A hybrid dynamic economic environmental dispatch model for balancing operating costs and pollutant emissions in renewable energy: A novel improved mayfly algorithm

Li, L.-L., Lou, J.-L., Tseng, M.-L., Lim, M. K. and Tan, R. R. (2022) A hybrid dynamic economic environmental dispatch model for balancing operating costs and pollutant emissions in renewable energy: A novel improved mayfly algorithm. Expert Systems with Applications, 203, 117411. (doi: 10.1016/j.eswa.2022.117411)

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Abstract

This study proposes a hybrid dynamic economic environmental dispatch model combining thermal power units, wind turbines, photovoltaic and energy storage device to achieve the balance between operating costs and pollutant emissions under the premise of stabilizing the output of renewable energy. Most studies have addressed economic and environmental issues to optimize dispatch as more and more renewable energy is connected to the grid, while ignoring the stability of renewable energy output. Aiming to solve the problem of instability of renewable energy output, a wind-photovoltaic stable output strategy is proposed, and an energy storage device is used to control the dispatched power of renewable energy. The fitness function is improved and an improved mayfly (IMA) algorithm using chaotic initialization, inertia weight and mutation strategy is proposed to find the optimal solution, and the performance of the algorithm is verified on two systems with different configurations. In addition, constraints such as the power balance, output of each power generation device and energy storage device are considered. The results show that the operating costs of the IMA algorithm is 4.12%, 13.21% and 15.14% lower than those of the MA, MFO and PSO algorithms, and the proposed model using the IMA algorithm can effectively achieve the balance between economy and environment and obtain stable renewable energy output. This study provides a useful reference for the stable operation of power grid under a variety of renewable energy access conditions.

Item Type:Articles
Additional Information:This study was supported by the key project of Tianjin Natural Science Foundation [Project No. 19JCZDJC32100] and the Natural Science Foundation of Hebei Province of China [Project No. E2018202282].
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lim, Professor Ming
Authors: Li, L.-L., Lou, J.-L., Tseng, M.-L., Lim, M. K., and Tan, R. R.
College/School:College of Social Sciences > Adam Smith Business School > Management
Journal Name:Expert Systems with Applications
Publisher:Elsevier
ISSN:0957-4174
ISSN (Online):1873-6793
Published Online:11 May 2022
Copyright Holders:Copyright © 2022 Elsevier Ltd.
First Published:First published in Expert Systems with Applications 203: 117311
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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