Economic operation of a multi-energy system based on adaptive learning rate firefly algorithm

doi:10.3969/j.issn.2097-0706.2022.07.006

Abstract

Abstract:

With the extensive application of renewable energy as power sources and the stepwise maturity of synergetic techniques, multi-energy system（MES） is drawing increasing attention,turning into the main carrier of energy on the way to achieving carbon neutrality. However, the economic operation of MES is confronting challenges due to the complexity of energy production, energy storage and energy consumption in the system. To tackle the problem, an economic optimization model of an MES including new energy power plants, a battery energy storage system（BESS） and a combined cooling heating and power（CCHP） system is formulated, taking minimizing the penalty of abandoning wind and solar power, power discharge loss of BESS, fuel costs of gas turbines, carbon emission penalty and other costs as the objective function. The optimization model is solved with the charging and discharging characteristics of BESS, the output characteristics of photovoltaic units and wind turbines and the balance between cooling, heating and electricity load as constraints. Afterward, a novel adaptive learning rate firefly algorithm（ALRFA） is proposed to prevent the problem solving from falling into local optimum and slow convergence by taking adaptive learning rate. Taking the cooling, heating and power loads of an industrial park as study case,the effectiveness and feasibility of the proposed model and algorithm is verified.

Key words: carbon neutrality, renewable energy, multi-energy system, CCHP, comprehensive optimization, battery energy storage system, carbon emission penalty, adaptive learning rate firefly algorithm

CLC Number:

TK018：TP301.6

ZHANG Rongquan, LI Gangqiang, BU Siqi, LIU Fang, ZHU Yuxiang. Economic operation of a multi-energy system based on adaptive learning rate firefly algorithm[J]. Integrated Intelligent Energy, 2022, 44(7): 49-57.

Figures/Tables 9

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References 29

[1]	张俊锋, 许文娟, 王跃锜, 等. 面向碳中和的中国碳排放现状调查与分析[J]. 华电技术, 2021, 43(10): 1-10.
	ZHANG Junfeng, XU Wenjuan, WANG Yueqi, et al. Investigation and analysis on carbon emission status in China on the path to carbon neutrality[J]. Huadian Technology, 2021, 43(10): 1-10.
[2]	蒋文坤, 韩颖慧, 薛智文, 等. 多能互补能源系统中储能原理及其应用[J]. 综合智慧能源, 2022, 44(1): 63-71. doi: 10.3969/j.issn.2097-0706.2022.01.009
	JIANG Wenkun, HAN Yinghui, XUE Zhiwen, et al. Energy storage technologies and their applications in multi-energy complementary power system[J]. Integrated Intelligent Energy, 2022, 44(1): 63-71. doi: 10.3969/j.issn.2097-0706.2022.01.009
[3]	CHEN Y, PARK B, KOU X, et al. A comparison study on trading behavior and profit distribution in local energy transaction games[J]. Applied Energy, 2020, 280 :115941. doi: 10.1016/j.apenergy.2020.115941
[4]	MIAO B, LIN J, LI H, et al. Day-ahead energy trading strategy of regional integrated energy system considering energy cascade utilization[J]. IEEE Access, 2020, 8: 138021-138035. doi: 10.1109/ACCESS.2020.3007224
[5]	WANG Y, QI C, DONG H, et al. Optimal design of integrated energy system considering different battery operation strategy[J]. Energy, 2020, 212: 118537. doi: 10.1016/j.energy.2020.118537
[6]	WANG H, ZHANG R, PENG J, et al. GPNBI-inspired MOSFA for Pareto operation optimization of integrated energy system[J]. Energy Conversion and Management, 2017, 151:524-537. doi: 10.1016/j.enconman.2017.09.005
[7]	石立宝, 翟放. 考虑风-光-荷不确定性的数据驱动型机组组合模型[J]. 综合智慧能源, 2022, 44(1): 18-25. doi: 10.3969/j.issn.2097-0706.2022.01.003
	SHI Libao, ZHAI Fang. Data-driven unit commitment model incorporating the uncertainty of wind-PV-load[J]. Integrated Intelligent Energy, 2022, 44(1): 18-25. doi: 10.3969/j.issn.2097-0706.2022.01.003
[8]	YANG X, YAO K, MENG W, et al. Optimal scheduling of CCHP with distributed energy resources based on water cycle algorithm[J]. IEEE Access, 2019, 7:105583-105592. doi: 10.1109/ACCESS.2019.2926803
[9]	张荣权, 王怀智, 王贵斌, 等. 基于改进萤火虫算法的冷热电联供系统多目标优化调度[J]. 华北电力大学学报(自然科学版), 2018, 45(1):1-6.
	ZHANG Rongquan, WANG Huaizhi, WANG Guibin, et al. Multi-objective optimal dispatch of combined cooling heating and power systems based on improved firefly algorithm[J]. Journal of North China Electric Power University (Natural Science Edition), 2018, 45(1):1-6.
[10]	RAUF H T, SHOAIB U, LALI M I, et al. Particle swarm optimization with probability sequence for global optimization[J]. IEEE Access, 2020, 8: 110535-110549. doi: 10.1109/ACCESS.2020.3002725
[11]	FISTER I, JR I F, YANG X S, et al. A comprehensive review of firefly algorithms[J]. Swarm & Evolutionary Computation, 2013, 13(1): 34-46.
[12]	FISTER JR I, YANG X S, FISTER I, et al. Memetic firefly algorithm for combinatorial optimization[J]. Mathematics, 2012, 5:1-14. doi: 10.3390/math5010001
[13]	FANG F. A novel optimal operational strategy for the CCHP system based on two operating modes[J]. IEEE Transactions on Power Systems, 2012, 27(2):1032-1041. doi: 10.1109/TPWRS.2011.2175490
[14]	蓝静, 朱继忠, 李盛林, 等. 考虑碳惩罚的电化学储能消纳风光与调峰研究[J]. 综合智慧能源, 2022, 44(1): 9-17. doi: 10.3969/j.issn.2097-0706.2022.01.002
	LAN Jing, ZHU Jizhong, LI Shenglin, et al. Research on electrochemical energy storage to assist new energy consumption and peak load regulation considering carbon penalty[J]. Integrated Intelligent Energy, 2022, 44(1): 9-17. doi: 10.3969/j.issn.2097-0706.2022.01.002
[15]	WANG H Z, PENG J C, LIU Y T, et al. GPNBI inspired MOSDE for electric power dispatch considering wind energy penetration[J]. Energy, 2018, 144:404-419. doi: 10.1016/j.energy.2017.12.005
[16]	AHMADIAHANGAR R, KARAMI H, HUSEV O, et al. Analytical approach for maximizing self-consumption of nearly zero energy buildings——Case study: Baltic region[J]. Energy, 2022, 238:121744. doi: 10.1016/j.energy.2021.121744
[17]	YAO W, ZHAO J, WEN F, et al. A Hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles[J]. IEEE Transactions on Power Systems, 2013, 28(3): 2768-2778. doi: 10.1109/TPWRS.2013.2256937
[18]	LIU X, GAO B, ZHU Z, et al. Non-cooperative and cooperative optimisation of battery energy storage system for energy management in multi-microgrid[J]. IET Generation Transmission & Distribution, 2018, 12(10): 2369-2377. doi: 10.1049/iet-gtd.2017.0401
[19]	KIM K, CHOI Y, KIM H. Data-driven battery degradation model leveraging average degradation function fitting[J]. Electronics Letters, 2017, 53(2): 102-104. doi: 10.1049/el.2016.3096
[20]	LIU C, WANG D, YIN Y. Two-stage optimal economic scheduling for commercial building multi-energy system through Internet of Things[J]. IEEE Access, 2019, 7: 174562-174572. doi: 10.1109/ACCESS.2019.2957267
[21]	WANG H, WANG W, ZHOU X, et al. Firefly algorithm with neighborhood attraction[J]. Information Sciences, 2016, 382: 374-387.
[22]	ZHANG R, LI G, MA Z. A deep learning based hybrid framework for day-ahead electricity price forecasting[J]. IEEE Access, 2020, 8: 143423-143436. doi: 10.1109/ACCESS.2020.3014241
[23]	欧阳喆, 周永权. 自适应步长萤火虫优化算法[J]. 计算机应用, 2011, 31(7):1804-1807.
	OUYANG Zhe, ZHOU Yongquan. Self-adaptive step glowworm swarm optimization algorithm[J]. Journal of Computer Applications, 2011, 31(7):1804-1807.
[24]	WANG Huaizhi, LI Gangqiang, WANG Guibin, et al. Deep learning based ensemble approach for probabilistic wind power forecasting[J]. Applied Energy, 2017, 188(15): 56-70. doi: 10.1016/j.apenergy.2016.11.111
[25]	ZHU Q, LUO X, ZHANG B, et al. Mathematical modeling and optimization of a large-scale combined cooling, heat, and power system that incorporates unit changeover and time-of-use electricity price[J]. Energy Convers Manage, 2017, 133(1): 385-398. doi: 10.1016/j.enconman.2016.10.056
[26]	ZHANG R, AZIZ S, FAROOQ M U, et al. A wind energy supplier bidding strategy using combined EGA-inspired HPSOIFA optimizer and deep learning predictor[J]. Energies, 2021, 14(11):3059. doi: 10.3390/en14113059
[27]	ZHOU H, ZHANG Y, YANG L, et al. Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism[J]. IEEE Access, 2019, 7: 78063-78074. doi: 10.1109/ACCESS.2019.2923006
[28]	王天峰, 刘丙栋, 张一晨, 等. 考虑调峰响应的峰谷型售电套餐效用评估方法[J]. 华电技术, 2021, 43(9): 78-84.
	WANG Tianfeng, LIU Bingdong, ZHANG Yichen, et al. Utility evaluation method for peak-valley electricity retail packages considering peak regulation responses[J]. Huadian Technology, 2021, 43(9):78-84.
[29]	胡倩, 孙志达, 江坷滕, 等. 基于短期负荷打捆预测的售电公司偏差考核控制方法[J]. 华电技术, 2021, 43(4): 47-55.
	HU Qian, SUN Zhida, JIANG Keteng, et al. Deviation assessment and control method for electricity sales companies based on short-term load bundling forecast[J]. Huadian Technology, 2021, 43(4): 47-55.

算法	最小值	最大值	均值	方差
PSO	44 487	44 552	44 525	1 158
FA	44 474	44 534	44 509	674
MFA	44 524	44 539	44 531	55
ALRFA	44 350	44 353	44 352	3

时刻	MES	MESWBESS	MESWREG	CCHP
01:00	1 830	1 831	1 854	1 838
02:00	1 810	1 796	1 822	1 826
03:00	1 778	1 772	1 794	1 817
04:00	1 712	1 705	1 700	1 742
05:00	1 750	1 762	1 758	1 765
06:00	1 703	1 642	1 675	1 696
07:00	1 752	1 690	1 754	1 758
08:00	1 771	1 756	1 782	1 823
09:00	1 800	1 793	1 865	1 880
10:00	1 756	1 760	2 000	2 019
11:00	1 754	1 796	2 132	2 177
12:00	1 967	1 945	2 245	2 236
13:00	1 969	1 995	2 423	2 379
14:00	1 971	1 973	2 272	2 322
15:00	1 905	1 961	2 191	2 241
16:00	1 868	1 830	2 093	2 141
17:00	1 284	1 362	1 666	1 756
18:00	1 588	1 645	1 837	1 901
19:00	2 144	2 142	2 218	2 205
20:00	2 145	2 157	2 174	2 181
21:00	2 104	2 129	2 138	2 149
22:00	2 057	2 055	2 052	2 069
23:00	2 009	1 991	1 994	1 997
24:00	1 923	1 942	1 945	1 964
合计	44 350	44 430	47 384	47 882