Generator Fuel Mix Optimization via MILP Linearization
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Solution Overview
Problem
Conventional methods for optimizing power generator operations, particularly those using multiple fuels, are complex and require significant manual calculations, lacking automated systems for generating an optimized fuel mix and resource schedule based on accurate models.
Innovation Solution
A method and system using mixed-integer linear programming (MILP) and quadratic constraint programming (QCP) to create an optimized operating schedule, fuel mix, and allocation plan by linearizing quadratic objective and gas constraints functions, allowing for automated control of generator resources to meet energy output requirements efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for optimizing power generator operations, then detailed technical understanding and manual calculations are required, but the complexity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual calculation methods with an automated computer-based optimization system that uses mathematical programming (mixed-integer linear programming and quadratic constraint programming). The system automatically processes generator parameters, fuel costs, and operational constraints to generate optimized schedules, eliminating the need for manual technical calculations while maintaining or improving optimization accuracy.
2Productivity
If automated systems are implemented for generating optimized fuel mix and resource schedules, then manual effort is reduced, but system complexity and modeling requirements increase
Solution Approach 1:
The patent introduces a computer-based optimization system as an intermediary between input data (generator parameters, fuel costs, constraints) and output results (optimized schedules, fuel mix). This intermediary automatically performs the complex modeling and calculation tasks, requiring users only to input data and interpret results, thereby reducing manual effort while managing modeling complexity through specialized software.
Solution Approach 2:
The patent transforms the optimization problem by changing the mathematical representation of constraints and objective functions. It converts quadratic constraints into linear forms suitable for mixed-integer linear programming, and uses quadratic constraint programming for specific gas constraints. This parameter transformation enables automated processing while maintaining solution accuracy.
3Productivity
If quadratic objective functions and gas constraints functions are linearized, then computational efficiency improves, but modeling accuracy may be compromised
Solution Approach 1:
The patent employs dynamic modeling approaches where the mathematical representation of constraints adapts to the specific problem context. It uses quadratic constraint programming for gas constraints that require non-linear accuracy, while applying linear programming for other constraints where linearization is sufficient. This dynamic selection of modeling approaches balances computational efficiency with required accuracy for different aspects of the optimization problem.
Data Source
AI summary
Embodiments of the invention provide systems, methods, and apparatus for receiving input information including at least one of generator parameters, energy output requirements, fuel cost, fuel availability, and a maintenance schedule; creating a resource model based on the input information wherein creating the resource model includes linearizing a quadratic objective function and linearizing a quadratic gas constraints function; generating an optimized operating schedule, fuel mix, and allocation plan from the resource model based on the input information; and controlling the generator resources to optimally meet energy output requirements using the operating schedule fuel mix, and allocation plan. Numerous other aspects are provided.