Е-Публикации
Технически университет - София

Abstract: This study presents the development and implementation of a Binary Particle Swarm Optimization (BPSO) algorithm for energy management in a grid-connected microgrid composed of photovoltaic (PV) generation and battery energy storage. The optimization aims to minimize the total operational cost over a one-month horizon with hourly resolution, using real-world consumption and solar generation data. The model accounts for import and export pricing asymmetries and battery efficiency constraints. Simulation results indicate that BPSO effectively schedules battery charging and discharging cycles, maximizes self-consumption of PV energy, and reduces peak grid imports. The system achieves favorable net energy costs by strategically exporting surplus energy and leveraging battery flexibility. The findings confirm that BPSO is a robust, computationally efficient tool for real-time or offline energy optimization in distributed renewable systems. Its adaptability to fluctuating demand and generation patterns makes it suitable for scalable energy management in smart grid environments.

References

1. Ali Avar, Ehsan Ghanbari, "Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach, Computers and Electrical Engineering, Volume 117, 2024, 109280, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2024.109280.
2. M, Thirunavukkarasu & Lala, Himadri & Sawle, Yashwant. (2022). Reliability Index Based Optimal Sizing and Statistical Performance Analysis of Stand-Alone Hybrid Renewable Energy System Using Metaheuristic Algorithms. SSRN Electronic Journal. 74. 10.2139/ssrn.4243739.
3. S. Sankarananth, M. Karthiga, Suganya E., Sountharrajan S., Durga Prasad Bavirisetti, "AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids," Energy Reports, Volume 10, 2023, Pages 1299-1312, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2023.08.005.
4. Benhabsa, Imadeddine & Rahmani, Ala & Kouadri, Ramzi & Slimani, Linda. (2024). A new Metaheuristic Method for Emission and Cost generation Reduction considering Stochastic Solar Power. 1-6. 10.1109/ICAECOT62402.2024.10829125.
5. Syama, S & Ramprabhakar, J & Rajendran, Anand & Guerrero, Josep. (2024). An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems. Scientific Reports. 14. 10.1038/s41598-024-75743-0.
6. Maheshwari, Ankur & Sood, Yog. (2023). Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm. Wind Engineering. 48. 10.1177/0309524X231204992.
7. Abdel-Mawgoud, H., Kamel, S. (2023). Stochastic Optimization of Renewable Energy Sources in Distribution Networks. In: Dulhare, U.N., Houssein, E.H. (eds) Machine Learning and Metaheuristics: Methods and Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-6645-5_4.3
8. T, Senthilkumar & T, Jayasankar & P, Vijayarajan. (2024). Artificial Intelligence with Metaheuristic Algorithm Based Optimization for a Hybrid Energy Storage System. International Journal of Advanced Trends in Engineering and Management. III. 10.59544/BKXT5123/IJATEMV03I05P1.
9. Ying Meng, Yu Wang, Sizhou Sun, Shengpeng Wang, Hong Zhang, "Multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty of renewable energy generations based on two-level iterative strategy," Energy Reports, Volume 9, 2023, Pages 1842-1858, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2022.12.149.
10. Al-Shamma’a, A. A., Hussein Farh, H. M., & Alsharabi, K. (2024). Integrating Firefly and Crow Algorithms for the Resilient Sizing and Siting of Renewable Distributed Generation Systems under Faulty Scenarios. Sustainability, 16(4), 1521. https://doi.org/10.3390/su16041521

Issue

Copyright IEEE