CN111555364A - 一种使用改进的灰狼优化器的微电网能量管理方法 - Google Patents

一种使用改进的灰狼优化器的微电网能量管理方法 Download PDF

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CN111555364A
CN111555364A CN202010285701.XA CN202010285701A CN111555364A CN 111555364 A CN111555364 A CN 111555364A CN 202010285701 A CN202010285701 A CN 202010285701A CN 111555364 A CN111555364 A CN 111555364A
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module
power
onoff
microgrid
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陈腾鹏
曹宇豪
肖钰洁
张景瑞
李钷
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Xiamen University
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Abstract

一种使用改进的灰狼优化器微电网能量管理方法，其特征在于：设有光伏模块、风力发电机模块、蓄电池储能单元模块、可控发电机模块及与公用电网的互动模块，通过将采集的信息输入对应模块得到输出功率、技术约束和运营成本；采用MGWO算法对目标函数进行优化计算，在满足功率平衡约束的前提下，根据优化的目标函数对微网进行运行成本和污染性气体排放量的优化。本发明可有效降低运营成本和污染排放。

Description

一种使用改进的灰狼优化器的微电网能量管理方法
技术领域
本发明涉及微电网能量管理领域，特别是指一种使用改进的灰狼优化器的微电网能量管理方法。
背景技术
随着电力需求的迅速增长以及环境保护带来的巨大压力，建立可靠、高效、清洁的电网已成为至关重要的问题。微电网通常集成了可再生能源(Renewable Energy System，RES)，分布式发电单元和其他单元。由于它们可用于改善电网的整体性能，所以已引起人们的广泛关注。然而，RES输出功率的波动性和不可避免的负荷需求预测误差会使微电网的可靠运行复杂化。因此，有效的能量管理方法对于缓解上述不确定性、优化微电网运行中的经济和环境效益至关重要。
目前，针对微电网能量管理涉及的不确定性已经进行了广泛的研究，根据不同的建模方法可分为两类：随机规划(Stochastic Programming，SP)和鲁棒优化(RobustOptimization,RO)。在考虑RES的间歇性和可变性的情况下，SP方法可用于最大程度地降低运营成本。但是，在使用的同时需给出不确定变量的概率分布或精确的数据参数。RO能量管理优化问题可以通过商业求解器解决，也可以通过现有的数学编程方法解决。然而在某些非线性情况下，其优化结果不能保证全局最优。灰狼优化器(Grey Wolf Optimizer,GWO)是一种相对较新的群优化算法，与其他元启发式算法相比，它具有高效的性能。然而，GWO仍可能过早地陷入局部最优。
发明内容
本发明的主要目的在于克服现有技术中的上述缺陷，考虑到灰狼优化器(GWO)具有较高的性能，提出一种使用改进的灰狼优化器的微电网能量管理方法。
本发明采用如下技术方案：
一种使用改进的灰狼优化器微电网能量管理方法，其特征在于：设有光伏模块、风力发电机模块、蓄电池储能单元模块、可控发电机模块及与公用电网的互动模块，通过将采集的信息输入对应模块得到输出功率、技术约束和运营成本；采用MGWO算法对目标函数进行优化计算，在满足功率平衡约束的前提下，根据优化的目标函数对微网进行运行成本和污染性气体排放量的优化。
优选的，所述光伏模块的输出功率为：
Figure BDA0002448421290000021
Figure BDA0002448421290000022
其中，Ppv,stc是标准测试条件下光伏模块的输出功率，G是光伏电池板上的实际辐射度，γ是功率温度系数，Tamb是环境温度，Tnoct表示标称工作电池温度；
光伏发电必须满足技术约束：
Figure BDA0002448421290000023
其中，
Figure BDA0002448421290000024
和
Figure BDA0002448421290000025
分别表示输出功率Ppv的上下限；
运营成本Cpv公式的线性函数为
Figure BDA0002448421290000026
其中，λpv是光伏模块的成本系数。
优选的，所述风力发电机模块输出功率Pwt为：
Figure BDA0002448421290000027
其中，Pr，vci，vco和vr是WT的额定输出功率，切入风速，切出风速和额定风速，v是风速，风力发电机的输出功率须满足以下技术约束
Figure BDA0002448421290000031
其中，
Figure BDA0002448421290000032
和
Figure BDA0002448421290000033
是Pwt的上下限，整个运行周期WT的运营成本为：
Figure BDA0002448421290000034
其中，λwt表示风力发电机的成本系数。
优选的，所述蓄电池储能单元模块中，蓄电池荷电状态为
Figure BDA0002448421290000035
其中σ、Qbat、ηbc和ηbdc分别是BAT自放电率、额定容量、充电和放电效率，η是引入的辅助变量，t代表当前时刻，Δt是时间间隔，Pbat(t)的正负值对应于蓄电池储能单元的放电和充电功率，蓄电池储能单元的运行约束为SOCmin≤SOC(t)≤SOCmax
Figure BDA0002448421290000036
Figure BDA0002448421290000037
Figure BDA0002448421290000038
Figure BDA0002448421290000039
SOCmin、SOCmax分别是蓄电池储能单元荷电状态的上、下限；为了可持续和稳定地使用蓄电池储能单元，在微网最后一个运行时间间隔(T)的蓄电池储能单元容量应等于初始容量值：
SOC(0)＝SOC(T)
微网运行中蓄电池储能单元的总经济成本为
Figure BDA0002448421290000041
其中，Ibat和λbat分别是BAT的初始投资成本和运行成本系数，αbat和βbat是损耗系数。
优选的，所述可控发电机模块，维持可控发电机可靠运行的技术约束为
Figure BDA0002448421290000042
Figure BDA0002448421290000043
δonoff(t)-δonoff(t-Δt)≤δonoff(γon)
γon＝t,t+1,…,min(t+Ton-1,T)
δonoff(t-Δt)-δonoff(t)≤1-δonoff(γoff)
γoff＝t,t+1,…,min(t+Toff-1,T)
Pcg(t)代表t时刻可控发电机的输出功率，
Figure BDA0002448421290000044
分别是其输出功率上、下限，
Figure BDA0002448421290000045
表示爬坡率，δonoff(t)表示可控发电机的工作状态；引入两个二进制变量δon(t)和δoff(t)用来表示可控发电机在t时刻是否有开机或者停机操作，定义为
δon(t)＝max(δonoff(t)-δonoff(t-Δt),0)
δoff(t)＝max(δonoff(t-Δt)-δonoff(t),0)
可控发电机的运行成本如下，αcg、βcg、γcg表示可控发电机的燃料成本系数，λcg、λon、λoff分别表示运维成本因子、开机成本系数和停机成本系数，
Figure BDA0002448421290000046
可控发电机运行时会排放温室气体，排放量用
Figure BDA0002448421290000047
表示,对应惩罚成本
Figure BDA0002448421290000048
为
Figure BDA0002448421290000049
其中λem代表污染物气体成本系数，
Figure BDA00024484212900000410
是污染物气体排放因子。
优选的，所述与公用电网的互动模块中，与公用电网的交易功率以Pug(t)表示,
Figure BDA0002448421290000051
其中
Figure BDA0002448421290000052
和
Figure BDA0002448421290000053
是t时刻的交易限额，Pug(t)的正负值对应于微网购电和售电，
与公用电网的交易成本定义为
Figure BDA0002448421290000054
λug＝λugp,Pug(t)≥0；λug＝λugs,Pug(t)＜0
其中λugp和λugs分别是微网购电和售电的价格，假设从公用电网购电时的污染气体等效排放量表示为
Figure BDA0002448421290000055
则惩罚成本
Figure BDA0002448421290000056
为
Figure BDA0002448421290000057
其中
Figure BDA0002448421290000058
是污染物气体排放因子。
优选的，所述功率平衡约束为：
Pl(t)＝Ppv(t)+Pwt(t)+Pbat(t)+Pcg(t)+Pug(t)。
优选的，所述目标函数为：
min Ccost＝Com+Cem
s.t.Com＝Cpv+Cwt+Cbat+Ccg+Cug
Figure BDA0002448421290000059
其中Com表示微电网总的运行和维护费用，Cem是环境污染的惩罚成本，计算模拟期间的污染性气体排放量的公式为
Figure BDA00024484212900000510
优选的，还包括鲁棒能量管理，假设每个不确定变量位于指定的区间内,即
Figure BDA00024484212900000511
Figure BDA00024484212900000512
表示t时刻光伏模块实际输出功率，
Figure BDA00024484212900000513
表示t时刻光伏模块输出功率的预测值,
Figure BDA0002448421290000061
和
Figure BDA0002448421290000062
表示最大负偏差和正偏差，则功率平衡约束的鲁棒对应项为
Figure BDA0002448421290000063
Figure BDA0002448421290000064
Figure BDA0002448421290000065
Figure BDA0002448421290000066
Figure BDA0002448421290000067
Figure BDA0002448421290000068
Figure BDA0002448421290000069
其中
Figure BDA00024484212900000610
分别是负荷需求和风机模块输出功率的预测值；Γl和Γres是引入的RO参数以调整RES输出和负载需求的不确定度，0≤Γl≤1，0≤Γres≤1，zl，zres，
Figure BDA00024484212900000611
为引入的辅助变量。
优选的，所述GWO算法中，GWO算法的包围猎物过程为
Figure BDA00024484212900000612
Figure BDA00024484212900000613
其中
Figure BDA00024484212900000614
表示种群中灰狼与猎物的距离，k代表当前迭代，
Figure BDA00024484212900000615
是猎物的位置矢量，
Figure BDA00024484212900000616
代表灰狼的位置矢量，
Figure BDA00024484212900000617
和
Figure BDA00024484212900000618
是随机系数向量，
Figure BDA00024484212900000619
Figure BDA00024484212900000620
其中
Figure BDA00024484212900000621
和
Figure BDA00024484212900000622
是介于0至1之间的随机变量，
Figure BDA00024484212900000623
采用非线性递减，
Figure BDA00024484212900000624
其中Maxk表示最大迭代次数，若某次迭代中存在决策变量越限，以决策变量和界值之差的随机数校正
Figure BDA0002448421290000071
其中
Figure BDA0002448421290000072
和
Figure BDA0002448421290000073
分别表示第j维决策变量在第次和次迭代后的取值，r是0到1之间的随机值，Ubj和Lbj是第j维决策变量的上下限。
由上述对本发明的描述可知，与现有技术相比，本发明具有如下有益效果：
1、本发明提出了一种改进的GWO算法来解决不确定性微网的鲁棒能量管理问题，可节省微网大量的运营成本并减少污染性气体排放。与Active-set算法和传统的GWO相比，可进一步优化微网的运营成本并减少污染性气体排放。
2、本发明应用前景良好，符合电力需求的迅速增长以及环境保护发展趋势。
附图说明
图1是本发明实施例的典型微电网结构示意图。
图2是发明能量管理与其它方法对比图。
以下结合附图和具体实施例对本发明作进一步详述。
具体实施方式
以下通过具体实施方式对本发明作进一步的描述。
一种使用改进的灰狼优化器微电网能量管理方法，设有光伏模块、风力发电机模块、蓄电池储能单元模块、可控发电机模块、与公用电网的互动模块和，；通过将采集的信息输入对应模块得到输出功率、技术约束和运营成本；采用MGWO算法模块对系统目标函数进行优化计算；在满足系统功率平衡约束的前提下，根据目标函数对微网进行运行成本和污染性气体排放量的优化。
读取光伏模块所需环境温度和光辐射强度等信息，并将信息带入光伏模块的能量管理模型中计算其可发功率，光伏模块PV输出功率Ppv为：
Figure BDA0002448421290000081
Figure BDA0002448421290000082
其中Ppv,stc是标准测试条件下的输出功率，G是光伏电池板上的实际辐射度，γ是功率温度系数，Tamb是环境温度，Tnoct表示额定工作条件下电池温度，PV输出功率须满足以下技术约束
Figure BDA0002448421290000083
其中
Figure BDA0002448421290000084
和
Figure BDA0002448421290000085
分别表示Ppv的上下限，微网运行周期内PV运行成本为
Figure BDA0002448421290000086
其中λpv是PV的成本系数；
读取风力发电机模块所需风速等信息，并将信息带入风力发电机模块的能量管理模型中计算其可发功率，风机输出功率Pwt为
Figure BDA0002448421290000087
其中Pr，vci，vco和vr分别是风力发电机WT的额定输出功率，切入风速，切出风速和额定风速，v是风速，Pwt须满足相应约束
Figure BDA0002448421290000088
其中
Figure BDA0002448421290000089
和
Figure BDA00024484212900000810
是Pwt的上下限，WT在微网整个运行周期的经济成本为
Figure BDA00024484212900000811
其中λwt表示WT的成本系数。
读取蓄电池模块所需蓄电池技术参数信息，并将信息带入蓄电池(BAT)模块的能量管理模型中计算蓄电池的充放电能力，蓄电池荷电状态(SOC)为
Figure BDA0002448421290000091
其中σ，Qbat，ηbc和ηbdc分别是BAT的自放电率，额定容量，充电和放电效率，η是引入的辅助变量，t代表当前时刻，Δt是时间间隔，Pbat(t)的正负值对应于BAT的放电和充电功率，BAT运行时须满足以下约束
SOCmin≤SOC(t)≤SOCmax
Figure BDA0002448421290000092
Figure BDA0002448421290000093
Figure BDA0002448421290000094
Figure BDA0002448421290000095
SOCmin、SOCmax分别是蓄电池储能单元荷电状态的上、下限；为了可持续和稳定地使用BAT，在微网最后一个仿真时间间隔(T)的BAT容量应等于仿真初始时刻容量值,
SOC(0)＝SOC(T)
整个微网运行周期BAT的总经济成本为
Figure BDA0002448421290000096
其中Ibat和λbat分别是BAT的初始投资成本和运行成本系数，αbat和βbat是损耗系数。
读取可控发电机(CG)模块所需技术参数信息，并将信息带入可控发电机模块的能量管理模型中计算其发电能力，维持CG安全可靠运行的技术约束为
Figure BDA0002448421290000097
Figure BDA0002448421290000098
δonoff(t)-δonoff(t-Δt)≤δonoff(γon)
γon＝t,t+1,…,min(t+Ton-1,T)
δonoff(t-Δt)-δonoff(t)≤1-δonoff(γoff)
γoff＝t,t+1,…,min(t+Toff-1,T)
Pcg(t)代表t时刻可控发电机的输出功率，
Figure BDA0002448421290000101
分别是其输出功率上、下限，
Figure BDA0002448421290000102
表示爬坡率，δonoff(t)表示可控发电机的工作状态；同时，为表示在t时刻CG是否开机或者停机，我们引入两个二进制变量δon(t)和δoff(t)，定义式如下
δon(t)＝max(δonoff(t)-δonoff(t-Δt),0)
δoff(t)＝max(δonoff(t-Δt)-δonoff(t),0)
αcg、βcg、γcg表示可控发电机的燃料成本系数，λcg、λon、λoff分别表示运维成本因子和开/停机成本系数。CG的运行成本为
Figure BDA0002448421290000103
CG运行时会排放污染性气体气体，其量表示为
Figure BDA0002448421290000104
惩罚成本
Figure BDA0002448421290000105
为
Figure BDA0002448421290000106
其中，λem代表污染物气体成本系数，
Figure BDA0002448421290000107
是污染物气体排放因子；
读取与公用电网的互动模块所需电价等信息，并将信息带入与公用电网的互动模块的能量管理模型中计算与公用电网功率交互的范围，以Pug(t)表示微网和公用电网之间的功率交互,
Figure BDA0002448421290000108
其中，
Figure BDA0002448421290000109
和
Figure BDA00024484212900001010
是t时刻的交易限额，Pug(t)的正负值分别对应微网购电和售电，微网与公用电网的电力交易成本定义为
Figure BDA00024484212900001011
λug＝λugp,Pug(t)≥0；λug＝λugs,Pug(t)＜0
其中，λugp和λugs分别是微网购电和售电的价格，假设微网从公用电网购电时，污染性气体的等效排放量表示为
Figure BDA0002448421290000111
则相应惩罚成本
Figure BDA0002448421290000112
为
Figure BDA0002448421290000113
其中，
Figure BDA0002448421290000114
是污染物气体排放因子。
还可设置功率平衡约束模块，读取其所需各发电单元输出功率和对应负荷需求信息，并将信息带入功率平衡约束模块的能量管理模型中，功率平衡约束为
Pl(t)＝Ppv(t)+Pwt(t)+Pbat(t)+Pcg(t)+Pug(t)。
还设置目标功能模块，读取其所需各发电单元发电成本信息，并将信息带入目标功能模块的目标函数以及能量管理模型中，同时优化微网运行经济性和污染性气体排放量的目标函数如下：
min Ccost＝Com+Cem
s.t.Com＝Cpv+Cwt+Cbat+Ccg+Cug
Figure BDA0002448421290000115
其中，Com表示微网总运行和维护费用，Cem是环境污染的惩罚成本，计算模拟期间的污染性气体排放量的公式为
Figure BDA0002448421290000116
进一步的，设置鲁棒能量管理模型模块，读取其所需RO参数和光伏电池、风力发电机及负荷需求预测信息，并将信息带入微电网的鲁棒能量管理模型模块的能量管理模型中，假设每个不确定变量位于指定的区间内(以PV为例),即
Figure BDA0002448421290000117
其中
Figure BDA0002448421290000118
表示t时刻PV的输出功率预测值,
Figure BDA0002448421290000121
表示t时刻光伏模块实际输出功率，
Figure BDA0002448421290000122
和表示最大负偏差和正偏差。微电网功率平衡约束的鲁棒对应项为
Figure BDA0002448421290000124
Figure BDA0002448421290000125
Figure BDA0002448421290000126
Figure BDA0002448421290000127
Figure BDA0002448421290000128
Figure BDA0002448421290000129
Figure BDA00024484212900001210
其中，
Figure BDA00024484212900001211
分别是负荷需求和风机模块输出功率的预测值；Γl(0≤Γl≤1)和Γres(0≤Γres≤1)是引入的RO参数以调整RES输出功率和负荷需求的不确定度，zl，zres，
Figure BDA00024484212900001212
为引入的辅助变量。
读取MGWO算法模块所需的种群大小、迭代次数和各发电单元的发电能力及发电成本系数等信息，并将信息带入MGWO算法中计算得到微网各发电单元实际输出功率，GWO算法的包围猎物过程为
Figure BDA00024484212900001213
Figure BDA00024484212900001214
其中
Figure BDA00024484212900001215
表示种群中灰狼与猎物的距离，k表示当前迭代步数，
Figure BDA00024484212900001216
是猎物的位置矢量，
Figure BDA00024484212900001217
代表灰狼的位置矢量，
Figure BDA00024484212900001218
和
Figure BDA00024484212900001219
是随机系数向量，
Figure BDA00024484212900001220
Figure BDA00024484212900001221
其中
Figure BDA00024484212900001222
和
Figure BDA00024484212900001223
是介于0至1之间的随机变量。MGWO采用了非线性的递减公式，
Figure BDA0002448421290000131
其中Maxk表示算法最大迭代次数，MGWO中则以决策变量和所越界值之差的随机数校正，
Figure BDA0002448421290000132
其中
Figure BDA0002448421290000133
和
Figure BDA0002448421290000134
分别表示第j维决策变量在第次和次迭代后的取值，r是0到1之间的随机值，Ubj和Lbj是第j维决策变量的上下限，j的取值视具体情况定。采用MGWO算法求解微网能量管理优化问题，得到如图2所示的结果图，其中(a)为Active-set算法得到的Ccost，(b)为Active-set算法得到的Eem，(c)为GWO算法得到的Ccost，(d)为GWO算法得到的Eem，(e)为本发明MGWO算法得到的Ccost，(f)为本发明MGWO算法得到的Eem。
综上，本发明方法，用于优化涉及新能源发电和负荷消耗不确定性的并网微电网的鲁棒能量管理。除微电网运行的经济性，目标函数还考虑了污染性气体的排放，这对促进微网实际应用和环境保护有重要意义。
上述仅为本发明的具体实施方式，但本发明的设计构思并不局限于此，凡利用此构思对本发明进行非实质性的改动，均应属于侵犯本发明保护范围的行为。

Claims (10)

1.一种使用改进的灰狼优化器微电网能量管理方法，其特征在于：设有光伏模块、风力发电机模块、蓄电池储能单元模块、可控发电机模块及与公用电网的互动模块，通过将采集的信息输入对应模块得到输出功率、技术约束和运营成本；采用MGWO算法对目标函数进行优化计算，在满足功率平衡约束的前提下，根据优化的目标函数对微网进行运行成本和污染性气体排放量的优化。
2.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述光伏模块的输出功率为：
Figure FDA0002448421280000011
Figure FDA0002448421280000012
其中，Ppv,stc是标准测试条件下光伏模块的输出功率，G是光伏电池板上的实际辐射度，γ是功率温度系数，Tamb是环境温度，Tnoct表示标称工作电池温度；
光伏发电必须满足技术约束：
Figure FDA0002448421280000013
其中，
Figure FDA0002448421280000014
和
Figure FDA0002448421280000015
分别表示输出功率Ppv的上下限；
运营成本Cpv公式的线性函数为
Figure FDA0002448421280000016
其中，λpv是光伏模块的成本系数。
3.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述风力发电机模块输出功率Pwt为：
Figure FDA0002448421280000017
其中，Pr，vci，vco和vr是WT的额定输出功率，切入风速，切出风速和额定风速，v是风速，风力发电机的输出功率须满足以下技术约束
Figure FDA0002448421280000021
其中，
Figure FDA0002448421280000022
和
Figure FDA0002448421280000023
是Pwt的上下限，整个运行周期WT的运营成本为：
Figure FDA0002448421280000024
其中，λwt表示风力发电机的成本系数。
4.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述蓄电池储能单元模块中，蓄电池荷电状态为
Figure FDA0002448421280000025
其中σ、Qbat、ηbc和ηbdc分别是BAT自放电率、额定容量、充电和放电效率，η是引入的辅助变量，t代表当前时刻，Δt是时间间隔，Pbat(t)的正负值对应于蓄电池储能单元的放电和充电功率，蓄电池储能单元的运行约束为
SOCmin≤SOC(t)≤SOCmax
Figure FDA0002448421280000026
Figure FDA0002448421280000027
Figure FDA0002448421280000028
Figure FDA0002448421280000029
SOCmin、SOCmax分别是蓄电池储能单元荷电状态的上、下限；为了可持续和稳定地使用蓄电池储能单元，在微网最后一个运行时间间隔(T)的蓄电池储能单元容量应等于初始容量值：
SOC(0)＝SOC(T)
微网运行中蓄电池储能单元的总经济成本为
Figure FDA00024484212800000210
其中，Ibat和λbat分别是BAT的初始投资成本和运行成本系数，αbat和βbat是损耗系数。
5.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述可控发电机模块，维持可控发电机可靠运行的技术约束为
Figure FDA0002448421280000031
Figure FDA0002448421280000032
δonoff(t)-δonoff(t-Δt)≤δonoff(γon)
γon＝t,t+1,…,min(t+Ton-1,T)
δonoff(t-Δt)-δonoff(t)≤1-δonoff(γoff)
γoff＝t,t+1,…,min(t+Toff-1,T)
Pcg(t)代表t时刻可控发电机的输出功率，
Figure FDA0002448421280000033
分别是其输出功率上、下限，
Figure FDA0002448421280000034
表示爬坡率，δonoff(t)表示可控发电机的工作状态；引入两个二进制变量δon(t)和δoff(t)用来表示可控发电机在t时刻是否有开机或者停机操作，定义为
δon(t)＝max(δonoff(t)-δonoff(t-Δt),0)
δoff(t)＝max(δonoff(t-Δt)-δonoff(t),0)
可控发电机的运行成本如下，αcg、βcg、γcg表示可控发电机的燃料成本系数，λcg、λon、λoff分别表示运维成本因子、开机成本系数和停机成本系数，
Figure FDA0002448421280000035
可控发电机运行时会排放温室气体，排放量用
Figure FDA0002448421280000036
表示,对应惩罚成本
Figure FDA0002448421280000037
为
Figure FDA0002448421280000038
其中λem代表污染物气体成本系数，
Figure FDA0002448421280000039
是污染物气体排放因子。
6.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述与公用电网的互动模块中，与公用电网的交易功率以Pug(t)表示,
Figure FDA0002448421280000041
其中
Figure FDA0002448421280000042
和
Figure FDA0002448421280000043
是t时刻的交易限额，Pug(t)的正负值对应于微网购电和售电，与公用电网的交易成本定义为
Figure FDA0002448421280000044
λug＝λugp,Pug(t)≥0；λug＝λugs,Pug(t)＜0
其中λugp和λugs分别是微网购电和售电的价格，假设从公用电网购电时的污染气体等效排放量表示为
Figure FDA0002448421280000045
则惩罚成本
Figure FDA0002448421280000046
为
Figure FDA0002448421280000047
其中
Figure FDA0002448421280000048
是污染物气体排放因子。
7.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述功率平衡约束为：
Pl(t)＝Ppv(t)+Pwt(t)+Pbat(t)+Pcg(t)+Pug(t)。
8.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述目标函数为：
min Ccost＝Com+Cem
s.t.Com＝Cpv+Cwt+Cbat+Ccg+Cug
Figure FDA0002448421280000049
其中Com表示微电网总的运行和维护费用，Cem是环境污染的惩罚成本，计算模拟期间的污染性气体排放量的公式为
Figure FDA00024484212800000410
9.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：还包括鲁棒能量管理，假设每个不确定变量位于指定的区间内,即
Figure FDA00024484212800000411
Figure FDA00024484212800000412
表示t时刻光伏模块实际输出功率，
Figure FDA00024484212800000413
表示t时刻光伏模块输出功率的预测值,
Figure FDA0002448421280000051
和
Figure FDA0002448421280000052
表示最大负偏差和正偏差，则功率平衡约束的鲁棒对应项为
Figure FDA0002448421280000053
Figure FDA0002448421280000054
Figure FDA0002448421280000055
Figure FDA0002448421280000056
Figure FDA0002448421280000057
Figure FDA0002448421280000058
Figure FDA0002448421280000059
其中
Figure FDA00024484212800000510
分别是负荷需求和风机模块输出功率的预测值；Γl和Γres是引入的RO参数以调整RES输出和负载需求的不确定度，0≤Γl≤1，0≤Γres≤1，zl，zres，
Figure FDA00024484212800000511
为引入的辅助变量。
10.如权利要求1所述的一种使用改进的灰狼优化器的微电网能量管理方法，其特征在于：所述GWO算法中，GWO算法的包围猎物过程为
Figure FDA00024484212800000512
Figure FDA00024484212800000513
其中
Figure FDA00024484212800000514
表示种群中灰狼与猎物的距离，k代表当前迭代，
Figure FDA00024484212800000515
是猎物的位置矢量，
Figure FDA00024484212800000516
代表灰狼的位置矢量，
Figure FDA00024484212800000517
和
Figure FDA00024484212800000518
是随机系数向量，
Figure FDA00024484212800000519
Figure FDA00024484212800000520
其中
Figure FDA00024484212800000521
和
Figure FDA00024484212800000522
是介于0至1之间的随机变量，
Figure FDA00024484212800000523
采用非线性递减，
Figure FDA00024484212800000524
其中Maxk表示最大迭代次数，若某次迭代中存在决策变量越限，以决策变量和界值之差的随机数校正
Figure FDA0002448421280000061
其中
Figure FDA0002448421280000062
和
Figure FDA0002448421280000063
分别表示第j维决策变量在第次和次迭代后的取值，r是0到1之间的随机值，Ubj和Lbj是第j维决策变量的上下限。
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