Microgrid energy management system with degradation cost and carbon trading mechanism: A multi-objective artificial hummingbird algorithm

• Published in: Applied energy 2025-01, Vol.378, p.124853, Article 124853
• Main Authors: Li, Ling-Ling, Ji, Bing-Xiang, Li, Zhong-Tao, Lim, Ming K., Sethanan, Kanchana, Tseng, Ming-Lang
• Format: Article
• Language: English
• Subjects: Microgrid energy management system; Multi-objective artificial hummingbird algorithm; Optimal energy management; Renewable energy, hybrid energy storage
• ISSN: 0306-2619
• DOI: 10.1016/j.apenergy.2024.124853

Microgrid is an important way to optimize the distributed power generation and its optimal scheduling to ensure reliable and economical operation. This study constructs a multi-objective optimization model for a microgrid energy management system involving degradation cost and carbon trading mechanism. A carbon trading mechanism is to reduce greenhouse gas emissions; meanwhile, a demand response strategy is employed to optimize energy load demand. The energy storage system mathematical model is considered and degradation cost is introduced to change the corresponding control strategy. A hybrid energy storage is used in this model to smooth out the solar power and wind power fluctuations. Hence, a multi-objective artificial hummingbird optimization algorithm is proposed and uses to solve the optimal operation strategy of the microgrid. The final optimal operation strategy is obtained from the Pareto solution set using TOPSIS. The results show that the proposed microgrid system has 20.2 % lower total operating costs, 4.5 % lower carbon emissions, and 32.6 % longer battery life than the conventional microgrid system, which is critical for improving the operation stability, economy, low carbon of the system, and extending the service life of the battery. [Display omitted] •This study constructs a multi-objective artificial hummingbird algorithm.•A microgrid energy management system is involving degradation cost and carbon trading mechanism.•A hybrid energy storage is used in this model to smooth out the solar power and wind power fluctuations.•The final optimal operation strategy is obtained from the Pareto solution set using TOPSIS.•The system has 20.2 % lower total operating costs, 4.5 % lower carbon emissions, and 32.6 % longer battery life.