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

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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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