Capacity configuration optimization of wind-solar-storage systems for oil well groups at drilling sites

doi:10.3969/j.issn.2097-0706.2026.05.001

Abstract

Abstract:

To address the power supply-demand imbalances and low renewable energy consumption rates caused by uncertainties in wind and solar power and power consumption of oil pump wells， an optimized capacity configuration model for the wind-solar-storage multi-energy system for oil well groups was developed. Models for wind turbine output， photovoltaic panel output， battery charging and discharging power， and power consumption of oil wells were established. Taking the total investment cost as the objective function and the renewable energy consumption rate as a constraint， an optimized model for wind-solar-storage system capacity configuration and corresponding algorithms were proposed. Using a specific well site as an example， a capacity configuration scheme for production sites for the wind-solar-storage system of oil well groups was designed. The research and case analysis showed that the proposed optimized capacity configuration model and algorithms realized optimal capacity configuration of various sources in the multi-energy system of oil pump well groups. While considering the economic efficiency， the model significantly improved the renewable energy consumption rate at the well site to 86.15%. Additionally， the scheme effectively mitigated fluctuations in renewable energy output and periodic energy consumption， showing substantial practical application benefits.

Key words: oil well group, wind-solar-storage system, configuration optimization, heuristic algorithm, consumption rate, renewable energy

CLC Number:

TK011

JING Linru, TAN Chaodong, CHEN Peiyao, FENG Gang, WANG Jun, LIU Bin. Capacity configuration optimization of wind-solar-storage systems for oil well groups at drilling sites[J]. Integrated Intelligent Energy, 2026, 48(5): 1-9.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Fig.7

Table 2

Fig.8

Fig.9

Fig.10

Fig.11

Table 3

References 27

[1]	GHAFFARI A, FADAEINEDJAD R, ASKARZADEH A. Optimal allocation of energy storage systems, wind turbines and photovoltaic systems in distribution network considering flicker mitigation[J]. Applied Energy, 2022, 319:119253. doi: 10.1016/j.apenergy.2022.119253
[2]	ALI H, SAYYAH A Z, YOUNES A, et al. Configuration optimization of a renewable hybrid system including biogas generator, photovoltaic panel and wind turbine: Particle swarm optimization and genetic algorithms[J]. International Journal of Modern Physics C, 2023, 34(5): 1-22.
[3]	NUVVULA R S S, TEEGALA K S, DEVARAJ E, et al. Multi-objective mutation-enabled adaptive local attractor quantum behaved particle swarm optimisation based optimal sizing of hybrid renewable energy system for smart cities in India[J]. Sustainable Energy Technologies and Assessments, 2022, 49:101689. doi: 10.1016/j.seta.2021.101689
[4]	NAZLOO H T, BABAZADEH R, VARMAZYAR M. Optimal configuration and planning of distributed energy systems considering renewable energy resources[J]. Journal of Environment Informatics, 2024, 43(1):50-64.
[5]	李明轩, 范越, 汪莹, 等. 新能源大基地风光储容量协调优化配置[J]. 电力自动化设备, 2024, 44(3): 1-8.
	LI Mingxuan, FAN Yue, WANG Ying, et al. Coordinated optimal configuration of wind-photovoltaic-energy storage capacity for large-scale renewable energy bases[J]. Electric Power Automation Equipment, 2024, 44(3): 1-8.
[6]	刘丹, 刘方. 风光储系统储能容量协调优化[J]. 热力发电, 2021, 50(6): 54-59.
	LIU Dan, LIU Fang. Cooperative optimization of energy storage capacity in wind/PV/storagehybrid system[J]. Thermal Power Generation, 2021, 50(6): 54-59.
[7]	周成伟. 基于风光储微电网下的储能容量优化配置研究[D]. 南京: 南京信息工程大学, 2023.
	ZHOU Chengwei. Research on optimal configuration of energy storage capacity based on wind-solar storage microgrid[D]. Nanjing: Nanjing University of Information Science & Technology, 2023.
[8]	年兰雨. 基于随机生产模拟的多微网储能容量优化配置与运行控制[D]. 北京: 北京交通大学, 2021.
	NIAN Lanyu. Optimal configuration and operation control of multi-microgrid energy storage capacity based on stochastic production simulation[D]. Beijing: Beijing Jiaotong University, 2021.
[9]	张晶晶, 陈俊, 曹辉, 等. 计及风光资源不确定性的独立型微电网容量优化配置[J]. 电力与能源, 2024, 45(3): 347-354.
	ZHANG Jingjing, CHEN Jun, CAO Hui, et al. Capacity optimization configuration of stand-alone microgrid considering uncertainty of wind and solar resources[J]. Power & Energy, 2024, 45(3): 347-354.
[10]	苏宇生. 计及负荷响应的风光储微网容量优化研究[D]. 湘潭: 湖南科技大学, 2022.
	SU Yusheng. Research on capacity optimization of wind-solar storage microgrid considering load response[D]. Xiangtan: Hunan University of Science and Technology, 2022.
[11]	周成伟, 李鹏, 俞斌, 等. 风光储微电网储能系统容量优化配置[J]. 综合智慧能源, 2022, 44(12): 56-61. doi: 10.3969/j.issn.2097-0706.2022.12.008
	ZHOU Chengwei, LI Peng, YU Bin, et al. Optimal configuration for energy storage system capacity of wind-solar-storage microgrid[J]. Integrated Intelligent Energy, 2022, 44(12): 56-61. doi: 10.3969/j.issn.2097-0706.2022.12.008
[12]	王尧, 鞠立伟, 王佳伟, 等. 微网多能协同优化运行及效益评价模型[M]. 北京: 中国电力出版社, 2020.
	WANG Yao, JU Liwei, WANG Jiawei, et al. Multi-energy collaborative optimization operation and benefit evaluation model of microgrid[M]. Beijing: China Electric Power Press, 2020.
[13]	陈君, 唐秀明, 苏宇生. 一种基于多目标优化的独立微电网容量配置方法[J]. 兵工自动化, 2024, 43(9): 66-72, 96.
	CHEN Jun, TANG Xiuming, SU Yusheng. An independent microgrid capacity allocation method based on multi-objective optimization[J]. Ordnance Industry Automation, 2024, 43(9): 66-72, 96.
[14]	李维波, 彭智明, 张浩, 等. 基于自适应蚁群算法的岛礁混合发电系统电源容量优化方法[J]. 中国舰船研究, 2024, 19(4):139-147.
	LI Weibo, PENG Zhiming, ZHANG Haole, et al. Optimization method for island and reef hybrid power generation systems' power capacity based on adaptive ant colony algorithm[J]. Chinese Journal of Ship Research, 2024, 19(4):139-147.
[15]	杨赛. 微电网短期电力负荷预测及优化调度研究[D]. 天津: 天津理工大学, 2022.
	YANG Sai. Research on short-term power load forecasting and optimal dispatching of microgrid[D]. Tianjin: Tianjin University of Technology, 2022.
[16]	鲍冰. 考虑油机负荷特性的油田电网可靠性分析[D]. 北京: 华北电力大学, 2015.
	BAO Bing. Reliability analysis of oilfield power grid considering load characteristic of pumping unit[D]. Beijing: North China Electric Power University, 2015.
[17]	孟昭辉. 低渗透油田合理间抽制度确定及其应用效果分析[J]. 石油石化节能与计量, 2024, 14(5): 11-15.
	MENG Zhaohui. Determination of reasonable interval pumping system in low permeability oilfield and analysis of its application effect[J]. Energy Conservation and Measurement in Petroleum & Petrochemical Industry, 2024, 14(5): 11-15.
[18]	刘金亮. 抽油机井不停机间抽制度优化分析[J]. 石油石化节能与计量, 2024, 14(6): 66-70.
	LIU Jinliang. Optimization analysis of non-stop interval pumping system for pumping wells[J]. Energy Conservation and Measurement in Petroleum & Petrochemical Industry, 2024, 14(6): 66-70.
[19]	高东升. 基于改进自适应遗传算法的油田多源微网双层优化调度[J]. 吉林电力, 2023, 51(3): 40-45.
	GAO Dongsheng. Two-layer optimization scheduling of oilfield multi-source microgrid based on improved adaptive genetic algorithm[J]. Jilin Electric Power, 2023, 51(3): 40-45.
[20]	谢菁, 吴政声, 万航羽, 等. 微电网风光储容量优化配置[J]. 云南电力技术, 2024, 52(4): 19-24.
	XIE Jing, WU Zhengsheng, WAN Hangyu, et al. Optimization of power capacity allocation of wind-wind microgrid based on NGO algorithm[J]. Yunnan Electric Power, 2024, 52(4): 19-24.
[21]	王林, 孔小民, 周忠玉, 等. 云储能模式下的配电网分布式光伏-储能无功优化方法[J]. 综合智慧能源, 2024, 46(6): 44-53. doi: 10.3969/j.issn.2097-0706.2024.06.006
	WANG Lin, KONG Xiaomin, ZHOU Zhongyu, et al. Distributed photovoltaic-energy storage reactive power optimization method for distribution networks under cloud energy storage mode[J]. Integrated Intelligent Energy, 2024, 46(6): 44-53. doi: 10.3969/j.issn.2097-0706.2024.06.006
[22]	王广玲. 微网风光储容量优化配置[D]. 北京: 北方工业大学, 2021.
	WANG Guangling. Optimal configuaration of micro-grid scenery storage capacity[D]. Beijing: North China University of Technology, 2021.
[23]	赵宇洋, 赵钰欢, 郭英军, 等. 离网型风光氢储系统容量配置与控制优化[J]. 太阳能学报, 2024, 45(7): 50-59.
	ZHAO Yuyang, ZHAO Yuhuan, GUO Yingjun, et al. Capacity configuration and control optimization of off-grid wind solar hydrogen storage system[J]. Acta Energiae Solaris Sinica, 2024, 45(7): 50-59.
[24]	田雨明. 并网型微电网电源容量优化配置研究[D]. 沈阳: 沈阳工程学院, 2023.
	TIAN Yuming. research on power capacity optimization allocation of grid-connected microgrid[D]. Shenyang: Shenyang Insitute of Engineering, 2023.
[25]	管晓虎, 王长云, 张燕. 基于配网状态的分布式电源接入位置研究[J]. 综合智慧能源, 2024, 46(10): 26-31. doi: 10.3969/j.issn.2097-0706.2024.10.004
	GUAN Xiaohu, WANG Changyun, ZHANG Yan. Research on the access location of distributed generations based on distribution network status[J]. Integrated Intelligent Energy, 2024, 46(10): 26-31. doi: 10.3969/j.issn.2097-0706.2024.10.004
[26]	蒋伟, 陈照光. 基于改进的人工蜂群算法的微电网储能系统容量优化配置[J]. 上海电力大学学报, 2021, 37(5): 415-421, 427.
	JIANG Wei, CHEN Zhaoguang. Optimization of microgrid energy storage system capacity based on improved artifical bee colony algorithm[J]. Journal of Shanghai University of Electric Power, 2021, 37(5): 415-421, 427.
[27]	马永翔, 韩子悦, 闫群民, 等. 考虑电动汽车充电负荷及储能寿命的充电站储能容量配置优化[J]. 电网与清洁能源, 2024, 40(4): 92-101.
	MA Yongxiang, HAN Ziyue, YAN Qunmin, et al. Optimization of storage capacity allocation at charging stations considering EV charging load and storage lifetime[J]. Power System and Clean Energy, 2024, 40(4): 92-101.

项目	风机	光伏板	蓄电池
额定功率	30 kW	109 W	200 A·h （5 kW）
工况参数	v_in=3 m/s; v_r=12 m/s; ${v}_{\mathrm{o}\mathrm{u}\mathrm{t}}$=25 m/s	工作电压=5.99 A；工作电流=18.2 V	标准电压=25.6 V $\eta =$80%
建设成本/（元·kW^-1）	3 700	2 000	2 500
运维成本/（元·kW^-1）	0.029 6	0.009 6	0.009 0
使用寿命/a	20	20	8

参数	ABC算法	GA
风机容量/kW	199	199
光伏板容量/kW	212	250
蓄电池容量/（kW·h）	100	100
总投资成本/万元	116.099 0	122.201 5
新能源消纳率/%	84.25	81.86

参数	ABC算法	GA
风电装机容量/kW	187	187
光伏装机容量/kW	266	286
蓄电池容量/（kW·h）	100	100
总投资成本/万元	120.849 2	122.940 4
新能源消纳率/%	86.15	85.28