CN111445092A - 基于改进jaya算法的多微电网优化方法 - Google Patents
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Abstract
基于改进JAYA算法的多微电网优化方法,包括步骤1:建立多微电网并网优化模型;步骤2:建立微电网经济运行约束条件;步骤3:引入非线性变异算子改进JAYA算法;步骤4:利用改进JAYA算法对多微电网并网优化模型进行求解,从而得到多微电网内各电源的输出功率。本发明一种基于改进JAYA算法的多微电网优化方法,该方法求解精度高,具有较强的抗早熟收敛能力。
Description
技术领域
本发明属于微电网优化运行技术领域,具体涉及一种基于改进JAYA算法的多微电网优化方法。
背景技术
微电网作为分布式能源就地消纳的有效方式得到了迅速发展,随着微电网的发展,局部区域内由部分联系紧密的微电网形成的多微电网系统开始出现。多微电网相对于单微网具有更高的运行稳定性和经济性,更有利于新能源的高渗透综合利用。为了实现对分布式发电单元的最优调度,使多微电网内微电网所有者经济效益最大化的同时提高多微电网的运行稳定性,有必要对多微电网经济运行策略展开深入的研究。
现有技术文献中:A hybrid harmony search algorithm with differentialevolution for day-ahead scheduling problem of a microgrid with considerationof power flow constraints(ZHANG Jingrui,WU Yihong,GUO Yiran.A hybrid harmonysearch algorithm with differential evolution for day-ahead scheduling problemof a microgrid with consideration of power flow constraints[J].AppliedEnergy,183:791-804.)针对由光伏发电、柴油发电机、风力发电机和储能电池组成的微电网的最优日前调度模型,提出了一种将协调搜索与进化微分相结合的方法。混合粒子群算法在微电网经济优化运行的应用(吴定会,高聪,纪志成.混合粒子群算法在微电网经济优化运行的应用[J].控制理论与应用,2018,35(4):457-467.)使用混合粒子群算法对微电网中可控分布式发电单元的出力安排进行优化,以提升微电网运行经济性。基于人工蜂群算法的微电网环保经济调度(薛贵挺,张健.基于人工蜂群算法的微电网环保经济调度[J].电工电气,2016,(6):57-60.)针对考虑了系统运行安全约束的微电网环保经济调度模型,采用人工蜂群算法进行求解。基于遗传算法的微电网经济运行优化(陈天翼,刘宏勋.基于遗传算法的微电网经济运行优化[J].电器与能效管理技术,2017,(21):54-58,)考虑了微电网的运行成本和环境污染排放成本,采用遗传算法对优化模型进行求解。
上述群体智能算法具有简单、易于实现和收敛迅速的优点,因此在微电网领域受到了较多的关注。然而,上述群体优化算法均存在早熟收敛的问题。
发明内容
为改进上述现有技术中存在的不足,本发明提出一种基于改进JAYA算法的多微电网优化方法,该方法求解精度高,具有较强的抗早熟收敛能力。
本发明采取的技术方案为:
基于改进JAYA算法的多微电网优化方法,包括以下步骤:
步骤1:建立多微电网并网优化模型;
步骤2:建立微电网经济运行约束条件;
步骤3:引入非线性变异算子改进JAYA算法;
步骤4:利用改进JAYA算法对多微电网并网优化模型进行求解,从而得到多微电网内各电源的输出功率。
所述步骤1中,多微电网并网优化模型的目标函数表达式为:
其中,N为多微电网中微电网集合,T为多微电网优化周期;
ρt为t时段配电网的售购电电价;
所述步骤2中,微电网经济运行约束条件包括:储能电池输出功率约束、储能电池SOC约束、微型燃气轮机输出功率约束、微型燃气轮机输出功率爬坡速率约束、微电网n电功率平衡约束、微电网n联络线功率平衡约束;这些约束条件的表达式如下:
所述步骤3中,改进JAYA算法方程为:
Xi+1,j,k=Xi,j,k+βi,j[ri,j,1(Xi,j,best-|Xi,j,k|)-ri,j,2(Xi,j,worst-|Xi,j,k|)]
其中,Xi+1,j,k为更新后的解,Xi,j,k为更新前的解,βij为第i次迭代过程中,第j个种群的变异算子,ri,j,1与ri,j,2是第i次迭代中第j个变量在区间[0,1]中的两个随机数,Xi,j,best与Xi,j,worst为变量K在第i次迭代中最优值与最差值。
所述步骤4中,改进JAYA算法求解多微电网并网优化模型时,在获取多微电网设备参数以及各微电网输出功率与负荷功率的基础上,首先通过步骤3基于改进JAYA算法获取种群最优解Xi,j,best与最差解Xi,j,worst,然后对变异算子βi,j进行动态调整,并利用改进JAYA算法方程更新解Xi+1,j,k。变异算子方程为:
其中,βi,j为第i次迭代过程中,第j个种群的变异算子,k为总迭代次数,βmax和βmin分别为变异算子的最大值与最小值并分别取值,Δfi为第i次迭代过程中,n个种群的适应度集合,fi,j为第i次迭代过程中,第j个种群的适应度,fi,best为第i次迭代过程中最有种群的适应度。
最后,将更新解Xi+1,j,k与当前局部最优解Xi,j,k进行比较,若Xi+1,j,k<Xi,j,k,说明更新解有更好适应性,则取代当前局部最优解Xi,j,k,否则保持当前局部最优解Xi,j,k,直至找到并输出符合标准的全局最优解X,从而使多微电网内各电源工作在最优状态。全局最优解应满足如下公式:
X=min(X1,X2,...,Xn),i=1,...,n。
本发明一种基于改进JAYA算法的多微电网优化方法,优点在于:
改进的JAYA算法在传统JAYA算法基础上引入非线性变异算子,在迭代过程中通过计算种群适应度与最优种群适应度之间的差值大小对变异算子进行动态调整。通过调整变异算子大小的方法,增强了算法的自适应性,更好地平衡了算法的搜索行为,减少了算法陷入局部极值的概率,提高了JAYA算法的求解精度与抗早熟收敛能力。
附图说明
图1是并网多微电网控制结构图。
图2是改进的JAYA算法流程图。
图3是采用标准JAYA算法与本发明改进的JAYA算法求解模型时适应度变化曲线对比图。
具体实施方式
基于改进JAYA算法的多微电网优化方法,实施例如下:
图1为并网多微电网控制结构图。该多微电网由两个微电网组成,每个微电网包括负荷,储能电池和由光伏,风机及微型燃气轮机组成的分布式发电单元。各个微电网连接于多微电网系统公共母线,并通过多微电网功率节点连接于配电网。
其中,多微电网参数为:风机WT1额定功率为100kW,光伏PV1额定功率为70kW,PV2额定功率为80kW微型燃气轮机MT1额定功率为100kW,MT2额定功率为90kW,储能电池BAT1额定容量为250kW·h,BAT2额定容量为100kW·h。
图2为改进的JAYA算法流程图。首先获取多微电网设备参数以及各微电网输出功率与负荷功率,然后以微电网总运行成本最低为目标建立多微电网经济优化模型。引入非线性变异算子改进传统JAYA算法,改进JAYA算法求解经济优化模型时,获取种群最优解与最差解,然后基于迭代方程更新解,如果所更新的解有更好的适应性则接受并取代已有解,否则保持已有解,直至找到符合标准的最优解,最后输出该最优解,使多微电网内各电源工作在最优状态。
图3为分别采用标准JAYA算法与本发明改进的JAYA算法求解模型时适应度变化曲线。由图3可知,在算法迭代过程中,两种算法的适应度均在减少,但标准JAYA算法不仅在迭代初期有较慢的迭代速度,且迭代后期陷入了早熟收敛之中。而改进的JAYA算法能找到合适的全局最优解,相比标准JAYA算法具有更好的求解精度与抗早熟能力。
Claims (5)
1.基于改进JAYA算法的多微电网优化方法,其特征在于包括以下步骤:
步骤1:建立多微电网并网优化模型;
步骤2:建立微电网经济运行约束条件;
步骤3:引入非线性变异算子改进JAYA算法;
步骤4:利用改进JAYA算法对多微电网并网优化模型进行求解,从而得到多微电网内各电源的输出功率。
2.根据权利要求1所述基于改进JAYA算法的多微电网优化方法,其特征在于:所述步骤1中,多微电网并网优化模型的目标函数表达式为:
其中,N为多微电网中微电网集合,T为多微电网优化周期;
ρt为t时段配电网的售购电电价;
3.根据权利要求1所述基于改进JAYA算法的多微电网优化方法,其特征在于:所述步骤2中,微电网经济运行约束条件包括:储能电池输出功率约束、储能电池SOC约束、微型燃气轮机输出功率约束、微型燃气轮机输出功率爬坡速率约束、微电网n电功率平衡约束、微电网n联络线功率平衡约束;这些约束条件的表达式如下:
4.根据权利要求1所述基于改进JAYA算法的多微电网优化方法,其特征在于:所述步骤3中,改进JAYA算法方程为:
Xi+1,j,k=Xi,j,k+βi,j[ri,j,1(Xi,j,best-|Xi,j,k|)-ri,j,2(Xi,j,worst-|Xi,j,k)|]
其中,Xi+1,j,k为更新后的解,Xi,j,k为更新前的解,βij为第i次迭代过程中,第j个种群的变异算子,ri,j,1与ri,j,2是第i次迭代中第j个变量在区间[0,1]中的两个随机数,Xi,j,best与Xi,j,worst为变量K在第i次迭代中最优值与最差值。
5.根据权利要求1所述基于改进JAYA算法的多微电网优化方法,其特征在于:所述步骤4中,改进JAYA算法求解多微电网并网优化模型时,在获取多微电网设备参数以及各微电网输出功率与负荷功率的基础上,首先通过步骤3基于改进JAYA算法获取种群最优解Xi,j,best与最差解Xi,j,worst,然后对变异算子βi,j进行动态调整,并利用改进JAYA算法方程更新解Xi+1,j,k;变异算子方程为:
其中,βi,j为第i次迭代过程中,第j个种群的变异算子,k为总迭代次数,βmax和βmin分别为变异算子的最大值与最小值并分别取值,Δfi为第i次迭代过程中,n个种群的适应度集合,fi,j为第i次迭代过程中,第j个种群的适应度,fi,best为第i次迭代过程中最有种群的适应度;
最后,将更新解Xi+1,j,k与当前局部最优解Xi,j,k进行比较,若Xi+1,j,k<Xi,j,k,说明更新解有更好适应性,则取代当前局部最优解Xi,j,k,否则保持当前局部最优解Xi,j,k,直至找到并输出符合标准的全局最优解X,从而使多微电网内各电源工作在最优状态;全局最优解应满足如下公式:
X=min(X1,X2,...,Xn),i=1,...,n。
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CN113722853A (zh) * | 2021-08-30 | 2021-11-30 | 河南大学 | 一种面向智能计算的群智能进化式优化方法 |
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