Document Type : Research paper


1 Department of Electrical Engineering, Faculty of Engineering, University of Urmia, Urmia, Iran

2 Faculty of Electrical and Computer Engineering, University of Urmia, Urmia, Iran

3 Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran.


The optimal management of distributed generation (DG) enhances the efficiency of the distribution system; On the other hand, increasing the interest of customers in optimizing their consumption improves the performance of DG. This act is called demand side management. In this study, a new method based on the intelligent algorithm is proposed to optimal operate the demand side management in the presence of DG units and demand response. Firstly, the best location and capacity of different technologies of DG are selected by optimizing the technical index including the active and reactive loss and the voltage profile. Secondly, the daily performance of multi-DG and grid is optimized with and without considering the demand response. The economic and environmental indices are optimized in this step. In both steps, the non-dominated sorting firefly algorithm is utilized to multi-objective optimize the objective functions and then the fuzzy decision-making method is used to select the best result from the Pareto optimal solutions. Finally, the proposed method is implemented on the IEEE 33-bus distribution system and actual 101-bus distribution systems in Khoy-Iran. The obtained numerical results indicate the impact of the proposed method on improving the technical, economic and environmental indices of the distribution system.


Main Subjects

[1]    L. Gelazanskas, A. Gamage, “Demand side management in smart grid: a review and proposals for future direction”, Sustainable Cities Soc., vol. 11, pp. 22-30, 2014.
[2]    M. Behrangrad, “A review of demand side management business models in the electricity market”, Renewable Sustainable Energy Rev., vol. 47, pp. 270-283, 2015.
[3]    H. Shayeghi, M. Alilou, “Application of multi objective hfapso algorithm for simultaneous placement of DG, capacitor and protective device in radial distribution network”, J. Oper. Autom. Power Eng., vol. 3, p.131-146, 2015.
[4]    E. Heydarian, H. A. Aalami, “Multi objective scheduling of utility-scale energy storages and demand response programs portfolio for grid integration of wind power”, J. Oper. Autom. Power Eng., vol. 4, pp. 104-116, 2016.
[5]    D. Kotur, Z. Durisic, “Optimal spatial and temporal demand side management in a power system comprising renewable energy sources”, Renewable Energy, vol. 108, pp. 533-547, 2017.
[6]    M. Wang, Y. Ting, Y. Mu, H. Jia, L Shiguang, “A unified management and control model of demand-side resources”, Energy Procedia, vol. 105, pp. 2935-2940, 2017.
[7]    F. Verrilli, G. Gambino, S. Srinivasan, G. Palmieri, C. Vecchio, L. Glielmo, “Demand side management for heating controls in microgrids”, Int. Fed. Autom. Control, pp. 611- 616, 2016.
[8]    D. Müller, A. Monti, S. Stinner, T. Schlosser, Th. Schütz, P. Matthes, H. Wolisz, Ch. Molitor, H. Harb, R. Streblow, “Demand side management for city districts”, Build. Environ., vol. 91, pp. 283-293, 2015.
[9]    H. Shakouri, A. Kazemi, “Multi-objective cost-load optimization for demand side management of a residential area in smart grids”, Sustainable Cities Soc., vol. 32, pp. 171-180, 2017.
[10]    H. Li, Q. An, B. Yu, J. zhao, L. Cheng, Y. Wang, “Strategy analysis of demand side management on distributed heating driven by wind power”, Energy Procedia, vol. 105, pp. 2207-2213, 2017.
[11]    Z. Wu, H. Tazvinga, X. Xia, “Demand side management of photovoltaic-battery hybrid system”, Appl. Energy, vol. 148, pp. 294-304, 2015.
[12]    E. Yao, P. Samadi, V. Wong, R. Schober, “Residential demand side management under high penetration of rooftop photovoltaic units”, IEEE Trans. Smart Grid, vol. 7, pp. 1597-1608, 2016.
[13]    K. Ma, C. Wang, J. Yang, Z. Tian, X. Guan, “Energy management based on demand-side pricing: a supermodular game approach”, IEEE Access, vol. 5, pp. 18219-18228, 2017.
[14]    M. Tushar, A. Zeineddine, Ch. Assi, “Demand-side management by regulating charging and discharging of the EV, ESS, and utilizing renewable energy”, IEEE Trans. Ind. Inf., vol. 14, pp. 117-126, 2018.
[15]    M. Aman, G. Jasmon, A. Bakar, H. Mokhlis, “A new approach for optimum simultaneous multi-DG distributed generation unit’s placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm”, Energy, vol. 66, pp. 202-215, 2014.
[16]    H. Bagheri, M. H. Ali, and M. Rizwan, “Novel hybrid fuzzy-intelligent water drops approach for
optimal feeder multi objective reconfiguration by considering multiple-distributed generation”, J. Oper. Autom. Power Eng., vol. 2, pp. 91-102, 2014.
[17]    M. Moghaddam, A. Abdollahi, M. Rashidinejad, “Flexible demand response programs modeling in competitive electricity markets”, Appl. Energy, vol. 88, pp. 3257-3269, 2011.
[18]    A. Zangeneh, Sh. Jadid, A. Rahimi-Kian, “A fuzzy environmental-technical-economic model for distributed generation planning”, Energy, vol. 36, pp. 3437-3445, 2011.
[19]    Xin-she. Yang, “Firefly algorithms for multimodal optimization”, arXiv: 1003.1466v1 [math.OC], , (7 Mar 2010).
[20]    K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Trans. Evol. Comput., vol. 6, pp. 182-197, 2002.