.png){kind=icon}
返回
Microgrid energy optimization management considering consumer segmentation optimization and dynamic time intervals
.png){kind=icon}
PRE
AI
DOI：10.1080/15567036.2024.2422570.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To prioritize critical loads and enhance microgrid energy management efficiency, this study introduces a method that combines consumer segmentation optimization and dynamic time intervals. A penalty-based approach is used to minimize unmet load demands, prioritizing energy for critical loads. A joint classification model for multi-type consumers is developed using a convolutional neural network autoencoder and hierarchical clustering. Dynamic time intervals are applied to reduce iterations and improve memory and processor efficiency in solving the energy management model. Simulation tests on a microgrid with distributed generation, battery storage, and consumption units confirm the method's effectiveness. Results show that configuring dynamic time intervals and segmenting loads by priority yield solutions similar to fixed interval methods. Finally, the energy optimization management effects of the proposed method were compared with those of two latest methods. The comparison results demonstrate that if a microgrid underwent four different disconnection scenarios from the main distribution network, the proposed method saves 23.15%, 23.08%, 23.79%, and 34.61% time to achieve energy optimization management compared with that of the first latest method, and 24.20%, 23.87%, 25.11%, and 36.18% time than that of the second latest method.
Keyword:
Consumer segmentation optimization
dynamic time intervals
energy optimization management
load power supply priority
microgrid
期刊
E
IF:
2.2
论文数:
512
被引数:
1.1W
机构
引用论文
A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Multi-service battery energy storage system optimization and control多业务电池储能系统优化与控制APPLIED ENERGY
OAAI
APPLIED ENERGYIF11
OAAI分享收藏Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand SideSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Real-time energy optimization and scheduling of buildings integrated with renewable microgrid集成可再生微电网的建筑物实时能量优化与调度APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid基于启发式算法优化的深度学习微电网能量管理预测模型APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
查看更多内容