考虑多风场出力相关性的可再生能源场景生成 风电场景生成，并通过聚类算法场景削减成几个场景，每个场景都有确定的出现概率 完美复现
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考虑多风场出力相关性的可再生能源场景生成 风电场景生成，并通过聚类算法场景削减成几个场景，每个场景都有确定的出现概率。完美复现《考虑多风电场出力 Copula 相关关系的场景生成方法》Copula 函数(连接函数)描述空间相邻风电场间的相关性，提出一种基于 Copula 函数生成风电场出力场景的方法。该方法对边缘分布没有限制，能捕捉变量之间非线性、非对称性以及尾部相关关系。阐述了多个风电场出力的边缘分布函数及 Copula函数的构造和确定过程。拟合出最优Copula函数，并生成场景。编程语言：MATLAB这段程序主要是对风电场的出力进行分析和建模。下面我将逐步解释代码的功能和工作。首先，程序导入了一个名为"windpower.csv"的数据文件，其中包含了风电场的出力数据。然后，程序绘制了机组1和机组2的频率直方图，以及原始数据的二元频数直方图。接下来，程序对机组1和机组2的数据进行了正态性检验。它使用了三种不同的检验方法：Jarque-Bera检验、Kolmogorov-Smirnov检验和Lilliefors检验。如果数据不服从正态分布，程序会输出相应的提示信 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89765652/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89765652/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">考虑多风场出力相关性的可再生能源场景生成</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">提出了一种基于<span class="ff2"> Copula </span>函数生成风电场出力场景的方法<span class="ff3">，</span>该方法能够捕捉风电场之间的相关性<span class="ff3">，</span></div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">并将风电场的出力数据分成多个场景<span class="ff3">，</span>每个场景都有确定的出现概率<span class="ff4">。</span>该方法不限制边缘分布<span class="ff3">，</span>能够</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">处理变量之间的非线性<span class="ff4">、</span>非对称性以及尾部相关关系<span class="ff4">。</span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">首先<span class="ff3">，</span>导入风电场的出力数据文件<span class="ff2">"windpower.csv"<span class="ff3">，</span></span>该文件包含了风电场的出力数据<span class="ff4">。</span>然后<span class="ff3">，</span>绘</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">制机组<span class="_ _0"> </span><span class="ff2">1<span class="_ _1"> </span></span>和机组<span class="_ _0"> </span><span class="ff2">2<span class="_ _1"> </span></span>的频率直方图以及原始数据的二元频数直方图<span class="ff3">，</span>从直观上观察风电场的出力特征<span class="ff4">。</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">接下来<span class="ff3">，</span>进行机组<span class="_ _0"> </span><span class="ff2">1<span class="_ _1"> </span></span>和机组<span class="_ _0"> </span><span class="ff2">2<span class="_ _1"> </span></span>的正态性检验<span class="ff4">。</span>通过<span class="_ _0"> </span><span class="ff2">Jarque-Bera<span class="_ _1"> </span></span>检验<span class="ff4">、<span class="ff2">Kolmogorov-Smirnov<span class="_ _1"> </span></span></span>检</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">验和<span class="_ _0"> </span><span class="ff2">Lilliefors<span class="_ _1"> </span></span>检验<span class="ff3">，</span>判断数据是否服从正态分布<span class="ff4">。</span>如果数据不服从正态分布<span class="ff3">，</span>需要进行相应的处</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">理<span class="ff4">。</span></div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">然后<span class="ff3">，</span>使用非参数法确定机组<span class="_ _0"> </span><span class="ff2">1<span class="_ _1"> </span></span>和机组<span class="_ _0"> </span><span class="ff2">2<span class="_ _1"> </span></span>的分布<span class="ff4">。</span>通过经验分布函数和核光滑方法进行估计<span class="ff3">，</span>绘制经</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">验分布函数图和核分布估计图<span class="ff4">。</span>观察两个机组之间的关系<span class="ff4">。</span></div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">接下来<span class="ff3">，</span>利用极大似然法估计<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>模型中的参数<span class="ff4">。</span>分别对二元正态<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>和二元<span class="_ _0"> </span><span class="ff2">t-Copula</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">的线性相关参数进行估计<span class="ff4">。</span>同时<span class="ff3">，</span>估计<span class="_ _0"> </span><span class="ff2">Gumbel<span class="ff4">、</span>Clayton<span class="_ _1"> </span></span>和<span class="_ _0"> </span><span class="ff2">Frank Copula<span class="_ _1"> </span></span>模型的参数<span class="ff4">。</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">然后<span class="ff3">，</span>计算<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>模型的概率密度和累积分布<span class="ff4">。</span>分别使用二元正态<span class="_ _0"> </span><span class="ff2">Copula<span class="ff4">、</span></span>二元<span class="_ _0"> </span><span class="ff2">t-Copula<span class="ff4">、</span></span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">Gumbel Copula<span class="ff4">、</span>Clayton Copula<span class="_ _1"> </span><span class="ff1">和<span class="_ _0"> </span></span>Frank Copula<span class="_ _1"> </span><span class="ff1">模型进行计算<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">接下来<span class="ff3">，</span>计算<span class="_ _0"> </span><span class="ff2">Kendall<span class="_ _1"> </span></span>秩相关系数和<span class="_ _0"> </span><span class="ff2">Spearman<span class="_ _1"> </span></span>秩相关系数<span class="ff4">。</span>分别计算二元正态<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>和二元</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">t-Copula<span class="_ _1"> </span><span class="ff1">模型的相关系数<span class="ff4">。</span>同时<span class="ff3">，</span>根据原始观测数据直接计算<span class="_ _0"> </span></span>Kendall<span class="_ _1"> </span><span class="ff1">秩相关系数和<span class="_ _0"> </span></span>Spearman</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">秩相关系数<span class="ff4">。</span></div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">然后<span class="ff3">，</span>对多个<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>模型进行评价<span class="ff4">。</span>引入经验<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>的概念<span class="ff3">，</span>计算经验<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>与拟合的</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">Copula<span class="_ _1"> </span><span class="ff1">模型之间的距离<span class="ff3">，</span>以评估每个<span class="_ _0"> </span></span>Copula<span class="_ _1"> </span><span class="ff1">模型的优劣<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">接下来<span class="ff3">，</span>进行采样<span class="ff4">。</span>利用拟合的<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>模型生成<span class="_ _0"> </span><span class="ff2">10000<span class="_ _1"> </span></span>个样本<span class="ff3">，</span>并将结果保存<span class="ff4">。</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">最后<span class="ff3">，</span>进行聚类分析<span class="ff4">。</span>使用<span class="_ _0"> </span><span class="ff2">k-means<span class="_ _1"> </span></span>或<span class="_ _0"> </span><span class="ff2">k-medoids<span class="_ _1"> </span></span>算法对样本进行聚类<span class="ff3">，</span>并绘制聚类结果和质心</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">的图形<span class="ff4">。</span>同时<span class="ff3">，</span>计算每个聚类的概率<span class="ff3">，</span>并将结果保存<span class="ff4">。</span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">综上所述<span class="ff3">，</span>本方法通过基于<span class="_ _0"> </span><span class="ff2">Copula<span class="_ _1"> </span></span>函数生成风电场出力场景<span class="ff3">，</span>能够全面分析风电场数据的特性和行</div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">为<span class="ff4">。</span>通过对风电场数据进行正态性检验<span class="ff4">、</span>分布估计<span class="ff4">、</span>相关性分析<span class="ff4">、</span>模型评价<span class="ff4">、</span>采样和聚类分析等步骤</div><div class="t m0 x1 h2 y1a ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff1">可以更好地理解和应用风电场的相关性</span>，<span class="ff1">为可再生能源领域的场景生成提供了一种有效的方法<span class="ff4">。</span></span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>