Research article Special Issues

Fuzzy logic controller and game theory based distributed energy resources allocation

  • Received: 30 November 2020 Accepted: 13 May 2020 Published: 15 June 2020
  • Energy management and demand control through conventional energy generation sources are challenging for energy providers. Distributed energy resources (DERs) allocation near load centers may provide a suitable solution. The main contribution of the paper improves the voltage profile and reduce the active and reactive power losses in the distribution network. DERs are integrated with IEEE 33 bus system using fuzzy logic controller (FLC) and game theory for two different cases with unity and 0.9 power factor (PF) and compares with conventional methods of integration (i.e., modified novel method, power loss sensitivity method, voltage sensitivity analysis method). The capacity of DERs is optimized by FLC with the help of three triangular input functions voltage profile, active power loss, and reactive power loss. The location of integration of DERs in the radial distribution network is identified by game theory. Game theory is a mathematical algorithm, the results of multiple runs of DERs integration with the desired capacity calculated by FLC, identify the location for integration. The results comparison of DERs integration with the proposed methodology and conventional method shows the effectiveness of the proposed methodology. The voltage profile of the IEEE 33 bus system is increased by 6.70% with unity PF and 7.10% with 0.9 PF most among the applied methodologies and reduce the active and reactive power losses for both unity and 0.9 PF cases.

    Citation: Akash Talwariya, Pushpendra Singh, Mohan Lal Kolhe, Jalpa H. Jobanputra. Fuzzy logic controller and game theory based distributed energy resources allocation[J]. AIMS Energy, 2020, 8(3): 474-492. doi: 10.3934/energy.2020.3.474

    Related Papers:

  • Energy management and demand control through conventional energy generation sources are challenging for energy providers. Distributed energy resources (DERs) allocation near load centers may provide a suitable solution. The main contribution of the paper improves the voltage profile and reduce the active and reactive power losses in the distribution network. DERs are integrated with IEEE 33 bus system using fuzzy logic controller (FLC) and game theory for two different cases with unity and 0.9 power factor (PF) and compares with conventional methods of integration (i.e., modified novel method, power loss sensitivity method, voltage sensitivity analysis method). The capacity of DERs is optimized by FLC with the help of three triangular input functions voltage profile, active power loss, and reactive power loss. The location of integration of DERs in the radial distribution network is identified by game theory. Game theory is a mathematical algorithm, the results of multiple runs of DERs integration with the desired capacity calculated by FLC, identify the location for integration. The results comparison of DERs integration with the proposed methodology and conventional method shows the effectiveness of the proposed methodology. The voltage profile of the IEEE 33 bus system is increased by 6.70% with unity PF and 7.10% with 0.9 PF most among the applied methodologies and reduce the active and reactive power losses for both unity and 0.9 PF cases.
    加载中


    [1] Justo JJ, Mwasilu F, Lee J, et al. (2013) AC-microgrids versus DC-microgrids with distributed energy resources: A review. Renewable Sustainable Energy Rev 24: 387-405. doi: 10.1016/j.rser.2013.03.067
    [2] Ehsan A, Yang Q (2018) Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques. Appl Energy 210: 44-59. doi: 10.1016/j.apenergy.2017.10.106
    [3] Singh P, Kothari DP, Singh M (2014) Interconnected distribution network for the integration of distributed energy resources. Res J Appl Sci, Eng Technol 7: 240-250. doi: 10.19026/rjaset.7.247
    [4] World Energy Council, World Energy Scenario-2019. Exploring Innovation Pathways to 2040. 2019. Available from: https://www.worldenergy.org/assets/downloads/2019_Scenarios_Full_Report.pdf
    [5] Keane A, Ochoa LF, Borages CL, et al. (2012) State-of-the-art techniques and challenges ahead for distributed generation planning and optimization. IEEE Trans Power Syst 28: 1493-1502.
    [6] Alarcon-Rodriguez A, Ault G, Galloway S (2010) Multi-objective planning of distributed energy resources: A review of the state-of-the-art. Renewable Sustainable Energy Rev 14: 1353-1366. doi: 10.1016/j.rser.2010.01.006
    [7] Ameli A, Bahrami S, Khazaeli F, et al. (2014) A multiobjective particle swarm optimization for sizing and placement of DGs from DG owner's and distribution company's viewpoints. IEEE Trans Power Delivery 29: 1831-1840.
    [8] Syahputra R, Robandi I, Ashari M (2015) Reconfiguration of distribution network with distributed energy resources integration using PSO algorithm. Telkomnika 13: 759-766. doi: 10.12928/telkomnika.v13i3.1790
    [9] Kothari DP, Singh P, Singh M (2010) Concept of energy highways for industrial growth and national prosperity. Int J Electron Electr Eng 9: 9-15.
    [10] Singh P, Kothari DP, Singh P (2010) Voltage control in distribution networks having DGs by using UPFC. Int J Electro Electr Eng Syst 2: 31-38.
    [11] Talwariya A, Singh P (2019) Optimization of distribution networks with integration of distributed generators using cooperative Game Theory. Int J Power Energy Syst 39: 1-7.
    [12] Mumtaz F, Syed MH, Al Hosani M, et al. (2015) A novel approach to solve power flow for islanded microgrids using modified Newton Raphson with droop control of DG. IEEE Trans Sustainable Energy 7: 493-503.
    [13] Imran AM, Kowsalya M, Kothari DP (2014) A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks. Int J Electr Power Energy Syst 63: 461-472. doi: 10.1016/j.ijepes.2014.06.011
    [14] Abdmouleh Z, Gastli A, Ben-Brahim L, et al. (2017) Review of optimization techniques applied for the integration of distributed generation from renewable energy sources. Renewable Energy 113: 266-280. doi: 10.1016/j.renene.2017.05.087
    [15] Rao RS, Ravindra K, Satish K, et al. (2012) Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE Trans Power Syst 28: 317-325.
    [16] Prakash P, Khatod DK (2016) Optimal sizing and siting techniques for distributed generation in distribution systems: A review. Renewable Sustainable Energy Rev 57: 111-130. doi: 10.1016/j.rser.2015.12.099
    [17] Talwariya A, Sharma D, Pandey AK, et al. An execution of smart grid with game theory. In: 2016 International Conference on Recent Advances and Innovations in Engineering Dec 23-25, 2016. IEEE; 1-4.
    [18] Talwariya A, Sharma SK, Singh P, et al. (2018) A game theory approach and tariff strategy for demand side management, In: 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering Nov 22-25, 2018. IEEE; 1-5.
    [19] Talwariya A, Singh P, Kolhe M (2019) A stepwise power tariff model with game theory based on Monte-Carlo simulation and its applications for household, agricultural, commercial and industrial consumers. Int J Electr Power Energy Syst 111: 14-24. doi: 10.1016/j.ijepes.2019.03.058
    [20] Mei S, Wei W, Liu F (2017) On engineering game theory with its application in power systems. Control Theory Technol 15: 1-12. doi: 10.1007/s11768-017-6186-y
    [21] Lalitha MP, Reddy VV, Reddy NS, et al. (2011) DG source allocation by fuzzy and clonal selection algorithm for minimum loss in distribution system. Distrib Gener Altern Energy J 26: 17-35.
    [22] Murthy VVSN, Kumar A (2013) Comparison of optimal DG allocation methods in radial distribution systems based on sensitivity approaches. Int J Electr Power Energy Syst 53: 450-467. doi: 10.1016/j.ijepes.2013.05.018
    [23] Qian K, Zhou C, Allan M, et al. (2011) Effect of load models on assessment of energy losses in distribution generation planning. Int J Electr Power Energy Syst 33: 1243-1250. doi: 10.1016/j.ijepes.2011.04.003
    [24] Rueda-Medina AC, Franco JF, Rider MJ, et al. (2013) A mixed-integer linear programming approach for optimal type, size and allocation of distributed generation in radial distribution systems. Electr Power Syst Res 97:133-43. doi: 10.1016/j.epsr.2012.12.009
    [25] Hasanien HM, Matar M (2014) A fuzzy logic controller for autonomous operation of a voltage source converter-based distributed generation system. IEEE Trans Smart Grid 6: 158-165.

    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
  • Reader Comments
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(190) PDF downloads(179) Cited by(0)

Article outline

Figures and Tables

Figures(14)  /  Tables(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog