Multi-Objective Micro Energy Grid Control via Fuzzy Pareto Optimization
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Solution Overview
Problem
Current micro energy grid operation methods primarily focus on minimizing operation cost, failing to adapt to complex and evolving energy structures, and thus struggle to balance economic gains and efficiency effectively.
Innovation Solution
A multi-objective operation control method for micro energy grids that maximizes comprehensive income and energy utilization rate, using GAMS software to solve optimization objectives through a weighting method, acquiring Pareto optimal solutions, and selecting a compromise solution based on fuzzy membership degrees for scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If single-objective optimized scheduling is used to minimize operation cost, then economic cost is reduced, but the solution is difficult to adapt to complicated comprehensive energy supply and use environment and constantly transformed energy structure
Solution Approach 1:
The patent transforms the single-objective optimization problem into a multi-objective optimization problem by introducing multiple parameters (comprehensive income, energy utilization rate) to reflect the complicated energy supply environment. This parameter expansion allows the system to adapt to varying energy structures while maintaining optimization capability.
Solution Approach 2:
The patent employs dynamic weighting coefficients that can be adjusted according to different operating conditions and energy structures. This dynamic approach enables the optimization model to adapt to constantly transformed energy structures by changing the relative importance of different objectives based on current system state.
2Productivity
If comprehensive income maximization is pursued, then economic gains are improved, but energy utilization efficiency may be compromised
Solution Approach 1:
The patent merges multiple conflicting objectives (comprehensive income maximization and energy utilization rate maximization) into a unified multi-objective optimization framework. By combining these objectives with appropriate weighting, the system achieves a balanced solution that considers both economic gains and energy efficiency simultaneously.
Solution Approach 2:
The patent uses iterative optimization with feedback mechanisms where the weighting coefficients are adjusted based on the trade-off between comprehensive income and energy utilization rate. This feedback loop allows the system to find optimal balance points by learning from previous optimization results and adjusting parameters accordingly.
Data Source
AI summary
The present invention disclosed a multi-objective operation control method for a micro energy grid, comprising the specific steps of: (1) establishing optimization objectives of the micro energy grid, the optimization objectives comprising comprehensive income maximization and comprehensive energy utilization rate maximization; (2) using GAMS software to solve for an optimal solution and a worst solution for each optimization objective; (3) processing the optimization objectives by means of a weighting method, uniformly changing a weighting coefficient, and acquiring a Pareto frontier by the GAMS software; (4) acquiring reference satisfaction levels of Pareto optimal solutions according to a fuzzy membership degree, and selecting the Pareto optimal solution having the maximum reference satisfaction level as an optimal compromise solution; and (5) executing scheduling of the micro energy grid according to the optimal compromise solution.