Low-carbon collaborative optimization for the commitment and maintenance of units considering hydrogen production equipment

doi:10.3969/j.issn.2097-0706.2022.05.009

Abstract

Abstract:

The rapid growth of electricity consumption has brought challenges to power systems and environment deterioration to our society.In order to tackle the problems,it is urgent for power sector to develop clean energy and take efficient emission mitigation measures.Considering the conditions of the hydrogen production equipment on power demand side,discussion on the multi-objective collaborative optimization for the monthly commitment and maintenance of power units under the existent carbon emission trading mechanism is made.By taking the theory of deep reinforcement learning and controlling the contraction-expansion coefficient in multi-objective quantum-behaved particle swarm optimization algorithm flexibly,the optimization efficiency of the algorithm is improved.The simulation results of an IEEE 118-bus system show that the improved algorithm performs better than the traditional algorithm.The stability of the locational marginal price and the reliability of the system calculated by the multi-objective collaborative optimization model exceeds the ones by the single-objective model by about 10% and 30%,respectively,and the line security margin of the former model is significantly better than that of the latter model.These results have proven that the multi-objective optimization model can find a solution for unit scheduling which fully considers multiple objectives,and ensure the safe,stable and low-carbon operation of the power system.

Key words: clean energy, unit maintenance, carbon emission right, energy conservation and emission reduction, multi-objective optimization model, hydrogen production equipment, low-carbon operation

CLC Number:

TM73：TK01

Hengyuan GUO, Xiaofeng FENG, Guodong LI, Zhiguo DUAN, Yuanzheng LI. Low-carbon collaborative optimization for the commitment and maintenance of units considering hydrogen production equipment[J]. Integrated Intelligent Energy, 2022, 44(5): 78-87.

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机组编号	检修持续时长	检修起始时长	检修结束时长
^#2	3	1	30
^#4	3	1	30
^#9	3	1	15
^#12	3	1	30
^#16	3	1	30
^#19	2	16	30
^#25	3	1	30
^#27	3	1	30
^#31	2	1	30
^#32	2	1	30

机组编号	碳排放系数	机组编号	碳排放系数
^#1	0.98	^#17	0.88
^#2	0.98	^#18	0.88
^#3	0.98	^#19	0.96
^#4	0.97	^#20	0.96
^#5	0.97	^#21	1.13
^#6	0.97	^#22	1.13
^#7	1.21	^#23	1.13
^#8	1.21	^#24	1.17
^#9	1.21	^#25	1.17
^#10	1.21	^#26	1.17
^#11	1.15	^#27	0.42
^#12	1.15	^#28	0.42
^#13	1.15	^#29	0.42
^#14	1.15	^#30	0.97
^#15	1.08	^#31	0.97
^#16	1.08	^#32	0.97

机组编号	检修调度计划	机组编号	检修调度计划
^#2	27~29	^#19	22~23
^#4	9~11	^#25	1~3
^#9	5~7	^#27	21~23
^#12	14~16	^#31	14~15
^#16	18~20	^#32	15~16

目标	单目标模型	多目标模型	变化/%
F₁/万美元	94.045	97.039	+3.18
F₂	0.175 3	0.129 5	-28.18
F₃/[美元·(MW·h)^-1]	23.128 0	20.942 7	-9.45
F₄/MW	5.261 2	30.464 5	+479
F₅/万t	7.875 6	7.961 6	-1.09