Multi-verse Optimization-based MPPT Design for PV-TEG System

doi:10.11930/j.issn.1004-9649.202210127

Abstract

Abstract:

A centralized hybrid photovoltaic thermoelectric generator (PV-TEG) system shows multiple local maximum power points (LMPPs) under a partial shading condition (PSC). The multi-verse optimization (MVO) is used for the maximum power point tracking (MPPT) of the PV-TEG system under PSC. MVO can effectively identify the unique global maximum power point (GMPP) among multiple LMPPs by balancing global search and local search, avoiding search results trapped in LMPP and improving generation efficiency and energy utilization. The simulation results of the example show that the MPPT based on the MVO algorithm can collect higher power in a shorter time and achieve the minimum power fluctuation.

Key words: centralized photovoltaic thermoelectric generator system, maximum power point tracking, multi-verse optimization, partial shading

Dahu LI, Hongyu ZHOU, Yue ZHOU, Yuze RAO, Wei YAO. Multi-verse Optimization-based MPPT Design for PV-TEG System[J]. Electric Power, 2023, 56(11): 197-205.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202210127

https://www.electricpower.com.cn/EN/Y2023/V56/I11/197

Figures/Tables 12

References 31

1	武群丽, 席曼. 基于电力供应链博弈的可再生能源政策效应研究[J]. 中国电力, 2022, 55 (5): 12- 20, 38.
	WU Qunli, XI Man. Research on effects of renewable energy policy based on power supply chain game[J]. Electric Power, 2022, 55 (5): 12- 20, 38.
2	许洪华, 邵桂萍, 鄂春良, 等. 我国未来能源系统及能源转型现实路径研究[J]. 发电技术, 2023, 44 (4): 484- 491.
	XU Honghua, SHAO Guiping, E Chunliang, et al. Research on China's future energy system and the realistic path of energy transformation[J]. Power Generation Technology, 2023, 44 (4): 484- 491.
3	MAHIDIN E, HUSIN H, ZAKI N. M, et al. Critical review of the integration of renewable energy sources with various technologies[J]. Protection and Control of Modern Power Systems, 2021, 6 (1): 37- 54. DOI
4	YANG B, WU S, ZHANG H, et al. Wave energy converter array layout optimization: A critical and comprehensive overview[J]. Renewable and Sustainable Energy Reviews, 2022, 167, 112668. DOI
5	董洁,乔建强. “双碳”目标下先进煤炭清洁利用发电技术研究综述,2022,55(8):202-212.[J]. 中国电力, 2022, 55 (8): 202- 212.
	DONG Jie, QIAO Jianqiang. A review on advanced clean coal power generation technology under "Carbon Peaking and Carbon Neutrality" goal[J]. Electric Power, 2022, 55 (8): 202- 212.
6	曹钰, 房磊. “双碳”背景下热电机组-储热联合运行消纳弃风策略[J]. 中国电力, 2022, 55 (10): 142- 149, 160.
	CAO Yu, FANG Lei. Combined operation strategy of CHP unit and heat accumulator for eliminate abandoned wind under "Double Carbon" background[J]. Electric Power, 2022, 55 (10): 142- 149, 160.
7	YANG B, WU S, LI Q, et al. Jellyfish search algorithm based optimal thermoelectric generation array reconfiguration under non-uniform temperature distribution condition[J]. Renewable Energy, 2023, 204, 197- 217. DOI
8	GUCHHAIT P K, BANERJEE A. Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm[J]. Protection and Control of Modern Power Systems, 2020, 5 (1): 1- 13.
9	王奔, 牛洪海, 徐卫峰, 等. 基于PLC的槽式光热太阳能追踪控制系统的研究与应用[J]. 中国电力, 2020, 53 (11): 185- 194.
	WANG Ben, NIU Honghai, XU Weifeng, et al. Research and application of parabolic sun-tracking system based on PLC[J]. Electric Power, 2020, 53 (11): 185- 194.
10	薛飞, 马鑫, 田蓓, 等. 基于改进蜻蜓算法的光伏全局最大功率追踪[J]. 中国电力, 2022, 55 (2): 131- 137.
	XUE Fei, MA Xin, TIAN Bei, et al. Photovoltaic global maximum power tracking based on improved dragonfly algorithm[J]. Electric Power, 2022, 55 (2): 131- 137.
11	LUND J W, TOTH A N. Direct utilization of geothermal energy 2020 worldwide review[J]. Geothermics, 2021, 90, 101915. DOI
12	李鹏, 信鹏飞, 窦鹏冲, 等. 计及光伏发电最大功率跟踪的光储微电网功率协调控制方法[J]. 电力系统自动化, 2014, 38 (4): 8- 13, 103. DOI
	LI Peng, XIN Pengfei, DOU Pengchong, et al. Power coordinated control of photovoltaic energy-storage system in microgrid under photovoltaic maximum power point tracking condition[J]. Automation of Electric Power Systems, 2014, 38 (4): 8- 13, 103. DOI
13	HÜBNER S, ECK M, STILLER C, et al. Techno-economic heat transfer optimization of large scale latent heat energy storage systems in solar thermal power plants[J]. Applied Thermal Engineering, 2016, 98, 483- 491. DOI
14	LU F L, ZHU Y, PAN M Z, et al. Thermodynamic, economic, and environmental analysis of new combined power and space cooling system for waste heat recovery in waste-to-energy plant[J]. Energy Conversion and Management, 2020, 226, 113511. DOI
15	王立舒, 白龙, 房俊龙, 等. 基于双曲正切函数的光伏/温差自适应MPPT控制策略研究[J]. 农业工程学报, 2021, 37 (16): 184- 191.
	WANG Lishu, BAI Long, FANG Junlong, et al. Self-adaptive photovoltaic/temperature difference MPPT control strategy based on hyperbolic tangent function[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37 (16): 184- 191.
16	CALISE F, CAPPIELLO F L, D'ACCADIA M D, et al. Smart grid energy district based on the integration of electric vehicles and combined heat and power generation[J]. Energy Conversion and Management, 2021, 234 (8): 113932.
17	LI K W, GARRISON G, MOORE M, et al. An expandable thermoelectric power generator and the experimental studies on power output[J]. International Journal of Heat and Mass Transfer, 2020, 160, 120205.
18	CHEN M, LUND H, ROSENDAHL L A, et al. Energy efficiency analysis and impact evaluation of the application of thermoelectric power cycle to today's CHP systems[J]. Applied Energy, 2021, 87 (4): 1231- 1238.
19	TORRECILLA M C, MONTECUCCO A, SIVITER, J, et al. Novel model and maximum power tracking algorithm for thermoelectric generators operated under constant heat flux[J]. Applied Energy, 2019, 256, 113930. DOI
20	韩鹏, 李银红, 何璇, 等. 结合量子粒子群算法的光伏多峰最大功率点跟踪改进方法[J]. 电力系统自动化, 2016, 40 (23): 101- 108.
	HAN Peng, LI Yinhong, HE Xuan, et al. Improved maximum power point tracking method for photovoltaic multi-peak based on quantum-behaved particle swarm optimization algorithm[J]. Automation of Electric Power Systems, 2016, 40 (23): 101- 108.
21	REZK H, ELTAMALY A M. A comprehensive comparison of different MPPT techniques for photovoltaic systems[J]. Solar Energy, 2015, 112 (2): 1- 11.
22	殷明慧, 蒯狄正, 李群, 等. 风机最大功率点跟踪的失效现象[J]. 中国电机工程学报, 2011, 31 (18): 40- 47.
	YIN Minghui, KUAI Dizheng, LI Qun, et al. A phenomenon of maximum power point tracking invalidity of wind turbines[J]. Proceedings of the CSEE, 2011, 31 (18): 40- 47.
23	MUZATHIK A M, LANKA S. Photovoltaic modules operating temperature estimation using a simple correlation[J]. International Journal of Energy Engineering, 2014, 4 (4): 151.
24	BALATO M, COSTANZO L, LO SCHIAVO A, et al. Optimization of both perturb & observe and open circuit voltage MPPT techniques for resonant piezoelectric vibration harvesters feeding bridge rectifiers[J]. Sensors and Actuators A Physical, 2018, 278: 85-97.
25	REZK H, FATHY A, ABDELAZIZ A Y. A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading condition[J]. Renewable and Sustainable Energy Reviews, 2017, 74, 377- 386.
26	ZAKI DIAB A A, REZK H. Global MPPT based on flower pollination and differential evolution algorithms to mitigate partial shading in building integrated PV system[J]. Solar Energy, 2017, 157, 171- 186. DOI
27	MOHAMED M A, ZAKI DIAB A A, REZK H. Partial shading mitigation of PV systems via different meta-heuristic techniques[J]. Renewable Energy, 2019, 130: 1159–1175.
28	FARIS H, MAFARJA M M, HEIDARI A A, et al. An efficient binary salp swarm algorithm with crossover scheme for feature selection problems[J]. Knowledge-Based Systems, 2018, 154, 43- 67. DOI
29	EL-FERGANY A. Extracting optimal parameters of PEM fuel cells using salp swarm optimizer[J]. Renewable Energy, 2018, 119, 641- 648. DOI
30	JATELY V, ARORA S. Development of a dual-tracking technique for extracting maximum power from PV systems under rapidly changing environmental conditions[J]. Energy, 2017, 133, 557- 571. DOI
31	LI X S, WEN H Q, HU Y H, et al. Modified beta algorithm for GMPPT and partial shading detection in photovoltaic systems[J]. IEEE Transactions on Power Electronics, 2018, 33 (3): 2172- 2186. DOI

PV系统
模型		模块数量	最大功率/W		开路电压/V	短路电流/A	电压最大值/V	电流最大值/A
A10 Green Technology A10 J-M60-225		60	224.9856		36.24	8.04	30.24	7.44

TEG系统			升压转换器
塞贝克系数		温度 T₀/K	转换式	开关频率 f_s/kHz	负载 R/Ω	电感 L/mH	电容/μF
α₀/(μV·K^–1)	变化率 α₁/(μV·(sK)^–1)	温度 T₀/K	转换式	开关频率 f_s/kHz	负载 R/Ω	电感 L/mH	C₁	C₂
210	120	300	V_out=V_in/(1–D_C)	20	3	250	66	200

PV系统
模型		模块数量	最大功率/W		开路电压/V	短路电流/A	电压最大值/V	电流最大值/A
A10 Green Technology A10 J-M60-225		60	224.9856		36.24	8.04	30.24	7.44

TEG系统			升压转换器
塞贝克系数		温度 T₀/K	转换式	开关频率 f_s/kHz	负载 R/Ω	电感 L/mH	电容/μF
α₀/(μV·K^–1)	变化率 α₁/(μV·(sK)^–1)	温度 T₀/K	转换式	开关频率 f_s/kHz	负载 R/Ω	电感 L/mH	C₁	C₂
210	120	300	V_out=V_in/(1–D_C)	20	3	250	66	200

算法	上界	下界	种群数量	初始值	最大迭代次数	参数1	参数2	参数3
MVO	1	0	5	0.5	5	WEP_max=1	WEP_min=0.2	膨胀率 c=0.6
MFO	1	0	5	0.5	5	选择参数 t=0.6	路径数量 k=–1	火焰螺旋参数 f=0.5
GWO	1	0	5	0.5	5	α=1	β=1.5	δ=1.5
FA	1	0	5	0.5	5	步长因子 α=0.25	吸引度 β=0.2	光强度吸收系数 γ=1
PSO	1	0	5	0.5	5	加速常数 c₁, c₂=2	惯性因子 w=0.6	速度最大值 V_max=0.8

算法	上界	下界	种群数量	初始值	最大迭代次数	参数1	参数2	参数3
MVO	1	0	5	0.5	5	WEP_max=1	WEP_min=0.2	膨胀率 c=0.6
MFO	1	0	5	0.5	5	选择参数 t=0.6	路径数量 k=–1	火焰螺旋参数 f=0.5
GWO	1	0	5	0.5	5	α=1	β=1.5	δ=1.5
FA	1	0	5	0.5	5	步长因子 α=0.25	吸引度 β=0.2	光强度吸收系数 γ=1
PSO	1	0	5	0.5	5	加速常数 c₁, c₂=2	惯性因子 w=0.6	速度最大值 V_max=0.8

算法	温度的阶跃变化				启动测试
算法	功率/ W	电流/ A	电压/ V	能量/ (W·s)	功率/ W	电流/ A	电压/ V	能量/ (W·s)
MVO	130.0	1.89	68.8	236.5	195.6	2.62	74.7	92.6
GWO	114.5	1.78	64.0	225.6	195.1	2.62	74.5	90.5
FA	92.1	1.71	53.9	186.3	194.9	2.61	74.6	89.2
MFO	110.8	1.81	61.2	214.7	194.7	2.61	74.6	88.7
PSO	110.7	1.80	61.5	220.1	195.0	2.62	74.6	91.2