论文标题
电源分配系统中动态匹配市场的在线算法
Online Algorithms for Dynamic Matching Markets in Power Distribution Systems
论文作者
论文摘要
本文提出了用于电源分配系统中动态匹配市场的在线算法，该算法在任何实时操作实例中都决定匹配或延迟供应 - 具有可用可再生能源生成的灵活负载，目的是最大程度地提高系统中交易所的社会福利。更具体地说，针对以下一代加载方案提出了两种在线匹配算法：（i）当可再生生成的平均值大于灵活负载的平均值时，以及（ii）当条件（i）逆转时。通过直觉，这种算法的性能随供求的随机性而降低了，提出了两种属性来评估算法的性能。第一个属性是融合到最优性（CO），因为可再生生成和客户负载的潜在随机性为零。第二个属性是偏离最优性，是根据可再生生成和客户负载的基本随机性的标准偏差来测量的。对于第一种情况，提出的算法显示出满足CO的满足，并且与最佳的偏差与标准偏差的变化线性变化。但是，在第二种情况下，同样的算法不满足CO。然后，我们表明，针对第二种情况提出的算法满足CO和与最佳的偏差，该算法随标准偏差的变化以及偏移量的变化而变化。
This paper proposes online algorithms for dynamic matching markets in power distribution systems, which at any real-time operation instance decides about matching -- or delaying the supply of -- flexible loads with available renewable generation with the objective of maximizing the social welfare of the exchange in the system. More specifically, two online matching algorithms are proposed for the following generation-load scenarios: (i) when the mean of renewable generation is greater than the mean of the flexible load, and (ii) when the condition (i) is reversed. With the intuition that the performance of such algorithms degrades with increasing randomness of the supply and demand, two properties are proposed for assessing the performance of the algorithms. First property is convergence to optimality (CO) as the underlying randomness of renewable generation and customer loads goes to zero. The second property is deviation from optimality, is measured as a function of the standard deviation of the underlying randomness of renewable generation and customer loads. The algorithm proposed for the first scenario is shown to satisfy CO and a deviation from optimal that varies linearly with the variation in the standard deviation. But the same algorithm is shown to not satisfy CO for the second scenario. We then show that the algorithm proposed for the second scenario satisfies CO and a deviation from optimal that varies linearly with the variation in standard deviation plus an offset.
