Grid-Connected Optimization Dispatch of Mobile Wave Energy Power Generation Platform Considering the Coordination of Source, Network and Storage
He Weijie, Feng Zhongnan, Lin Xiangning, Wei Fanrong, Gu Benshuo
State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electric Engineering Huazhong University of Science and Technology Wuhan 430074 China
Abstract：As a new type of renewable energy, wave energy power generation output is periodic and impulsive. Since island microgrids in the marine environment lack inertia and rotating backup, meeting the access requirements for periodic and impulsive power supplies is challenging. In real-world application scenarios, wave energy power generation adopts a maximum power control strategy and energy storage to smooth out impacts when connected to the grid. However, traditional strategies ignore the huge charge and discharge loss of battery storage during this process, resulting in the economic costs of the island microgrid. This paper proposes a grid-connected strategy for the wave-energy power generation platform, considering source, grid, and storage coordination. This strategy modulates the power of wave energy power generation from the disturbance source and uses the microgrid and energy storage to jointly absorb the power impact. The simulation results show that the proposed strategy can reduce battery losses and improve grid connection friendliness. First, a mathematical model for the output power of the hydraulic wave-energy power generation device is constructed. The valve opening power of the device in each generation cycle can be changed to modulate the power curve and improve its impulsive characteristics. Secondly, since the microgrid's ability to absorb power impact is related to the inertia, an island microgrid inertia evaluation model and a post-disturbance frequency response model are established. The difference between the grid-connected disturbance of the wave energy power generation and the general step disturbance is subdivided, and the changing rate of frequency and transient frequency extreme values in the frequency response process are calculated. These two indicators are used to quantify the ultimate bearing capacity of the microgrid. Finally, with the minimum economic cost as the objective function, a global grid-connected dispatch model is established under multiple source, network, and storage constraints. In this model, the energy flow interaction processes of the three sides are flexibly coupled, thereby greatly reducing economic costs. The simulation with the actual island microgrid data shows that when the wave energy power generation adopts the maximum power point tracking (MPPT) control strategy, the equivalent battery loss is 1 440.6 kW·h, and the maximum power impact is 100 kW. In the flexible control strategy adopted by the wave energy power generation, the equivalent battery loss is 516.2 kW·h, and the maximum power impact is reduced to less than 70 kW. In addition, if the energy storage and microgrid jointly absorb the power impact, the battery loss is only 302.1 kW·h. Movable and high penetration scenarios of wave energy are further simulated to verify the proposed model. The following conclusions can be drawn. (1) The output power curve of the wave energy power generation using the flexible control strategy is smoother than the MPPT strategy, which greatly reduces the rate loss and charge and discharge loss of energy storage. The wave energy device can also flexibly adjust the impact disturbance when connected to the grid to ensure frequency safety. (2) According to the simulation results, the economic benefits of the global consumption strategy that comprehensively considers the coordination of source, network, and storage are 17.8% higher than the traditional strategy that only uses energy storage. The economy and security of the microgrid operation are further improved if mobility is considered.
贺玮杰, 冯忠楠, 林湘宁, 魏繁荣, 顾本硕. 考虑源网储协同配合下的移动式波浪能发电平台并网优化调度[J]. 电工技术学报, 2024, 39(20): 6502-6516.
He Weijie, Feng Zhongnan, Lin Xiangning, Wei Fanrong, Gu Benshuo. Grid-Connected Optimization Dispatch of Mobile Wave Energy Power Generation Platform Considering the Coordination of Source, Network and Storage. Transactions of China Electrotechnical Society, 2024, 39(20): 6502-6516.
[1] 王伟宏, 王焕栋. 波浪能发电技术及其相关专利[J]. 中国科技信息, 2023(7): 11-14. Wang Weihong, Wang Huandong.Wave power gen- eration technology and its related patents[J]. China Science and Technology Information, 2023(7): 11-14. [2] 王坤林, 盛松伟, 叶寅, 等. 波浪能装置中液压发电系统Boost变换机理及控制策略[J]. 电力系统自动化, 2017, 41(12): 173-178. Wang Kunlin, Sheng Songwei, Ye Yin, et al.Boost converter converting mechanism and control strategy for hydraulic power generation system in wave energy converter[J]. Automation of Electric Power Systems, 2017, 41(12): 173-178. [3] 肖曦, 摆念宗, 康庆, 等. 波浪发电系统发展及直驱式波浪发电系统研究综述[J]. 电工技术学报, 2014, 29(3): 1-11. Xiao Xi, Bai Nianzong, Kang Qing, et al.A review of the development of wave power system and the research on direct-drive wave power system[J]. Transactions of China Electrotechnical Society, 2014, 29(3): 1-11. [4] 王颖, 王晓文, 陆铭阳. 新能源高占比电力系统惯量相关问题研究[J]. 东北电力技术, 2023, 44(2): 20-27, 46. Wang Ying, Wang Xiaowen, Lu Mingyang.Research on inertia related problems of high proportion of new energy resources in power system[J]. Northeast Electric Power Technology, 2023, 44(2): 20-27, 46. [5] 李和丰. 微电网冲击性负荷下飞轮储能与电源的运行优化[D]. 济南: 山东大学, 2018. Li Hefeng.Optimal operation of flywheel energy storage and power supply under impact load of microgrid[D]. Jinan: Shandong University, 2018. [6] 赵靖英, 乔珩埔, 姚帅亮, 等. 考虑储能SOC自恢复的风电波动平抑混合储能容量配置策略[J]. 电工技术学报, 2024, 39(16): 5206-5219. Zhao Jingying, Qiao Hengpu, Yao Shuailiang, et al.Hybrid energy storage system capacity configuration strategy for stabilizing wind power fluctuation considering SOC self-recovery[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5206-5219. [7] 郭怿, 明波, 黄强, 等. 考虑输电功率平稳性的水-风-光-储多能互补日前鲁棒优化调度[J]. 电工技术学报, 2023, 38(9): 2350-2363. Guo Yi, Ming Bo, Huang Qiang, et al.Day-ahead robust optimal scheduling of hydro-wind-PV-storage complementary system considering the steadiness of power delivery[J]. Transactions of China Electro- technical Society, 2023, 38(9): 2350-2363. [8] 罗晓乐, 宋洋, 徐翔, 等. 计及风电不确定性的综合能源系统储能优化配置研究[J]. 东北电力技术, 2021, 42(12): 18-25, 46. Luo Xiaole, Song Yang, Xu Xiang, et al.Research on optimal allocation of energy storage of integrated energy system considering wind power uncertainty[J]. Northeast Electric Power Technology, 2021, 42(12): 18-25, 46. [9] 鲍谚, 石锦凯, 陈世豪. 考虑负荷预测不确定性的快充站储能鲁棒实时控制策略[J]. 电力系统自动化, 2023, 47(10): 107-116. Bao Yan, Shi Jinkai, Chen Shihao.Robust real-time control strategy for energy storage in fast charging station considering load forecasting uncertainty[J]. Automation of Electric Power Systems, 2023, 47(10): 107-116. [10] Farrok O, Ahmed K, Tahlil A D, et al.Electrical power generation from the oceanic wave for sustainable advancement in renewable energy tech- nologies[J]. Sustainability, 2020, 12(6): 2178. [11] 王坤林. 基于海岛直流纳电网波浪能发电场关键技术研究[D]. 广州: 华南理工大学, 2015. Wang Kunlin.The key issues of wave energy farms based on DC nano grid in islands[D]. Guangzhou: South China University of Technology, 2015. [12] 黄磊, 魏莱, 杨建龙, 等. 基于模型预测的直驱式波浪发电机机侧最优功率控制技术研究[J]. 电工技术学报, 2024, 39(14): 4391-4404. Huang Lei, Wei Lai, Yang Jianlong, et al.Research on optimal power control technology of direct-drive wave power generation system based on model prediction[J]. Transactions of China Electro- technical Society, 2024, 39(14): 4391-4404. [13] Rasool S, Muttaqi K M, Sutanto D.A multi-filter based dynamic power sharing control for a hybrid energy storage system integrated to a wave energy converter for output power smoothing[J]. IEEE Transactions on Sustainable Energy, 2022, 13(3): 1693-1706. [14] 吴清, 邢涛, 张小店, 等. 基于波浪能技术为海岛供电的方案研究[J]. 科学技术创新, 2020(25): 1-4. Wu Qing, Xing Tao, Zhang Xiaodian, et al.Research on power supply scheme for island based on wave energy technology[J]. Scientific and Technological Innovation, 2020(25): 1-4. [15] 李言华, 王喜莲, 王小君. 液压蓄能式波浪能发电系统及其控制方案研究[J]. 太阳能学报, 2022, 43(1): 219-227. Li Yanhua, Wang Xilian, Wang Xiaojun.Research on hydraulic energy-storage wave energy generation system and its control scheme[J]. Acta Energiae Solaris Sinica, 2022, 43(1): 219-227. [16] 曹飞飞, 史宏达, 赵晨羽, 等. 振荡浮子波浪发电装置物理模型试验研究[J]. 太阳能学报, 2020, 41(5): 244-249. Cao Feifei, Shi Hongda, Zhao Chenyu, et al.Experimental study on a heaving buoy wave energy converter[J]. Acta Energiae Solaris Sinica, 2020, 41(5): 244-249. [17] 叶寅, 王坤林, 盛松伟, 等. 波浪能装置液压发电系统实验研究[J]. 可再生能源, 2021, 39(12): 1699-1704. Ye Yin, Wang Kunlin, Sheng Songwei, et al.Experi- mental study on hydraulic power generation system of wave energy converter[J]. Renewable Energy Resources, 2021, 39(12): 1699-1704. [18] 陈佳, 兰飞, 郭昊霖, 等. 波浪能发电控制技术研究综述[J]. 电力自动化设备, 2023, 43(6): 124-136. Chen Jia, Lan Fei, Guo Haolin, et al.Review on wave energy power generation control technology[J]. Electric Power Automation Equipment, 2023, 43(6): 124-136. [19] 李飞宇, 顾延勋, 魏繁荣, 等. 波浪能液压发电系统灵活接入下的孤岛微电网细粒化调度[J]. 电工技术学报, 2023, 38(13): 3499-3511. Li Feiyu, Gu Yanxun, Wei Fanrong, et al.Fine- grained dispatching of isolated microgrids under flexible access of wave energy hydraulic power generation system[J]. Transactions of China Elec- trotechnical Society, 2023, 38(13): 3499-3511. [20] Feng Zhongnan, Wei Fanrong, Wu Chuantao, et al.Novel source-storage coordination strategy adaptive to impulsive generation characteristic suitable for isolated island microgrid scheduling[J]. IEEE Transa- ctions on Smart Grid, 2023, 14(5): 3791-3803. [21] 曾繁宏, 张俊勃. 电力系统惯性的时空特性及分析方法[J]. 中国电机工程学报, 2020, 40(1): 50-58, 373. Zeng Fanhong, Zhang Junbo.Temporal and spatial characteristics of power system inertia and its analysis method[J]. Proceedings of the CSEE, 2020, 40(1): 50-58, 373. [22] 张祥宇, 胡剑峰, 付媛, 等. 风储联合系统的虚拟惯量需求与协同支撑[J]. 电工技术学报, 2024, 39(3): 672-685. Zhang Xiangyu, Hu Jianfeng, Fu Yuan, et al.Virtual inertia demand and collaborative support of wind power and energy storage system[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 672-685. [23] 张恒旭, 高志民, 曹永吉, 等. 高比例可再生能源接入下电力系统惯量研究综述及展望[J]. 山东大学学报(工学版), 2022, 52(5): 1-13. Zhang Hengxu, Gao Zhimin, Cao Yongji, et al.Review and prospect of research on power system inertia with high penetration of renewable energy source[J]. Journal of Shandong University (Engin- eering Science), 2022, 52(5): 1-13. [24] Pepiciello A, Vaccaro A.An optimization-based method for estimating critical inertia in smart grids[C]//2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019: 2237-2241. [25] 江一航, 赵书强, 王慧, 等. 计及风电, 调相机支撑特性的频率安全约束分布鲁棒机组组合调度方法[J/OL]. 电工技术学报. https://doi.org/10.19595/j. cnki.1000-6753.tces.232164. Jiang Yihang, Zhao Shuqiang, Wang Hui, et al. Distributionally robust frequency constrained unit commitment with frequency support of wind power and synchronous condenser[J/OL]. Transactions of China Electrotechnical Society, https://doi.org/10. 19595/j.cnki.1000-6753.tces.232164. [26] 文云峰, 林晓煌. 孤岛与并网模式下微电网最低惯量需求评估[J]. 中国电机工程学报, 2021, 41(6): 2040-2052, 12. Wen Yunfeng, Lin Xiaohuang.Minimum inertia requirement assessment of microgrids in islanded and grid-connected modes[J]. Proceedings of the CSEE, 2021, 41(6): 2040-2052, 12. [27] 李越. 基于间歇性液压蓄能的波浪能发电系统研究[D]. 太原: 太原科技大学, 2017. Li Yue.Research on wave energy power generation system based on intermittent hydraulic energy storage[D]. Taiyuan: Taiyuan University of Science and Technology, 2017. [28] Wen Yunfeng, Li Wenyuan, Huang Gang, et al.Frequency dynamics constrained unit commitment with battery energy storage[J]. IEEE Transactions on Power Systems, 2016, 31(6): 5115-5125. [29] 王荔妍, 陈启鑫, 何冠楠, 等. 考虑电池储能寿命模型的发电计划优化[J]. 电力系统自动化, 2019, 43(8): 93-100. Wang Liyan, Chen Qixin, He Guannan, et al.Optimization of generation scheduling considering battery energy storage life model[J]. Automation of Electric Power Systems, 2019, 43(8): 93-100. [30] 罗青龙. 基于储电船舶时空分布特性的远洋海岛微电网能量优化管理研究[D]. 武汉: 华中科技大学, 2016. Luo Qinglong.Research on energy optimization management of offshore island microgrid based on the temporal and spatial distribution characteristics of power storage ships[D]. Wuhan: Huazhong University of Science and Technology, 2016.
2024, Vol. 39 
