Optimal Configuration of Distributed Generation Considering Carbon Trading and Reactive Power Compensation

doi:10.12096/j.2096-4528.pgt.22069

Abstract

Abstract:

Under the background of “double carbon”, the introduction of carbon trading and reactive power compensation mechanism in the planning of distributed generation is an important means to realize the low-carbon and stability of power system. Therefore, a optimal configuration method of distributed generation considering carbon trading and reactive power compensation was proposed. Firstly, the carbon trading mechanism was introduced to establish the calculation model of carbon trading cost. Then, a two-tier optimal configuration model of distributed generation considering carbon trading and reactive power compensation was established. The upper layer took the annual comprehensive cost as the optimization objective, and the decision variables were the location and capacity of photovoltaic and gas turbine connected to the distribution network. The lower layer took the system network loss, voltage offset and node voltage stability as the optimization objectives. The decision variables were the location and capacity of the reactive power compensation capacitor in the distribution network. Finally, the optimal configuration model was solved by the improved adaptive genetic algorithm. Combined with the example of IEEE33 node structure, the simulation analysis was carried out. The results show that the model can effectively reduce the loss of network and improve the stability of system voltage on the basis of ensuring the economy and environmental protection of the system.

Key words: carbon trading mechanism, reactive power compensation, distributed generation, adaptive genetic algorithm, optimal configuration

CLC Number:

TK 73

Jianhao SUN, Zhuang CHU. Optimal Configuration of Distributed Generation Considering Carbon Trading and Reactive Power Compensation[J]. Power Generation Technology, 2024, 45(1): 142-150.

Figures/Tables 10

Fig. 1 Solution flow chart

Fig. 2 Node system diagram

Tab. 1 Simulation data

参数	数值
运行年限 $Y$ /a	20
年等值回报率 $r$ /(元/a)	0.1
外购单位电价 $γ$ /[元/(kW⋅h)]	0.5
年最大负荷损耗时间 $T l o s s$ /h	3 200
年最大负荷利用时间 $T m a x$ /h	5 000
单位电量无偿碳排放配额 $η$ /[kg/(kW⋅h)]	0.613
外购单位电量碳排放强度 $δ$ /[kg/(kW⋅h)]	0.841
微型燃气轮机碳排放强度 $μ$ /[kg/(kW⋅h)]	0.184
燃气轮机单位电量无偿碳排放配额 $λ$ /[kg/(kW⋅h)]	0.342
无功补偿装置投资(运行)成本 $C c (C c h)$ /[元/(kW⋅h)]	60(10)
光伏投资(运行)成本 $C P V (C P V M)$ /[元/(kW⋅h)]	6 500(70)
燃气轮机投资(运行)成本 $C M T (C M T M)$ /[元/(kW⋅h)]	1 500(11.8)

Tab. 1 Simulation data

参数	数值
运行年限 $Y$ /a	20
年等值回报率 $r$ /(元/a)	0.1
外购单位电价 $γ$ /[元/(kW⋅h)]	0.5
年最大负荷损耗时间 $T l o s s$ /h	3 200
年最大负荷利用时间 $T m a x$ /h	5 000
单位电量无偿碳排放配额 $η$ /[kg/(kW⋅h)]	0.613
外购单位电量碳排放强度 $δ$ /[kg/(kW⋅h)]	0.841
微型燃气轮机碳排放强度 $μ$ /[kg/(kW⋅h)]	0.184
燃气轮机单位电量无偿碳排放配额 $λ$ /[kg/(kW⋅h)]	0.342
无功补偿装置投资(运行)成本 $C c (C c h)$ /[元/(kW⋅h)]	60(10)
光伏投资(运行)成本 $C P V (C P V M)$ /[元/(kW⋅h)]	6 500(70)
燃气轮机投资(运行)成本 $C M T (C M T M)$ /[元/(kW⋅h)]	1 500(11.8)

Tab. 2 Node voltage stability margin sequence table

次序	节点	电压稳定裕度/pu	次序	节点	电压稳定裕度/pu
1	19	0.642 93	18	8	0.766 07
2	18	0.646 98	19	27	0.778 78
3	17	0.649 96	20	7	0.785 60
4	16	0.656 40	21	6	0.799 30
5	33	0.659 43	22	26	0.883 91
6	15	0.661 40	23	5	0.885 98
7	32	0.662 16	24	25	0.908 94
8	31	0.668 82	25	4	0.923 78
9	14	0.669 20	26	24	0.925 45
10	13	0.678 40	27	23	0.939 54
11	30	0.686 73	28	22	0.959 25
12	29	0.698 04	29	3	0.960 76
13	12	0.700 59	30	21	0.961 07
14	11	0.706 35	31	20	0.969 05
15	10	0.711 72	32	2	0.984 63
16	28	0.730 40	33	1	1.000 00
17	9	0.736 24

Tab. 3 Optimized configuration results under different schemes

方案	外购电容量/kW	光伏		燃气轮机		网损/kW	碳排放量/t	成本/万元						总成本/ 万元
方案	外购电容量/kW	节点	容量/ kW	节点	容量/ kW	网损/kW	碳排放量/t	光伏	燃气轮机	无功补偿	网损	负荷电费	碳交易	总成本/ 万元
1	3 927	—	—	—	—	211.92	16 191.89	0	0	0	33.91	928.75	362.21	1 324.87
2	2 988	14	30	16	350	93.48	13 133.46	2.5	14.85	0	14.95	723.75	0	756.05
2	2 988	30	20	31	420	93.48	13 133.46	2.5	14.85	0	14.95	723.75	0	756.05
3	2 985	14	310	16	70	90.76	12 592.54	52.52	3.6	0	14.52	723.75	78.22	872.61
3	2 985	30	320	31	120	90.76	12 592.54	52.52	3.6	0	14.52	723.75	78.22	872.61
4	2 972	12	290	17	80	77.19	12 436.01	52.52	3.6	10.81	12.35	723.75	65.87	868.82
4	2 972	28	340	31	110	77.19	12 436.01	52.52	3.6	10.81	12.35	723.75	65.87	868.82

Tab. 4 Final double-layer optimal configuration results

类型	节点	容量
光伏	12	290 kW
光伏	28	340 kW
燃气轮机	17	80 kW
燃气轮机	31	110 kW
无功补偿装置	13	170 kV⋅A
	14	70 kV⋅A
	15	70 kV⋅A
	16	110 kV⋅A
	17	130 kV⋅A
	18	20 kV⋅A
	19	210 kV⋅A
	29	190 kV⋅A
	30	210 kV⋅A
	31	290 kV⋅A
	32	280 kV⋅A
	33	70 kV⋅A

Fig. 3 Node voltage comparison diagram

Fig. 4 Comparison of voltage offset and stability margin

Fig. 5 Comparison curve of algorithm convergence

Tab. 5 Comparison results of algorithm optimization

参数	改进的自适应遗传算法	传统的遗传算法
求解时间/s	128.3	245.7
迭代收敛次数	98	124
总成本/万元	868.82	874.94

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