﻿ 新能源多时空尺度仿真系统
 计算机系统应用  2020, Vol. 29 Issue (8): 105-112 PDF

1. 中国科学院 计算机网络信息中心, 北京 100190;
2. 中国科学院大学, 北京 100049;
3. 郑州大学, 郑州 450001

Multiple Spatial and Temporal Scales Simulation System of New Energy
YANG Zhi-Ce1,2, WANG Jue1, CAO Hai-Zhou3, WANG Xiao-Guang1, WANG Yan-Gang1
1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Zhengzhou University, Zhengzhou 450001, China
Foundation item: National Key Research and Development Program of China (2017YFB0202202); Headquarter Science and Technology Project of State Grid Corporation of China (SGGR0000JSJS1800569)
Abstract: Compared with fossil energy power generation, the use of new energy such as wind power and photovoltaic power generation is conducive to energy security and social sustainable development. However, large-scale wind and photovoltaic power in national grid are seriously challenged by the overall dispatch of the power system, due to the regional, intermittent, random and unpredictable of wind and light. This study optimizes the objective function and the inter-regional tie-lines of the mid-to-long-term wind power acceptance capacity assessment model, and adds photovoltaic unit output constraints. The system has been practically applied in a province and a region, assisting the grid power system to determine power generation plans and unit maintenance arrangements. It can reduce the occurrence of new energy abandonment, and provide effective guidance for the actual dispatch and planning of power systems through visualized results.
Key words: new energy generation     multiple spatial and temporal scales     constraints of photovoltaic unit model     new energy consumption capacity     simulation system

1 多时空尺度新能源仿真系统 1.1 目标函数优化

 $\begin{split} &\max \displaystyle\sum\limits_{t = 1}^T {\displaystyle\sum\limits_{n = 1}^N {{C_1}newenergy(t,n)} } -\\ & {C_2}\left({\displaystyle\sum\limits_{j = 1}^{{S_{\rm kind}}} {{A_j}S_{j,{\rm on}}^{t,n} + {B_j}S_{j,{\rm off}}^{t,n} + CP_j^{t,n}}} \right) -\\ & {C_3}\displaystyle\sum\limits_{j = 1}^{{S_{\rm kind}}} {|{S_j}(t,n) - {S_j}(t - 1,n)|} \end{split}$ (1)

1.2 约束条件优化

 $0 \le P_v^{t,n} \le N_{{V}}^n\overline {P_{{V}}^{t,n}}$ (2)

1.3 区域间联络线优化

1.4 多时空尺度新能源仿真系统设计

 图 1 多时空尺度新能源仿真系统设计

2 多时空尺度新能源仿真系统应用

2.1 电网数据预处理

(1)电网机组数据类型: 常规机组以及光伏风电机组的机组个数、装机容量、常规机组爬坡功率等信息. 电网机组数据类型具体抽象见表1.

(2)时间序列数据类型: 将一年按周、天、小时的方式划分, 以周进行迭代.

(3)电网电厂数据类型: 与外省联络线数据、风光电预测功率、电网正备用容量及分区信息.

2.2 某省全网新能源消纳计算 2.2.1 计算条件

 图 2 某省电网等效聚合图

2.2.2 回算验证

2.2.3 全网2020年消纳计算

4个区域N1、N2、N3和N4的风电和光伏具体发电量见表4.

2.2.4 机组启停安排

N1地区凝气机组出力和台数见表5.

2.2.5 机组启停安排

 图 3 预计2020年4个地区新能源出力曲线

 图 4 N1地区网供负荷和凝气30万千瓦机组启停机关系

2.2.6 敏感性分析

2.3 某地区新能源消纳计算 2.3.1 计算条件

2020年某地区风电装机113.2万千瓦、光伏装机35万千瓦, 常规机组按等效聚合分类见表9.

 图 5 某地区电网等效聚合图

2.3.2 某地区2020年消纳计算

2.3.3 机组启停安排

 图 6 某地区新能源接纳电量

2.3.4 某地区新能源最大承载能力

2021~2022年, 某地区无新增常规机组, 预计负荷零增长. 预计某地区电网2020~2021年风电、光伏装机容量见表10.

2021、2022年某地区新能源弃风、弃光时段如图7所示, 结果表明多发生在春秋冬季.

 图 7 2020和2021年某地区限电率

3 总结

 [1] 陈学桦, 宋敏. 河南省清洁能源“风光”无限. 河南日报, 2016-01-12. [2] Zhao Z, Tong XJ. Economic dispatch of wind integrated power systems with a conditional risk method. Proceedings of the 9th IET International Conference on Advances in Power System Control, Operation and Management. Hong Kong, China. 2012. 1–6. [3] 李星雨, 邱晓燕, 史光耀. 考虑环境成本和需求响应的风电并网优化. 电测与仪表, 2018, 55(2): 33-38. DOI:10.3969/j.issn.1001-1390.2018.02.006 [4] 戴剑丰, 汤奕, 曲立楠, 等. 太阳能光热与风力发电协调优化控制研究. 计算机仿真, 2017, 34(10): 73-77. DOI:10.3969/j.issn.1006-9348.2017.10.016 [5] Makarov YV, Etingov PV, Ma J, et al. Incorporating uncertainty of wind power generation forecast into power system operation, dispatch, and unit commitment procedures. IEEE Transactions on Sustainable Energy, 2011, 2(4): 433-442. DOI:10.1109/TSTE.2011.2159254 [6] Ummels BC, Gibescu M, Pelgrum E, et al. Impacts of wind power on thermal generation unit commitment and dispatch. IEEE Transactions on Energy Conversion, 2007, 22(1): 44-51. DOI:10.1109/TEC.2006.889616 [7] Lowery C, O’Malley M. Impact of wind forecast error statistics upon unit commitment. IEEE Transactions on Sustainable Energy, 2012, 3(4): 760-768. DOI:10.1109/TSTE.2012.2210150 [8] Ela E, O’Malley M. Studying the variability and uncertainty impacts of variable generation at multiple timescales. IEEE Transactions on Power Systems, 2012, 27(3): 1324-1333. DOI:10.1109/TPWRS.2012.2185816 [9] Jeong YW, Park JB, Jang SH, et al. A new quantum-inspired binary PSO: Application to unit commitment problems for power systems. IEEE Transactions on Power Systems, 2010, 25(3): 1486-1495. DOI:10.1109/TPWRS.2010.2042472 [10] Cao Y, Liu C, Huang YH, et al. Wind power accommodation capability of large-scale interconnected power grid based on equivalent aggregation method. High Voltage Engineering, 2016, 42(9): 2792-2799. [11] Zhang LZ, Zhou N, Wang N. Economic comparison for different generation schedulings with large scale wind power connected power system. Automation of Electric Power Systems, 2011, 35(22): 105-110. [12] 刘文颖, 文晶, 谢昶, 等. 考虑风电消纳的电力系统源荷协调多目标优化方法. 中国电机工程学报, 2015, 35(5): 1079-1088. [13] 赵振宇, 苑曙光, 胡明辉. 基于空间聚类统计模型的风电消纳潜力区域分析. 电网技术, 2019, 43(10): 3641-3647. [14] Liu C, Cao Y, Huang YH, et al. An annual wind power planning method based on time sequential simulations. Automation of Electric Power Systems, 2014, 38(11): 13-19. [15] Liu C, Wu H, Gao CZ, et al. Study on analysis method of accommodated capacity for wind power. Power System Protection and Control, 2014, 42(4): 61-66. [16] Xu ZY, Luo XJ, Niu T. Thermal unit commitment scheme considering electricity market and energy-saving dispatch. Automation of Electric Power Systems, 2009, 33(22): 14-17. [17] Wang TQ, Liu F. A mixed integer model for large-scale new energy medium-term operation problem. International Journal of Performability Engineering, 2017, 13(8): 1381-1388. DOI:10.2390/ijpe.17.08.p19.13811388 [18] Karlsson K, Meibom P. Optimal investment paths for future renewable based energy systems-using the optimisation model Balmorel. International Journal of Hydrogen Energy, 2008, 33(7): 1777-1787. DOI:10.1016/j.ijhydene.2008.01.031 [19] 孙明一, 孙力勇, 葛延峰. 基于时序生产模拟法的辽宁电网风电接纳能力研究. 2015年中国电机工程学会年会论文集. 武汉, 中国. 2015. 1–11. [20] 陈永华, 李明应, 史开礼. 安全稳定控制装置在大规模风电并网控制中的应用. 中国电机工程学会第十二届青年学术会议论文集. 杭州, 中国. 2012. 1–5. [21] Liu DW, Huang YH, Wang WS, et al. Analysis on provincial system available capability of accommodating wind power considering peak load dispatch and transmission constraints. Automation of Electric Power Systems, 2011, 35(22): 77-81. [22] GAMS development corp. https://www.gams.com. [2020-01-09]. [23] CPLEX. Technical Report. IBM ILOG. http://www.ilog.com/products/cplex. [2020-01-09]. [24] SCIP. http://scip.zib.de. [2020-01-09].