GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration
GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration
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GPNBI 启发 MOSDE 考虑风能渗透率的电力调度
DOI：
10.1016/j.energy.2017.12.005
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发表时间：
2018
期刊：
影响因子：
9
通讯作者：
Hui Jiang
中科院分区：
文献类型：
--
作者：
Xian Zhang;Huaizhi Wang;Jian-chun Peng;Yitao Liu;Guibin Wang;Hui Jiang
本文旨在从两个角度解决风电综合电力调度问题。随机建模和多目标优化。首先，竞争的目标在。对现代电力能源系统进行了分析，提出了一种新的区间优化模型。针对风力发电的不确定性，提出了目标。然后，一种新颖的力量。针对区间优化模型，提出了差分进化（SDE）算法。SDE的。采用基于混沌序列和玻尔兹曼分布的种群选择过程进行平衡。本地开采和全球勘探之间的权衡。在此基础上，建立了多目标EPD模型，提出了一种新的广义分段法向边界相交（GPNBI）方法。将多目标EPD问题有效地转化为一系列单目标子问题。由SDE解决。为了处理GPNBI中的高约束，提出了一种新的启发式约束处理方法。提出了加快收敛速度的策略。最后，提出了一种基于超平面的决策策略，以确定所得到的最优妥协解。帕累托边界。区间优化模型的可行性和有效性。GPNBI启发的多目标SDE （MOSDE）在改进的ieee .30总线系统和118总线系统上进行了综合评价。统计结果证实了所提出的区间优化方法。模型可以近似地量化每个目标的潜在不确定性，并进行论证。所提出的MOSDE算法表现出比状态算法更好的性能。因此,。结果表明，本文提出的优化模型和方法具有较好的应用前景。风能渗透电力和能源系统的实际实施问题。
This paper aims to solve wind energy integrated electric power dispatch (EPD) problem from the perspectives.of stochastic modelling and multiobjective optimization. At first, the competing objectives in.modern electric power and energy system is analyzed and a new interval optimization model for each.objective is proposed based on the uncertainties with respect to wind power. Then, a novel strength.differential evolution (SDE) algorithm is developed to address the interval optimization model. The SDE.adopts a population selection process based on chaotic sequence and Boltzmann distribution to balance.the tradeoff between local exploitation and global exploration. Afterwards, a multiobjective EPD model is.established and a novel generalized piecewise normal boundary intersection (GPNBI) method is mooted.to transform multiobjective EPD into a series of single-objective sub-problems which can be effectively.solved by SDE. In order to deal with the highly constrains in GPNBI, a new heuristic constraint handling.strategy is proposed accordingly to accelerate the convergence speed. At last, a hyper-plane based.decision-making strategy is originally developed to identify the best compromise solution in the obtained.Pareto frontiers (PFs). The feasibility and effectiveness of the interval optimization model and.GPNBI inspired multiobjective SDE (MOSDE) have been comprehensively evaluated on a modified IEEE.30-bus system and a 118-bus system. The statistical results confirm that the proposed interval optimization.model can approximately quantify the potential uncertainty in each objective and also demonstrate.that the proposed MOSDE exhibits better performance than the algorithms of the state. Therefore,.it is convinced that the proposed optimization model and method have high potentials to address the.practical implementation problems in electric power and energy systems with wind energy penetration.