Multi-Objective Demand Side Management to Improve Economic and ‎Environmental Issues of a Smart Microgrid ‎

Document Type : Research paper


1 Energy Management Research Centre, University of Mohaghegh Ardabili, Ardabil, Iran

2 Department of Electrical Engineering, Urmia University, Urmia, Iran


In the last years, microgrids have been introduced for better managing the overall power network. The two-way communication between supplier and consumer sides of a smart microgrid causes to better apply the demand side management methods to this type of system. For this reason, the multi-objective demand side management of a smart microgrid is investigated in this study. The economic and environmental indices of the microgrid are considered as the primary objective functions of the proposed demand side management method. The load variations of the microgrid are improved based on the applied demand response program. The operator of the microgrid can provide the demand of the system using a wind turbine, photovoltaic panel, diesel generator, micro turbine, fuel cell, energy storage system and the upstream network. The stochastic behavior of renewable units is also considered to evaluate the proposed method in a more realistic condition. The combination of the multi-objective ant lion optimization algorithm and the analytical hierarchy process method is utilized to solve the demand side management problem. Numerical results, which are obtained from evaluating the proposed method in a sample microgrid, demonstrate the high efficiency of the proposed demand side management method in improving the economic and environmental indices of the microgrid.


[1]    M. Behrangrad, “A review of demand side management business models in the electricity market”, Renew. Sustain. Energy Rev., vol. 47, pp. 270-83, 2015.
[2]    M. Honarmand, A. Zakariazadeh and S. Jadid, “Integrated scheduling of renewable generation and electric vehicles parking lot in a smart microgrid”, Energy Convers. Manage., vol. 86, pp. 745-55, 2014.
[3]    X. Yan, Y. Ozturk, Z. Hu and Y. Song, “A review on price-driven residential demand response”, Renew. Sustain. Rev., vol. 96, pp. 411-19, 2018.
[4]    S. Aghajani and M. Kalantar, “Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach”, Energy, vol. 139, pp. 422-32, 2017.
[5]    H. Akbaria, M.C. Browne, A. Ortega, M. J. Huang, N.  J. Hewitt, B. Norton, S.  J. McCormacka, “Efficient energy storage technologies for photovoltaic systems”, Solar Energy, vol. 192, pp. 144-68, 2019.
[6]    Z. Wu, H. Tazvinga and X. Xia, “Demand side management of photovoltaic-battery hybrid system”, Appl. Energy, vol. 148, pp. 294-04, 2015.
[7]    G. Aghajani, H. Shayanfar and H. Shayeghi, “Demand side management in a smart micro-grid in the presence of renewable generation and demand response”, Energy, vol. 126, pp. 622-37, 2017.
[8]    G. Xu, H. Cheng, S. Fang, Z. Ma, P. Zeng, L. Yao, “Optimal size and location of battery energy storage systems for reducing the wind power curtailments”, Electr. Power Compon. Syst., vol. 46, pp. 342-52, 2018.
[9]    D. Miao and S. Hossain, “Improved gray wolf optimization algorithm for solving placement and sizing of electrical energy storage system in micro-grids”, ISA Trans., vol. 102, pp. 376-87, 2020.
[10]    P. Premadasa and D. Chandima, “An innovative approach of optimizing size and cost of hybrid energy storage system with state of charge regulation for stand-alone direct current microgrids”, J. Energy Storage, vol. 32, pp. 101703, 2020.
[11]    A. Y. Ali, A. Basit, A. Basit, A. Qamar, J. Iqbal, “Optimizing coordinated control of distributed energy storage system in microgrid to improve battery life”, Comput. Electr. Eng., vol. 86, pp. 106741, 2020.
[12]    H. Shakouri and A. Kazemi, “Multi-objective cost-load optimization for demand side management of a residential area in smart grids”, Sustain. Cities Soc., vol. 32, pp. 171-80, 2017.
[13]    A.R. Kalair, N. Abas, Q.U. Hasan, M. Seyedmahmoud-ian, N. Khan, “Demand side management in hybrid rooftop photovoltaic integrated smart nano grid”, J. Cleaner Prod., vol. 258, pp. 120747, 2020.
[14]    A. Roy, F. Auger, F. Dupriez-Robin, S. Bourguet, Q.T. Tran, “A multi-level demand-side management algorithm for offgrid multi-source systems”, Energy, vol. 191, pp. 116536, 2020.
[15]    S. Avril, G. Arnaud, A. Florentin and M. Vinard, “Multi-objective optimization of batteries and hydrogen storage technologies for remote photovoltaic systems”, Energy, vol. 35, pp. 5300-08, 2010.
[16]    A. Sharma and M. Kolhe, “Techno-economic evaluation of PV based institutional smart micro-grid under energy pricing dynamics”, J. Cleaner Prod., vol. 264, pp. 121486, 2020.
[17]    M. Alilou, D. Nazarpour and H. Shayeghi, “Multi-objective optimization of demand side management and multi dg in the distribution system with demand response”, J Oper. Autom. Power Eng., vol. 6, pp. 230-42, 2018.
[18]    M. Aman, G. Jasmon, A. Bakar and H. Mokhlis, “A New approach for optimum simultaneous multi-dg distributed generation units placement and sizing based on maximization of system loadability using hpso (hybrid particle swarm optimization) algorithm”, Energy, vol. 66, pp. 202-15, 2014.
[19]    M. Mazidi, A. Zakariazadeh, Sh. Jadid and P. Siano, “Integrated scheduling of renewable generation and demand response programs in a microgrid”, Energy Conver. Manage., vol. 86, pp. 1118-27, 2014.
[20]    H. Aalami, M. Moghaddam and G. Yousefi, “Modeling and prioritizing demand response programs in power markets”, Electr. Power Syst. Res., vol. 80, pp. 426-35, 2010.
[21]    S. Mirjalili, P. Jangir and S. Saremi, “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems”, Appl. Intel., vol. 45, 2016.
[22]    H. Shayeghi and 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.131-46, 2015.
[23]    M. Alilou, B. Tousi and H. Shayeghi, “Multi-objective unit and load commitment in smart homes considering uncertainties”, Int. Trans. Electr. Energy Syst., vol. 30, pp. 12614, 2020.
[24]    M. Alilou, B. Tousi and H. Shayeghi, “Home energy management in a residential smart micro grid under stochastic penetration of solar panels and electric vehicles”, Solar Energy, vol. 212, pp. 6-18, 2020.