电力系统研究所

个人主页

www.zhaohaoran.top

学术身份

国家特聘教授（2017年入选），博士生导师，齐鲁青年学者（2016年入选）。目前为IEEE高级会员、CIGRE工作组C6.C1.33（综合能源系统）工作组成员、CIGRE C4.56 （大规模电力电子接入的大电网电磁暂态仿真模型）工作组成员、IEC SC 8A工作组专家、山东能源研究会副理事长、电机工程学会配电网控制运行专委会委员、电工技术学会人工智能与电气应用专委会委员、仿真学会综合能源数字孪生专委会委员、可再生能源学会综合能源专委会委员。

目前担任IEEE Transactions on Sustainable Energy，IEEE Power Engineering Letters，IET Renewable Power Generation/Journal of Engineering等国际期刊Associate Editor，电力保护与控制青年编委，受邀担任IEEE Transactions on Energy Conversion特刊Associate Editor，International Journal of Electrical Power & Energy Systems特刊主编。

工作经历

2001-2005 3308维多利亚优惠大厅，工学学士

2005-2007 国家电网济南供电公司，工程师

2007-2010 德国柏林工业大学电气系，工学硕士

2009-2010 德国Younicos AG，兼职工程师

2010-2011 德国DIgSILENT GmbH，项目工程师

2011-2014 丹麦科技大学电力能源中心，工学博士

2014-2017 丹麦科技大学电力能源中心，博士后

2017-至今 3308维多利亚优惠大厅，教授

个人信息

姓名

赵浩然

性别

男

出生日期

1983.04

籍贯

山东济宁

职称

教授

职务

副院长

电话

0531-81696100

Email

hzhao@sdu.edu.cn

研究方向

◆新能源发电与并网

◆新型电力系统建模与仿真

◆综合能源优化运行与控制

学术著作

近年代表性期刊论文，会议论文与书章节未列出。

[1]Yu J ,Zhao H,Jiang Y , et al.Efficient electromagnetic transient simulation for DFIG-based wind farms using fine-grained network partitioning[J].International Journal of Electrical Power and Energy Systems,2024,162110297-110297.

[2]Hu X, Ma J, Zhao H, et al. A weak leak location method based on signal attenuation mechanism for energy transportation system[J]. Energy, 2024: 132786.

[3]Tian H, Zhao H, Xin S, et al. A Mechanism-based Data-driven Interval Energy Flow Calculation Method for Integrated Energy Systems via Affine Arithmetic-based Optimization[J]. IEEE Transactions on Sustainable Energy, 2024.

[4]江艺宝,于佳乐,赵浩然,等.新型电力系统电磁暂态并行仿真关键技术及展望[J].高电压技术,2024,50(07):3145-3160.

[5]赵浩然,孟铃涵,江艺宝,等.面向新型电力系统的实时仿真平台综述与展望[J].高电压技术,2024,50(10):4611-4626.

[6]Wang P, Ma Y, Zhao H*. Online assessment of multi-parameter stability region and stability margin of wind power plants[J]. International Journal of Electrical Power & Energy Systems, 2024, 155: 109413.

[7]马悦鑫，王鹏*，赵浩然，贺敬，等.基于分段仿射阻抗模型的电压源变流器小扰动稳定域在线构造[J]，中国电机工程学报，2023.

[8]Li B, Zhao H, Jiang Y, et al. Real-time simulation for detailed wind turbine model based on heterogeneous computing[J]. International Journal of Electrical Power & Energy Systems, 2024, 155: 109486.

[9]Ma H, Liu C*, Zhao H, et al. A novel analytical unified energy flow calculation method for integrated energy systems based on holomorphic embedding[J]. Applied Energy, 2023, 344: 121163.

[10]Huang X, Tian H, Zhao H*, et al. Digital twins of multiple energy networks based on real-time simulation using holomorphic embedding method, Part I: Mechanism-driven modeling[J]. International Journal of Electrical Power & Energy Systems, 2023, 154: 109419.

[11]Tian H, Zhao H*, Li H, et al. Digital twins of multiple energy networks based on real-time simulation using holomorphic embedding method, Part II: Data-driven simulation[J]. International Journal of Electrical Power & Energy Systems, 2023, 153: 109325.

[12]Liu H, Liu C, Zhao H, et al. Non-intrusive Load Monitoring Method for Multi-Energy Coupling Appliances Considering Spatio-Temporal Coupling[J]. IEEE Transactions on Smart Grid, 2023.

[13]王梦雪,赵浩然,刘春阳等.基于碳熵指标的电-热互联综合能源系统碳轨迹追踪方法[J].电力系统自动化,2023,47(09):13-22.

[14]Tian H, Zhao H*, Li H, et al. Interval-probabilistic Electricity-heat-gas Flow Calculation by Dual-level Surrogate Structure[J].IEEE Transactions on Smart Grid, 2023.

[15]王金龙,赵浩然,王鹏,罗嘉,孙开宁.基于阻抗法的并网逆变器小信号稳定功率极限分析与提高[J].电力系统保护与控制,2022,50(18):18-28.

[16]马钊,张恒旭,赵浩然等.双碳目标下配用电系统的新使命和新挑战[J].中国电机工程学报,2022,42(19):6931-6945.

[17]刘俊伟,刘春阳,赵浩然*,等.基于知识引导深度神经网络的电-热综合能源系统状态估计[J].电网技术.

[18]王俊杰,赵浩然*,刘春阳,陈常念,等.基于熵指标的热能输动态特性建模与仿真分析[J].中国电机工程学报.

[19]LI B, ZHAO H R, ZHANG K P. A heterogeneous accelerated simulation framework for wind field dynamic model [J]. Iet Renewable Power Generation.

[20]Mengxue Wang, Haoran Zhao, Hang Tian and Qiuwei Wu.Distributed Collaborative Optimization of Multi-region Integrated Energy System Based on Edge Computing Unit[J]. Frontiers in Energy Research

[21]程建东,赵浩然,韩明哲.市场机制下推动风电参与电力市场的实践总结与启示[J].电网技术

[22]Tian H, Zhao H, Liu C, Chen J. Iterative Linearization Approach for Optimal Scheduling of Multi-regional Integrated Energy System[J]. Frontiers in Energy Research, 2021: 208.

[23]Tian H, Zhao H, Liu C, Chen J, Wu Q, Terzija V. A dual-driven linear modeling approach for multiple energy flow calculation in electricity–heat system[J]. Applied Energy, 2022, 314: 11887

[24]Guo Y, Gao H, Wu Q, et al. Enhanced voltage control of VSC-HVDC-connected offshore wind farms based on model predictive control[J]. IEEE Transactions on Sustainable Energy, 2017, 9(1): 474-487.

[25]Gao S, Zhao H, Wang P, et al. Comparative Study of Symmetrical Controlled Grid-Connected Inverters[J]. IEEE Transactions on Power Electronics, 2021, 37(4): 3954-3968.

[26]罗嘉,赵浩然,高术宁,王鹏,孙开宁.基于显式模型预测控制和改进虚拟阻抗的双馈风机低电压穿越策略[J].电网技术,2021,45(05):1716-1723.

[27]Zhao H, Lin Z, Wu Q, et al. Model predictive control based coordinated control of multi-terminal HVDC for enhanced frequency oscillation damping[J]. International Journal of Electrical Power & Energy Systems, 2020, 123: 106328.

[28]Kim C, Gui Y, Zhao H, et al. Coordinated LVRT control for a permanent magnet synchronous generator wind turbine with energy storage system[J]. Applied Sciences, 2020, 10(9): 3085.

[29]陈健,林咨良,赵浩然,吴秋伟,宋关羽.考虑信息耦合的电–气综合能源系统韧性优化方法[J].中国电机工程学报,2020,40(21):6854-6864.

[30]王梦雪,赵浩然,田航,陈健,吴秋伟.典型综合能源系统仿真与规划平台综述[J].电网技术,2020,44(12):4702-4712.

[31]Li B, Zhao H, Gao S, et al. Digital real-time co-simulation platform of refined wind energy conversion system[J]. International Journal of Electrical Power & Energy Systems, 2020, 117: 105676.

[32]Luo J , Zhao H , Gao S , et al. A Low Voltage Ride Through Strategy of DFIG based on Explicit Model Predictive Control [J]. International Journal of Electrical Power & Energy Systems. 2020,119, 105783.

[33]Gao S, Zhao H, Gui Y, et al. Impedance analysis of voltage source converter using direct power control[J]. IEEE Transactions on Energy Conversion, 2020, 36(2): 831-840.

[34]Gao S, Zhao H, Gui Y, et al. An improved direct power control for doubly fed induction generator[J]. IEEE Transactions on Power Electronics, 2020, 36(4): 4672-4685.

[35]Gao S, Zhao H, Gui Y, et al. A novel direct power control for DFIG with parallel compensator under unbalanced grid condition[J]. IEEE Transactions on Industrial Electronics, 2020, 68(10): 9607-9618.

[36]Huang S, Wu Q, Zhao H, et al. Distributed optimization-based dynamic tariff for congestion management in distribution networks[J]. IEEE Transactions on Smart Grid, 2017, 10(1): 184-192.

[37]Zhao H, Wu Q, Huang S, et al. Hierarchical control of thermostatically controlled loads for primary frequency support[J]. IEEE Transactions on Smart Grid, 2016, 9(4): 2986-2998.

[38]Guo Y, Gao H, Wu Q, et al. Enhanced voltage control of VSC-HVDC-connected offshore wind farms based on model predictive control[J]. IEEE Transactions on Sustainable Energy, 2017, 9(1): 474-487.

[39]Zhao H, Wu Q, Wang J, et al. Combined active and reactive power control of wind farms based on model predictive control[J]. IEEE Transactions on Energy Conversion, 2017, 32(3): 1177-1187.

[40]Zhao H, Wu Q, Huang S, et al. Fatigue load sensitivity-based optimal active power dispatch for wind farms[J]. IEEE Transactions on sustainable energy, 2017, 8(3): 1247-1259.

[41]Zhao H, Wu Q, Guo Q, et al. Coordinated voltage control of a wind farm based on model predictive control[J]. IEEE Transactions on Sustainable Energy, 2016, 7(4): 1440-1451.

[42]Huang S, Wu Q, Zhao H, et al. Geometry of power flows and convex-relaxed power flows in distribution networks with high penetration of renewables[J]. Energy Procedia, 2016, 100: 1-7.

[43]Zhao H, Wu Q, Guo Q, et al. Distributed model predictive control of a wind farm for optimal active power controlpart I: Clustering-based wind turbine model linearization[J]. IEEE transactions on sustainable energy, 2015, 6(3): 831-839.

[44]Zhao H, Wu Q, Guo Q, et al. Distributed model predictive control of a wind farm for optimal active power controlpart II: Implementation with clustering-based piece-wise affine wind turbine model[J]. IEEE Transactions on Sustainable Energy, 2015, 6(3): 840-849.

[45]Zhao H, Wu Q, Rasmussen C N, et al. ${\cal L} _1 $ adaptive speed control of a small wind energy conversion system for maximum power point tracking[J]. IEEE Transactions on Energy Conversion, 2014, 29(3): 576-584.

[46]Huang S, Wu Q, Cheng L, et al. Uncertainty management of dynamic tariff method for congestion management in distribution networks[J]. IEEE Transactions on Power Systems, 2016, 31(6): 4340-4347.

[47]Zhao H, Wu Q, Guo Q, et al. Optimal active power control of a wind farm equipped with energy storage system based on distributed model predictive control[J]. IET Generation, Transmission & Distribution, 2016, 10(3): 669-677.

[48]Korompili A, Wu Q, Zhao H. Review of VSC HVDC connection for offshore wind power integration[J]. Renewable and Sustainable Energy Reviews, 2016, 59: 1405-1414.

[49]Zhao H, Wu Q, Hu S, et al. Review of energy storage system for wind power integration support[J]. Applied energy, 2015, 137: 545-553.

[50]Zhao H, Wu Q, Margaris I, et al. Implementation and validation of IEC generic type 1A wind turbine generator model[J]. International Transactions on Electrical Energy Systems, 2015, 25(9): 1804-1813.

[51]Zhao H, Wu Q, Wang C, et al. Fuzzy logic based coordinated control of battery energy storage system and dispatchable distributed generation for microgrid[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(3): 422-428.

承担科研项目

1.海外高层次人才专项基金，主持。

2.国家重点研发计划课题：信息能源耦合节点能量一致标度及其自适应持续进化建模，主持。

3.国家重点研发计划子课题：电网故障下风电机组电压/频率暂态主动支撑技术研究，主持。

4.国家重点研发计划子课题：大容量风电机组电网友好型控制技术，主持。

5.国家自然科学基金项目：基于分布式模型预测控制的风电场有功控制系统的研究，主持。

6.国家自然科学基金子项目：综合能源系统统一建模与精细化仿真，主持。

7.国网科技项目：基于大型风电场实时仿真等值模型的电力系统动态仿真技术研究，主持。

8.国网科技项目：基于DIgSILENT的MMC模型开发与交流系统分析研究，主持。

9.国网科技项目：大规模风电场并网宽频震荡风险评估模块开发，主持。

10.国网科技项目：混合直流数字物理混合仿真接口算法研究，主持。

11.国网科技项目：“新能源场站+共享储能”协同控制与综合评价关键技术研究，主持。

12.国网科技项目：新能源电站的实测建模与模型参数优化技术研究，主持。

13.电科院项目：风电场精细化模型开发及实时在线仿真技术，主持。

14.电科院项目：创新自筹-大规模风电场并网宽频震荡风险评估模块开发，主持。

15.南方电网：支持BPA数据转化的DIgSILENTPower Factory电网模型搭建，主持。

16.上电科项目：逆变器DIgSILENT建模测试，主持。

17.上电科项目：逆变器的PowerFactory建模 ，主持。

学术类型

可培养学术型、专业型研究生

课题组常年诚招相关研究方向的硕士、博士研究生及博士后，有意者可电话或邮件联系（邮件请附个人简历），成绩优秀者可与丹麦、德国的科研单位联合培养。

我们立足能源低碳转型大背景，研究方向覆盖风电场并网控制与稳定性分析、综合能源系统建模与运行优化、能源数字孪生等广泛领域，通过理论研究和工程实践解决新型电力系统的重大挑战。课题组热忱欢迎自我驱动、求知若渴的本科生加入，并为同学提供各种软硬件科研平台、学长指导、科创发明比赛和研究补助。REDNet课题组欢迎你的加入！