An Energy-Saving Optimization Method of Air-Conditioning System for Electric Drive Workshop Based on IBK-IPS Algorithm

doi:10.12141/j.issn.1000-565X.240412

Abstract

Abstract:

To address the problems of high operational energy consumption and low efficiency in air-conditioning system for electric drive workshops, this paper proposed a dynamic energy-saving optimization method based on the IBK-IPS algorithm, taking into account the mutual constraints of each equipment of the air-conditioning system. Firstly, the influence mechanism among system components were analyzed, and mathematical models of energy consumption and constraints conditions for each device were established to an objective function for system energy consumption. Then, an improved Black Kite-Particle Swarm (IBK-IPS) algorithm was introduced to optimize operational parameters such as water temperature, flow rate, and air volume, thereby improving the accuracy and effectiveness of system parameter control. Subsequently, a simulation model of the cooling water system and chilled water system of the air-conditioning system is developed using the Simulink platform to evaluate the performance and accuracy of the parameter optimization. Finally, the method is practically applied in an electric drive workshop to verify the practical effect and feasibility of the proposed method. The results of simulation experiments and practical application tests show that: the operational energy consumption of the system is effectively reduced, achieving an energy-saving rate of 11.23%~34.68%, and improves operational energy efficiency by 11.53%~41.75%. Compared with the PS, BK, and BK-PS algorithms, the IBK-IPS algorithm delivers superior energy-saving performance, with convergence speeds improved by 27.27%, 61.90%, and 69.23%, respectively. In real-world testing under five different load conditions, the optimized system achieved energy-saving rates of 22.61%, 17.24%, 7.48%, 14.97%, and 12.64%, respectively. In summary, the energy-saving optimization method proposed in this paper can effectively solve the problem of high energy consumption and low efficiency of the air-conditioning system operation in the electric drive workshop, which has good energy-saving effect and practicability, and can provide new ideas for the research of energy-saving optimization of air-conditioning system.

Key words: electric drive workshop, air-conditioning system, mutual constraint, improved black-winged kite and particle swarm algorithm, dynamic energy-saving optimization method

CLC Number:

TP18

GONG Xiaorong, WANG Xin, XIONG Weiqing, WANG Tangliang, ZHANG Hongming. An Energy-Saving Optimization Method of Air-Conditioning System for Electric Drive Workshop Based on IBK-IPS Algorithm[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(7): 80-92.

Figures/Tables 17

Fig.1

Table 1

Fig.2

Fig.3

Table 2

Optimization results of operating parameters of the air-conditioning system in electric drive workshop under different loads"

Q_e/kW	θ_sq/℃	θ_d，o/℃	G_d/（m³‧h^-1）	θ_q，i/℃	G_q/（m³‧h^-1）	F_3a/（m³‧h^-1）	V_76a/（m³‧h^-1）	W_o/（kW·h）	W_o0/（kW·h）	λ/%
15.38	33.05	10.93	101.32	29.98	120.68	58 668.73	3 360.28	91.46	140.02	34.68
19.21	26.97	10.78	105.00	29.56	125.77	60 452.14	3 420.67	96.21	142.64	32.55
21.69	40.98	10.54	108.00	29.74	131.75	63 356.00	3 990.42	102.63	150.51	31.81
29.07	36.60	10.09	101.00	30.01	133.86	142 209.74	4 000.63	109.80	154.80	29.07
35.73	25.99	10.01	106.15	30.00	137.71	162 688.85	4 371.26	115.76	162.84	28.91
44.92	25.75	9.78	107.00	30.20	151.36	170 000.00	4 580.04	121.08	167.42	27.68
50.02	28.13	9.61	109.00	30.08	165.69	180 261.00	5 300.25	127.82	175.22	27.05
57.80	28.98	9.55	111.00	29.65	168.01	185 262.17	5 824.57	130.04	177.50	26.74
65.12	38.18	9.40	118.37	30.00	170.18	190 768.50	6 035.25	134.25	179.24	25.10
74.18	28.25	9.40	125.56	30.90	179.37	190 261.00	6 277.21	149.63	193.10	22.51
82.24	37.23	9.35	134.26	30.04	186.32	200 716.74	6 450.25	162.00	205.66	21.23
86.17	39.03	9.13	153.55	29.89	193.26	210 663.50	6 625.46	179.37	226.71	20.88
91.89	40.83	9.02	167.65	29.70	200.21	220 611.00	6 973.72	192.74	241.35	20.14
101.42	30.13	8.86	171.66	30.54	205.36	227 143.61	7 321.98	206.11	257.00	19.80
108.51	28.13	8.71	174.45	30.69	207.16	230 558.51	7 670.24	220.48	272.40	19.06
115.08	28.79	8.56	179.59	29.01	211.87	238 675.27	7 862.44	237.85	292.56	18.70
127.41	31.11	8.45	183.47	30.00	214.10	240 506.69	8 018.49	254.22	310.93	18.24
136.73	28.89	8.36	187.67	30.64	218.56	242 456.07	8 366.75	271.06	330.44	17.97
141.06	33.54	8.20	190.57	30.12	221.05	247 803.25	8 445.78	289.15	348.84	17.11
147.39	30.62	8.05	192.52	29.87	228.00	250 062.52	8 525.48	296.59	355.07	16.47
152.02	35.35	8.04	196.52	30.02	234.94	253 476.87	8 634.62	308.96	368.03	16.05
166.04	28.54	7.90	199.57	30.18	241.89	258 453.50	8 715.01	315.57	373.63	15.54
174.37	33.42	7.70	202.35	30.06	248.83	260 401.00	9 063.26	334.63	393.31	14.92
179.70	34.36	7.61	203.13	31.34	255.78	265 832.81	9 480.52	351.52	409.60	14.18
186.02	36.57	7.41	208.34	29.82	262.73	268 793.05	9 759.78	370.33	428.87	13.65
189.05	40.13	7.30	214.63	29.65	269.67	271 034.26	10 108.03	382.71	440.30	13.08
195.28	29.64	7.10	219.47	30.89	276.62	275 473.35	10 259.25	398.08	457.04	12.90
202.68	29.24	7.01	227.30	29.76	283.56	284 366.00	10 547.26	406.52	463.75	12.34
208.00	28.48	6.71	231.04	30.01	286.18	287 261.00	10 680.89	424.75	481.69	11.82
215.00	28.26	6.40	235.61	31.23	290.91	290 261.00	10 791.73	428.43	482.63	11.23

Table 2

Table 3

Fig.4

Fig.5

Table 4

Fig.6

Table 5

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 6

References 21

[1]	ZHAO Y， LI W， ZHANG J，et al ．Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network［J］．Energy and Buildings，2023，298：113527/1-13．
[2]	LU S， QI Y， CAI Z，et al ．Optimization model analysis of centralized groundwater source heat pump system in heating season［J］．Frontiers in Energy，2015，9：343-361．
[3]	LI S， CHEN X， BU L，et al ．Two-stage optimization for the air conditioning system in public buildings with flexible control of indoor load［J］．Energy and Buildings，2024，312：114162/1-13．
[4]	CEN J， ZENG L， LIU X，et al ．Research on energy-saving optimization method for central air conditioning system based on multi-strategy improved sparrow search algorithm ［J］．International Journal of Refrigeration，2024，160：263-274．
[5]	叶灿滔，马伟斌，刘金平，等．基于谐波反应法的净化空调系统节能研究［J］．华南理工大学学报（自然科学版），2013，41（12）：81-89．
	YE Can-tao， MA Wei-bin， LIU Jin-ping，et al ．Study on energy saving of purified air conditioning system based on harmonic response method［J］．Journal of South China University of Technology （Natural Science Edition），2013，41（12）：81-89．
[6]	ZHAO J， LIU D， YUAN X，et al ．Model predictive control for the ice-storage air-conditioning system coupled with multi-objective optimization［J］．Applied Thermal Engineering，2024，243：122595/1-19.
[7]	刘金平，卢智涛，刘雪峰，等．基于次优化解群的冷冻水泵组全年能耗评价方法［J］．华南理工大学学报（自然科学版），2015，43（7）：106-117．
	LIU Jin-ping， LU Zhi-tao， LIU Xue-feng，et al ．An evaluation method of annual energy consumption of chilled water pumps group based on suboptimal solutions［J］．Journal of South China University of Technology （Natural Science Edition），2015，43（7）：106-117．
[8]	YANG J， WU J， XIAN T，et al ．Research on energy-saving optimization of commercial central air-conditioning based on data mining algorithm［J］．Energy and Buildings，2022，272：112326/1-15．
[9]	杨秀，刘欣雨，孙改平，等．基于改进粒子群算法的中央空调系统节能优化控制［J］．电力科学与技术学报，2023，38（3）：65-75，93．
	YANG Xiu， LIU Xinyu， SUN Gaiping，et al ．Energy-saving optimization control of central air-conditioning system based on improved particle swarm algorithm［J］．Journal of Electric Power Science and Technology，2023，38（3）：65-75，93．
[10]	闫军威，黄琪，周璇．基于Double-DQN的中央空调系统节能优化运行［J］．华南理工大学学报（自然科学版），2019，47（1）：135-144．
	YAN Junwei， HUANG Qi， ZHOU Xuan ．Energy-saving optimization operation of central air-conditioning system based on double-DQN algorithm［J］．Journal of South China University of Technology （Natural Science Edition），2019，47（1）：135-144．
[11]	TU M， HUANG H， LIU Z H，et al ．Factor analysis and optimization of operational parameters in a liquid desiccant air-conditioning system［J］．Energy，2017，139：767-781．
[12]	周璇，莫浩华，闫军威．基于改进H-AC算法的冷源系统节能优化控制策略［J］．华南理工大学学报（自然科学版），2025，53（1）：21-31．
	ZHOU Xuan， MO Haohua， YAN Junwei ．Investigating an enhanced H-AC algorithm-based strategy for energy-saving optimization control in cold source system［J］．Journal of South China University of Technology （Natural Science Edition），2025，53（1）：21-31．
[13]	LIU Q， CHENG X， SHI J，et al ．Modeling and predicting energy consumption of chiller based on dynamic spatial-temporal graph neural network［J］．Journal of Building Engineering，2024，91：109657/1-21．
[14]	CAI J， YANG H ．Attention mechanism-aided model ensemble method of chiller energy consumption prediction［J］．International Journal of Refrigeration，2024，165：111-121．
[15]	WANG H， WANG F， WANG C，et al ．A prospective assessment of scale effects of energy conversion in ultra-low-head pumped hydro energy storage units［J］．Energy Conversion and Management，2024，315：118798/1-22．
[16]	BARNIER M A， BOURRET B ．Pumping energy and variable frequency drives［J］．ASHRAE Journal，1999，41（12）：37-40．
[17]	王明伟，刘天天，高琦，等．填料蒸发预冷进风的机械通风空冷塔设计及其运行性能［J］．中国电力，2024，57（6）：225-234．
	WANG Mingwei， LIU Tiantian， GAO Qi，et al ．Design and operating performance of mechanical draft dry cooling tower pre-cooled with wet medium［J］．Electric Power，2024，57（6）：225-234．
[18]	乔义友，方健珉，殷翔，等．送风温度对车用跨临界CO₂制冷系统影响的仿真研究［J］．制冷学报，2022，43（4）：96-102．
	QIAO Yiyou， FANG Jianmin， YIN Xiang，et al ．Simulation study on the effect of supply air temperature on vehicle transcritical CO₂ refrigeration system［J］．Journal of Refrigeration，2022，43（4）：96-102．
[19]	WANG J， WANG W， HU X，et al ．Black-winged kite algorithm：a nature-inspired meta-heuristic for solving benchmark functions and engineering problems［J］．Artificial Intelligence Review，2024，57：98/1-53．
[20]	JIANG M， FENG X， WANG C，et al ．Robust color image watermarking algorithm based on synchronization correction with multi-layer perceptron and Cauchy distribution model［J］．Applied Soft Computing，2023，140：110271/1-15．
[21]	BO Y， GUO X， LIU Q，et al ．Prediction of tunnel deformation using PSO variant integrated with XGBoost and its TBM jamming application［J］．Tunnelling and Underground Space Technology，2024，150：105842/1-20．

设备名称	参数名称	参数值		台数
设备名称	参数名称	单台设备	总体	台数
冷水机组	制冷量/kW	217	1 953	9
冷水机组	功率/kW	40.0	360.0	9
冷却水泵	流量/（m³‧h^-1）	300	900	3
冷却水泵	功率/kW	20.0	620.0	3
冷冻水泵	流量/（m³‧h^-1）	250	750	3
冷冻水泵	功率/kW	15.0	415.0	3
冷却塔	流量/（m³‧h^-1）	300	900	3
	风量/（m³‧h^-1）	103 333.00	310 000.00
	功率/kW	12.0	36.0
空调风机	流量/（m³‧h^-1）	16	1 216	76
	风量/（m³‧h^-1）	157.89	12 000.00
	功率/kW	1.5	114.0

运行参数	原始值	优化值
冷冻水出水温度/℃	［5，12］	［6.40，10.93］
冷冻水泵流量/（m³‧h^-1）	［100，250］	［101.32，235.61］
冷却水进水温度/℃	［24，32］	［29.01，31.34］
冷却水泵流量/（m³‧h^-1）	［120，300］	［120.68，290.91］
冷却塔风机风量/（m³‧h^-1）	［0，310 000］	［58 668.73，290 261.00］
空调风机风量/（m³‧h^-1）	［0，12 000］	［3 360.28，10 791.73］

需求负荷		空气湿球温度/℃	W_o/（kW·h）				节能效果/（kW·h）
数值/kW	占比/%	空气湿球温度/℃	PS	BK	BK-PS	IBK-IPS	W_o（IBK-IPS）-W_o（PS）	W_o（IBK-IPS）-W_o（BK）	W_o（IBK-IPS）-W_o（BK-PS）
21.69	10	40.98	131.67	124.02	115.84	102.63	-29.04	-21.39	-13.21
65.12	30	38.18	142.02	139.72	137.29	134.25	-7.77	-5.47	-3.04
108.51	50	28.13	270.38	253.87	237.28	220.48	-49.90	-33.39	-16.80
152.02	70	35.35	361.33	342.10	325.74	308.96	-52.37	-33.14	-16.78
195.28	90	29.64	442.18	428.69	416.77	398.08	-44.10	-30.61	-18.69

负荷/kW	空气湿球温度/℃	能耗/（kW·h）		λ/%
负荷/kW	空气湿球温度/℃	优化后	优化前	λ/%
0.1Q_e	26.51	108.26	139.89	22.61
0.3Q_e	27.38	135.87	164.18	17.24
0.5Q_e	31.35	219.25	236.97	7.48
0.7Q_e	36.14	316.01	371.66	14.97
0.9Q_e	37.29	421.28	482.23	12.64