GPNBI-inspired MOSFA for Pareto operation optimization of integrated energy system
GPNBI-inspired MOSFA for Pareto operation optimization of integrated energy system
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GPNBI启发的MOSFA用于综合能源系统帕累托运行优化
DOI：
10.1016/j.enconman.2017.09.005
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发表时间：
2017-11
影响因子：
10.4
通讯作者：
Liu Wenxin
中科院分区：
文献类型：
--
作者：
Wang Huaizhi;Zhang Rongquan;Peng Jianchun;Wang Guibin;Liu Yitao;Jiang Hui;Liu Wenxin
随着分布式发电的快速发展和多种能源的强烈相互依赖性，综合能源系统（IES）的优化运行变得比以往任何时候都更加重要。然而，目前的IES的最佳操作策略可能是不切实际的，因为它们对公用电网的潜在负面影响一般不被考虑。因此，在这项研究中，制定了一个原始的多目标优化模型IES操作，以最大限度地减少运营成本，一次能源消耗，和二氧化碳排放的IESs，并以最佳方式减少电网的功率损耗和电压幅度偏差。然后，提出了一种新的Pareto优化算法，称为多目标强度萤火虫算法（MOSFA），解决IES的多目标操作问题。强度萤火虫算法（SFA）使用玻尔兹曼分布和基于混沌序列的种群选择过程，以促进局部开发和全局探索。此外，广义分段法向边界相交（GPNBI）方法的发展，将多目标操作问题转化为一系列的高度约束的单目标优化子问题，可以有效地解决的SFA。最后，一个超平面为基础的决策策略，以确定最佳的妥协解决方案所获得的帕累托边界。在一个由天然气、风力和光伏发电机供电的新型IES上，对GPNBI启发的MOSFA进行了全面评估。标准IEEE 39节点系统被视为公用电网。数值计算结果表明，所提出的MOSFA具有竞争力的性能相比，算法的状态，这意味着优化的操作策略提供了一个更好的权衡所有目标之间的研究。
With the rapid growth of penetration of distributed generations and the strong interdependencies of multiple energy sources, the optimal operation of integrated energy systems (IESs) is becoming more important than ever before. However, current optimal operation strategies for IESs may be impractical because their potential negative impacts on utility grids are generally not considered. Therefore, in this study, an original multiobjective optimization model for IES operation is formulated to minimize the operational cost, primary energy consumption, and carbon dioxide emission of IESs and to optimally reduce the power loss and voltage magnitude deviation of the utility grid. Then, a novel Pareto optimization algorithm, called multiobjective strength firefly algorithm (MOSFA), is proposed to solve the multiobjective operation problem of IES. The strength firefly algorithm (SFA) uses a Boltzmann distribution and a chaotic-sequence-based population selection process to facilitate local exploitation and global exploration. In addition, a generalized piecewise normal boundary intersection (GPNBI) method is developed to transform the multiobjective operation problem into a series of highly constrained single-objective optimization sub-problems that can be effectively solved by the SFA. Finally, a hyper-plane-based decision making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers. The GPNBI-inspired MOSFA was comprehensively evaluated on a novel IES powered by natural gas, wind and photovoltaic generators. The standard IEEE 39-bus system is considered as the utility grid. The numerical results demonstrated that the proposed MOSFA exhibits competitive performance when compared to the algorithms of the state, which means that the optimized operation strategy provides a better trade-off between all objectives considered in this study.
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DOI：
10.1109/pes.2010.5588053
发表时间：
2010-07
期刊：
IEEE PES General Meeting
影响因子：
--
作者：
C. Colson;M. H. Nehrir;S. A. Pourmousavi
通讯作者：
C. Colson;M. H. Nehrir;S. A. Pourmousavi
基于冷热电联产压缩空气储能系统的多目标优化与能源经济分析
影响因子：
10.4
作者：
Yao Erren;Wang Huanran;Wang Ligang;Xi Guang;Marechal Francois
通讯作者：
Marechal Francois
DOI：
10.1016/j.cnsns.2012.06.009
发表时间：
2013-01-01
影响因子：
3.9
作者：
Gandomi, A. H.;Yang, X-S.;Alavi, A. H.
通讯作者：
Alavi, A. H.
影响因子：
9
作者：
R. Hemmati;H. Saboori;M. A. Jirdehi
通讯作者：
R. Hemmati;H. Saboori;M. A. Jirdehi
DOI：
10.1109/pess.2001.970254
发表时间：
2001-07
期刊：
2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262)
影响因子：
--
作者：
A.A. Abido
通讯作者：
A.A. Abido