Central Plant Control Using Maintenance Cost Rate Selection
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Solution Overview
Problem
Existing energy cost optimization systems face challenges in incorporating maintenance contracts with complex cost structures into economic optimization algorithms for energy facilities, particularly when maintenance costs vary based on equipment usage, leading to inefficiencies in operational cost management.
Innovation Solution
A control system that includes a controller capable of optimizing energy facility operations by modifying the cost function to account for maintenance costs, simulating different scenarios, and selecting optimal setpoints to minimize costs, incorporating both base and marginal maintenance costs based on equipment usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maintenance contracts with complex cost structures are incorporated into economic optimization algorithms, then cost management accuracy is improved, but system complexity increases
Solution Approach 1:
The maintenance cost function is segmented into distinct components: a base cost component that is independent of equipment usage, and a marginal cost component that varies with equipment usage hours. This segmentation allows the complex maintenance contract to be broken down into manageable mathematical components that can be easily integrated into the optimization algorithm, improving cost management accuracy without proportionally increasing system complexity.
Solution Approach 2:
The patent transforms the maintenance contract terms into parameterized mathematical functions where base cost and marginal cost become adjustable parameters. By expressing maintenance costs as a function of equipment usage hours with configurable parameters, the system can accurately model complex cost structures while maintaining algorithmic simplicity through standardized mathematical relationships.
2Productivity
If maintenance costs are accurately accounted for in optimization, then operational cost efficiency is improved, but computational requirements increase
Solution Approach 1:
The maintenance cost function is pre-defined and structured before the optimization process begins. By establishing the base cost and marginal cost parameters in advance, and formulating the cost function in a ready-to-use mathematical form, the system eliminates the need for complex real-time calculations during optimization, thereby improving operational cost efficiency without significantly increasing computational time.
3Productivity
If equipment usage is optimized to minimize costs, then operational expenses are reduced, but maintenance contract compliance becomes more difficult to manage
Solution Approach 1:
The optimization algorithm incorporates feedback mechanisms that continuously monitor equipment usage hours against maintenance contract thresholds. By integrating the maintenance cost function directly into the optimization process, the system provides real-time feedback on how equipment operation decisions affect both operational costs and maintenance compliance, enabling cost reduction while automatically ensuring contract adherence.
Solution Approach 2:
The maintenance cost function serves multiple purposes simultaneously: it acts as a cost component in the optimization algorithm, a compliance monitoring mechanism, and a decision-making guide. This multi-functionality allows the system to reduce operational expenses while inherently managing maintenance compliance through the same mathematical framework, eliminating the need for separate compliance management systems.
Data Source
AI summary
A control system for cost optimal operation of an energy facility including equipment includes a controller configured to provide a cost function comprising a cost term defining a cost as a function of a rate variable and an equipment usage variable, simulate the cost of operating the energy facility over an optimization period at each of a plurality of different values of the rate variable which define a plurality of different costs per unit of the equipment usage variable, select a value of the rate variable that results in a lowest cost of operating the energy facility over the optimization period, perform 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 operate the equipment during the optimization period in accordance with the generated setpoints.