计及风、光消纳的风电-光伏-光热互补发电二层优化调度

熊伟; 马志程; 张晓英; 王琨; 周强; 陈伟

doi:10.19912/j.0254-0096.tynxb.2020-1146

PDF(1891 KB)

太阳能学报 ›› 2022, Vol. 43 ›› Issue (7) : 39-48. DOI: 10.19912/j.0254-0096.tynxb.2020-1146

计及风、光消纳的风电-光伏-光热互补发电二层优化调度

熊伟¹, 马志程², 张晓英¹, 王琨³, 周强², 陈伟¹

作者信息 +

TWO-LAYER OPTIMAL DISPATCH OF WF-PV-CSP HYBRID POWER GENERATION CONSIDERING WIND POWER AND PHOTOVOLTAIC CONSUMPTION

Xiong Wei¹, Ma Zhicheng², Zhang Xiaoying¹, Wang Kun³, Zhou Qiang², Chen Wei¹

Author information +

文章历史 +

摘要

以风电、光伏、光热构成新能源互补发电模式,计及风、光资源消纳,建立将高载能负荷作为可调度资源参与新能源互补发电系统的二层优化调度模型,采用NSGA-Ⅱ和二进制粒子群算法求解模型。上层优化模型以互补系统输出功率波动最小和并网效益最大为优化指标确定各机组的出力,并且计算出弃风弃光量。在此基础上,下层优化模型针对上层优化模型造成的弃风弃光量,选取能够有效消纳风、光的高载能负荷参与电网调度。最后,以甘肃地区实际数据为例,验证了所提方法的可行性和有效性。仿真结果表明,光热电站的参与增加了44.3%的经济效益,减小了65.8%的输出功率波动,高载能负荷的参与减小了86.3%的弃风弃光量。

Abstract

This paper makes a new energy hybrid power generation model, composed of wind power, photovoltaics, and concentrating solar power （CSP）. Considering the consumption of wind and solar resources, a two-tier optimal scheduling model that uses high-load energy loads as schedulable resources to participate in the new energy complementary power generation system has been established. The fast non-dominated sorting genetic algorithm （NSGA-Ⅱ） with elite strategy and binary particle swarm algorithm are used to solve the two-layer model. The upper-level optimization model uses the minimum output power fluctuation of the hybrid system and the maximum grid connection benefit as the optimization indicators to determine the output of each unit, to calculates the amount of wind and solar abandonment. On this basis, the lower-level optimization model selects the high-energy loads that can effectively absorb wind and light to participate in the grid dispatching according to the amount of wind and light abandonment caused by the upper-level optimization model. Finally, the actual data in Gansu district of China verifies the feasibility and effectiveness of the proposed method. The simulation results show that the participation of CSP stations increases the economic benefits by 44.3%, the output power fluctuations drops by 65.8%, and the participation of high-energy loads reduces the amount of wind and solar abandonment by 86.3%

导出引用

熊伟, 马志程, 张晓英, 王琨, 周强, 陈伟. 计及风、光消纳的风电-光伏-光热互补发电二层优化调度[J]. 太阳能学报. 2022, 43(7): 39-48 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1146

Xiong Wei, Ma Zhicheng, Zhang Xiaoying, Wang Kun, Zhou Qiang, Chen Wei. TWO-LAYER OPTIMAL DISPATCH OF WF-PV-CSP HYBRID POWER GENERATION CONSIDERING WIND POWER AND PHOTOVOLTAIC CONSUMPTION[J]. Acta Energiae Solaris Sinica. 2022, 43(7): 39-48 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1146

中图分类号： TM761

参考文献

[1] 周玮, 孙辉, 顾红, 等. 含风电场的电力系统经济调度研究综述[J]. 电力系统保护与控制, 2011, 39(24): 148-154.
ZHOU W, SUN H, GU H, et al.Research review on economic scheduling of power systems including wind farms[J]. Power system protection and control, 2011, 39(24) : 148-154.
[2] 杨雨瑶, 张勇军, 林国营, 等. 含光储联合发电系统的配电网双目标拓展无功优化[J]. 电力系统保护与控制, 2018, 46(22): 39-46.
YANG Y Y, ZHANG Y J, LIN G Y, et al.Dual-objective expansion reactive power optimization of distribution network with optical storage combined power generation system[J]. Power system protection and control, 2018, 46(22): 39-46.
[3] 郑太一, 范国英, 孙勇, 等. 大规模风电接入电网多目标电源的协调控制[J]. 电网技术, 2013, 37(11): 3091-3095.
ZHENG T Y, FAN G Y, SUN Y, et al.Coordinated control of large-scale wind power access to grid multi-target power supply[J]. Power system technology, 2013, 37(11) : 3091-3095.
[4] 车泉辉, 娄素华, 吴耀武, 等. 计及条件风险价值的含储热光热电站与风电电力系统经济调度[J]. 电工技术学报, 2019, 34(10): 2047-2055.
CHE Q H, LOU S H, WU Y W, et al.Economical dispatching of thermal power stations with thermal storage and wind power system considering conditional value at risk[J]. Transactions of China Electrotechnical Society, 2019, 34(10): 2047-2055.
[5] XU T, ZHANG N.Coordinated operation of concentrated solar power and wind resources for the provision of energy and reserve services[J]. IEEE transactions on power system, 2017, 32(2): 1260-1271.
[6] 崔杨, 杨志文, 张节潭, 等. 计及综合成本的风电-光伏-光热联合出力调度策略[J]. 高电压技术, 2019, 31(1): 269-275.
CUI Y, YANG Z W, ZHANG J T, et al.Combined output scheduling strategy of wind power, photovoltaic and photothermal with comprehensive cost[J]. High voltage technology, 2019, 31(1): 269-275.
[7] 张宏, 陈钊, 黄蓉, 等. 风电-光伏-光热联合发电系统的模糊多目标优化模型[J]. 电源学报, 2019, 26(1): 1-13.
ZHANG H, CHEN Z, HUANG R, et al.Multi-objective optimization model of wind power-PV-CSP power generation system[J]. Journal of power sources, 2019, 26(1): 1-13.
[8] 付菊霞, 陈洁, 滕扬新, 等. 基于集合经验模态分解的风电混合储能系统能量管理协调控制策略[J]. 电工技术学报, 2019, 34(10): 2038-2046.
FU J, CHEN J, TENG Y X, et al.Energy management coordinated control strategy for wind energy hybrid energy storage system based on ensemble empirical mode decomposition[J]. Transactions of China Electrotechnical Society, 2019, 34(10): 2038-2046.
[9] 李军徽, 张嘉辉, 穆钢, 等. 储能辅助火电机组深度调峰的分层优化调度[J]. 电网技术, 2019, 43(11): 3961-3970.
LI J H, ZHANG J H, MU G, et al.Hierarchical optimal dispatch of energy storage assisted thermal power unit deep peak shaving[J]. Power system technology, 2019, 43(11): 3961-3970.
[10] 彭院院, 周任军, 李斌, 等. 计及光热发电特性的光-风-火虚拟电厂双阶段优化调度[J]. 电力系统及其自动化学报, 2020, 32(4): 21-28.
PENG Y Y, ZHOU R J, LI B, et al.Two-stage optimal dispatch of photovoltaic-wind-thermal virtual power plant considering the characteristics of solar thermal power generation[J]. Power system and automation, 2020, 32(4): 21-28.
[11] 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[J]. 中国电机工程学报, 2015, 35(5): 1079-1088.
LIU W J, WEN J, XIE W, et al.Multi-objective optimization of source-source coordination for power system considering wind power consumption[J]. Proceedings of the CSEE, 2015, 35(5): 1079-1088.
[12] 郭鹏, 文晶, 朱丹丹, 等. 基于源-荷互动的大规模风电消纳协调控制策略[J]. 电工技术学报, 2017, 32(3): 1-9.
GUO P, WEN J, ZHU D D, et al.Coordinated control strategy for large-scale wind power consumption based on source-charge interaction[J]. Transactions of China Electrotechnical Society, 2017, 32(3): 1-9.
[13] 黄伟, 熊伟鹏, 华亮亮, 等. 基于动态调度优先级的主动配电网多目标优化调度[J]. 电工技术学报, 2018, 33(15): 3486-3498.
HUANG W, XIONG W P, HUA L L, et al.Multi-objective optimal scheduling for active distribution networks based on dynamic scheduling priority[J]. Transactions of China Electrotechnical Society, 2018,33(15): 3486-3498.
[14] 刘聪, 刘文颖, 王维洲, 等. 高载能负荷参与的电网消纳风/光电能力定量预评估方法[J]. 电网技术, 2015, 39(1): 223-229.
LIU C, LIU W Y, WANG W Z, et al.Quantitative pre-evaluation method for grid wind/photoelectric capacity with high energy Load participation[J]. Power system technology, 2015, 39(1): 223-229.
[15] 张晓英, 廖顺, 张蜡宝, 等. 高载能负荷参与的集群风电场并网系统经济调度模式[J]. 兰州理工大学学报, 2018, 44(2): 79-85.
ZHANG X Y, LIAO S, ZHANG L B, et al.Economic dispatch mode of grid-connected wind farms with high energy Load participation[J]. Journal of Lanzhou University of Technology, 2018, 44(2): 79-85.
[16] 冯智慧, 吕林, 许立雄. 基于能量枢纽的沼-风-光全可再生能源系统日前-实时两阶段优化调度模型[J]. 电网技术, 2019, 43(9): 3101-3109.
FENG Z Z, LYU L, XU L X.Two-stage optimal dispatch model of day-ahead and real-time for biogas-wind-solar fully renewable energy system based on energy hub[J]. Power system technology, 2019, 43(9): 3101-3109.
[17] 许丹, 夏少连, 丁强, 等. 基于启发式混合整数规划法求解大规模机组组合问题[J]. 电力系统保护与控制, 2012, 40(21): 1-6.
XU D, XIA S L, DING Q, et al.Solving large-scale unit combination problems based on heuristic mixed-Integer programming[J]. Power system protection and control, 2012, 40(21): 1-6.
[18] 黄凯, 郭永芳, 李志刚. 基于信息反馈粒子群的高精度锂离子电池模型参数辨识[J]. 电工技术学报, 2019, 34(S1): 378-387.
HUANG K, GUO Y F, LI Z G.Parameter identification of high-precision lithium ion battery model based on information feedback particle swarm[J]. Transactions of China Electrotechnical Society, 2019,34(S1): 378-387.
[19] 张福民, 崔海波, 李占凯, 等. 基于改进NSGA-Ⅱ算法的微网交互式多目标优化[J]. 电力系统保护与控制, 2018, 46(12): 24-31.
ZHANG F M, CUI H B, LI Z K, et al.Interactive multi-objective optimization of microgrid based on improved NSGA-Ⅱ algorithm[J]. Power system protection and control, 2018, 46(12): 24-31.
[20] 苏玉刚, 陈龙, 吴学颖, 等. 基于遗传算法的SS型磁耦合WPT系统负载与互感识别方法[J]. 电工技术学报, 2018, 33(18): 4199-4206.
SU Y G, CHEN L, WU X Y, et al.Method of load and mutual inductance of SS magnetically coupled WPT system based on genetic algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(18): 4199-4206.
[21] 靳晓凌, 赵建国. 基于改进二进制粒子群优化算法的负荷均衡化配电网重构[J]. 电网技术, 2005, 29(23): 40-43.
JIN X L, ZHAO J G.Reconfiguration of load balancing distribution network based on improved binary particle swarm optimization algorithm[J]. Power system technology, 2005, 29(23): 40-43.