Capacity coordination and optimization considering multi-scale uncertainty of micro-grid energy storage system

doi:10.19799/j.cnki.2095-4239.2021.0152

Abstract

Abstract:

The random problem of distributed power and load demand in the micro-grid system makes the selection of micro-grid energy storage capacity an important research topic. This paper proposes an energy storage capacity optimization method for micro-grid systems based on the randomness of source-load and the uncertainty of predicted output deviation over multiple time scales. his method is used to establish the system energy balance relationship and the robust economic coordination index, as well as to depict the quantitative relationship between the energy storage system capacity optimization method and the micro-grid stochastic factor Furthermore, the goal of the micro-grid operation economy is considered. In this paper, a two-layer optimization model of the energy storage capacity of the micro-grid with distributed power sources is established and solved using the multi-objective particle swarm optimization (MOPSO) algorithm. The simulation results demonstrate that the proposed method not only ensures the optimal configuration of the energy storage system capacity but also achieves good economic efficiency.

Key words: micro-grid energy storage, capacity optimization, energy balance, robustness, multi-objective algorithm

CLC Number:

TM 7

Yinghui LU. Capacity coordination and optimization considering multi-scale uncertainty of micro-grid energy storage system[J]. Energy Storage Science and Technology, 2021, 10(6): 2235-2243.

Figures/Tables 12

Fig. 1

Fig. 2

Fig. 3

Table 1

Simulation parameter table"

优化参数	数值	优化参数	数值
风机容量	450	K_line/元·kW^-1	0.3
负荷容量	600	光伏容量(峰值)/kW	400
$K E S S P$ /元·kW^-1	1085	yEss/y	20
$K E S S E$ /元·(kW·h)^-1	3224	C_con/万元	3
d/%	5	$K E S S m$ /元·kW^-1	155
K_inv/元·kW^-1	1380	K_pen/元·kW^-1	0.35
$P l i n e m a x$ /kW	200	S_ESS,0	0.3
$P l i n e m i n$ /kW	-200	$S E S S m a x$	0.9
L/%	0.3	$S E S S m i n$	0.1
K_b/元·(kW·h)^-1	0.69	η_c&η_d	0.9
K_s/元·(kW·h)^-1	0.58	N_rep	2000

Table 1

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 2

Table 3

Table 4

Table 5

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情景分类	WT/台	PV/台	ESS/台	总成本/元
r_k_,_t=0	373	44	28	6345.95
r_k_,_t=1；N=6	328	42	15	5537.68
r_k_,_t=1；N=12	298	38	21	4036.48
r_k_,_t=1；N=24	265	30	28	5645.75

联络线设定功率/kW	优化指标
联络线设定功率/kW	P_ESS,_n/kW	C/元	弃风、弃光率/%
±100	65.84	987.54	0
±80	66.47	984.38	0
±60	65.65	986.24	0
±48.5	—	—	—

联络线设定功率/kW	优化指标
联络线设定功率/kW	P_ESS,_n/kW	C/元	弃风、弃光率/%
±100	43.68	587.32	0
±80	45.03	590.94	1.24
±60	46.12	591.57	5.38
±56.47	—	—	—

方法	出力随机性		随机预测误差		B_ESS,_n/kW·h	P_ESS,_n/kW	总成本/元
方法	源	荷	源	荷	B_ESS,_n/kW·h	P_ESS,_n/kW	总成本/元
法1	Y	Y	N	N	1375.35	30.75	9867.38
法2	N	N	Y	Y	982.17	45.67	6198.31
本文	Y	Y	Y	Y	1275.98	63.29	7839.74

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