基于非对称纳什谈判的多微网电能共享运行优化策略MATLAB代码，电网技术文献复现：关键词：纳什谈判 合作博弈 微网 电转气
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基于非对称纳什谈判的多微网电能共享运行优化策略MATLAB代码，电网技术文献复现：关键词：纳什谈判 合作博弈 微网 电转气-碳捕集 P2P电能交易交易 参考文档：《基于非对称纳什谈判的多微网电能共享运行优化策略》完美复现仿真平台：MATLAB CPLEX+MOSEK IPOPT主要内容：该代码主要做的是微网间基于非对称纳什谈判的P2P电能交易共享问题，基于纳什谈判理论建立了多微网电能共享合作运行模型，进而将其分解为微网联盟效益最大化子问题和合作收益分配子问题，选择交替方向乘子法分布式求解，从而有效保护各主体隐私。在合作收益分配子问题中，提出以非线性能量映射函数量化各参与主体贡献大小的非对称议价方法，各微网分别以其在合作中的电能贡献大小为议价能力相互谈判，以实现合作收益的公平分配。同时，微电网模型中考虑了电转气以及碳捕集设备，实现了低碳调度。Step1_纳什谈判破裂点求解这是一个分布式优化迭代模型，涉及到三个微网（MG1、MG2、MG3）的程序。下面我将对每个微网的程序进行详细的分析和解释。首先是MG1微网的程序分析：该程序主要是为了解决微网的电负荷和 <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/89765625/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/89765625/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">基于<span class="_ _0"> </span></span>BES<span class="_ _1"> </span><span class="ff2">秃鹰优化算法与<span class="_ _0"> </span></span>LSSVM<span class="_ _1"> </span><span class="ff2">的多特征变量输入预测模型研究</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">在大数据时代背景下<span class="ff3">，</span>特征变量的选取和模型的优化对于预测任务的准确性至关重要<span class="ff4">。</span>本文主要探讨</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">利用<span class="_ _0"> </span><span class="ff1">BES<span class="_ _1"> </span></span>秃鹰优化算法优化最小二乘支持向量机<span class="ff3">（<span class="ff1">LSSVM</span>）</span>模型<span class="ff3">，</span>构建多特征变量输入<span class="ff4">、</span>单个因变量</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">输出的拟合预测模型<span class="ff4">。</span>我们选用<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _1"> </span></span>作为程序实现语言<span class="ff3">，</span>以提供一套灵活且高效的解决方案<span class="ff4">。</span></div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">一<span class="ff4">、</span>引言</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">面对复杂的数据处理问题<span class="ff3">，</span>如回归预测<span class="ff4">、</span>时间序列分析等<span class="ff3">，</span>机器学习算法扮演着至关重要的角色<span class="ff4">。</span>最</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">小二乘支持向量机<span class="ff3">（<span class="ff1">LSSVM</span>）</span>作为一种基于支持向量机<span class="ff3">（<span class="ff1">SVM</span>）</span>的改进算法<span class="ff3">，</span>具有运算效率高<span class="ff4">、</span>参数</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">少等优点<span class="ff4">。</span>而<span class="_ _0"> </span><span class="ff1">BES<span class="_ _1"> </span></span>秃鹰优化算法作为一种新兴的智能优化算法<span class="ff3">，</span>具有全局搜索能力强<span class="ff4">、</span>参数自适应等</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">特点<span class="ff3">，</span>能够很好地解决复杂优化问题<span class="ff4">。</span>将两者结合<span class="ff3">，</span>利用<span class="_ _0"> </span><span class="ff1">BES<span class="_ _1"> </span></span>秃鹰优化算法对<span class="_ _0"> </span><span class="ff1">LSSVM<span class="_ _1"> </span></span>进行优化<span class="ff3">，</span>有</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">望提高预测模型的性能<span class="ff4">。</span></div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">二<span class="ff4">、</span>背景知识</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span>LSSVM<span class="_ _1"> </span><span class="ff2">简介<span class="ff3">：</span>最小二乘支持向量机<span class="ff3">（</span></span>LSSVM<span class="ff3">）<span class="ff2">是一种基于<span class="_ _0"> </span></span></span>SVM<span class="_ _1"> </span><span class="ff2">的回归预测方法<span class="ff3">，</span>通过最小化误</span></div><div class="t m0 x2 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">差平方和来求解回归问题<span class="ff4">。</span>它通过将数据映射到高维特征空间<span class="ff3">，</span>并寻找最优决策函数来实现回归</div><div class="t m0 x2 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">预测<span class="ff4">。</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span>BES<span class="_ _1"> </span><span class="ff2">秃鹰优化算法<span class="ff3">：</span></span>BES<span class="_ _1"> </span><span class="ff2">秃鹰优化算法是一种模拟自然界中生物进化过程的智能优化算法<span class="ff4">。</span>它</span></div><div class="t m0 x2 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">具有全局搜索能力强<span class="ff4">、</span>参数自适应等优点<span class="ff3">，</span>能够很好地解决复杂的优化问题<span class="ff4">。</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">三<span class="ff4">、</span>方法论述</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">本研究采用<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _1"> </span></span>作为实现工具<span class="ff3">，</span>构建基于<span class="_ _0"> </span><span class="ff1">BES<span class="_ _1"> </span></span>秃鹰优化算法的<span class="_ _0"> </span><span class="ff1">LSSVM<span class="_ _1"> </span></span>预测模型<span class="ff4">。</span>具体步骤如下</div><div class="t m0 x1 h3 y13 ff3 fs0 fc0 sc0 ls0 ws0">：</div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">数据准备<span class="ff3">：</span>收集并整理多特征变量输入和单个因变量输出的数据集<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff2">数据预处理<span class="ff3">：</span>对收集到的数据进行标准化处理<span class="ff3">，</span>消除量纲差异<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff2">模型构建<span class="ff3">：</span>利用<span class="_ _0"> </span></span>Matlab<span class="_ _1"> </span><span class="ff2">实现<span class="_ _0"> </span></span>LSSVM<span class="_ _1"> </span><span class="ff2">模型<span class="ff3">，</span>并利用<span class="_ _0"> </span></span>BES<span class="_ _1"> </span><span class="ff2">秃鹰优化算法对模型进行优化<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff2">模型训练<span class="ff3">：</span>将处理后的数据输入到优化后的<span class="_ _0"> </span></span>LSSVM<span class="_ _1"> </span><span class="ff2">模型中进行训练<span class="ff3">，</span>得到最优预测模型<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">5.<span class="_ _2"> </span><span class="ff2">模型评估<span class="ff3">：</span>利用测试数据集对训练得到的模型进行评估<span class="ff3">，</span>计算相关评价指标如均方误差<span class="ff3">（</span></span>MSE<span class="ff3">）</span></div><div class="t m0 x2 h2 y19 ff4 fs0 fc0 sc0 ls0 ws0">、<span class="ff2">决定系数<span class="ff3">（<span class="ff1">R2</span>）</span>等</span>。</div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">四<span class="ff4">、</span>实验结果与分析</div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">为了验证本方法的有效性<span class="ff3">，</span>我们在<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _1"> </span></span>环境下进行了实验<span class="ff4">。</span>实验结果表明<span class="ff3">，</span>基于<span class="_ _0"> </span><span class="ff1">BES<span class="_ _1"> </span></span>秃鹰优化算</div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">法的<span class="_ _0"> </span><span class="ff1">LSSVM<span class="_ _1"> </span></span>模型在拟合预测方面具有优良的性能<span class="ff3">，</span>与传统的<span class="_ _0"> </span><span class="ff1">LSSVM<span class="_ _1"> </span></span>模型相比<span class="ff3">，</span>具有更高的预测精度</div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">和稳定性<span class="ff4">。</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>