Pareto Fronts of Mixed Coal Quality and Cost in Power Plant Based on Chance Constraints

doi:10.12096/j.2096-4528.pgt.21042

Abstract

Abstract:

Coal blending models usually take the composition or properties of coal as constraints to optimize the cost of mixed coal for the blending scheme in power plant. Properties of coal are essentially not optimized with this kind of blending model. Therefore, the maximum likelihood quality of design coal of boiler was defined, when the compositions of raw coal can be regarded as random variables. A multi-objective optimization model of coal properties and cost based on chance constraint was established. Pareto fronts of the multi-objective model were presented by genetic algorithm. Analysis of Pareto fronts shows that the optimal data of coal properties and cost are reasonable, and the chance constraints are met well. The blending model can employ the stability of mixed coal as the optimization objective additionally. According to different characteristics of the generator unit and the actual coal to be blended, the optimization model can have different objectives and constraint combinations, which means the good flexibility and practicability.

Key words: thermal power generation, coal blending, maximum likelihood principle, chance constraint, multi-objective programming, Pareto fronts

CLC Number:

TK 16

Fuguo LIU, Ke LIU, Shouen WANG. Pareto Fronts of Mixed Coal Quality and Cost in Power Plant Based on Chance Constraints[J]. Power Generation Technology, 2022, 43(1): 160-167.

Figures/Tables 8

References 15

1	陈广伟，黄建平，徐鹏志．兰炭在600 MW无烟煤“W”火焰锅炉上的掺烧应用研究[J]．发电技术，2021，42(2)：267-272． doi:10.12096/j.2096-4528.pgt.20029
	CHEN G W， HUANG J P， XU P Z ．Application research of semi-coke blending on a 600 MW anthracite “W” flame boiler[J]．Power Generation Technology，2021，42(2)：267-272． doi:10.12096/j.2096-4528.pgt.20029
2	SLOSS L L ．Blending of coals to meet power station requirements[R]．London：IEA Clean Coal Centre，2014．
3	GUO X J， CHEN M， WU J W ．Coal blending optimization of coal preparation production process based on improved GA[J]．Procedia Earth & Planetary Science，2009，1(1)：654-660． doi:10.1016/j.proeps.2009.09.103
4	SHIH J S， FREY H C ．Coal blending optimization under uncertainty[J]．European Journal of Operational Research，1995，83(3)：452-465． doi:10.1016/0377-2217(94)00243-6
5	CARNIATO A， CAMPONOGARA E ．Integrated coal-mining operations planning： modeling and case study[J]．Coal Preparation，2011，31(6)：299-334． doi:10.1080/19392699.2011.576656
6	AMINI S H， VASS C， SHAHABI M，et al ．Optimization of coal blending operations under uncertainty-robust optimization approach[J]．International Journal of Coal Preparation and Utilization，2019，42(1)：1-21．
7	BERETTA F S， COSTA J F C L， KOPPE J C ．Reducing coal quality attributes variability using properly designed blending piles helped by geostatistical simulation[J]．International Journal of Coal Geology，2010，84(2)：83-93． doi:10.1016/j.coal.2010.08.007
8	CHAKRABORTY A， CHAKRABORTY M ．Multi criteria genetic algorithm for optimal blending of coal[J]．Opsearch，2012，49(4)：386-399． doi:10.1007/s12597-012-0089-y
9	高继录，邹天舒，李志山，等．1000 MW机组神华煤掺烧霍林河褐煤的试验研究[J]．动力工程学报，2012，32(6)：430-434． doi:10.3969/j.issn.1674-7607.2012.06.002
	GAO J L， ZHOU T S， LI Z S，et al ．Experimental study on mixed combustion of Shenhua coal with Huolinhe coal in a 1000 MW power unit[J]．Journal of Chinese Society of Power Engineering，2012，32(6)：430-434． doi:10.3969/j.issn.1674-7607.2012.06.002
10	LINEBERRY G T， GILLENWATER E L ．Linear programming to improve raw coal blending：a new twist to an old tool[J]．Coal Preparation，1987，4(3/4)：227-239． doi:10.1080/07349348708945534
11	夏季，华志刚，彭鹏，等．基于非支配排序遗传算法的无约束多目标优化配煤模型[J]．中国电机工程学报，2011，31(2)：85-90． doi:10.1631/jzus.A1000135
	XIA J， HUA Z G， PENG P，et al ．A model of unconstrained multi-objective optimization of coal blending based on the non-dominated sorting genetic algorithm[J]．Proceedings of the CSEE，2011，31(2)：85-90． doi:10.1631/jzus.A1000135
12	张宗振，李德波，李新虎，等．污泥掺烧对燃煤机组锅炉效率及环保系统的影响[J]．浙江电力，2020，39(7)：93-101． doi:10.19585/j.zjdl.202007016
	ZHANG Z Z， LI D B， LI X H，et al ．Impact of co-combustion of sludge with coal on boiler efficiency and environmental protection system of coal-fired generating units[J]．Zhejiang Electric Power，2020，39(7)：93-101． doi:10.19585/j.zjdl.202007016
13	任耀峰，王玉琢．正态随机变量线性组合的分布问题[J]．高等数学研究，2013，16(4)：104-106． doi:10.3969/j.issn.1008-1399.2013.04.037
	REN Y F， WANG Y Z ．On linear combinations of normal random variables[J]．Studies in College Mathematics，2013，16(4)：104-106． doi:10.3969/j.issn.1008-1399.2013.04.037
14	孙有发．倒数正态分布与证券收益异象原因研究[J]．统计与决策，2007(9)：21-22． doi:10.3969/j.issn.1002-6487.2007.09.010
	SUN Y F ．Research on the reciprocal normal distribution and the causes of abnormal returns[J]．Statistics and Decision Making，2007(9)：21-22． doi:10.3969/j.issn.1002-6487.2007.09.010
15	蒋猛，黄宇，廖伟涵，等．基于改进NSGA-II 算法的电-气-热综合能源系统多目标优化[J]．发电技术，2020，41(2)：131-136．
	JIANG M， HUANG Y， LIAO WH，et al ．Multi-objective optimization of electricity-gas-heat integrated energy system[J]．Power Generation Technology，2020，41(2)：131-136．

参数	i	j
参数	i	1	2	3
μ_ij /%	1	10.98	7.08	13.48
	2	33.62	31.57	11.07
	3	26.78	22.67	32.29
	4	37.44	44.60	51.23
	5	0.63	1.29	0.63
	6	17 996	19 856	24 125
σ_ij /%	1	2.00	1.65	1.69
	2	5.86	3.98	3.31
	3	1.75	2.02	1.28
	4	4.24	3.47	2.21
	5	0.08	0.40	0.15
	6	1 860	1 377	955
P_j /(美元/t)		482	525	672

参数	i	j
参数	i	1	2	3
μ_ij /%	1	10.98	7.08	13.48
	2	33.62	31.57	11.07
	3	26.78	22.67	32.29
	4	37.44	44.60	51.23
	5	0.63	1.29	0.63
	6	17 996	19 856	24 125
σ_ij /%	1	2.00	1.65	1.69
	2	5.86	3.98	3.31
	3	1.75	2.02	1.28
	4	4.24	3.47	2.21
	5	0.08	0.40	0.15
	6	1 860	1 377	955
P_j /(美元/t)		482	525	672

优化解	x₁	x₂	x₃	p₁	E(p₂)	ω_H1		ω_H2		ω_H3		ω_H4		ω_H5		ω_H6
优化解	x₁	x₂	x₃	p₁	E(p₂)	μ_H1	σ_H1	μ_H2	σ_H2	μ_H3	σ_H3	μ_H4	σ_H4	μ_H5	σ_H5	μ_H6	σ_H6
1	0.22	0.46	0.32	26.99	15.26	9.98	1.03	25.48	2.48	26.67	1.09	45.21	1.98	0.94	0.19	20 836	815
2	0.33	0.40	0.27	26.95	15.65	10.11	1.04	26.69	2.66	26.66	1.05	44.06	2.06	0.89	0.17	20 414	864
3	0.43	0.33	0.24	26.92	16.26	10.27	1.09	27.55	2.94	26.72	1.05	43.14	2.21	0.85	0.14	20 085	946
4	0.51	0.27	0.22	26.90	16.70	10.47	1.18	28.16	3.27	26.87	1.09	42.39	2.41	0.81	0.12	19 837	1 043
5	0.64	0.20	0.15	26.80	17.70	10.56	1.36	29.78	3.89	26.78	1.22	41.00	2.84	0.76	0.10	19 307	1 239
6	0.69	0.18	0.13	26.77	18.03	10.59	1.42	30.30	4.12	26.73	1.27	40.52	3.00	0.75	0.09	19 123	1 309
7	0.73	0.15	0.12	26.75	18.11	10.71	1.50	30.58	4.34	26.85	1.32	40.18	3.15	0.73	0.09	19 020	1 379
8	0.86	0.08	0.06	26.64	18.93	10.81	1.72	32.05	5.03	26.78	1.51	38.88	3.64	0.68	0.08	18 527	1 597
9	0.96	0.03	0.02	26.54	19.56	10.93	1.92	33.17	5.61	26.77	1.68	37.86	4.06	0.65	0.08	18 151	1 781

优化解	x₁	x₂	x₃	p₁	E(p₂)	ω_H1		ω_H2		ω_H3		ω_H4		ω_H5		ω_H6
优化解	x₁	x₂	x₃	p₁	E(p₂)	μ_H1	σ_H1	μ_H2	σ_H2	μ_H3	σ_H3	μ_H4	σ_H4	μ_H5	σ_H5	μ_H6	σ_H6
1	0.22	0.46	0.32	26.99	15.26	9.98	1.03	25.48	2.48	26.67	1.09	45.21	1.98	0.94	0.19	20 836	815
2	0.33	0.40	0.27	26.95	15.65	10.11	1.04	26.69	2.66	26.66	1.05	44.06	2.06	0.89	0.17	20 414	864
3	0.43	0.33	0.24	26.92	16.26	10.27	1.09	27.55	2.94	26.72	1.05	43.14	2.21	0.85	0.14	20 085	946
4	0.51	0.27	0.22	26.90	16.70	10.47	1.18	28.16	3.27	26.87	1.09	42.39	2.41	0.81	0.12	19 837	1 043
5	0.64	0.20	0.15	26.80	17.70	10.56	1.36	29.78	3.89	26.78	1.22	41.00	2.84	0.76	0.10	19 307	1 239
6	0.69	0.18	0.13	26.77	18.03	10.59	1.42	30.30	4.12	26.73	1.27	40.52	3.00	0.75	0.09	19 123	1 309
7	0.73	0.15	0.12	26.75	18.11	10.71	1.50	30.58	4.34	26.85	1.32	40.18	3.15	0.73	0.09	19 020	1 379
8	0.86	0.08	0.06	26.64	18.93	10.81	1.72	32.05	5.03	26.78	1.51	38.88	3.64	0.68	0.08	18 527	1 597
9	0.96	0.03	0.02	26.54	19.56	10.93	1.92	33.17	5.61	26.77	1.68	37.86	4.06	0.65	0.08	18 151	1 781

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