# Short-term Scheduling of Restructured Distribution Networks with Demand Response using Symbiotic Organism Search (SOS) Algorithm

Keywords:
distributed network, symbiotic organism search algorithm, short-term scheduling, demand response, distributed generation.

### Abstract

Recently, power system restructuring, demand response (DR) program and using of distributed generation (DG) are important issues to enhance reliability, power flow continuity and power quality for costumers. In this paper, scheduling of distributed networks with DR program for a 24-hours optimization problem is modelled. The DR program is based on load side participation and in order to solve this optimization problem, a new algorithm called Symbiotic Organism Search (SOS) has been used. Objective functions are system losses and operation costs reduction. After exact definition of the problem, objective functions and constraints, proposed method for short-term scheduling is simulated on a 33-bus standard network with MATLAB software for different scenarios. Simulation results show that adoption of demand response programs with proposed method have desirable performance to reduce losses and costs.### References

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[2] S. Kalambe and G. Agnihotri, “Loss minimization techniques used in distribution network: bibliographical survey,” Renewable and Sustainable Energy Reviews, vol. 139, pp. 98–112, 2014.

[3] M.H. Albadi and E.F. El-Saadany, “A summary of demand response in electricity markets,” Electric Power Systems Research, vol. 78, pp. 1989–1996, 2008.

[4] A. Zakariazadeh, S. Jadid and P. Siano, “Smart microgrid energy and reserve scheduling with demand response using stochastic optimization,” Electrical Power and Energy Systems, vol. 63, pp. 523–533, 2014.

[5] YFB Jiang, “Dynamic residential demand response and distributed generation management in smart microgrid with hierarchical agents,” Energy Proc., vol. 12, pp. 76–90, 2011.

[6] M. Cepeda and M. Saguan, “Assessing long-term effects of demand response policies in wholesale electricity markets,” Electrical Power and Energy Systems, vol. 74, pp. 142–152, 2016.

[7] P. Faria, Z. Vale and J. Baptista, “Constrained consumption shifting management in the distributed energy resources scheduling considering demand response,” Energy Conversion and Management, vol. 93, pp. 309–320, 2015.

[8] R. Wang, P. Wang and G. Xiao, “A robust optimization approach for energy generation scheduling in microgrids,” Energy Conversion and Management, vol. 106, pp. 597–607, 2015.

[9] M. Alipour, B. Mohammadi-Ivatloo and K. Zare, “Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs,” Applied Energy, vol. 136, pp. 393–404, 2014.

[10] F. Ruelens, B. J. Claessens, S. Vandael, B. D. Schutter, R. Babuska, and R. Belmans, “Residential demand response of thermostatically controlled loads using batch reinforcement learning,” IEEE Trans. on Smart Grid, vol. 8, pp. 2149-2159, 2016.

[11] M. Silva, H. Morais, Z. Vale and P. Faria, “Short-term Scheduling Considering Five-minute and Hour-ahead Energy Resource Management,” IEEE Power and Energy Society General Meeting, 2012.

[12] Q. Wang, J. Wang, and Y. Guan, “Stochastic unit commitment with uncertain demand response,” IEEE Trans. Power Syst., vol. 28, pp. 562–563, 2013.

[13] B. Kim, Y. Zhang, M. van der Schaar, and J. Lee, “Dynamic pricing and energy consumption scheduling with reinforcement learning,” IEEE Trans. on Smart Grid, vol. 7, pp. 2187–2198, 2016.

[14] P. S. Georgilakis and N. D. Hatziargyriou, “Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research,” IEEE Trans. on power system, vol. 28, pp. 3420-3428, 2013.

[15] P. Samal and S. Ganguly, “A Modified Forward Backward Sweep Load Flow Algorithm for Unbalanced Radial Distribution Systems,” IEEE Power & Energy Society General Meeting, 2015.

[16] S.M. Moghaddas-Tafreshi and Elahe Mashhour, “Distributed generation modeling for power flow studies and a three-phase unbalanced power flow solution for radial distribution systems considering distributed generation,” Electric Power Systems Research, vol. 79, pp. 680–686, 2009.

[17] J. A. Michline Rupa and S. Ganesh, “Power Flow Analysis for Radial Distribution System Using Backward/Forward Sweep Method,” World Academy of Science, Engineering and Technology, vol. 8, pp. 1621-1625, 2014.

[18] M. Cheng and D. Prayogo, “Symbiotic Organisms Search: A new metaheuristic optimization algorithm,” Computers & Structures, vol. 139, pp. 98–112, 2014.

[19] H. Kahraman, M. Dosoglu, U. Guvenc, S. Duman and Y. Sonmez, “Optimal scheduling of short-term hydrothermal generation using symbiotic organisms search algorithm,” 4th International Istanbul Smart Grid Congress and Fair (ICSG), April 2016.

[20] M. Padma Lalitha, P.Suresh babu and B.Adivesh, “SOS algorithm for DG placement for loss minimization considering reverse power flow in the distribution systems,” International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2016.

[21] P. Balachennaiah and M. Suryakalavathi, “Real Power Loss minimization using symbiotic organisms search algorithm,” IEEE India Conference (INDICON), Dec. 2015.

[22] H. M. Hasanien and A. A. El-Fergany, “Symbiotic organisms search algorithm for automatic generation control of interconnected power systems including wind farms,” IET Generation, Transmission & Distribution, vol. 11, pp. 1692–1700, 2017.

[23] M.M. Amran, G.B. Jasmon, A.H.A. Bakar and H. Mokhlis, “Optimal network reconfiguration based on maximization of system loadability using continuation power flow theorem,” International Journal of Electrical Power & Energy Systems, vol. 54, pp. 123–133, 2014.

[24] M. Rostami, A. Kavousi-Fard, and T. Niknam. “Expected Cost Minimization of Smart Grids With Plug-In Hybrid Electric Vehicles Using Optimal Distribution Feeder Reconfiguration,” IEEE Transactions on Industrial Informatics, vol. 11, pp. 388-397, 2015.

Published

2018-01-01

How to Cite

*Majlesi Journal of Electrical Engineering*,

*12*(1), 23-30. Retrieved from http://mjee.iaumajlesi.ac.ir/index/index.php/ee/article/view/2447

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Articles