Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2024F8403

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 279–287 (2024)

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Abstract. This work provides a comprehensive analysis of the optimization of a Virtual Power Plant (VPP), that consider the presence of energy storage systems and controllable loads, through the benchmarking of various solvers. It delves into the development of a Mixed Integer Linear Programming (MILP) algorithm aiming at optimizing energy management and exchange within a VPP, that takes into account the operation of shift electric appliances and battery storage systems among different houses. The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electrical appliance is scheduled. Furthermore, the experimental comparisons, the study evaluates the performance of commercial and open-source solvers in handling the complex dynamics of energy demand and supply. The findings highlight the importance of solver selection in enhancing the management, scalability, and reliability of VPP optimization strategies, offering insights into the optimal combination of programming interfaces and solvers for efficient VPP operation.

References

1. K. O. Adu-Kankam and L. M. Camarinha-Matos, “Renewable energy communities or ecosystems: An analysis of selected cases,” Heliyon, vol. 8, no. 12, 2022. http://dx.doi.org/https://doi.org/10.1016/j.heliyon.2022.e12617
2. N. Naval and J. M. Yusta, “Virtual power plant models and electricity markets - a review,” Renewable and Sustainable Energy Reviews, vol. 149, p. 111393, 2021. http://dx.doi.org/https://doi.org/10.1016/j.rser.2021.111393
3. X. Wang, Z. Liu, H. Zhang, Y. Zhao, J. Shi, and H. Ding, “A review on virtual power plant concept, application and challenges,” in 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), 2019. http://dx.doi.org/10.1109/ISGT-Asia.2019.8881433 pp. 4328–4333.
4. H. M. Rouzbahani, H. Karimipour, and L. Lei, “A review on virtual power plant for energy management,” Sustainable energy technologies and assessments, vol. 47, p. 101370, 2021. http://dx.doi.org/https://doi.org/10.1016/j.seta.2021.101370
5. R. Faia, P. Faria, and Z. Vale, “Optimizing house energy management with milp considering shifting of electric appliances and battery storage system,” in 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), 2023. http://dx.doi.org/10.1109/ISGTEUROPE56780.2023.10407660 pp. 1–5.
6. E. A. Bhuiyan, M. Z. Hossain, S. Muyeen, S. R. Fahim, S. K. Sarker, and S. K. Das, “Towards next generation virtual power plant: Technology review and frameworks,” Renewable and Sustainable Energy Reviews, vol. 150, p. 111358, 2021. http://dx.doi.org/https://doi.org/10.1016/j.rser.2021.111358
7. S. M. Nosratabadi, R.-A. Hooshmand, and E. Gholipour, “A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems,” Renewable and Sustainable Energy Reviews, vol. 67, pp. 341–363, 2017. http://dx.doi.org/https://doi.org/10.1016/j.rser.2016.09.025
8. D. Ilic, P. G. Da Silva, S. Karnouskos, and M. Griesemer, “An energy market for trading electricity in smart grid neighbourhoods,” in 2012 6th IEEE international conference on digital ecosystems and technologies (DEST). IEEE, 2012. http://dx.doi.org/10.1109/DEST.2012.6227918 pp. 1–6.
9. D. E. Olivares, A. Mehrizi-Sani, A. H. Etemadi, C. A. Cañizares, R. Iravani, M. Kazerani, A. H. Hajimiragha, O. Gomis-Bellmunt, M. Saeedifard, R. Palma-Behnke et al., “Trends in microgrid control,” IEEE Transactions on smart grid, vol. 5, no. 4, pp. 1905–1919, 2014. http://dx.doi.org/10.1109/TSG.2013.2295514
10. S. Mashayekh, M. Stadler, G. Cardoso, and M. Heleno, “A mixed integer linear programming approach for optimal der portfolio, sizing, and placement in multi-energy microgrids,” Applied Energy, vol. 187, pp. 154–168, 2017. http://dx.doi.org/https://doi.org/10.1016/j.apenergy.2016.11.020
11. J. Mohamed, A. Muqbel, A. T. Al-Awami, and I. Elamin, “Optimal demand response bidding and pricing mechanism in distribution network: application for a virtual power plant,” in 2018 IEEE Industry Applications Society Annual Meeting (IAS). IEEE, 2018. http://dx.doi.org/10.1109/IAS.2018.8544514 pp. 1–8.
12. G. Weishang, W. Qiang, L. Haiying, and W. Jing, “A trading optimization model for virtual power plants in day-ahead power market considering uncertainties,” Frontiers in Energy Research, vol. 11, p. 1152717, 2023. http://dx.doi.org/https://doi.org/10.3389/fenrg.2023.1152717
13. Y. Gao, L. Gao, P. Zhang, and Q. Wang, “Two-stage optimization scheduling of virtual power plants considering a user-virtual power plant-equipment alliance game,” Sustainability, vol. 15, no. 18, p. 13960, 2023. http://dx.doi.org/10.3390/su151813960
14. G. Ruan, D. Qiu, S. Sivaranjani, A. S. Awad, and G. Strbac, “Data-driven energy management of virtual power plants: A review,” Advances in Applied Energy, p. 100170, 2024. http://dx.doi.org/https://doi.org/10.1016/j.adapen.2024.100170
15. W. Nafkha-Tayari, S. Ben Elghali, E. Heydarian-Forushani, and M. Benbouzid, “Virtual power plants optimization issue: A comprehensive review on methods, solutions, and prospects,” Energies, vol. 15, no. 10, p. 3607, 2022. http://dx.doi.org/10.3390/en15103607
16. L. Gurobi Optimization, “Gurobi optimizer reference manual,” 2021.
17. S. Nickel, C. Steinhardt, H. Schlenker, and W. Burkart, Decision Optimization with IBM ILOG CPLEX Optimization Studio: A Hands-On Introduction to Modeling with the Optimization Programming Language (OPL). Springer Nature, 2022.
18. C. I. O. R. G. (COIN-OR), CBC (Coin-or Branch and Cut) Solver Documentation, 2011. [Online]. Available: https://projects.coin-or.org/Cbc
19. S. Mitchell et al., PuLP: A Linear Programming Toolkit for Python, 2005. [Online]. Available: https://coin-or.github.io/pulp/
20. T. P.-M. Contributors, “Python-MIP: Modeling and solving mixed-integer linear programming problems,” 2019, available at https://www.python-mip.com/.