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A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering Distributed Generators

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  • Niknam, Taher

Abstract

In recent years, Distributed Generators (DGs) connected to the distribution network have received increasing attention. The connection of enormous DGs into existing distribution network changes the operation of distribution systems. Because of the small X/R ratio and radial structure of distribution systems, DGs affect the daily Volt/Var control. This paper presents a new algorithm for multiobjective daily Volt/Var control in distribution systems including Distributed Generators (DGs). The objectives are costs of energy generation by DGs and distribution companies, electrical energy losses and the voltage deviations for the next day. A new optimization algorithm based on a Chaotic Improved Honey Bee Mating Optimization (CIHBMO) is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. Since objectives are not the same, a fuzzy system is used to calculate the best solution. The plausibility of the proposed algorithm is demonstrated and its performance is compared with other methods on a 69-bus distribution feeder. Simulation results illustrate that the proposed algorithm has better outperforms the other algorithms.

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  • Niknam, Taher, 2011. "A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering Distributed Generators," Applied Energy, Elsevier, vol. 88(3), pages 778-788, March.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:3:p:778-788
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    5. Hung, Duong Quoc & Mithulananthan, N. & Bansal, R.C., 2014. "An optimal investment planning framework for multiple distributed generation units in industrial distribution systems," Applied Energy, Elsevier, vol. 124(C), pages 62-72.
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    7. Muttaqi, K.M. & Le, An D.T. & Aghaei, J. & Mahboubi-Moghaddam, E. & Negnevitsky, M. & Ledwich, G., 2016. "Optimizing distributed generation parameters through economic feasibility assessment," Applied Energy, Elsevier, vol. 165(C), pages 893-903.
    8. Jung, Jaesung & Onen, Ahmet & Arghandeh, Reza & Broadwater, Robert P., 2014. "Coordinated control of automated devices and photovoltaic generators for voltage rise mitigation in power distribution circuits," Renewable Energy, Elsevier, vol. 66(C), pages 532-540.
    9. Samimi, Abouzar & Nikzad, Mehdi & Siano, Pierluigi, 2017. "Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs," Renewable Energy, Elsevier, vol. 109(C), pages 22-40.
    10. Ahmet Onen, 2016. "Energy Saving of Conservation Voltage Reduction Based on Load-Voltage Dependency," Sustainability, MDPI, vol. 8(8), pages 1-11, August.
    11. Mortazi, Mohammad & Moradi, Ahmad & Khosravi, Mohsen, 2020. "Simultaneous optimization of transformer tap changer and network capacitors to improve the distribution system’s static security considering distributed generation sources," MPRA Paper 109052, University Library of Munich, Germany, revised 01 Jul 2020.
    12. Zare, Mohsen & Niknam, Taher, 2013. "A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources," Energy, Elsevier, vol. 55(C), pages 236-252.
    13. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
    14. Fu, Xueqian & Chen, Haoyong & Cai, Runqing & Yang, Ping, 2015. "Optimal allocation and adaptive VAR control of PV-DG in distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 173-182.
    15. Petinrin, J.O. & Shaabanb, Mohamed, 2016. "Impact of renewable generation on voltage control in distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 770-783.
    16. Zhou, Bin & Xu, Da & Chan, Ka Wing & Li, Canbing & Cao, Yijia & Bu, Siqi, 2017. "A two-stage framework for multiobjective energy management in distribution networks with a high penetration of wind energy," Energy, Elsevier, vol. 135(C), pages 754-766.
    17. Tang, Jia & Wang, Dan & Wang, Xuyang & Jia, Hongjie & Wang, Chengshan & Huang, Renle & Yang, Zhanyong & Fan, Menghua, 2017. "Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques," Applied Energy, Elsevier, vol. 204(C), pages 143-162.
    18. Manbachi, Moein & Farhangi, Hassan & Palizban, Ali & Arzanpour, Siamak, 2016. "Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification," Applied Energy, Elsevier, vol. 174(C), pages 69-79.
    19. Ruiz-Romero, Salvador & Colmenar-Santos, Antonio & Mur-Pérez, Francisco & López-Rey, África, 2014. "Integration of distributed generation in the power distribution network: The need for smart grid control systems, communication and equipment for a smart city — Use cases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 223-234.
    20. Merahi, Farid & Berkouk, El Madjid & Mekhilef, Saad, 2014. "New management structure of active and reactive power of a large wind farm based on multilevel converter," Renewable Energy, Elsevier, vol. 68(C), pages 814-828.
    21. Zhang, Lu & Shen, Chen & Chen, Ying & Huang, Shaowei & Tang, Wei, 2018. "Coordinated allocation of distributed generation, capacitor banks and soft open points in active distribution networks considering dispatching results," Applied Energy, Elsevier, vol. 231(C), pages 1122-1131.

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