Optimal Capacity Configuration of Pumped-Storage Power Station in Wind-PV-Fire-Pump Storage System

doi:10.12204/j.issn.1000-7229.2021.11.008

Abstract

Abstract:

Aiming at the capacity allocation of pumped-storage power stations in the association system of wind power, photovoltaic, thermal power and pumped storage, a bi-level programming of association system model is constructed and a solve loop is proposed. The upper-level model takes the minimum total curtailment of photovoltaic and wind power as the goal to determine the capacity allocation of the pumped-storage power station. The lower-level model aims to maximize the economic and environmental benefits of the association system, while improving the operating conditions of the system and solving the operation scheduling problem of pumped-storage power stations. The targets of the upper and lower-level are solved by the improved grey wolf optimizer (GWO) based on chaos optimization of Tent map. The effectiveness of the model and algorithm is verified by simulation analysis of two typical daily scenes in a certain area in winter and summer. The results show that the constructed bi-level programming model is effective for scientifically determining the capacity of pumped-storage power stations in the association system. And under the condition of satisfying the optimization of operation scheduling, it can improve the operating conditions of the system and realize the expected goal of maximizing economic and environmental benefits.

Key words: pumped-storage power station, capacity configuration, bi-level programming

CLC Number:

TM61

CHENG Mengzeng, TANG Yijin, SHANG Wenying, MAN Linkun, ZHANG Nan, ZHANG Na, LI Hongze. Optimal Capacity Configuration of Pumped-Storage Power Station in Wind-PV-Fire-Pump Storage System[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(11): 72-81.

Figures/Tables 17

Fig.1 Structure of the two-layer optimization model

Fig.2 The solution flow of GWO algorithm based on Tent mapping chaos optimization

Fig.3 Typical daily loads, wind power and photovoltaic output in winter and summer

Fig.4 Electricity of prices of wind, photovoltaic, thermal power and time-of-use electricity price

Table 1 Technical and economic parameters of thermal power units

Table 2 Technical and economic parameters of pumped-storage units

Fig.5 Wind power abandoned on a typical day in winter and summer

Fig.6 Typical daily operation scheduling results without pumping-storage system

Fig.7 Optimal results of typical daily operation scheduling of the combined system with a pumped-storage power station of 320 MW

Fig.8 Optimal results of typical daily operation scheduling of the combined system with a pumped-storage power station of 280 MW

Fig.9 Optimal results of typical daily operation scheduling of the combined system with a pumped-storage power station of 240 MW

Fig.10 Optimal results of typical daily operation scheduling of the combined system with a pumped-storage power station of 200 MW

Table 3 Typical daily system economics with different pumped-storage capacity

Table 4 Cost recovery period for construction of pumped storage plants with different capacity configurations

Table 5 Environmental values in a typical day with different pumped-storage capacity

Table 6 Results of a typical day of improved system operating conditions with different pumped-storage capacity

Table 7 Wind power abandonment with different pumped-storage capacity

References 23

[1]	康重庆, 姚良忠. 高比例可再生能源电力系统的关键科学问题与理论研究框架[J]. 电力系统自动化, 2017, 41(9): 2-11.
	KANG Chongqing, YAO Liangzhong. Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41(9): 2-11.
[2]	林鸿基, 闫园, 文福拴, 等. 高比例可再生能源电力系统中计及灵活调节产品的实时调度[J]. 电力建设, 2019, 40(10): 18-27.
	LIN Hongji, YAN Yuan, WEN Fushuan, et al. Real-time dispatch considering flexible ramping products in a power system with high proportion of renewable energy generation[J]. Electric Power Construction, 2019, 40(10): 18-27.
[3]	国家能源局. 2020年我国可再生能源并网运行情况[EB/OL]. (2021-03-21)[2021-05-16]. http://www.nea.gov.cn/2021-03/30/c_139846095.htm.
[4]	李江, 马昊天, 宋田宇. 风储联合系统爬坡事件的日前能量最优平抑方法[J]. 中国电机工程学报, 2021, 41(12): 4153-4164.
	LI Jiang, MA Haotian, SONG Tianyu. An event-based day-ahead energy optimization method to smooth ramp events of combined wind storage systems[J]. Proceedings of the CSEE, 2021, 41(12): 4153-4164.
[5]	张经纬, 黄宁馨, 陈柏柯, 等. 电力市场环境下抽水蓄能参与电能量市场的优化运行策略[J]. 电力建设, 2020, 41(4): 45-52.
	ZHANG Jingwei, HUANG Ningxin, CHEN Baike, et al. Optimal operation strategy for pumped-storage power station in energy market under power market environment[J]. Electric Power Construction, 2020, 41(4): 45-52.
[6]	陈爱康, 胡静哲, 陆轶祺, 等. 梯级水光蓄系统规划关联模型的建模[J]. 中国电机工程学报, 2020, 40(4): 1106-1116,1403.
	CHEN Aikang, HU Jingzhe, LU Yiqi, et al. Research on correlation model of cascade hydro-photovoltaic-pumped storage hybrid generation planning[J]. Proceedings of the CSEE, 2020, 40(4): 1106-1116,1403.
[7]	杨丽君, 张钊, 吕雪姣, 等. 计及备用容量及考虑禁止区间的含风-蓄电力系统经济调度[J]. 电网技术, 2017, 41(5): 1548-1554.
	YANG Lijun, ZHANG Zhao, LÜ Xuejiao, et al. Economic dispatch on power systems with wind power and pumped-storage station considering prohibited zones under influence of reserve capacity[J]. Power System Technology, 2017, 41(5): 1548-1554.
[8]	邹金, 赖旭, 汪宁渤. 以减少电网弃风为目标的风电与抽水蓄能协调运行[J]. 电网技术, 2015, 39(9): 2472-2477.
	ZOU Jin, LAI Xu, WANG Ningbo. Mitigation of wind curtailment by coordinating with pumped storage[J]. Power System Technology, 2015, 39(9): 2472-2477.
[9]	张国斌, 陈玥, 张佳辉, 等. 风-光-水-火-抽蓄联合发电系统日前优化调度研究[J]. 太阳能学报, 2020, 41(8): 79-85.
	ZHANG Guobin, CHEN Yue, ZHANG Jiahui, et al. Research on optimization of day-ahead dispatching of wind power-photovoltaic-hydropower-thermal power-pumped storage combined power generation system[J]. Acta Energiae Solaris Sinica, 2020, 41(8): 79-85.
[10]	王辉, 崔建勇. 应对光伏并网的抽水蓄能电站优化运行[J]. 电网技术, 2014, 38(8): 2095-2101.
	WANG Hui, CUI Jianyong. Optimal operation of pumped hydro energy storage in power system with large integration of photovoltaic generation[J]. Power System Technology, 2014, 38(8): 2095-2101.
[11]	崔勇, 李鹏, 姬德森, 等. 基于多边收益的风光水能源联合运营策略[J]. 电力自动化设备, 2019, 39(4): 161-166,173.
	CUI Yong, LI Peng, JI Desen, et al. Joint operation strategy of wind power-photovoltaic-pumped storage hydro energy based on multilateral income[J]. Electric Power Automation Equipment, 2019, 39(4): 161-166,173.
[12]	肖白, 杨宇, 姜卓, 等. 风电-抽水蓄能联合系统中抽蓄电站容量的优化规划[J]. 太阳能学报, 2020, 41(12): 270-277.
	XIAO Bai, YANG Yu, JIANG Zhuo, et al. Optimal planning of capacity of pumped storage power station in wind power-pumped storage system[J]. Acta Energiae Solaris Sinica, 2020, 41(12): 270-277.
[13]	蔺梦雪, 顾圣平. 基于贝叶斯评价模型的抽水蓄能电站容量规划[J]. 水电能源科学, 2019, 37(1): 156-159.
	LIN Mengxue, GU Shengping. Capacity planning of pumped storage power station based on Bayesian evaluation model[J]. Water Resources and Power, 2019, 37(1): 156-159.
[14]	张占安, 蔡兴国. 采用熵理论确定抽水蓄能容量[J]. 电机与控制学报, 2014, 18(3): 34-39.
	ZHANG Zhan’an, CAI Xingguo. Determination of pumped storage capacity based on entropy[J]. Electric Machines and Control, 2014, 18(3): 34-39.
[15]	罗仕华, 胡维昊, 黄琦, 等. 市场机制下光伏/小水电/抽水蓄能电站系统容量优化配置[J]. 电工技术学报, 2020, 35(13): 2792-2804.
	LUO Shihua, HU Weihao, HUANG Qi, et al. Optimization of photovoltaic/small hydropower/pumped storage power station system sizing under the market mechanism[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2792-2804.
[16]	宁志, 丛星亮, 陈永龙. 300 MW和1 000 MW燃煤机组能耗和污染物排放特性[J]. 电站辅机, 2019, 40(1): 28-33.
	NING Zhi, CONG ZXingliang, CHEN ZYonglong. Energy consumption and pollutant emission performance of 300 MW and 1 000 MW coal-fired power units[J]. Power Station Auxiliary Equipment, 2019, 40(1): 28-33.
[17]	李军徽, 付英男, 李翠萍, 等. 提升风电消纳的储热电混合储能系统经济优化配置[J]. 电网技术, 2020, 44(12): 4547-4557.
	LI Junhui, FU Yingnan, LI Cuiping, et al. Economic optimal configuration of hybrid energy storage system for improving wind power consumption[J]. Power System Technology, 2020, 44(12): 4547-4557.
[18]	韩平平, 范桂军, 孙维真, 等. 基于数据测试和粒子群优化算法的光伏逆变器LVRT特性辨识[J]. 电力自动化设备, 2020, 40(2): 49-54.
	HAN Pingping, FAN Guijun, SUN Weizhen, et al. Identification of LVRT characteristics of photovoltaic inverters based on data testing and PSO algorithm[J]. Electric Power Automation Equipment, 2020, 40(2): 49-54.
[19]	LEE U, PARK S, LEE I. Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)[J]. Energy, 2020, 196: 117090. doi: 10.1016/j.energy.2020.117090 URL
[20]	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. doi: 10.1109/4235.996017 URL
[21]	单梁, 强浩, 李军, 等. 基于Tent映射的混沌优化算法[J]. 控制与决策, 2005, 20(2): 179-182.
	SHAN Liang, QIANG Hao, LI Jun, et al. Chaotic optimization algorithm based on Tent map[J]. Control and Decision, 2005, 20(2): 179-182.
[22]	MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61. doi: 10.1016/j.advengsoft.2013.12.007 URL
[23]	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.