Abstract
By-product gas, steam, and electricity coupling system plays an important role in providing stable energy supply for steelworks. In this paper, a multi-period GSECS scheduling model with the objectives of economics and in-plant grid reliability was constructed. Economics is reflected in the reduction of gasholder penalty cost, electricity interaction cost, and carbon emission cost while the reliability is shown as the variance of the electric load. In GPC, a new method of defining gasholder penalty factor was designed to distinguish different types and operating intervals of gasholders. The NSGA-II was used to obtain the multi-objective Pareto solution set, and the optimal solution was filtered based on the improved AHP—entropy weight method. With a calculated example, compared to manual operations, the gasholder penalty cost was reduced by − 51.37% after optimization, as well as a reduction of 25.23% in the deviation of the gasholder, enhancing its stability. In addition, the variance of the in-plant grid was improved by 45.15%.
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References
He DF, Liu PZ, Feng K (2019) Collaborative optimization of rolling plan and energy dispatching in steel plants. China Metall 29(04):75–80. https://doi.org/10.13228/j.boyuan.issn1006-9356.20180306
Chen HD, Meng F, Wang Q (2023) Influence of installed capacity of energy storage system and renewable energy power generation on power system performance. Energ Storage Sci Technol 12(02):477–485. https://doi.org/10.19799/j.cnki.2095-4239.2022.0439
Zeng YJ, Sun YG (2015) Short-term scheduling of steam power system in iron and steel industry under time-of-use power price. J Iron Steel Res Int 22(9):795–803. https://doi.org/10.1016/S1006-706X(15)30073-X
Hu ZB, He DF (2022) Operation scheduling optimization of gas-steam-power conversion systems in iron and steel enterprises. Appl Therm Eng 206. https://doi.org/10.1016/j.applthermaleng.2022.118121
Wei ZQ, Zhai XQ, Zhang Q (2020) A MINLP model for multi-period optimization considering couple of gas-steam-electricity and time of use electricity price in steel plant. Appl Therm Eng 168. https://doi.org/10.1016/j.applthermaleng.2019.114834
Lu XH, Zhou KL, Yang SL (2018) Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles. J Clean Prod 195:187–199. https://doi.org/10.1016/j.jclepro.2018.05.190
Zhang XZ, Wang ZY, Lu ZY (2021) Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm. Appl Energ 306. https://doi.org/10.1016/j.apenergy.2021.118018
Wang HQ, Xie ZS, Pu L (2022) Energy management strategy of hybrid energy storage based on Pareto optimality. Appl Energ 327. https://doi.org/10.1016/j.apenergy.2022.120095
Jin ZS, Liu HC, Kou W (2021) Selection of purification technology for small and medium-sized biogas projects based on AHP and entropy weight method. Renew Energ Resour 39(10):1294–1300. https://doi.org/10.13941/j.cnki.21-1469/tk.2021.10.003
Hu YJ, Wu LZ, Shi C (2020) Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS. Int J Prod Res 58(3):748–757. https://doi.org/10.1080/00207543.2019.1600760
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Tian, W., An, H., Zhao, X., Bai, H. (2024). A Multi-objective Scheduling Model for a Gas-Steam-Electricity Coupling System in the Steelwork Based on Time-of-Use Electricity Pricing. In: Iloeje, C., et al. Energy Technology 2024. TMS 2024. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-031-50244-6_18
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