CAPACITY CONFIGURATION OF WIND-PHOTOVOLTAIC-ELECTRIC HYDROGEN MICROGRID BASED ON COOPERATIVE GAME

Chen Yi; He Shan; Xie Shaohua; Hu Bing; Chen Jie; Yuan Zhi

doi:10.19912/j.0254-0096.tynxb.2022-1683

PDF(1760 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (2) : 395-405. DOI: 10.19912/j.0254-0096.tynxb.2022-1683

CAPACITY CONFIGURATION OF WIND-PHOTOVOLTAIC-ELECTRIC HYDROGEN MICROGRID BASED ON COOPERATIVE GAME

Chen Yi¹, He Shan^1,2, Xie Shaohua¹, Hu Bing¹, Chen Jie^1,2, Yuan Zhi^1,2

Author information +

History +

Abstract

When wind farm, photovoltaic power station and hybrid electric-hydrogen energy storage system belong to different investors, there is a contradiction between the optimal operation of the microgrid as a whole and the optimal operation of each investor. Meanwhile, the uncertainty of wind and photovoltaic output also affects the capacity configuration. To solve the above problems, a capacity configuration method of wind-photovoltaic-electric hydrogen microgrid based on cooperative game is proposed, considering the uncertainty of wind and photovoltaic. Firstly, the typical monthly scenes of wind-photovoltaic-load are extracted based on ordered clustering and K-means clustering algorithm. Secondly, the factors affecting the operation of the microgrid are comprehensively considered, and an economic model based on different investors is constructed. Under the scenario of a typical month, the revenue relationship between each investor and the alliance is analyzed under the mode of full cooperation game, partial cooperation game and non-cooperation game. Finally, the highest month income is used as the optimization objective, the improved grey wolf optimization algorithm is used to allocate the installed capacity of investors, and the investor income of the cooperative alliance is allocated based on shapley value. According to the results of the example and the sensitivity analysis of key parameters, it can be seen that in the full cooperative game mode, economy and low carbon are taken into account, and the interests of each investor are distributed reasonably. The fluctuations of electricity price and wind-photovoltaic subsidy price have different effects on each investor.

Key words

microgrid / optimization / energy storage / distributed generation / cooperative game / capacity configuration

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Chen Yi, He Shan, Xie Shaohua, Hu Bing, Chen Jie, Yuan Zhi. CAPACITY CONFIGURATION OF WIND-PHOTOVOLTAIC-ELECTRIC HYDROGEN MICROGRID BASED ON COOPERATIVE GAME[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 395-405 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1683

References

[1] 刘道兵, 袁野, 李世春, 等. 利用氢储能在含可再生能源系统容量配置综述[J]. 电测与仪表, 2021, 59(12): 1-13.
LIU D B, YUAN Y, LI S C, et al.A review of capacity allocation of renewable energy system using hydrogen storage[J]. Electrical measurement & instrumentation, 2021, 59(12): 1-13.
[2] 荆涛, 陈庚, 王子豪, 等. 风光互补发电耦合氢储能系统研究综述[J]. 中国电力, 2022, 55(1): 75-83.
JING T, CHEN G, WANG Z H, et al.Research overview on the integrated system of wind-solar hybrid power generation coupled with hydrogen-based energy storage[J]. Electric power, 2022, 55(1): 75-83.
[3] 张红, 袁铁江, 谭捷, 等. 面向统一能源系统的氢能规划框架[J]. 中国电机工程学报, 2022, 42(1): 83-94.
ZHANG H, YUAN T J, TAN J, et al.Hydrogen energy system planning framework for unified energy system[J]. Proceedings of the CSEE, 2022, 42(1): 83-94.
[4] 来益博, 樊立波, 罗少杰, 等.基于多目标优化的分布式能源系统配置优化方法[A]//浙江省电力学会2021年度优秀论文集, 2022: 306-317.
LAI Y B, FAN L B, LUO S J, et al.Distributed energy systems based on multi-objective optimization configuration optimization method[A]//Zhejiang Electric Power Society 2021 Annual Excellent Proceedings, 2022: 306-317.
[5] 王侃宏, 赵政通, 罗景辉, 等. 风光互补发电制氢储能系统模拟仿真研究及性能分析[J]. 节能, 2019, 38(11): 79-84.
WANG K H, ZHAO Z T, LUO J H, et al.Simulation and performance analysis of hydrogen storage system for wind and solar complementary power generation[J]. Energy conservation, 2019, 38(11): 79-84.
[6] 马榕谷, 陈洁, 赵军超, 等. 非并网风氢互补系统的容量多目标优化[J]. 太阳能学报, 2019, 40(2): 422-429.
MA R G, CHEN J, ZHAO J C, et al.Multi-objective optimization for capacity of non-grid-connected wind/hydrogen hybrid power system[J]. Acta energiae solaris sinica, 2019, 40(2): 422-429.
[7] 希望·阿不都瓦依提, 吕海鹏, 晁勤. 基于非合作博弈的风-光-氢微电网容量优化配置[J]. 电力工程技术, 2022, 41(2): 110-118.
ABUDUWAYITI X, LYU H P, CHAO Q.Optimal capacity configuration of wind-photovoltaic-hydrogen microgrid based on non-cooperative game theory[J]. Electric power engineering technology, 2022, 41(2): 110-118.
[8] 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.
[9] 金顺平, 房方, 朱仲晏, 等. 不同投资模式下计及缺电率约束的微网容量配置博弈分析[J]. 中国电力, 2020, 53(8): 173-181.
JIN S P, FANG F, ZHU Z Y, et al.Game analysis of microgrid capacity configuration for different investment modes constrained by loss of power supply probability[J]. Electric power, 2020, 53(8): 173-181.
[10] 晏才鑫, 裘智峰, 王春生. 考虑合作博弈的风-光-液态空气储能集群日前市场不确定性优化方法研究[J]. 控制理论与应用, 2023, 40(11): 2028-2038.
YAN C X, QIU Z F, WANG C S.Template of paper for control theory & applications[J]. Control theory & applications, 2023, 40(11): 2028-2038.
[11] 韩莹, 于三川, 李荦一, 等. 计及阶梯式碳交易的风光氢储微电网低碳经济配置方法[J]. 高电压技术, 2022, 48(7): 2523-2533.
HAN Y, YU S C, LI L Y, et al.Low-carbon and economic configuration method for solar hydrogen storage microgrid including stepped carbon trading[J]. High voltage engineering, 2022, 48(7): 2523-2533.
[12] 张节潭, 程浩忠, 胡泽春, 等. 含风电场的电力系统随机生产模拟[J]. 中国电机工程学报, 2009, 29(28): 34-39.
ZHANG J T, CHENG H Z, HU Z C, et al.Power system probabilistic production simulation including wind farms[J]. Proceedings of the CSEE, 2009, 29(28): 34-39.
[13] WAHBAH M, FENG S, EL-FOULY T H M, et al. Root-transformed local linear regression for solar irradiance probability density estimation[J]. IEEE transactions on power systems, 2020, 35(1): 652-661.
[14] 吴传申, 刘宇, 高山, 等. 基于预测控制的储能系统多时间尺度动态响应优化研究[J]. 全球能源互联网, 2020, 3(3): 222-230.
WU C S, LIU Y, GAO S, et al.Multi-time scale dynamic response optimization of energy storage system based on predictive control[J]. Journal of global energy interconnection, 2020, 3(3): 222-230.
[15] CHEN Y E, WEI W, LIU F, et al.Energy trading and market equilibrium in integrated heat-power distribution systems[J]. IEEE transactions on smart grid, 2019, 10(4): 4080-4094.
[16] 滕志军, 吕金玲, 郭力文, 等. 一种基于Tent映射的混合灰狼优化的改进算法[J]. 哈尔滨工业大学学报, 2018, 50(11): 40-49.
TENG Z J, LYU J L, GUO L W, et al.An improved hybrid grey wolf optimization algorithm based on Tent mapping[J]. Journal of Harbin Institute of Technology, 2018, 50(11): 40-49.
[17] 刘文霞, 凌云頔, 赵天阳. 低碳经济下基于合作博弈的风电容量规划方法[J]. 电力系统自动化, 2015, 39(19): 68-74.
LIU W X, LING Y D, ZHAO T Y.Cooperative game based capacity planning model for wind power in low-carbon economy[J]. Automation of electric power systems, 2015, 39(19): 68-74.
[18] 白凯峰, 顾洁, 彭虹桥, 等. 融合风光出力场景生成的多能互补微网系统优化配置[J]. 电力系统自动化, 2018, 42(15): 133-141.
BAI K F, GU J, PENG H Q, et al.Optimal allocation for multi-energy complementary microgrid based on scenario generation of wind power and photovoltaic output[J]. Automation of electric power systems, 2018, 42(15): 133-141.
[19] 袁铁江, 曹继雷. 计及风电-负荷不确定性的风氢低碳能源系统容量优化配置[J]. 高电压技术, 2022, 48(6): 2037-2044.
YUAN T J, CAO J L.Capacity optimization allocation of wind hydrogen low-carbon energy system considering wind power-load uncertainty[J]. High voltage engineering, 2022, 48(6): 2037-2044.