@article{__nodate,
	title = {基于分布式神经动态优化的综合能源系统多目标优化调度},
	issn = {0254-4156},
	url = {https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=MOTO20201103004&v=},
	doi = {10.16383/j.aas.c200168},
	abstract = {本文研究了基于神经动态优化的综合能源系统(Integrated Energy Systems, IES)分布式多目标优化调度问题.首先,将IES元件单元(包含负荷)作为独立的决策主体,联合考量其运行成本和排放成本,并计及多能源设备间的传输损耗,提出了IES多目标优化调度模型,该模型可描述为一类非凸多目标优化问题.其次,针对此类问题的求解,提出了一种基于神经动力学系统的分布式多目标优化算法,该算法基于动态权重的神经网络模型,可以解决不可分离的不等式约束问题.该算法计算负担小,收敛速度快,并且易于硬件实现.仿真结果表明,所提算法能同时协调综合能源系统的经济性和环境性这两个冲突的目标,且获得了整个帕累托前沿,有效降低了综合能源系统的污染物排放量和综合运行成本.},
	language = {中文},
	journal = {自动化学报},
	author = {{黄博南} and {王勇} and {李玉帅} and {刘鑫蕊} and {杨超}},
	note = {{\textless}北大核心, EI, CSCD{\textgreater}},
	keywords = {/unread, distributed multi-objective optimization, recurrent neural networks(RNNs), 分布式多目标优化, 综合能源系统, 递归神经网络 Integrated energy systems},
	pages = {1--19},
}

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