Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

Abstract

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.