教育工作经历

2003年9月-2007年6月：华中科技大学水电与数字化工程学院学习，获工学学士学位

2007年9月-2010年4月：华中科技大学水电与数字化工程学院学习，获工学硕士学位

2010年7月-2014年7月：调峰调频发电公司检修中心工作，担任自动化班设备维修员

2014年9月-2017年12月：武汉大学电气工程学院学习，获工学博士学位

2018年2月至今：福州大学，电气工程与自动化学院，讲师

科研简介

1. 福州大学科研启动项目，计及柔性资源的含风电场电力系统多时间尺度协调调度建模研究，2018.04-2020.04，在研，主持

含新能源的电力系统优化运行

代表性论文

[1]YachaoZhang, Kaipei Liu, Liang Qin, et al. Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods.Energy Conversion and Management, 2016, 112: 208-219. (SCI, Top期刊, IF=6.377)

[2]YachaoZhang, Jian Le, Xiaobing Liao, et al.Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO. Renewable Energy, 2018, 128: 91-107. (SCI, Top期刊, IF=4.900)

[3]Yachao Zhang, Jian Le, Xiaobing Liao, et al. A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing. Energy, 2019, 168: 558-572. (SCI, Top期刊, IF=4.968)

[4]Yachao Zhang, Jian Le, Feng Zheng, et al. Two-stage distributionally robust coordinated scheduling for gas-electricity integrated energy system considering wind power uncertainty and reserve capacity configuration. Renewable Energy, 2019, 135: 122-135. (SCI, Top期刊, IF=4.900)

[5]YachaoZhang, Kaipei Liu, Xiaobing Liao, et al. Stochastic dynamic economic emission dispatch with unit commitment problem considering wind power integration.International Transactions on Electrical Energy Systems, 2018, 28(1). (SCI)

[6]YachaoZhang, Kaipei Liu, Xiaobing Liao, et al.A probabilistic scenario-based framework for solving stochastic dynamic economic emission dispatch with unit commitment.Turkish Journal of Electrical Engineering and Computer Sciences, 2017, 25(6): 4805-4817. (SCI)

[7]Kaipei Liu, Yachao Zhang, Liang Qin. A novel combined forecasting model for short-term wind power based on ensemble empirical mode decomposition and optimal virtual prediction. Journal of Renewable and Sustainable Energy, 2016, 8(1). (SCI)

[8]张亚超，刘开培，秦亮，等.基于聚类经验模态分解-样本熵和优化极限学习机的风电功率多步区间预测[J]. 电网技术，2016，40(7): 2045-2051. (EI)

[9]张亚超，刘开培，秦亮，等.计及柔性资源的含风电场电力系统多目标动态协调调度[J]. 高电压技术，2017，43(4): 1186-1193. (EI)

[10]张亚超，刘开培，廖小兵，等.含大规模风电的电力系统多时间尺度源荷协调调度模型研究[J].高电压技术，2019，45(2)：600-608. (EI)