Energy management in smart power grids with the presence of renewable sources, storage devices and electric vehicles using multi-objective water cycle optimization method

Document Type : Original Article

Authors

1 Master of Electrical Engineering Ray Power Planet Management Company, Tehran, Iran

2 Assistant Professor, Department of Electrical Engineering, Yadegar-e-Imam Khomeini(Rah) Shahre-Rey Branch, Islamic Azad University, Tehran, Iran

3 Master of Electrical Engineering Yadegar-e-Imam Khomeini(Rah) Shahre-Rey Branch, Islamic Azad University, Tehran, Iran

10.52547/jrenew.10.2.105

Abstract

Nowadays, the use of smart home appliances has made home energy management more advanced and sophisticated, so determining the optimal timing of home appliances is necessary to define the optimization problem and choose a suitable solution to achieve the necessary benefits for residential consumers. It also helped electricity suppliers by reducing energy consumption. Unlike hybrid electric vehicle (HEV) batteries, which can only charge with an electric motor plug-in hybrid electric vehicle (PHEV) can charged via a mains connection. To optimize the economics of PHEVs, it is necessary to determine an energy management strategy to coordinate the distribution of power between several energy sources. In this paper, after expressing the generalities and background of the subject, the modeling method and the formulation and problem-solving method for determining the optimal energy management strategy in smart power grids with the presence of connectable electric vehicles reviewed. The cost of electricity consumption in a micro grid including a smart home presented and the multi-objective water cycle optimization (MOWCA) method used to solve the resulting optimization problem. The simulation results show that the use of renewable energy sources, battery storage, and electric vehicles by then proposed method increases consumer benefit.

Keywords

Main Subjects


[1] E. Shirazi and S. Jadid, Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS, Energy and Buildings, vol. 93, pp. 40-49,  2015.
[2] M. Saadatmandi, S. Hakimi, and A. Hajizadeh, Management of Plug-in Hybrid Electrical Vehicle to Increase Renewable Energy Penetration in Smart Grid, 2018.
[3] X. Han, H. He, J. Wu, J. Peng, and Y. Li, Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle, Applied Energy, vol. 254, p. 113708, 2019.
[4] M. Shokri and H. Kebriaei, Mean Field Optimal Energy Management of Plug-In Hybrid Electric Vehicles, IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 113-120, 2019.
[5] X. Lu and H. Wang, Optimal Sizing and Energy Management for Cost-Effective PEV Hybrid Energy Storage Systems, IEEE Transactions on Industrial Informatics, pp. 1-1, 2019.
[6] Y. Bai, H. He, J. Li, S. Li, Y.-x. Wang, and Q. Yang, Battery anti-aging control for a plug-in hybrid electric vehicle with a hierarchical optimization energy management strategy, Journal of Cleaner Production, vol. 237, p. 117841, 2019.
[7] J. Guo, H. He, and J. Peng, Real-time Energy Management for Plug-in Hybrid Electric Vehicle based on Economy Driving Pro System, Energy Procedia, vol. 158, pp. 2689-2694,  2019.
[8] M. H. Hajimiri and F. R. Salmasi, A Fuzzy Energy Management Strategy for Series Hybrid Electric Vehicle with Predictive Control and Durability Extension of the Battery, in 2006 IEEE Conference on Electric and Hybrid Vehicles, 2006.
[9] T. Sousa, H. Morais, J. Soares, and Z. Vale, Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints, Applied Energy, vol. 96, pp. 183-193, 2012.
[10] J. Soares, T. Sousa, H. Morais, Z. Vale, B. Canizes, and A. Silva, Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid, Applied Soft Computing, vol. 13, no. 11, pp. 4264-4280,  2013.
[11] A. Zakariazadeh, S. Jadid, and P. Siano, Multi-objective scheduling of electric vehicles in smart distribution system, Energy Conversion and Management, vol. 79, pp. 43-53,  2014.
[12] M. H. Amini, J. Frye, M. D. Ilić, and O. Karabasoglu, Smart residential energy scheduling utilizing two stage Mixed Integer Linear Programming,  North American Power Symposium (NAPS), 2015.
[13] M. H. Amini and A. Sarwat, Optimal Reliability-based Placement of Plug-In Electric Vehicles in Smart Distribution Network, International Journal of Energy Science, vol. 4, p. 43,2014.
[14] F. Y. Melhem, O. Grunder, Z. Hammoudan, and N. Moubayed, Optimization and Energy Management in Smart Home Considering Photovoltaic, Wind, and Battery Storage System With Integration of Electric Vehicles, Canadian Journal of Electrical and Computer Engineering, vol. 40, no. 2, pp. 128-138, 2017.
[15] X. Wu, X. Hu, X. Yin, and S. J. Moura, Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage, IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 2065-2075, 2018.
[16] M. Govardhan and R. Roy, Generation scheduling in smart grid environment using global best artificial bee colony algorithm, International Journal of Electrical Power & Energy Systems, vol. 64, pp. 260-274,  2015.