Multi-Objective Peak-Shaving Optimization via Index Linkage Analysis
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Solution Overview
Problem
Current peak-shaving methods for power systems lack comprehensive analysis of load demand and are prone to uncertainty and irrationality due to subjective weight coefficient selection, leading to ineffective generation scheduling.
Innovation Solution
A multi-objective optimization method based on index linkage analysis is introduced, which calculates correlation coefficients between peak-shaving indexes, transforms the multi-objective function into a single-objective model using weight coefficients, and employs Genetic Algorithm to optimize generation schedules, considering variance, peak-valley difference, maximum variation rate, and load levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single peak-shaving index is used for optimization, then the optimization process is simple, but the peak-shaving results deviate from comprehensive load regulation demands
Solution Approach 1:
The patent combines multiple peak-shaving indexes (variance, peak-valley difference, maximum variation rate, maximum load, minimum load) into a comprehensive optimization model. By merging these five individual indexes into a unified multi-objective function, the model achieves more accurate and comprehensive peak-shaving results while avoiding the deviation caused by single-index optimization.
2Adaptability or versatility
If weight coefficients are subjectively selected in multi-objective processing, then the model can handle multiple objectives, but uncertainty and irrationality are introduced
Solution Approach 1:
The patent implements a feedback mechanism where the optimization results are evaluated against multiple peak-shaving indexes, and the weight coefficients are automatically adjusted based on the actual performance. This feedback loop eliminates subjective weight selection by using objective data-driven adjustments, thereby improving the reliability and objectivity of the optimization results.
3Measurement precision
If comprehensive load demand analysis is conducted with multiple indexes, then the peak-shaving effectiveness improves, but the model complexity increases
Solution Approach 1:
The patent transforms the complex multi-index optimization problem into a more manageable form by changing the parameter representation. It uses a weighted sum approach where multiple peak-shaving indexes are combined into a single objective function with dynamically adjusted weights. This parameter transformation maintains the comprehensive analysis capability while simplifying the optimization structure and improving computational efficiency.
Data Source
AI summary
The invention relates to a multi-objective optimization method for power system peak-shaving based on index linkage analysis. The invention comprehensively considers five peak shaving indexes, and introduces a linkage analysis theory in economics to test stationarity and causality of data series of peak-shaving indexes. The purpose is to effectively clarify linkage relationship between indexes. A regression analysis method is used to quantify the mutual influence relationship. A multi-index coupling peak shaving optimization model for generation scheduling is developed and finally solved by using genetic algorithm. The invention can objectively describe comprehensive peak shaving requirements of a provincial power grid, and is helpful for generation dispatcher to reduce subjective assumptions in the decision-making. Compared with a conventional single-index peak-shaving model, peak-shaving effect of our method is obviously improved, which provides a novel technical way for peak-shaving generation scheduling and decision-making in short-term operations of power systems.