A Bi-level optimization dispatch for hybrid shipboard microgrid considering electricity-gas-heat coupling

Abstract

With the increasingly severe problem of air pollution and energy crisis, new energy power generation technology in ship has quickly become the focus of attention. Compared with traditional ships, hybrid shipboard microgrid systems can achieve pollution-free, renewable and high use value. However, the integration of electricity-gas-heat in hybrid energy shipboard microgrid system also poses challenges to current optimization methods. Therefore, this paper develops a bi-level optimization dispatch model for hybrid shipboard microgrid system based on multi-objective particle swarm optimization algorithm. Taking the diesel generators, photovoltaic generation system, energy storage system (ESS) and thermal energy storage equipment into account, a hybrid shipboard microgrid system model considering electricity-gas-heat coupling is constructed. Based on this, a bi-level optimization dispatch model is established to reduce total cost, GHG (GHG) emissions and lifespan loss of ESS. The upper-level model achieves the optimization dispatch of power generation equipment and loads; a lower-level optimization model with the goal of reducing the lifespan loss of ESS is constructed. The improved multi-objective and single-objective particle swarm optimization algorithms are introduced to find the optimal dispatch solutions for bi-level optimization dispatch model. Finally, simulation results show that the proposed optimization method can not only reduce the cost and GHG emissions by 8.7% and 10.9%, but also improve the cycle life of ESS by 9.2%.

Keywords

Particle swarm optimization / GHG emissions / Energy storage system

Data availability

The data that support the findings of this study are available.

CRediT authorship contribution statement

Xinyu Wang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology. Zibin Li: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Writing - original draft, Writing - review & editing. Xiaoyuan Luo: Conceptualization, Data curation, Formal analysis, Investigation. Yu Zhang: Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Xinping Guan: Supervision, Writing - original draft.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported by National Nature Science Foundation of China under 62103357 and 62203380, and by Hebei Natural Science Foundation under F2021203043 and F2020203097, and by the Open Research Fund of Intelligent Electric Power Grid Key Laboratory of Sichuan Province under 2023-IEPGKLSP-KFYB05.

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