresponding linear cost function, rather than evaluating the entire piecewise function during optimization. This preliminary preparation significantly reduces computational complexity during the optimization execution phase.
2Productivity
If dynamic maintenance cost adjustment based on equipment usage is implemented, then cost optimization is improved, but the control system complexity increases
Solution Approach 1:
The maintenance cost parameter in the optimization function is made dynamic by linking it to equipment usage hours. The controller continuously monitors equipment usage and automatically adjusts the maintenance cost parameter based on the current usage level and the applicable segment of the piecewise cost function. This dynamic adjustment enables real-time cost optimization without requiring a completely complex control system, as it leverages existing usage monitoring capabilities.
Solution Approach 2:
The system changes the maintenance cost parameter values based on equipment usage thresholds defined in the piecewise function. When equipment usage crosses certain thresholds, the controller switches between different cost parameters (c1, c2, c3, etc.), each corresponding to a different usage segment. This parameter change approach allows dynamic cost adjustment while maintaining a relatively simple control structure that builds upon standard optimization frameworks.
Data Source
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Figure 5A
AI summary
A control system for cost optimal operation of an energy facility including equipment covered by a maintenance contract includes equipment configured to operate during an optimization period and a controller. The controller modifies a cost function to include a maintenance cost term that defines a maintenance cost as a function of a rate variable and an equipment usage variable. The controller simulates a cost of operating the energy facility over the optimization period at each of a plurality of different values of the rate variable, selects a value of the rate variable that results in a lowest cost of operating the energy facility over the optimization period, performs an online optimization of the cost function with the rate variable set to the selected value to generate one or more setpoints for the equipment, and operates the equipment during the optimization period in accordance with the generated setpoints.