Dynamic combined economic and emission dispatch is an important task in the power system that examines the optimal allocation of power generation resources that yield the least possible fuel and emission costs. In this study, the Moth Swarm Algorithm has been proposed for solving the combined economic and emission dispatch problem for a 24-hour period. The model has been implemented on a test system made up of a combination of thermal and solar photovoltaic plants, while also considering spinning reserve allocation. The results obtained are presented in comparison with commonly used state-of-the-art methods like Moth Flame Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, and Tunicate Swarm Algorithm. Two test systems have been considered in the model implementation. The first system consists of a combination of six thermal plants and thirteen solar plants whose load demand is the hourly energy demand of an anonymous data centre in South Africa. The second test system is made up of three thermal plants and thirteen solar plants to service its unique hourly load demand. Results showed that the proposed MSA gave preference to solar photovoltaic generation over thermal generation given that environmental impact minimization is a major component of the overall objective function. The proposed MSA also scheduled thermal generators to provide the required spinning reserve capacity since solar energy is intermittent in nature. Overall, analyses show that the proposed MSA outperformed the other methods in finding the best fuel, emission, solar generation, and spinning reserve costs that best serve the energy demand of the data centre and the second test system for the 24-hour period.

中文翻译：

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针对数据中心负载，使用蛾群算法将日前的经济和排放调度与旋转储备考虑相结合


动态组合经济和排放调度是电力系统中的一项重要任务，它检查发电资源的优化配置，以产生尽可能低的燃料和排放成本。在本研究中，提出了飞蛾群算法来解决 24 小时内的综合经济和排放调度问题。该模型已在由热电和太阳能光伏电站组成的测试系统上实施，同时还考虑了旋转储备分配。所获得的结果与常用的最先进方法（如飞蛾火焰优化、鲸鱼优化算法、蚁狮优化器和被囊动物群算法）进行了比较。模型实现中考虑了两个测试系统。第一个系统由六座火力发电厂和十三个太阳能发电厂组成，其负载需求是南非一个匿名数据中心每小时的能源需求。第二个测试系统由三个热电厂和十三个太阳能电厂组成，以满足其独特的每小时负载需求。结果表明，鉴于环境影响最小化是总体目标函数的主要组成部分，拟议的 MSA 优先考虑太阳能光伏发电而不是火力发电。由于太阳能本质上是间歇性的，拟议的 MSA 还安排了热发电机来提供所需的旋转备用容量。总体而言，分析表明，所提出的 MSA 在寻找最佳燃料、排放、太阳能发电和旋转备用成本方面优于其他方法，这些成本最能满足数据中心和第二个测试系统 24 小时的能源需求。