A Decision-making Optimization Method for Network Reconfiguration of Power System With New Energy Considering the Synergistic Support of Source-grid

doi:10.11930/j.issn.1004-9649.202312022

Abstract

Abstract:

Facing the complex and changeable international situation and the increasing number of extreme events, it is of great significance to study the system recovery scheme under the high proportion of new energy access to improve the new power system security defense system. In this context, a decision-making optimization method for grid reconfiguration of power system with new energy is proposed. Firstly, the uncertainty of new energy output is analyzed and modeled based on the kernel density method. Secondly, considering the requirements of new energy grid connection and operation on the strength of the grid, the linearized model of multiple renewable energy stations short-circuit ratio constraint is realized. On this basis, a grid reconfiguration optimization model that can coordinate new energy, energy storage, conventional units and transmission network restoration is established and a bi-level optimization strategy is proposed to improve the efficiency of model solution. The example results based on the New England 10-machine 39-bus system verify the effectiveness of the proposed method.

Key words: power system restoration, nuclear density method, multiple renewable energy stations short-circuit ratio (MRSCR), bi-level optimization

CLC Number:

TM744

Jian ZHOU, Nan FENG, Yiping JI, Yuyao FENG, Shuai WANG, Shaoyan LI. A Decision-making Optimization Method for Network Reconfiguration of Power System With New Energy Considering the Synergistic Support of Source-grid[J]. Electric Power, 2024, 57(10): 150-157.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202312022

https://www.electricpower.com.cn/EN/Y2024/V57/I10/150

Figures/Tables 8

References 23

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编号	$P_g^{\max } $/ MW	P_st,g/ MW	T_a,g/ h	P_r,g/ MW	$K_{p,g}^{\rm{max}} $/ (MW·h^–1)	$X'_d $(p.u.)
30	350	/	/	/	320	0.043
31	460	16.0	0.50	130	300	0.060
32	520	18.0	0.46	155	314	0.054
33	432	13.5	0.25	130	275	0.051
34	508	17.5	0.83	160	300	0.075
35	650	20.0	1.00	185	308	0.050
36	360	12.0	0.58	108	260	0.062
37	540	16.5	0.46	160	320	0.048
38	830	23.0	0.67	235	405	0.055

编号	$P_g^{\max } $/ MW	P_st,g/ MW	T_a,g/ h	P_r,g/ MW	$K_{p,g}^{\rm{max}} $/ (MW·h^–1)	$X'_d $(p.u.)
30	350	/	/	/	320	0.043
31	460	16.0	0.50	130	300	0.060
32	520	18.0	0.46	155	314	0.054
33	432	13.5	0.25	130	275	0.051
34	508	17.5	0.83	160	300	0.075
35	650	20.0	1.00	185	308	0.050
36	360	12.0	0.58	108	260	0.062
37	540	16.5	0.46	160	320	0.048
38	830	23.0	0.67	235	405	0.055

时步	新能源出力/MW	常规机组出力/MW	储能电站充电功率/MW		负荷恢复率/%
时步	新能源出力/MW	常规机组出力/MW	C1	C2	负荷恢复率/%
1	0	80	0	0	1.10
2	0	160	0	0	2.49
3	281	240	40	100	5.79
4	362	395	0	60	10.64
5	620	580	50	80	16.89
6	759	803	20	0	24.67
7	835	1096	–40	–60	33.14
8	1208	1467	0	50	43.12
9	1360	1950	0	50	54.12
10	1533	2476	20	70	65.54

时步	新能源出力/MW	常规机组出力/MW	储能电站充电功率/MW		负荷恢复率/%
时步	新能源出力/MW	常规机组出力/MW	C1	C2	负荷恢复率/%
1	0	80	0	0	1.10
2	0	160	0	0	2.49
3	281	240	40	100	5.79
4	362	395	0	60	10.64
5	620	580	50	80	16.89
6	759	803	20	0	24.67
7	835	1096	–40	–60	33.14
8	1208	1467	0	50	43.12
9	1360	1950	0	50	54.12
10	1533	2476	20	70	65.54

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