考虑风电和负荷不确定性的输电网多目标柔性规划

doi:10.11930/j.issn.1004-9649.202007274

摘要/Abstract

摘要： 风电大规模集中并网使输电网中电源出力的不确定性显著增强，并且当前考虑风电和负荷不确定性的输电网规划模型大多采用直流潮流，规划结果的准确性有待提高。以交流潮流为基础，构建了考虑风电和负荷不确定性的输电网多目标柔性规划双层模型。上层模型是以建设投资等年值费用、年网损费用和运行效率为目标的输电网多目标规划模型，采用带精英策略的非支配排序遗传算法求解，并将帕累托最优规划方案集合传递给下层；下层模型是以弃风和切负荷量最小为目标的多场景校验模型，用来校验帕累托最优规划方案集合对于风电和负荷不确定性的承受能力，根据下层模型的弃风和切负荷期望值修改上层模型的约束条件，从而将风电和负荷不确定性的影响传递给上层模型。对下层模型的交流潮流约束进行二阶锥松弛，将非线性且非凸的规划模型转化为具有凸可行域的二阶锥规划模型，利用Cplex求解器进行高效求解。最后，用调整后的39节点系统验证了所提模型的有效性。

关键词: 风电和负荷不确定性, 交流潮流, 二阶锥规划, 多目标柔性规划, 双层模型

Abstract: Large-scale centralized integration of wind power into power grid significantly enhances the uncertainty of power output in transmission network. In addition, most of the current transmission network planning models adopt DC power flows, which affects the accuracy of planning results. Based on AC power flow, this paper constructs a two-layer model for multi-objective flexible planning of transmission networks considering the uncertainty of wind power and load. The upper-level model is a multi-objective planning model of transmission networks, with consideration of the objective of the annual cost of construction investment, annual network loss and operating efficiency. The non-dominated sorting genetic algorithm-II is used to solve the problem, and the Pareto optimal planning scheme set is transmitted to the lower level. The lower-level model is a multi-scenario checking model with the minimum wind curtailment and load shedding as objective, which is used to test the endurance capacity of the Pareto optimal planning scheme set to the uncertainty of wind power and load. The constraints of the upper model is modified according to the wind curtailment and load shedding expectations, thus transmitting the impact of wind power and load uncertainty to the upper-level model. The AC power flow constraints of the lower-level model is conducted for second order cone relaxation, and the nonlinear nonconvex planning model is transformed into a second order cone planning model with convex feasible region, which is effectively solved using the Cplex solver. Finally, the effectiveness of the proposed model is verified by an adjusted 39-node system.

Key words: wind power and load uncertainty, AC power flow, second-order cone planning, multi-objective flexible planning, two-layer model

王娟娟, 王涛, 刘子菡, 朱海南, 李丰硕, 孙华忠. 考虑风电和负荷不确定性的输电网多目标柔性规划[J]. 中国电力, 2022, 55(1): 168-177.

WANG Juanjuan, WANG Tao, LIU Zihan, ZHU Hainan, LI Fengshuo, SUN Huazhong. Multi-objective Flexible Planning of Transmission Network Considering Wind Power and Load Uncertainties[J]. Electric Power, 2022, 55(1): 168-177.

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https://www.electricpower.com.cn/CN/Y2022/V55/I1/168

参考文献

[1] 鲁宗相, 李海波, 乔颖. 含高比例可再生能源电力系统灵活性规划及挑战[J]. 电力系统自动化, 2016, 40(13): 147–158
LU Zongxiang, LI Haibo, QIAO Ying. Power system flexibility planning and challenges considering high proportion of renewable energy[J]. Automation of Electric Power Systems, 2016, 40(13): 147–158
[2] 时智勇, 王彩霞, 李琼慧. “十四五”中国海上风电发展关键问题[J]. 中国电力, 2020, 53(7): 8–17
SHI Zhiyong, WANG Caixia, LI Qionghui. Key issues of China's offshore wind power development in the “14th five-year plan”[J]. Electric Power, 2020, 53(7): 8–17
[3] 程耀华, 张宁, 王佳明, 等. 面向高比例可再生能源并网的输电网规划方案综合评价[J]. 电力系统自动化, 2019, 43(3): 33–42, 57
CHENG Yaohua, ZHANG Ning, WANG Jiaming, et al. Comprehensive evaluation of transmission network planning for integration of high-penetration renewable energy[J]. Automation of Electric Power Systems, 2019, 43(3): 33–42, 57
[4] 李晖. 考虑大规模新能源接入的电力系统规划研究及应用[D]. 北京: 华北电力大学(北京), 2017.
LI Hui. Research and application of power system planning considering large-scale new energy power integration[D]. Beijing: North China Electric Power University, 2017.
[5] 黄裕春. 计及间歇性能源发电的输电系统综合规划方法[D]. 杭州: 浙江大学, 2013.
HUANG Yuchun. Integrated transmission system planning considering accommodation of intermittent generation sources[D]. Hangzhou: Zhejiang University, 2013.
[6] 蒋霖, 郑倩薇, 王枫, 等. 考虑直接负荷控制与风电不确定性的输电网扩展规划[J]. 电力系统保护与控制, 2020, 48(3): 138–146
JIANG Lin, ZHENG Qianwei, WANG Feng, et al. Transmission network expansion planning considering direct load control and wind power uncertainty[J]. Power System Protection and Control, 2020, 48(3): 138–146
[7] 任大伟, 金晨, 侯金鸣, 等. 基于时序运行模拟的新能源配置储能替代火电的规划模型[J]. 中国电力, 2021, 54(7): 18–26
REN Dawei, JIN Cheng, HOU Jinming, et al. Planning model for renewable energy with energy storage replacing thermal power based on time series operation simulation[J]. Electric Power, 2021, 54(7): 18–26
[8] 程浩忠, 李隽, 吴耀武, 等. 考虑高比例可再生能源的交直流输电网规划挑战与展望[J]. 电力系统自动化, 2017, 41(9): 19–27
CHENG Haozhong, LI Jun, WU Yaowu, et al. Challenges and prospects for AC/DC transmission expansion planning considering high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41(9): 19–27
[9] 张宁, 胡兆光, 周渝慧, 等. 计及随机模糊双重不确定性的源网荷协同规划模型[J]. 电力系统自动化, 2016, 40(1): 39–44, 142
ZHANG Ning, HU Zhaoguang, ZHOU Yuhui, et al. Source-grid-load coordinated planning model considering randomness and fuzziness[J]. Automation of Electric Power Systems, 2016, 40(1): 39–44, 142
[10] MOREIRA A, STRBAC G, MORENO R, et al. A five-level MILP model for flexible transmission network planning under uncertainty: a min–max regret approach[J]. IEEE Transactions on Power Systems, 2018, 33(1): 486–501.
[11] 王秀丽, 李淑慧, 陈皓勇, 等. 基于非支配遗传算法及协同进化算法的多目标多区域电网规划[J]. 中国电机工程学报, 2006, 26(12): 11–15
WANG Xiuli, LI Shuhui, CHEN Haoyong, et al. Multi-objective and multi-district transmission planning based on NSGA-Ⅱ and cooperative co-evolutionary algorithm[J]. Proceedings of the CSEE, 2006, 26(12): 11–15
[12] 罗娅. 考虑运行效率的输电网规划模型及方法研究[D]. 北京: 华北电力大学(北京), 2017.
LUO Ya. Studying on models and methods of transmission network planning considering operating efficiency[D]. Beijing: North China Electric Power University, 2017.
[13] 梁子鹏, 陈皓勇, 郑晓东, 等. 考虑风电极限场景的输电网鲁棒扩展规划[J]. 电力系统自动化, 2019, 43(16): 58–67
LIANG Zipeng, CHEN Haoyong, ZHENG Xiaodong, et al. Robust expansion planning of transmission network considering extreme scenario of wind power[J]. Automation of Electric Power Systems, 2019, 43(16): 58–67
[14] 徐浩. 考虑新能源接入的输电网规划研究[D]. 济南: 山东大学, 2019.
XU Hao. Transmission network planning considering the integration of new energy resources[D]. Jinan: Shandong University, 2019.
[15] 田书欣, 程浩忠, 曾平良, 等. 大型集群风电接入输电系统规划研究综述[J]. 中国电机工程学报, 2014, 34(10): 1566–1574
TIAN Shuxin, CHENG Haozhong, ZENG Pingliang, et al. Review of transmission planning for integrating large clusters of wind power[J]. Proceedings of the CSEE, 2014, 34(10): 1566–1574
[16] 叶健民, 蔡京陶, 王若愚, 等. 考虑风电场接入的输电网与储能扩展鲁棒规划[J]. 南方电网技术, 2019, 13(3): 25–32
YE Jianmin, CAI Jingtao, WANG Ruoyu, et al. Expansion robust planning of transmission network and energy storage considering wind farm integration[J]. Southern Power System Technology, 2019, 13(3): 25–32
[17] GBADAMOSI S L, NWULU N I. A multi-period composite generation and transmission expansion planning model incorporating renewable energy sources and demand response[J]. Sustainable Energy Technologies and Assessments, 2020, 39: 100726.
[18] 柳璐, 程浩忠, 马则良, 等. 考虑全寿命周期成本的输电网多目标规划[J]. 中国电机工程学报, 2012, 32(22): 46–54, 19
LIU Lu, CHENG Haozhong, MA Zeliang, et al. Multi-objective transmission expansion planning considering life cycle cost[J]. Proceedings of the CSEE, 2012, 32(22): 46–54, 19
[19] 姜惠兰, 安星, 王亚微, 等. 基于改进NSGA2算法的考虑风机接入电能质量的多目标电网规划[J]. 中国电机工程学报, 2015, 35(21): 5405–5411
JIANG Huilan, AN Xing, WANG Yawei, et al. Improved NSGA2 algorithm based multi-objective planning of power grid with wind farm considering power quality[J]. Proceedings of the CSEE, 2015, 35(21): 5405–5411
[20] 辛培坤. 考虑输电价格影响的输电网多场景概率规划方法研究[D]. 北京: 华北电力大学(北京), 2018.
XIN Peikun. Research on multi-scenario probabilistic transmission planning considering transmission price[D]. Beijing: North China Electric Power University, 2018.
[21] 张衡, 程浩忠, 张建平, 等. 高比例风电背景下计及N-1安全网络约束的发输电优化规划[J]. 中国电机工程学报, 2018, 38(20): 5929–5936
ZHANG Heng, CHENG Haozhong, ZHANG Jianping, et al. Generation and transmission expansion planning considering N-1 security constraints with high penetration of wind power[J]. Proceedings of the CSEE, 2018, 38(20): 5929–5936
[22] DING Y, WANG P, GOEL L, et al. Long-term reserve expansion of power systems with high wind power penetration using universal generating function methods[J]. IEEE Transactions on Power Systems, 2011, 26(2): 766–774.
[23] 杨知方, 钟海旺, 夏清, 等. 输电网结构优化问题研究综述和展望[J]. 中国电机工程学报, 2016, 36(2): 426–434
YANG Zhifang, ZHONG Haiwang, XIA Qing, et al. Review and prospect of transmission topology optimization[J]. Proceedings of the CSEE, 2016, 36(2): 426–434
[24] TRPOVSKI A, HAMACHER T. A comparative analysis of transmission system planning for overhead and underground power systems using AC and DC power flow[C]//2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). Bucharest, Romania. IEEE, 2019: 1–5.
[25] KHANABADI M, GHASEMI H, DOOSTIZADEH M. Optimal transmission switching considering voltage security and N-1 contingency analysis[J]. IEEE Transactions on Power Systems, 2013, 28(1): 542–550.
[26] HAGHIGHAT H, ZENG B. Bilevel conic transmission expansion planning[J]. IEEE Transactions on Power Systems, 2018, 33(4): 4640–4642.
[27] HONG S Y, CHENG H Z, ZENG P L, et al. Composite generation and transmission expansion planning with second order conic relaxation of AC power flow[C]//2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). Xi'an, China. IEEE, 2016: 1688–1693.
[28] BAI Y, ZHONG H W, XIA Q, et al. A conic programming approach to optimal transmission switching considering reactive power and voltage security[C]//2015 IEEE Power & Energy Society General Meeting. Denver, CO, USA. IEEE, 2015: 1–5.
[29] 洪绍云, 程浩忠, 曾平良, 等. 输电网扩展优化规划研究综述[J]. 电网技术, 2016, 40(10): 3102–3107
HONG Shaoyun, CHENG Haozhong, ZENG Pingliang, et al. Review of transmission network expansion optimization planning[J]. Power System Technology, 2016, 40(10): 3102–3107
[30] KE D, SHI W H, BIE Z H, et al. Probability modeling on multiple time scales of wind power based on wind speed data[C]//2014 International Conference on Power System Technology. Chengdu, China. IEEE, 2014: 2590–2595.
[31] GEORGIEV A, SULAKOV S. Modelling of hourly wind generation using pan-European climatic data base and weibull probability distribution[C]//2019 16th International Conference on the European Energy Market (EEM). Ljubljana, Slovenia. IEEE, 2019: 1–4.