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首页> 外文期刊>Renewable Power Generation, IET >Dynamic economic emission dispatch with wind power based on improved multi-objective brain storm optimisation algorithm
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Dynamic economic emission dispatch with wind power based on improved multi-objective brain storm optimisation algorithm

机译：基于改进多目标脑风暴优化算法的风力发射动态经济排放调度

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摘要

Based on the non-parametric kernel density estimation technique, the probability distribution of wind power output and its forecast error are accurately modelled. The confidence interval and forecast error upper and lower bounds of wind power output are estimated to build a dynamic economic emission dispatch (DEED) model with wind power penetration. To effectively solve the DEED problem with multi-objective, high-dimensional, non-linear and strong constraints, based on the basic brainstorm optimisation (BSO) algorithm, three improvement mechanisms, namely random clustering centre, differential mutation operation and individual crossover operation are introduced to enhance the converging and diverging operation of BSO. Based on these improvements and external archive mechanism, an improved multi-objective BSO (IMOBSO) algorithm is proposed. Simulations on a classical test system with ten thermal units are performed, where two case studies are investigated carefully. The simulation results demonstrate that: (i) the proposed IMOBSO can optimise the cost and emission objectives simultaneously and have achieved better performance than other algorithms; (ii) the proposed DEED incorporating wind power model is reasonable and effective because it can achieve the optimal wind power output scheduling by adjusting the system's spinning reserve capacity and the confidence level of wind power prediction interval.
机译：基于非参数核密度估计技术，风电输出的概率分布及其预测误差是精确的建模。估计风力输出的置信区间和预测误差上限和下限，以构建带风力渗透的动态经济排放调度（契约）模型。为了基于基础头脑风暴优化（BSO）算法，三种改进机制，即随机聚类中心，差异突变操作和单独的交叉操作，有效地解决了多目标，高维，非线性和强制的契约问题介绍增强BSO的聚合和发散操作。基于这些改进和外部归档机制，提出了一种改进的多目标BSO（IMOBSO）算法。进行有10个热单元的经典测试系统的模拟，其中仔细研究了两种案例研究。仿真结果表明：（i）所提出的IMOBSO可以同时优化成本和排放目标，并且比其他算法实现更好的性能; （ii）拟议的契约采用风电模型是合理且有效的，因为它可以通过调整系统的纺纱储备容量和风力预测间隔的置信度来实现最佳风力输出调度。

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