Capacity Allocation Method for Wind-Solar-Hydro-Storage Complementary System Considering Time and Spatial Transfer Characteristics of Load

doi:10.11930/j.issn.1004-9649.202002151

Abstract

Abstract: By taking advantage of the quick regulation ability of cascade hydropower and the flexible conversion of the working conditions between power generation and pumping of the pumping generator units, and on the basis of compensating the fluctuation and uncontrollability of wind and PV output, a wind-solar-hydro-storage complementary system model is established at the source end to enhance the regulation ability of the power generation system. At the same time, considering the market spot electricity price and the time and spatial transfer characteristics of load, a load-side model is established, and a source-grid-load interaction strategy is proposed based on multiple system indicators such as the economy and state evenness of system and the load tracking, with consideration of the randomness of wind/solar power output at source end and the system network constraints. Based on above study results, a multi-objective capacity allocation model of complementary power generation system is built. Finally, through a case simulation, the nonlinear optimization problem is solved by professional optimization software (LINGO), and the allocation capacities of the source end system under different planning years and scenarios are obtained, which has verified the effectiveness of the proposed method in improving the system indicators.

Key words: demand response, source-grid-load, wind-solar-hydro-storage system, complementary system, electricity market

WEN Jie, LIU Jichun, WEN Zhengnan, LI Jianhua, LI Chenxin. Capacity Allocation Method for Wind-Solar-Hydro-Storage Complementary System Considering Time and Spatial Transfer Characteristics of Load[J]. Electric Power, 2021, 54(2): 66-77,97.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202002151

https://www.electricpower.com.cn/EN/Y2021/V54/I2/66

References

[1] 邹云阳, 杨莉, 李佳勇, 等. 冷热电气多能互补的微能源网鲁棒优化调度[J]. 电力系统自动化, 2019, 43(14): 65-77
ZOU Yunyang, YANG Li, LI Jiayong, et al. Robust optimal dispatch of micro-energy grid with multi-energy complementation of cooling heating power and natural gas[J]. Automation of Electric Power Systems, 2019, 43(14): 65-77
[2] 何畅, 程杉, 徐建宇, 等. 基于多时间尺度和多源储能的综合能源系统能量协调优化调度[J]. 电力系统及其自动化学报, 2020, 32(2): 77-84, 97
HE Chang, CHENG Shan, XU Jianyu, et al. Coordinated optimal scheduling of integrated energy system considering multi-time scale and hybrid energy storage system[J]. Proceedings of the CSU-EPSA, 2020, 32(2): 77-84, 97
[3] 陈道君, 王璇, 左剑, 等. 计及风电出力不确定性的抽水蓄能-风电联合优化调度方法[J]. 电网与清洁能源, 2016, 32(8): 110-116
CHEN Daojun, WANG Xuan, ZUO Jian, et al. A joint optimal dispatching method for pumped storage power station and wind power considering wind power uncertainties[J]. Power System and Clean Energy, 2016, 32(8): 110-116
[4] 李丹, 刘俊勇, 刘友波, 等. 风电接入后考虑抽蓄-需求响应的多场景联合安全经济调度模型[J]. 电力自动化设备, 2015, 35(2): 28-34, 41
LI Dan, LIU Junyong, LIU Youbo, et al. Joint secure & economic dispatch considering wind power, pumped storage and demand response[J]. Electric Power Automation Equipment, 2015, 35(2): 28-34, 41
[5] 张宁, 代红才, 胡兆光, 等. 考虑系统灵活性约束与需求响应的源网荷协调规划模型[J]. 中国电力, 2019, 52(2): 61-69
ZHANG Ning, DAI Hongcai, HU Zhaoguang, et al. A source-grid-load coordinated planning model considering system flexibility constraints and demand response[J]. Electric Power, 2019, 52(2): 61-69
[6] 赵丰明, 樊艳芳. 分时电价影响下的多能源虚拟电厂双层优化调度[J]. 电力系统保护与控制, 2019, 47(20): 33-40
ZHAO Fengming, FAN Yanfang. Bi level optimal dispatching of multi energy virtual power plant influenced by TOU price[J]. Power System Protection and Control, 2019, 47(20): 33-40
[7] 罗纯坚, 李姚旺, 许汉平, 等. 需求响应不确定性对日前优化调度的影响分析[J]. 电力系统自动化, 2017, 41(5): 22-29
LUO Chunjian, LI Yaowang, XU Hanping, et al. Influence of demand response uncertainty on day-ahead optimization dispatching[J]. Automation of Electric Power Systems, 2017, 41(5): 22-29
[8] 张高航,李凤婷,周强,等. 考虑风电并网友好性的日前分层调度计划模型[J]. 电力系统保护与控制, 2019, 47(6): 118-124
ZHANG Gaohang, LI Fengting, ZHOU Qiang, et al. Day-ahead hierarchical dispatching model considering friendliness of wind power[J]. Power System Protection and Control, 2019, 47(6): 118-124
[9] 石玉东,刘晋源,徐松,等. 考虑时序特性的配电网风-光-储随机规划模型[J]. 电力系统保护与控制, 2019, 47(10): 23-32
SHI Yudong, LIU Jinyuan, XU Song, et al. Integrated wind-photovoltaic-storage stochastic planning model considering time-varying characteristics in distribution network[J]. Power System Protection and Control, 2019, 47(10): 23-32
[10] 肖白, 丛晶, 高晓峰, 等. 风电-抽水蓄能联合系统综合效益评价方法[J]. 电网技术, 2014, 38(2): 400-404
XIAO Bai, CONG Jing, GAO Xiaofeng, et al. A method to evaluate comprehensive benefits of hybrid wind power-pumped storage system[J]. Power System Technology, 2014, 38(2): 400-404
[11] 董文略, 王群, 杨莉. 含风光水的虚拟电厂与配电公司协调调度模型[J]. 电力系统自动化, 2015, 39(9): 75-81, 207
DONG Wenlue, WANG Qun, YANG Li. A coordinated dispatching model for a distribution utility and virtual power plants with wind/photovoltaic/hydro generators[J]. Automation of Electric Power Systems, 2015, 39(9): 75-81, 207
[12] 李健华, 刘继春, 陈雪, 等. 含可再生能源的多能互补发电系统容量配置方法[J]. 电网技术, 2019, 43(12): 4387-4398
LI Jianhua, LIU Jichun, CHEN Xue, et al. Capacity allocation of multi-energy complementary system including renewable energy[J]. Power System Technology, 2019, 43(12): 4387-4398
[13] TRAN N H, TRAN D H, REN S L, et al. How geo-distributed data centers do demand response: a game-theoretic approach[J]. IEEE Transactions on Smart Grid, 2015: 1.
[14] 吴鸣, 任学婧, 周丹, 等. 新电改背景下产业园区供电系统容量优化配置方法[J]. 电力系统自动化, 2018, 42(5): 2-8
WU Ming, REN Xuejing, ZHOU Dan, et al. Optimal allocation method for capacity of power supply system in industrial park under new electricity market reform[J]. Automation of Electric Power Systems, 2018, 42(5): 2-8
[15] 陈紫颖, 荆朝霞, 刘云仁, 等. 电力现货市场增量优化与全电量优化模式的比较[J]. 南方电网技术, 2019, 13(11): 1-8
CHEN Zijin, JING Zhaoxia, LIU Yunren, et al. Comparison of incremental optimization model and integrated optimization model for electricity spot market[J]. Southern Power System Technology, 2019, 13(11): 1-8
[16] 孙川, 汪隆君, 许海林. 用户互动负荷模型及其微电网日前经济调度的应用[J]. 电网技术, 2016, 40(7): 2009-2016
SUN Chuan, WANG Longjun, XU Hailin. An interaction load model and its application in microgrid day-ahead economic scheduling[J]. Power System Technology, 2016, 40(7): 2009-2016
[17] 孙伟卿, 王承民, 张焰, 等. 电力系统运行均匀性分析与评估[J]. 电工技术学报, 2014, 29(4): 173-180
SUN Weiqing, WANG Chengmin, ZHANG Yan, et al. Analysis and evaluation on power system operation homogeneity[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 173-180
[18] 朱兰, 周雪莹, 唐陇军, 等. 计及可中断负荷的微电网多目标优化运行[J]. 电网技术, 2017, 41(6): 1847-1854
ZHU Lan, ZHOU Xueying, TANG Longjun, et al. Multi-objective optimal operation for microgrid considering interruptible loads[J]. Power System Technology, 2017, 41(6): 1847-1854
[19] ZHANG X S, BAO T, YU T, et al. Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid[J]. Energy, 2017, 133: 348-365.