.png){kind=icon}
返回
Decentralized multi-area multi-agent economic dispatch model using select meta-heuristic optimization algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.epsr.2021.107128.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a decentralized multiarea multiagent economic dispatch (DMMED) model using select metaheuristic optimization (MO) algorithms. The previous multiarea economic dispatch (MAED) studies that used MO algorithms were centrally planned and did not include multiple independent local agents in their decisionmaking framework. The proposed model allows to optimize the operation of the power systems involving the system operators (TSOs) and multiple independent local aggregators (LAs) in a decentralized manner. The objective of the model is to minimize the total operation cost of the system in a time-efficient manner by effectively coordinating the operation of the TSOs and LAs. Each area accounts for its operational uncertainties and power reserves considering the worst-case scenarios of the uncertainty sets of the individual agent in the area. To respect each area ownership, the solution algorithm utilizes separate population sets and a dependent bilevel operation approach to solve the DMMED model for each area in parallel, allowing the areas to achieve optimal operation, independently. Case studies with select MO algorithms are performed on a modified Nigerian 330 kV 39-bus transmission systems having three areas each with one TSO and three LAs to demonstrate the effectiveness of the proposed model.
Keyword:
Decentralized
Multiagent
Multiarea
Meta-heuristics
Power systems
Uncertainties
期刊
IF:
4.2
论文数:
1.1W
被引数:
2.2W
机构
引用论文
.png){kind=icon}
分享收藏Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging评估EV用户行为对EV聚合器智能充电的技术和经济影响
OAAI
OAAI分享收藏分享收藏
查看更多内容