Renewable Energy Capacity Assessment in Power System Based on Linearized OPF

doi:10.11930/j.issn.1004-9649.202107061

Abstract

Abstract: With the deepening adjustment of energy structure, the penetration of renewable energy in power system is gradually increased. The fluctuation and randomness of renewable energy output have brought new challenges to the power system operation. It is of great significance for the planning and operation of power system with renewable energy to scientifically evaluate the capacity of renewable energy consumption. In this paper, a linear optimal power flow (OPF) model of renewable energy consumption capacity in power system was established. Considering the nonlinear characteristic of power system power flow, a linearization method was established to solve the problem that the traditional high-order nonconvex OPF model is difficult to obtain the global optimal solution. Considering the voltage magnitude, line power flow, power injection and the climbing constraints, the renewable energy consumption capacity of power system was evaluated. The effectiveness of the proposed OPF is verified by simulation analysis, and the main factors restricting renewable energy penetration are discussed with a standard testing system.

Key words: optimal power flow, power flow linearization, renewable energy, hosting capacity assessment

DONG Yu, DONG Cun, YU Ruoying, DING Jie. Renewable Energy Capacity Assessment in Power System Based on Linearized OPF[J]. Electric Power, 2022, 55(3): 1-8.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202107061

https://www.electricpower.com.cn/EN/Y2022/V55/I3/1

References

[1] 中华人民共和国国务院新闻办公室. 新时代的中国能源发展白皮书[R]. 北京, 2020.
[2] 国家电网报. 国家电网公司发布“碳达峰、碳中和”行动方案[R]. 北京, 2021.
[3] 辛保安. 为实现“碳达峰碳中和”目标贡献智慧和力量[N]. 中国电力报, 2021-02-24(1).
[4] 段卫国. 加快构建新型电力系统助力实现“双碳”目标[N]. 中国能源报, 2021-07-05(4).
[5] 董战峰. “双碳”目标下绿色产业发展迎来战略机遇[N]. 经济参考报, 2021-04-30(1).
[6] 王怡. 制定更积极新能源发展目标加快推动碳达峰、碳中和[N]. 中国电力报, 2021-03-10(1).
[7] 国家能源局. 2019年度全国可再生能源电力发展监测评价报告[R]. 北京, 2019.
[8] 张岩, 代贤忠, 李振伟, 等. 基于时序生产模拟的区域电网新能源消纳能力研究[J/OL]. 中国电力: 1–7[2020-08-31]. http://kns.cnki.net/kcms/detail/11.3265.TM.20200804.1425.002.html.
ZHANG Yan, DAI Xianzhong, LI Zhenwei, et al. Temporal production simulation based renewable energy integration capacity assessment in regional power system under the new circumstances[J/OL]. Electric Power: 1–7[2020-08-31]. http://kns.cnki.net/kcms/detail/11.3265.TM.20200804.1425.002.html.
[9] 姚明侠, 杨超, 陈国华, 等. 基于时序生产模拟的新能源消纳能力分析[J]. 江西电力, 2018, 42(8): 10–14
[10] 吴冠男, 张明理, 徐建源, 等. 适用于评估风电接纳能力的时序生产模拟算法研究[J]. 电力系统保护与控制, 2017, 45(23): 151–157
WU Guannan, ZHANG Mingli, XU Jianyuan, et al. Time series production algorithm for evaluating wind power accommodation capacity[J]. Power System Protection and Control, 2017, 45(23): 151–157
[11] 曹阳, 李鹏, 袁越, 等. 基于时序仿真的新能源消纳能力分析及其低碳效益评估[J]. 电力系统自动化, 2014, 38(17): 60–66
CAO Yang, LI Peng, YUAN Yue, et al. Analysis on accommodating capability of renewable energy and assessment on low-carbon benefits based on time sequence simulation[J]. Automation of Electric Power Systems, 2014, 38(17): 60–66
[12] 朱睿, 胡博, 谢开贵, 等. 含风电–光伏–光热–水电–火电–储能的多能源电力系统时序随机生产模拟[J]. 电网技术, 2020, 44(9): 3246–3253
ZHU Rui, HU Bo, XIE Kaigui, et al. Sequential probabilistic production simulation of multi-energy power system with wind power, photovoltaics, concentrated solar power, cascading hydro power, thermal power and battery energy storage[J]. Power System Technology, 2020, 44(9): 3246–3253
[13] 刘纯, 屈姬贤, 石文辉. 基于随机生产模拟的新能源消纳能力评估方法[J]. 中国电机工程学报, 2020, 40(10): 3134–3144
LIU Chun, QU Jixian, SHI Wenhui. Evaluating method of ability of accommodating renewable energy based on probabilistic production simulation[J]. Proceedings of the CSEE, 2020, 40(10): 3134–3144
[14] 王立国, 朱燕芳, 武晓冬, 等. 基于时序随机生产模拟的风电置信容量评估[J]. 电力系统及其自动化学报, 2018, 30(11): 114–119
WANG Liguo, ZHU Yanfang, WU Xiaodong, et al. Assessment on capacity value of wind power based on probabilistic chronological production cost simulation[J]. Proceedings of the CSU-EPSA, 2018, 30(11): 114–119
[15] 薛蕾, 井天军, 陈义, 等. 配电网光伏消纳能力定界模拟与消纳方案综合择优[J]. 电网技术, 2020, 44(3): 907–916
XUE Lei, JING Tianjun, CHEN Yi, et al. Boundary simulation of PV accommodation capacity of distribution network and comprehensive selection of accommodation scheme[J]. Power System Technology, 2020, 44(3): 907–916
[16] WANG S X, CHEN S J, GE L J, et al. Distributed generation hosting capacity evaluation for distribution systems considering the robust optimal operation of OLTC and SVC[J]. IEEE Transactions on Sustainable Energy, 2016, 7(3): 1111–1123.
[17] CHEN X, WU W C, ZHANG B M, et al. Data-driven DG capacity assessment method for active distribution networks[J]. IEEE Transactions on Power Systems, 2017, 32(5): 3946–3957.
[18] 范志成, 朱俊澎, 袁越, 等. 基于改进型直流潮流算法的主动配电网分布式电源规划模型及其线性化方法[J]. 电网技术, 2019, 43(2): 504–513
FAN Zhicheng, ZHU Junpeng, YUAN Yue, et al. Distributed generation planning model of active distribution network and linearization method based on improved DC power flow algorithm[J]. Power System Technology, 2019, 43(2): 504–513
[19] 邢海军, 程浩忠, 曾平良, 等. 基于二阶锥规划的间歇性分布式电源消纳研究[J]. 电力自动化设备, 2016, 36(6): 74–80
XING Haijun, CHENG Haozhong, ZENG Pingliang, et al. IDG accommodation based on second-order cone programming[J]. Electric Power Automation Equipment, 2016, 36(6): 74–80
[20] BARADAR M, HESAMZADEH M R. AC power flow representation in conic format[J]. IEEE Transactions on Power Systems, 2015, 30(1): 546–547.
[21] ZHU J P, YUAN Y, WANG W S. An exact microgrid formation model for load restoration in resilient distribution system[J]. International Journal of Electrical Power & Energy Systems, 2020, 116: 105568.
[22] 徐艳春, 罗凯, 谢莎莎, 等. 计及负荷电压静特性的有源配电网线性潮流计算[J]. 智慧电力, 2020, 48(11): 40–47
XU Yanchun, LUO Kai, XIE Shasha, et al. Linear power flow calculation of active distribution network considering static characteristics of load voltage[J]. Smart Power, 2020, 48(11): 40–47
[23] 梁宵, 焦彦军, 蒋晨阳. 计及分布式电源的改进配网潮流计算方法[J]. 华北电力大学学报(自然科学版), 2016, 43(4): 59–65
LIANG Xiao, JIAO Yanjun, JIANG Chenyang. Improved power flow calculation method for distribution network with DGs[J]. Journal of North China Electric Power University (Natural Science Edition), 2016, 43(4): 59–65
[24] 韩源, 刘健, 陈亮亮, 等. 含分布式电源的大规模配电网快速潮流计算[J]. 西北水电, 2021(3): 105–108,122
HAN Yuan, LIU Jian, CHEN Liangliang, et al. Fast load flow calculation of large-scale distribution networks with distributed power generation[J]. Northwest Hydropower, 2021(3): 105–108,122
[25] 邵华, 贺春光, 安佳坤, 等. 基于线性约束的有源配电网规划研究[J]. 电力科学与技术学报, 2020, 35(5): 66–74
SHAO Hua, HE Chunguang, AN Jiakun, et al. Active distribution network planning model based on linearized constraints[J]. Journal of Electric Power Science and Technology, 2020, 35(5): 66–74
[26] YIN H, LI Q, LIU Y B, et al. Power flow calculation for a distribution system with multi-port PETs: an improved AC-DC decoupling iterative method[J]. Global Energy Interconnection, 2020, 3(4): 313–323.
[27] 叶亮, 吕智林, 王蒙, 等. 基于最优潮流的含多微网的主动配电网双层优化调度[J]. 电力系统保护与控制, 2020, 48(18): 27–37
YE Liang, LÜ Zhilin, WANG Meng, et al. Bi-level programming optimal scheduling of ADN with a multi-microgrid based on optimal power flow[J]. Power System Protection and Control, 2020, 48(18): 27–37
[28] 张福民, 刘国鑫, 李占凯, 等. 基于二阶锥规划的交直流混合配电网优化调度[J]. 智慧电力, 2020, 48(3): 117–123
ZHANG Fumin, LIU Guoxin, LI Zhankai, et al. Optimal dispatch of AC/DC hybrid distribution network based on second-order cone programming[J]. Smart Power, 2020, 48(3): 117–123
[29] 李红伟, 张力丹, 韩璐, 等. 基于下垂控制的直流电网线性潮流计算研究[J]. 智慧电力, 2021, 49(12): 66–71
LI Hongwei, ZHANG Lidan, HAN Lu, et al. Linearized power flow calculation of DC power grid based on droop control[J]. Smart Power, 2021, 49(12): 66–71
[30] ZHUO Z Y, ZHANG N, YANG J W, et al. Transmission expansion planning test system for AC/DC hybrid grid with high variable renewable energy penetration[J]. IEEE Transactions on Power Systems, 2020, 35(4): 2597–2608.