基于大数据与粒子群的清洁能源协同优化调度方法

doi:10.13229/j.cnki.jdxbgxb.20220167

Abstract

Abstract:

If there is a delay in the operation node of the clean energy system， it will affect the effect of collaborative optimization scheduling of clean energy. To avoid this problem， a collaborative optimization scheduling method of clean energy based on big data technology and particle swarm optimization algorithm is proposed. According to the operation characteristics of the clean energy system， the big data technology is used to analyze the operation parameters，the operation node delay scheduling strategy is desgined， and then the output power scheduling model is constructed. Taking the maximum operating benefit and the minimum energy discarding cost as the optimization objectives， a collaborative optimization scheduling model of clean energy system is constructed， and then the particle swarm optimization algorithm is used to solve the model to obtain the optimal scheduling scheme. The experimental results show that this method can obtain better results of clean energy collaborative scheduling.

Key words: big data technology, particle swarm algorithm, clean energy, collaborative optimal scheduling

CLC Number:

TM734

Yang LIU,Ji-cheng LIU. Collaborative optimization scheduling method of clean energy based on big data and particle swarm optimization[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1443-1448.

Figures/Tables 7

Fig.1

Fig.2

Table 1

Table 2

Fig.3

Fig.4

Fig.5

References 12

1	马苗苗, 刘立成, 王鑫, 等. 风光发电与新能源汽车协同优化调度策略[J]. 吉林大学学报: 工学版, 2022, 52(9): 2096-2106.
	Ma Miao-miao, Liu Li-cheng, Wang Xin, et al. Coordinated optimal dispatch strategy of wind and photovoltaic power generation and new energy vehicles[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2096-2106.
2	刘芳芳, 吕林, 刘友波, 等. 高渗透清洁能源的城市高压配电网转供调度策略[J]. 电力系统保护与控制, 2020, 48(11): 1-9.
	Liu Fang-fang, Lin Lyu, Liu You-bo, et al. Transfer scheduling strategy for urban high-voltage distribution network with highly-penetrated renewable energy[J]. Power System Protection and Control, 2020, 48(11): 1-9.
3	金新峰, 廖胜利, 刘战伟, 等. 考虑风电置信区间的水风火短期优化调度方法[J]. 水力发电学报, 2020, 39(9): 33-42.
	Jin Xin-feng, Liao Sheng-li, Liu Zhan-wei, et al. Short-term optimal operation method of water, wind and thermal power considering wind power confidence intervals[J]. Journal of Hydroelectric Engineering, 2020, 39(9): 33-42.
4	杨明通, 周步祥, 董申, 等. 区块链支持下的微网电力市场设计及调度优化[J]. 电力自动化设备, 2019, 39(12): 155-161.
	Yang Ming-tong, Zhou Bu-xiang, Dong Shen, et al. Design and dispatch optimization of microgrid electricity market supported by blockchain[J]. Electric Power Automation Equipment, 2019, 39(12):155-161.
5	文达, 崔双喜, 樊小朝, 等. 考虑源荷互动的主动配电网两层优化调度[J]. 计算机工程与应用, 2021, 57(10): 259-265.
	Wen Da, Cui Shuang-xi, Fan Xiao-chao, et al. Two-level optimal dispatching of active distribution network considering source-load interaction[J]. Computer Engineering and Applicationss, 2021, 57(10): 259-265.
6	廖玮霖, 程杉, 尚冬冬, 等. 多策略融合的粒子群优化算法[J]. 计算机工程与应用, 2021, 57(1): 69-76.
	Liao Wei-lin, Cheng Shan, Shang Dong-dong, et al. Particle swarm optimization algorithm integrated with multiple-strategies[J]. Computer Engineering and Applications, 2021, 57(1): 69-76.
7	赵志欣, 唐慧, 刘仁云. 基于马尔可夫过程的风电系统可靠性分析[J]. 吉林大学学报: 工学版, 2021, 51(2): 697-703.
	Zhao Zhi-xin, Tang Hui, Liu Ren-yun. Reliability analysis of wind power generation system based on Markov process[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 697-703.
8	白青峰. 可再生能源发电与核电协调调度及发展[J]. 中国电力, 2020, 53(2): 105-111.
	Bai Qing-feng. Coordinative dispatch and development of renewable energy and nuclear power generation [J]. Electric Power, 2020, 53(2): 105-111.
9	程海花, 寇宇, 周琳, 等. 面向清洁能源消纳的流域型风光水多能互补基地协同优化调度模式与机制[J]. 电力自动化设备, 2019, 39(10): 61-70.
	Cheng Hai-hua, Kou Yu, Zhou Lin, et al. Collaborative optimal dispatching mode and mechanism of watershed-type wind-solar-water multi-energy complementary bases for clean energy absorption[J]. Electric Power Automation Equipment, 2019, 39(10): 61-70.
10	魏齐鸣, 杨太华, 高余奇. 基于混合潮流算法的多能源互补调度仿真研究[J]. 计算机仿真, 2021, 38(11): 77-81.
	Wei Qi-ming, Yang Tai-hua, Gao Yu-qi. Simulation research on multi-energy complementary dispatch based on hybrid power flow algorithm[J]. Computer Simulation, 2021, 38(11): 77-81.
11	牛焕娜, 钱立, 杨璐, 等. 计及灵活性辅助服务费用的配电网灵活型资源优化配置[J]. 电力自动化设备, 2021, 41(10): 52-59.
	Niu Huan-na, Qian Li, Yang Lu, et al. Optimal allocation of flexible resources in distribution network considering cost of flexible auxiliary services [J]. Electric Power Automation Equipment, 2021, 41(10): 52-59.
12	耿焕同, 周山胜, 陈哲, 等. 基于分解的预测型动态多目标粒子群优化算法[J]. 控制与决策, 2019, 34(6): 1307-1318.
	Geng Huan-tong, Zhou Shan-sheng, Chen Zhe, et al. Composition-based predictive dynamic multi-objective particle swarm optimization algorithm[J]. Control and Decision, 2019, 34(6): 1307-1318.

Related Articles 10

[1]	Zhao-ming CHEN,Jin-song ZOU,Wei WANG,Ming-quan SHI. Multi-objective optimization of casting-forging dynamic forming based on improved particle swarm neural network and finite element analysis [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1524-1533.
[2]	Cui-yu LI,Ya-meng HU,Ya-wei KANG,De-liang ZHANG. Coordination scheduling of electric vehicle charge and discharge using adaptive genetic algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2508-2513.
[3]	YU Fan-hua, LIU Ren-yun, ZHANG Yi-min, ZHANG Xiao-li, SUN Qiu-cheng. Swarm intelligence algorithm of dynamic reliability-based robust optimization design of mechanic components [J]. 吉林大学学报(工学版), 2017, 47(6): 1903-1908.
[4]	YU Fan-hua, LIU Ren-yun, ZHANG Yi-min, SUN Qiu-cheng, ZHANG Xiao-li. Intelligent algorithm for optimized dynamic reliability design of mechanic structure [J]. 吉林大学学报(工学版), 2016, 46(4): 1269-1275.
[5]	LIU Ren-yun, YU Fan-hua. Robust optimal design for reliability based on analytic hierarchy process-particle swarm method [J]. 吉林大学学报(工学版), 2012, 42(增刊1): 139-142.
[6]	YU Fan-hua, LIU Ren-yun. Multi-objective reliability based on robust optimization design for vehicle leaf spring [J]. 吉林大学学报(工学版), 2011, 41(增刊2): 226-230.
[7]	LIU Ren-Yun, YU Fan-Hua, ZHANG Yi-Min. Reliabilitybased robust optimization design for drag rod with fuzzy particle swarm algorithm [J]. 吉林大学学报(工学版), 2010, 40(增刊): 344-0348.
[8]	YU Fan-Hua, LIU Ren-Yun, ZHOU Chun-Guang. Structural damage detection based on residual force vector method and particle swarm algorithm [J]. 吉林大学学报(工学版), 2010, 40(增刊): 339-0343.
[9]	Yu Fan-hua,Liu Han-bing . Structural damage identification by support vector machine and particle swarm algorithm [J]. 吉林大学学报(工学版), 2008, 38(02): 434-0438.
[10]	Liu Ren-yun, Zhang Yi-min, Yu Fan-hua . Robust optimization design for reliability based on grey particle swarm algorithm [J]. 吉林大学学报(工学版), 2006, 36(06): 893-897.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

参数	风蓄风火（弃风）	风蓄水火（弃风）
火电开机台数/台	4	6
火电发电量/（MW·h）	31 524	32 584
火电启停次数/次	1	2
火电出力标准差/MW	253	265

调度模式	火电开机台数/台	火电发电量 /（MW·h）	火电启停次数/次	火电出力标准差/MW
风蓄风火（弃风）	3	35 624	0	201
风蓄水火（弃风）	4	37 524	0	222

Collaborative optimization scheduling method of clean energy based on big data and particle swarm optimization

RICH HTML

PDF (PC)