CN107134813A - 一种配电网光伏与储能有功功率输出平衡指数预测方法 - Google Patents

一种配电网光伏与储能有功功率输出平衡指数预测方法 Download PDF

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CN107134813A
CN107134813A CN201710304749.9A CN201710304749A CN107134813A CN 107134813 A CN107134813 A CN 107134813A CN 201710304749 A CN201710304749 A CN 201710304749A CN 107134813 A CN107134813 A CN 107134813A
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李春来
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State Grid Corp of China SGCC
Shenyang University of Technology
State Grid Qinghai Electric Power Co Ltd
Electric Power Research Institute of State Grid Qinghai Electric Power Co Ltd
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Shenyang University of Technology
State Grid Qinghai Electric Power Co Ltd
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Abstract

本发明提供了一种配电网光伏与储能有功功率输出平衡指数预测方法，通过建立配电网光伏与储能有功功率输出平衡指数演化系统的时间序列，对时间序列测量数据进行贝叶斯网络处理，进而进行配电网光伏与储能有功功率输出平衡指数计算，得到配电网光伏与储能有功功率输出平衡指数预测值。该方法能够根据监测参数对光伏与储能有功功率输出平衡指数进行预测计算，根据计算结果实时地对光储联合发电系统及配电网进行控制，能够有效避免配电网系统因光储接入带来的功率不匹配等问题，显著提高配电网电力系统在光储联合系统接入后的可靠性与经济性。

Description

一种配电网光伏与储能有功功率输出平衡指数预测方法
技术领域
本发明属于配电网技术领域，特别涉及一种配电网光伏与储能有功功率输出平衡指数预测方法。
背景技术
配电网电力系统中分布式光伏发电设备和储能设备组成了一个复杂的系统，如何根据分布式光储系统及配电网运行特点进行配电网光伏与储能有功功率输出平衡指数预测评估，使每个光储联合发电系统及其所接入的配电网能够安全、稳定、高效运行，以往配电网并网点功率平衡指数计算方法的特点是忽略分布式光伏及光伏储能与配电网间的相互作用关系，由区域电网或光储联合发电系统内各个系统独立进行功率分析，不能有效利用电网和分布式光伏发电运行数据资源，评估准确度和光伏利用效率不高。
有鉴于此，本发明提供一种配电网光伏与储能有功功率输出平衡指数预测方法，以满足实际应用需要。
发明内容
本发明的目的是：为克服现有技术的不足，本发明提供一种配电网光伏与储能有功功率输出平衡指数预测方法，从而获得配电网光伏与储能有功功率输出平衡指数。
本发明所采用的技术方案是：一种配电网光伏与储能有功功率输出平衡指数预测方法，其特征在于，包括如下步骤：
步骤1：建立配电网光伏与储能有功功率输出平衡指数演化系统的时间序列：
在固定时间间隔对发电系统并网点有功功率、电压、PM2.5值、温度进行测量，并网点有功功率历史最大值与并网点有功功率测量值之差除以并网点有功功率历史最大值与并网点有功功率历史最小值之差作为配电网光伏与储能有功功率输出平衡指数，即：
则，在一系列时刻tph1,tph2,...,tphn，n为自然数，n＝1,2,…，得到并网点有功功率pph、电压vph、PM2.5pmph、温度Tph的测量数据：
步骤2：测量数据的贝叶斯网络处理：
步骤2.1：建立测量数据动态数学模型：
其中，式中i＝1,2,...,4n,phxi为优化变量，为目标函数，τ(phxi)为函数约束项，yph为待求的配电网光伏与储能有功功率输出平衡指数；
步骤2.2：参数先验概率分布的确定：
设τ(phxi)服从带有漂移的随机游走过程：
τt(phxi)＝λ+τt-1(phxi)+ε (3)
其中λ为漂移项，λ服从gamma分布，ε为误差项,ε服从高斯分布；
步骤2.3：测量数据的Gibbs取样处理：
Gibbs抽样首先赋予各参数以随机值，然后在其他参数值给定的条件下逐次访问每一个参数值，并通过不断的迭代过程，最终形成多次抽样，该方法利用后验概率密度函数中每个参数的条件概率密度函数，进行随机取样，由于参数的条件概率密度函数是熟知的统计分布形式，故而这些参数的随机取样容易获得，而完成参数估计过程，只需将这些从条件概率密度函数得到的取样值取平均值；
步骤2.4：参数后验概率分布密度的确定：
将未知参数yph看作随机变量，记为ξ，ξ已知时，样本phxi联合分布密度可看成样本phxi对ξ的条件密度，记为p(phxi|ξ)，利用如下公式求得后验概率分布密度：
其中p(phxi|ξ)表示后验概率分布密度，π(ξ)表示先验概率分布密度；
步骤3：配电网光伏与储能有功功率输出平衡指数计算：
将后验概率分布密度与先验概率分布密度做比较，当误差小于设定值时，得到yph即为配电网光伏与储能有功功率输出平衡指数预测值。
本发明的有益效果是：本发明为配电网提供了一种配电网光伏与储能有功功率输出平衡指数预测方法，对配电网及其内光储系统运行参数及气象环境参数进行实时监测，并根据监测参数对光伏与储能有功功率输出平衡指数进行预测计算，根据计算结果实时地对光储联合发电系统及配电网进行控制，能够有效避免配电网系统因光储接入带来的功率不匹配等问题，显著提高配电网电力系统在光储联合系统接入后的可靠性与经济性。
附图说明
图1为本发明实施例的目标函数迭代运算图。
具体实施方式
为了更好地理解本发明，下面结合实施例进一步阐明本发明的内容，但本发明的内容不仅仅局限于下面的实施例。本领域技术人员可以对本发明作各种改动或修改，这些等价形式同样在本申请所列权利要求书限定范围之内。
如图1所示，本发明实施例提供的一种配电网光伏与储能有功功率输出平衡指数预测方法，包括如下步骤：
步骤1：建立配电网光伏与储能有功功率输出平衡指数演化系统的时间序列：
在固定时间间隔对发电系统并网点有功功率、电压、PM2.5值、温度进行测量，并网点有功功率历史最大值与并网点有功功率测量值之差除以并网点有功功率历史最大值与并网点有功功率历史最小值之差作为配电网光伏与储能有功功率输出平衡指数，即：
则，在一系列时刻tph1,tph2,...,tphn，n为自然数，n＝1,2,…，得到并网点有功功率pph、电压vph、PM2.5pmph、温度Tph的测量数据：
步骤2：测量数据的贝叶斯网络处理：
步骤2.1：建立测量数据动态数学模型：
其中，式中i＝1,2,...,4n,phxi为优化变量，为目标函数，τ(phxi)为函数约束项，yph为待求的配电网光伏与储能有功功率输出平衡指数。
步骤2.2：参数先验概率分布的确定：
设τ(phxi)服从带有漂移的随机游走过程：
τt(phxi)＝λ+τt-1(phxi)+ε (3)
其中λ为漂移项，λ服从gamma分布，ε为误差项,ε服从高斯分布。
步骤2.3：测量数据的Gibbs取样处理：
Gibbs抽样首先赋予各参数以随机值，然后在其他参数值给定的条件下逐次访问每一个参数值，并通过不断的迭代过程，最终形成多次抽样，该方法利用后验概率密度函数中每个参数的条件概率密度函数，进行随机取样，由于参数的条件概率密度函数是熟知的统计分布形式，故而这些参数的随机取样容易获得，而完成参数估计过程，只需将这些从条件概率密度函数得到的取样值取平均值。
步骤2.4：参数后验概率分布密度的确定：
将未知参数yph看作随机变量，记为ξ，ξ已知时，样本phxi联合分布密度可看成样本phxi对ξ的条件密度，记为p(phxi|ξ)，利用如下公式求得后验概率分布密度：
其中p(phxi|ξ)表示后验概率分布密度，π(ξ)表示先验概率分布密度。
步骤3：配电网光伏与储能有功功率输出平衡指数计算：
将后验概率分布密度与先验概率分布密度做比较，当误差小于设定值 0.00178时，得到的yph即为配电网光伏与储能有功功率输出平衡指数预测值。
以上仅为本发明的实施例而已，并不用于限制本发明，因此，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的权利要求范围之内。

Claims (1)

1.一种配电网光伏与储能有功功率输出平衡指数预测方法，其特征在于，包括如下步骤：
步骤1：建立配电网光伏与储能有功功率输出平衡指数演化系统的时间序列：
在固定时间间隔对发电系统并网点有功功率、电压、PM2.5值、温度进行测量，并网点有功功率历史最大值与并网点有功功率测量值之差除以并网点有功功率历史最大值与并网点有功功率历史最小值之差作为配电网光伏与储能有功功率输出平衡指数，即：
则，在一系列时刻tph1,tph2,...,tphn，n为自然数，n＝1,2,…，得到并网点有功功率pph、电压vph、PM2.5pmph、温度Tph的测量数据：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>pph</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>pph</mi> <mn>2</mn> </msub> <mo>,</mo> <mo>...</mo> <mo>,</mo> <msub> <mi>pph</mi> <mi>n</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>vph</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>vph</mi> <mn>2</mn> </msub> <mo>,</mo> <mo>...</mo> <mo>,</mo> <msub> <mi>vph</mi> <mi>n</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>pmph</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>pmph</mi> <mn>2</mn> </msub> <mo>,</mo> <mo>...</mo> <mo>,</mo> <msub> <mi>pmph</mi> <mi>n</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>Tph</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>Tph</mi> <mn>2</mn> </msub> <mo>,</mo> <mo>...</mo> <mo>,</mo> <msub> <mi>Tph</mi> <mi>n</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
步骤2：测量数据的贝叶斯网络处理：
步骤2.1：建立测量数据动态数学模型：
其中，式中i＝1,2,...,4n,phxi为优化变量，为目标函数，τ(phxi)为函数约束项，yph为待求的配电网光伏与储能有功功率输出平衡指数；
步骤2.2：参数先验概率分布的确定：
设τ(phxi)服从带有漂移的随机游走过程：
τt(phxi)＝λ+τt-1(phxi)+ε (3)
其中λ为漂移项，λ服从gamma分布，ε为误差项,ε服从高斯分布；
步骤2.3：测量数据的Gibbs取样处理：
Gibbs抽样首先赋予各参数以随机值，然后在其他参数值给定的条件下逐次访问每一个参数值，并通过不断的迭代过程，最终形成多次抽样，该方法利用后验概率密度函数中每个参数的条件概率密度函数，进行随机取样，由于参数的条件概率密度函数是熟知的统计分布形式，故而这些参数的随机取样容易获得，而完成参数估计过程，只需将这些从条件概率密度函数得到的取样值取平均值；
步骤2.4：参数后验概率分布密度的确定：
将未知参数yph看作随机变量，记为ξ，ξ已知时，样本phxi联合分布密度可看成样本phxi对ξ的条件密度，记为p(phxi|ξ)，利用如下公式求得后验概率分布密度：
<mrow> <mi>p</mi> <mrow> <mo>(</mo> <msub> <mi>phx</mi> <mi>i</mi> </msub> <mo>|</mo> <mi>&amp;xi;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>&amp;pi;</mi> <mrow> <mo>(</mo> <mi>&amp;xi;</mi> <mo>)</mo> </mrow> <mi>p</mi> <mrow> <mo>(</mo> <msub> <mi>phx</mi> <mi>i</mi> </msub> <mo>|</mo> <mi>&amp;xi;</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mo>&amp;Integral;</mo> <mi>&amp;pi;</mi> <mrow> <mo>(</mo> <mi>&amp;xi;</mi> <mo>)</mo> </mrow> <mi>p</mi> <mrow> <mo>(</mo> <msub> <mi>phx</mi> <mi>i</mi> </msub> <mo>|</mo> <mi>&amp;xi;</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>&amp;xi;</mi> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
其中p(phxi|ξ)表示后验概率分布密度，π(ξ)表示先验概率分布密度；
步骤3：配电网光伏与储能有功功率输出平衡指数计算：
将后验概率分布密度与先验概率分布密度做比较，当误差小于设定值时，得到yph即为配电网光伏与储能有功功率输出平衡指数预测值。
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