An Enhanced Evolutionary Algorithm for Providing Energy Management Schedule in the Smart Distribution Network

Lotfi, Hossein; Nikooei, Amir; Shojaei, Ali; Ghazi, Reza; Naghibi Sistani, Mohammad Bagher

Document Type : Review Article

Authors

¹ Department of Electrical Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.

² Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

³ Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Penetration of distributed generation resources including wind power and solar photovoltaic units in distribution system has been increased, and it is important to examine their impact on the distribution systems’ operation in term of reliability. In this paper, the multi-objective dynamic feeder reconfiguration is introduced as an efficient approach for providing an energy management schedule in the distribution grid considering energy loss and energy not supplied as the objective functions in the presence of renewable energy sources and capacitor units. In addition, the effect of uncertainty related to power demand is considered in the evaluations. To this end, an enhanced particle swarm optimization algorithm is provided in this paper, the proposed approach is applied to the 33-node testing system.

Keywords

References

[1] Azizivahed, A., et al., “Energy Storage Management Strategy in Distribution Networks Utilized by Photovoltaic Resources,” IET Generation, Transmission & Distribution, Vol. 12(21), pp. 5627-5638, 2018.

[2] Azizivahed, A., et al. “New Energy Management Approach in Distribution Systems Considering Energy Storages,” 20th International Conference on Electrical Machines and Systems (ICEMS), 2017, IEEE.

[3] Tabari, M. and A. Yazdani, “An Energy Management Strategy for a DC Distribution System for Power System Integration of Plug-In Electric Vehicles,” IEEE Transactions on Smart Grid, Vol. 7(2). pp. 659-668, 2015.

[4] B. Lin and W. Wu, "Economic Viability of Battery Energy Storage and Grid Strategy: A Special Case of China Electricity Market," Energy, Vol. 124, pp. 423-434, 2017

[5] Azizivahed, A., et al., “Multi-Objective Energy Management Approach Considering Energy Storages in Distribution Networks with Respect to Voltage Security”. 2018.

[6] Azizivahed, A., et al., “A New Bi-objective Approach to Energy Management in Distribution Networks with Energy Storage Systems”. IEEE Transactions on Sustainable Energy, Vol. 9(1). pp. 56-64, 2017.

[7] Reihani, E., et al., “Energy Management at the Distribution Grid using a Battery Energy Storage System (BESS),” International Journal of Electrical Power & Energy Systems, Vol. 77, pp. 337-344, 2016.

[8] Korada, N. and M.K. Mishra, “Grid Adaptive Power Management Strategy for an Integrated Micro-grid with Hybrid Energy Storage,” IEEE Transactions on Industrial Electronics, Vol. 64(4), pp. 2884-2892, 2016.

[9] Lv, T. and Q. Ai, “Interactive Energy Management of Networked Microgrids-based Active Distribution System Considering Large-Scale Integration of Renewable Energy resources,” Applied Energy, Vol. 163, pp. 408-422, 2016.

[10] Lei, Z., et al., “Dynamic Energy Management for a Novel Hybrid Electric System based on Driving Pattern Recognition,” Applied Mathematical Modelling, Vol. 45, pp. 940-954, 2017.

[11] Mahboubi-Moghaddam, E., et al., “Multi-objective Distribution Feeder Reconfiguration to Improve Transient Stability, And Minimize Power Loss and Operation Cost Using an Enhanced Evolutionary Algorithm at The Presence of Distributed Generations,” International Journal of Electrical Power & Energy Systems, Vol. 76, pp. 35-43, 2016.

[12] Narimani, M.R., et al., “Enhanced Gravitational Search Algorithm for Multi-Objective Distribution Feeder Reconfiguration Considering Reliability, Loss and Operational Cost,” IET Generation, Transmission & Distribution, Vol. 8(1), pp. 55-69, 2014.

[13] Azizivahed, A., et al., “A Hybrid Evolutionary Algorithm for Secure Multi-Objective Distribution Feeder Reconfiguration,” Energy, Vol. 138, pp. 355-373, 2017.

[14] Azizivahed, A., et al. “Dynamic Feeder Reconfiguration in Automated Distribution Network Integrated with Renewable Energy Sources with Respect to the Economic Aspect”. in 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). 2019. IEEE

[15] Ding, F. and K.A. Loparo, “Feeder Reconfiguration for Unbalanced Distribution Systems with Distributed Generation: A Hierarchical Decentralized Approach,” IEEE Transactions on Power Systems, Vol. 31(2), pp. 1633-1642, 2015.

[16] Larimi, S.M.M., M.R. Haghifam, and A. Moradkhani, “Risk-based Reconfiguration of Active Electric Distribution Networks,” IET Generation, Transmission & Distribution, Vol. 10(4), pp. 1006-1015, 2016.

[17] Lotfi, H., R. Ghazi, and M. bagher Naghibi-Sistani, “Multi-objective Dynamic Distribution Feeder Reconfiguration Along with Capacitor Allocation Using A New Hybrid Evolutionary Algorithm”. Energy Systems, pp. 1-31, 2019.

[18] Barani, M., et al., “Optimal Partitioning of Smart Distribution Systems into Supply-Sufficient Micro-grids,” IEEE Transactions on Smart Grid, 2018.

[19] Niknam, T., M. Zare, and J. Aghaei, “Scenario-based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources,” IEEE Transactions on Power Delivery, Vol. 27(4), pp. 2004-2019, 2012.

[20] Kennedy, J., Eberhart, R, “Particle Swarm Optimization,” In: IEEE international conference on neural networks Piscataway, 1995, NJ, 4: p.1942–48.

[21] Lotfi, H., M. Samadi, and A. Dadpour. “Optimal Capacitor Placement and Sizing in Radial Distribution System Using An Improved Particle Swarm Optimization Algorithm”. in 2016 21st Conference on Electrical Power Distribution Networks Conference (EPDC). 2016. IEEE

[22] Lotfi, H. and R. Ghazi. “An Optimal Co-operation of Distributed Generators and Capacitor Banks in Dynamic Distribution Feeder Reconfiguration”. in 2019 24th Electrical Power Distribution Conference (EPDC). 2019. IEEE.

[23] Eusuff, M., K. Lansey, and F. Pasha. “Shuffled Frog-Leaping Algorithm: A Memetic Meta-Heuristic for Discrete Optimization”. Engineering optimization, Vol. 38(2), pp. 129-154, 2006.

Majlesi Journal of Electrical Engineering

An Enhanced Evolutionary Algorithm for Providing Energy Management Schedule in the Smart Distribution Network

References

References

Volume 14, Issue 2 - Serial Number 2
June 2020
Pages 17-23

An Enhanced Evolutionary Algorithm for Providing Energy Management Schedule in the Smart Distribution Network

References

References

Volume 14, Issue 2 - Serial Number 2June 2020Pages 17-23

Volume 14, Issue 2 - Serial Number 2
June 2020
Pages 17-23