返回
Multi-agent modeling for solving profit based unit commitment problem
DOI:10.1016/j.asoc.2013.04.001.png)
摘要
En 中文
Profit based unit commitment problem (PBUC) from power system domain is a high-dimensional, mixed variables and complex problem due to its combinatorial nature. Many optimization techniques for solving PBUC exist in the literature. However, they are either parameter sensitive or computationally expensive. The quality of PBUC solution is important for a power generating company (GENCO) because this solution would be the basis for a good bidding strategy in the competitive deregulated power market. In this paper, the thermal generators of a GENCO is modeled as a system of intelligent agents in order to generate the best profit solution. A modeling for multi-agents is done by decomposing PBUC problem so that the profit maximization can be distributed among the agents. Six communication and negotiation stages are developed for agents that can explore the possibilities of profit maximization while respecting PBUC problem constraints. The proposed multi-agent modeling is tested for different systems having 10-100 thermal generators considering a day ahead scheduling. The results demonstrate the superiority of proposed multi-agent modeling for PBUC over the benchmark optimization techniques for generating the best profit solutions in substantially smaller computation time. (C) 2013 Elsevier B. V. All rights reserved.
Keyword:
Profit based unit commitment
Agent rules
Multi-agent modeling
Deregulation
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Preoperative open field behavior predicts levels of neuropathic pain-related behavior in mice术前旷场行为预测小鼠神经病理性疼痛相关行为水平
Solution to profit based unit commitment problem using particle swarm optimization基于粒子群算法求解基于利润的机组组合问题
A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems基于多代理的制造系统中机器和自动引导车辆动态调度方法

