Central Plant Control with Maintenance Cost Rate Optimization
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Solution Overview
Problem
Existing energy cost optimization systems face challenges in incorporating maintenance contracts with complex cost structures, such as piecewise-defined maintenance costs, into economic optimization algorithms for energy facilities, making it difficult to determine optimal equipment operation setpoints.
Innovation Solution
A control system that includes a controller configured to obtain a cost function, modify it to include a maintenance cost term, simulate operations at different rate variables, select the rate variable for lowest cost, and generate setpoints for optimal equipment operation, incorporating maintenance contracts into the optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a maintenance contract with piecewise-defined cost structure is incorporated into the optimization system, then maintenance cost accuracy is improved, but the complexity of the cost function increases
Solution Approach 1:
The maintenance cost function is segmented into multiple linear segments, each valid within a specific range of the equipment usage variable. The controller divides the piecewise-defined maintenance cost into discrete segments with defined breakpoints, allowing each segment to be handled as a separate linear relationship in the optimization process.
Solution Approach 2:
The cost function is made dynamic by allowing the applicable segment to change based on the current value of the equipment usage variable. The controller dynamically selects which linear segment to use based on real-time equipment usage data, enabling the cost function to adapt to varying operational conditions while maintaining computational tractability.
2Measurement precision
If the controller simulates operations at multiple different values of the rate variable to find the optimal cost, then cost optimization accuracy is improved, but the computational time increases
Solution Approach 1:
The controller performs preliminary simulations at multiple different values of the rate variable before final optimization to identify the range and approximate location of the optimal value. This preliminary action narrows down the search space, allowing the final optimization to converge faster with fewer iterations.
Solution Approach 2:
The simulation results from different rate variable values provide feedback to guide the optimization process. The controller uses this feedback to adjust subsequent simulation parameters and focus computational effort on the most promising regions of the solution space, reducing overall computational time.
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.