《基于改进粒子群算法及AOA、SSA优化的混合储能系统容量精细化配置》,《基于改进粒子群算法与AOA、SSA优化的混合储能系统容量配置研究》,《基于改进粒子群算法的混合储能系统容量优化》完全复现ma
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《基于改进粒子群算法及AOA、SSA优化的混合储能系统容量精细化配置》,《基于改进粒子群算法与AOA、SSA优化的混合储能系统容量配置研究》,《基于改进粒子群算法的混合储能系统容量优化》完全复现matlab。以全生命周期费用最低为目标函数，负荷缺电率作为风光互补发电系统的运行指标，得到蓄电池储能和超级电容个数，缺电率和系统最小费用。粒子群算法：权重改进、对称加速因子、不对称加速因子三种情况的优化结果和迭代曲线。另包含2020年最新提出的阿基米德优化算法AOA和麻雀搜索算法SSA对该lunwen的实现。（该算法收敛速度快，不存在pso的早熟收敛）,核心关键词：基于改进粒子群算法; 混合储能系统容量优化; 全生命周期费用最低; 负荷缺电率; 蓄电池储能; 超级电容个数; 权重改进; 对称加速因子; 不对称加速因子; 优化结果; 迭代曲线; 阿基米德优化算法AOA; 麻雀搜索算法SSA; 收敛速度快; 早熟收敛。,基于多算法优化的混合储能系统容量配置研究 <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/90425116/2/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/90425116/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于改进粒子群算法的混合储能系统容量优化与新型优化算法的实践</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">随着可<span class="_ _0"></span>再生能<span class="_ _0"></span>源如风<span class="_ _0"></span>能和太<span class="_ _0"></span>阳能的<span class="_ _0"></span>广泛应<span class="_ _0"></span>用，混<span class="_ _0"></span>合储能<span class="_ _0"></span>系统（<span class="ff2">H<span class="_ _0"></span>ESS</span>）在平<span class="_ _0"></span>衡能源<span class="_ _0"></span>供需、<span class="_ _0"></span>提</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">高<span class="_ _0"></span>系<span class="_ _1"></span>统<span class="_ _0"></span>稳<span class="_ _1"></span>定<span class="_ _0"></span>性<span class="_ _1"></span>和<span class="_ _0"></span>可<span class="_ _1"></span>靠<span class="_ _0"></span>性<span class="_ _1"></span>方<span class="_ _0"></span>面<span class="_ _1"></span>发<span class="_ _0"></span>挥<span class="_ _1"></span>着<span class="_ _0"></span>重<span class="_ _1"></span>要<span class="_ _0"></span>作<span class="_ _1"></span>用<span class="_ _0"></span>。<span class="_ _1"></span>本<span class="_ _0"></span>文<span class="_ _1"></span>旨<span class="_ _0"></span>在<span class="_ _1"></span>研<span class="_ _0"></span>究<span class="_ _1"></span>如<span class="_ _0"></span>何<span class="_ _1"></span>基<span class="_ _0"></span>于<span class="_ _1"></span>改<span class="_ _0"></span>进<span class="_ _1"></span>的<span class="_ _0"></span>粒<span class="_ _1"></span>子<span class="_ _0"></span>群<span class="_ _1"></span>算<span class="_ _0"></span>法</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">PSO</span>）对混合储<span class="_ _0"></span>能系统进行容量优化<span class="_ _0"></span>，并探讨其与<span class="_ _2"> </span><span class="ff2">2020<span class="_"> </span></span>年最新提出的阿基米德优化算法</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">（<span class="ff2">AOA</span>）和麻雀搜索算法（<span class="ff2">SSA</span>）之间的差异和联系。</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">二、问题描述</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">我们以全生命周期费用最低为目标函数，<span class="_ _3"></span>同时考虑负荷缺电率作为风光互补发电系统的运行</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">指标。<span class="_ _4"></span>通过优化算法，<span class="_ _4"></span>我们希望得到蓄电池储能和超级电容的个数，<span class="_ _4"></span>以及相应的缺电率和系</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">统最小费用。</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">三、粒子群算法及其改进</div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _5"> </span><span class="ff1">粒子<span class="_ _0"></span>群算<span class="_ _0"></span>法（<span class="_ _0"></span></span>PSO<span class="ff1">）<span class="_ _0"></span>是一种<span class="_ _0"></span>迭代<span class="_ _0"></span>优化<span class="_ _0"></span>算法<span class="_ _0"></span>，通<span class="_ _0"></span>过模<span class="_ _0"></span>拟鸟<span class="_ _0"></span>群觅<span class="_ _0"></span>食行<span class="_ _0"></span>为来<span class="_ _0"></span>寻找<span class="_ _0"></span>最优<span class="_ _0"></span>解。<span class="_ _0"></span>其基</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">本思想是让一群<span class="_ _6"></span>“粒子”在解空间中搜索，<span class="_ _6"></span>根据每个粒子的历史最优解和全局最优解来更新自</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">己的速度和位置。</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">2. <span class="_ _5"> </span><span class="ff1">权重改进：<span class="_ _7"></span>传统的<span class="_ _5"> </span><span class="ff2">PSO<span class="_"> </span></span>中，粒子的速度和位置更新往往依赖于固定的权重系数。改进后</span></div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">的<span class="_ _5"> </span><span class="ff2">PSO<span class="_"> </span></span>引入了动<span class="_ _0"></span>态调整权重<span class="_ _0"></span>的策略，<span class="_ _0"></span>使算法在<span class="_ _0"></span>搜索过程中<span class="_ _0"></span>能够根据<span class="_ _0"></span>实际情况调<span class="_ _0"></span>整搜索方<span class="_ _0"></span>向</div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">和力度。</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _5"> </span><span class="ff1">对称加速<span class="_ _0"></span>因子与<span class="_ _0"></span>不对称加<span class="_ _0"></span>速因子<span class="_ _0"></span>：通过调<span class="_ _0"></span>整这两<span class="_ _0"></span>个参数<span class="_ _0"></span>，我们可<span class="_ _0"></span>以控制<span class="_ _0"></span>粒子的<span class="_ _0"></span>速度和位</span></div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">置更新的比例，使得算法在搜索过程中更加灵活，能够更好地适应不同的优化问题。</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">4. <span class="_ _5"> </span><span class="ff1">优化结果与迭代曲线：<span class="_ _7"></span>在实施改进的<span class="_ _5"> </span><span class="ff2">PSO<span class="_"> </span></span>后，我们得到了不同情况下的优化结果和迭代</span></div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">曲线。通过对比分析，我们可以看出改进后的<span class="_ _5"> </span><span class="ff2">PSO<span class="_"> </span></span>在收敛速度和寻优能力上都有所提升。</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">四、新型优化算法的实践</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _5"> </span><span class="ff1">阿基米德优化算法（</span>AOA<span class="ff1">）<span class="_ _8"></span>：<span class="_ _9"></span>这是一种新型的优化算法，其特点是收敛速度快，不存在早</span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">熟收敛的问题。我们将<span class="_ _5"> </span><span class="ff2">AOA<span class="_"> </span></span>应用于混合储能系统的容量优化问题中，通过实验验证了其有</div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">效性。</div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">2. <span class="_ _5"> </span><span class="ff1">麻雀搜索算<span class="_ _0"></span>法（</span>SSA<span class="ff1">）<span class="_ _a"></span>：<span class="ff2">SSA<span class="_"> </span></span>是一种模拟麻<span class="_ _0"></span>雀觅食行为<span class="_ _0"></span>的优化算<span class="_ _0"></span>法，具有很<span class="_ _0"></span>好的全局<span class="_ _0"></span>搜索</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">能力和<span class="_ _0"></span>局部<span class="_ _0"></span>开发能<span class="_ _0"></span>力。<span class="_ _0"></span>我们尝<span class="_ _0"></span>试将<span class="_ _2"> </span><span class="ff2">SSA<span class="_"> </span></span>应用于混<span class="_ _0"></span>合储<span class="_ _0"></span>能系统<span class="_ _0"></span>的容量<span class="_ _0"></span>优化<span class="_ _0"></span>问题中<span class="_ _0"></span>，以<span class="_ _0"></span>探讨其</div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">与<span class="_ _5"> </span><span class="ff2">PSO<span class="_"> </span></span>和其他算法的差异和优势。</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">五、实验结果与分析</div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">我们分别使用改进的<span class="_ _5"> </span><span class="ff2">PSO</span>、<span class="ff2">AOA<span class="_"> </span></span>和<span class="_ _5"> </span><span class="ff2">SSA<span class="_"> </span></span>对混合储能系统的容量进行优化。通过对比分析实</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>