Micro-Grid Configuration Optimization Using Genetic Algorithms
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Solution Overview
Problem
Conventional methods for optimizing independent micro-grid systems fail to simultaneously optimize the configuration of diesel generators and energy storage devices, leading to suboptimal economic and environmental performance, as they primarily focus on total power optimization without considering device types and capacities.
Innovation Solution
A multi-objective genetic algorithm is employed to optimize the configuration of diesel generators, wind generators, photovoltaic arrays, and energy storage batteries, using a quasi-steady state simulation strategy that includes a hard charging and power smooth strategy to maximize renewable energy utilization and minimize costs and pollution, while ensuring stability and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional quasi-steady state hourly simulation optimizing design method is adopted, then detailed simulation of renewable energy variations and load during total life cycle can be achieved, but a lot of time is consumed for calculation
Solution Approach 1:
The patent segments the optimization problem into two parts: (1) discrete optimization of device types and capacities using genetic algorithm, and (2) continuous optimization of operational parameters using conventional simulation. This segmentation allows the computationally intensive simulation to be performed only for evaluating candidate solutions generated by the genetic algorithm, rather than for every possible parameter combination, significantly reducing total calculation time while maintaining simulation accuracy for the most promising configurations.
Solution Approach 2:
The patent performs preliminary optimization of device configuration (types and capacities) using genetic algorithm before conducting detailed quasi-steady state simulation. This preliminary action identifies the most promising configuration candidates, allowing the computationally expensive simulation to be applied only to these selected candidates rather than all possible configurations, thereby reducing overall calculation time while preserving simulation accuracy for the optimal solutions.
2Device complexity
If total power of diesel generator group is optimized only, then optimization process is simplified, but types of diesel generators and multi-device combination solution are not considered leading to suboptimal economic and environmental performance
Solution Approach 1:
The patent extends the optimization from a single dimension (total power of diesel generators) to multiple dimensions by simultaneously optimizing (1) types of diesel generators, (2) capacities of multiple devices including wind generators, photovoltaic arrays, and energy storage batteries, and (3) operational parameters. This multi-dimensional optimization approach, enabled by the genetic algorithm framework, considers the interactions between different device types and capacities, leading to superior economic and environmental performance compared to simple total power optimization.
Solution Approach 2:
The patent employs a universal optimization framework (genetic algorithm) that can handle multiple optimization objectives simultaneously - economic performance (cost minimization), environmental performance (emission reduction), and technical performance (reliability and renewable energy utilization). This multi-functional optimization approach replaces the conventional single-objective total power optimization, allowing the system to achieve balanced improvement across all performance dimensions by considering types and capacities of multiple devices together.
Data Source
AI summary
The present application discloses a method for optimized design of an independent micro-grid system, the independent micro-grid system comprising diesel generators, wind powered generators, a photovoltaic array, and an energy storage battery, the optimization method referring specifically to a multi-objective optimization design model based on the independent micro-grid system. In terms of the optimization planning design model, the method takes into account a combined start-up mode for a plurality of diesel generators and a control strategy for coordinating between the energy storage battery and the diesel generators, such that the usage rate of renewable energy in the independent micro-grid is higher, and operations more economical and environmentally friendly. Regarding the stability of the system optimization planning design model, the method takes into account the reserve capacity needed for stability of the independent micro-grid. In terms of the solving algorithm of the optimization planning design model, the method employs a multi-objective genetic algorithm based on NSGA-II to implement multi-objective problem-solving, thereby allowing a multi-objective optimization of the three major objectives of economy, reliability, and environmental friendliness of an independent micro-grid system.