计及微网储能系统多尺度不确定性容量协调优化

doi:10.19799/j.cnki.2095-4239.2021.0152

摘要/Abstract

摘要：

微网系统中的分布式电源和负荷需求的随机问题促使微网储能容量决策成为一个研究的热点话题。本文提出了多时间尺度下微网系统中源-荷的随机性和预测出力偏差的不确定性的储能容量优化方法。利用该方法建立了系统能量平衡关系和鲁棒性经济协调指标，刻画出了储能系统容量优化方法和微网随机因子间的定量关系，并兼顾微网运行经济性的目标。结合分层理论建立了含分布式电源的微网储能容量的双层优化模型，并采用多目标粒子群算法对本文的优化模型进行求解。仿真结果表明，所提方法能够保证储能系统容量优化配置，同时能获得良好的经济效益。

关键词: 微网储能, 容量优化, 能量平衡, 鲁棒性, 多目标算法

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

中图分类号:

TM 7

卢颖辉. 计及微网储能系统多尺度不确定性容量协调优化[J]. 储能科学与技术, 2021, 10(6): 2235-2243.

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.

图/表 12

图1

图2

图3

表1

仿真参数"

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

表1

图4

图5

图6

图7

表2

表3

表4

表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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