为了考虑风光不确定性给微网运行带来的风险,针对独立型微网的容量优化配置,提出一种基于生成对抗网络(generative adversarial network,GAN)场景模拟和条件风险价值(conditional value at risk,CVaR)的容量随机优化配置模型。首先利用...为了考虑风光不确定性给微网运行带来的风险,针对独立型微网的容量优化配置,提出一种基于生成对抗网络(generative adversarial network,GAN)场景模拟和条件风险价值(conditional value at risk,CVaR)的容量随机优化配置模型。首先利用GAN模拟大量风光出力场景,再用K-medoids聚类进行消减得到若干典型场景;其次,以微网供电可靠性为约束,以经济性和可再生能源利用率为目标函数,通过CVaR度量因风光资源不确定性给微网系统带来的运行风险,并将其以平均风险损失的形式与目标函数相结合,构建微网电源容量随机优化配置模型;最后,采用电源损失风险和负荷风险损失指标对配置结果进行评价。仿真算例表明,相比于仅采用典型年风光资源数据进行配置的传统方法,文中提出的模型对于规划周期内可能出现的运行场景适应性更好。展开更多
In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy...In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.展开更多
文摘为了考虑风光不确定性给微网运行带来的风险,针对独立型微网的容量优化配置,提出一种基于生成对抗网络(generative adversarial network,GAN)场景模拟和条件风险价值(conditional value at risk,CVaR)的容量随机优化配置模型。首先利用GAN模拟大量风光出力场景,再用K-medoids聚类进行消减得到若干典型场景;其次,以微网供电可靠性为约束,以经济性和可再生能源利用率为目标函数,通过CVaR度量因风光资源不确定性给微网系统带来的运行风险,并将其以平均风险损失的形式与目标函数相结合,构建微网电源容量随机优化配置模型;最后,采用电源损失风险和负荷风险损失指标对配置结果进行评价。仿真算例表明,相比于仅采用典型年风光资源数据进行配置的传统方法,文中提出的模型对于规划周期内可能出现的运行场景适应性更好。
基金Projects(42177164,52474121)supported by the National Science Foundation of ChinaProject(PBSKL2023A12)supported by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China。
文摘In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
