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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

邮发代号 80-972

2019 Impact Factor: 2.657

Frontiers in Energy  2020, Vol. 14 Issue (1): 139-151   https://doi.org/10.1007/s11708-017-0484-4
  本期目录
一种基于遗传算法的使用能量过滤法的太阳能-风电混合系统的改进最优尺寸策略
MAHESH Aeidapu(), Singh SANDHU Kanwarjit()
印度理工学院电气工程系
A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm
Aeidapu MAHESH(), Kanwarjit Singh SANDHU()
Department of Electrical Engineering, National Institute of Technology, Kurukshetra 136119, India
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摘要:

本文基于遗传算法(GA),使用了一种新的能量滤波算法以优化一种并网太阳能-光伏(PV)-风电混合系统。本文的主要目标是使混合系统的总成本最小,同时保持其可靠性。除了可靠性约束外,另一些重要参数,比如光伏和风能系统互补性的充分利用、注入电网的功率波动以及电池的荷电状态(SOC),也被考虑在内,以确定混合系统的有效尺寸。本文提出了一种新的平滑注入电网功率的能量滤波算法。为了验证所提方法的有效性,我们进行了详细的算例研究。不同算例(使用和不使用能量滤波算法)的结果证明了所提出的尺寸策略的有效性。

Abstract

In this paper, the genetic algorithm (GA) is applied to optimize a grid connected solar photovoltaic (PV)-wind-battery hybrid system using a novel energy filter algorithm. The main objective of this paper is to minimize the total cost of the hybrid system, while maintaining its reliability. Along with the reliability constraint, some of the important parameters, such as full utilization of complementary nature of PV and wind systems, fluctuations of power injected into the grid and the battery’s state of charge (SOC), have also been considered for the effective sizing of the hybrid system. A novel energy filter algorithm for smoothing the power injected into the grid has been proposed. To validate the proposed method, a detailed case study has been conducted. The results of the case study for different cases, with and without employing the energy filter algorithm, have been presented to demonstrate the effectiveness of the proposed sizing strategy.

Key wordsPV-wind-battery hybrid system    size optimization    genetic algorithm
收稿日期: 2016-07-05      出版日期: 2020-03-16
通讯作者: MAHESH Aeidapu,Singh SANDHU Kanwarjit     E-mail: mahesh.aeidapu@gmail.com;kjssandhu@rediffmail.com
Corresponding Author(s): Aeidapu MAHESH,Kanwarjit Singh SANDHU   
 引用本文:   
MAHESH Aeidapu, Singh SANDHU Kanwarjit. 一种基于遗传算法的使用能量过滤法的太阳能-风电混合系统的改进最优尺寸策略[J]. Frontiers in Energy, 2020, 14(1): 139-151.
Aeidapu MAHESH, Kanwarjit Singh SANDHU. A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm. Front. Energy, 2020, 14(1): 139-151.
 链接本文:  
https://academic.hep.com.cn/fie/CN/10.1007/s11708-017-0484-4
https://academic.hep.com.cn/fie/CN/Y2020/V14/I1/139
Fig.1  
Fig.2  
Fig.3  
ParameterPopulation sizeMaximum
generation
Crossover
probability
Mutation
probability
Elitism
probability
Value40500.90.0050.1
Tab.1  
Fig.4  
Fig.5  
ParameterValue
PV panel: SANYO HIT Power 200Maximum power/W200
OC voltage /V68.7
SC current/A3.83
Voltage at MPP/V55.8
Current at MPP/A3.59
Efficiency at STC17.2
Slope (fixed slope)/(°)40.98
Cost per panel (Cpv)/$420
O & M cost (Cpvo&m)/($·kW−1)15
Wind turbine: PGE 35 kWRated power/kW35
Cut-in wind speed/(m·s−1)3
Cut-out wind speed/(m·s−1)25
Hub height/m24
Rated wind speed/(m·s−1)11
Rotor diameter/m19.2
Blade length/m9
Cost per turbine (Cwt)/$25000
O & M cost (Cwto&m)/($ ·kW−1)30
Battery: Hoppecke 6OPzS 600Rated capacity/Ah600
Rated voltage/V2
Round trip efficiency/%85
Max. Ch./disch. rate/(A·Ah–1)0.5
Max. ch/dis. current/A100, 75
A self-discharge rate/%1
Cost per battery (Cbs)/$150
O & M cost (Cbso&m)/( $·kAh−1)20
Tab.2  
NpvNwtNbsLPSPMAD/kWFcomplibg/kWDgs/kWLCE
415020.05111.642.546.298.60.4223
Tab.3  
Fig.6  
Fig.7  
NpvNwtNbsLPSPMAD(kW)Fcomplibg/kWDgs/kWLCE
18431127330.05158.162.3121.638115.530.5856
Tab.4  
Fig.8  
Fig.9  
NpvNwtNbsLPSPMAD/kWFcomplibg/kWDgs/kWLCE
19021110560.04986.372.1125.53295.790.4024
Tab.5  
Fig.10  
Fig.11  
Fig.12  
Fig.13  
ParameterWithout filterWith energy filterWith proposed filter
CA(×105)/$2.8793.992.74
LCE/$0.42230.58560.4024
Cpc(×106) ($)5.671700
Dgs(kW/?t)98.6115.5395.79
bg(kW/?t)56.169.3115.5
Fcompl2.52.32.1
LPSP0.050.050.049
MAD111.64158.1686.37
Ngs(×108)9.23315.057.20
Ngp(×107)3.332.4613.352
Tab.6  
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