Bi-Level Energy Management Optimization in Multi-Area Smart Grids

Subject Areas : Renewable energy

Mohammad Ali Hormozi ¹ (Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran)
Bahman Bahmani Firoozi ² (Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran)
Taher Niknam ³ (Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran)

Received: 2020-01-04 Accepted : 2020-05-20 Published : 2020-08-05

Keywords: smart grid, Demand side management (DSM), Energy management, Bi-level optimization,

Abstract :

Optimal energy management in multi area smart grids will increase social welfare, reduce economic costs and environmental pollution. Power management solutions for smart grids include issues such as economical distribution of load, suitable load management, optimized charging and discharging of energy storages, and the availability of renewable resources considering limitation of power exchange in different area, all of which are issues in an intelligent grid, that in this paper has been considered. This paper presents a bi-level mixed integer quadratic programming (MIQP) model for energy management in multi-are smart grids with the aim of reducing economic costs and environmental pollution and increasing social welfare by considering energy storage systems, load management and Renewable resources are presented. In this paper presents a bi-level approach that the upper level is formulated to minimization economic cost and pollution of resource and lower level is presented to maximization social welfare in the form of Karush–Kuhn–Tucker (KKT) conditions. The simulation is implemented in MATLAB with Gurobi solver that the results show that the proposed bi-level model is also an efficient way to optimize energy in multi-area smart grids compared to Pareto front and Weight methods.

References:

[1]     B. Lokeshgupta, S. Sivasubramani, “Multi-objective dynamic economic and emission dispatch with demand side management”, International Journal of Electrical Power and Energy Systems, vol. 97, pp. 334-343, April 2018 (doi: 10.1016/j.ijepes.2017.11.020).

[2]     J. Lin, Z.J. Wang, “Multi-area economic dispatch using an improved stochastic fractal search algorithm”, Energy, vol. 166, pp. 47-58, Jan. 2019 (doi: 10.1016/j.energy.2018.10.065).

[3]     L. Wu, “A transformation-based multi-area dynamic economic dispatch approach for preserving information privacy of individual areas”, IEEE Trans. on Smart Grid, vol. 10, no. 1, pp. 722-731, Jan. 2019 (doi: 10.1­10­9­/TSG.2017.2751479).

[4]     E. Loukarakis, C. J. Dent and J. W. Bialek, “Decentralized multi-period economic dispatch for real-time flexible demand management”, IEEE Trans. on Power Systems, vol. 31, no. 1, pp. 672-684, Jan. 2016 (doi: 1­0.11­09/TPWRS.2015.2402518).

[5]     Basu M., “Quasi-oppositional group search optimization for multi-area dynamic economic dispatch”, International Journal of Electrical Power and Energy Systems, vol. 78, pp. 356-367, June. 2016 (doi: 10.1­0­1­6­/j.ijepes.2015.11.120).

[6]     GhasemiM, Aghaei J, Akbari E, Ghavidel S, Li L, “A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems”, Energy, vol. 107, pp. 182-195, July. 2016 (doi: 10.1016/j.energy.2016.04.002).

[7]     Zou DX, Li S, Wang GG, Li ZY, Ouyang HB, “An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects”, Appl Energy, vol. 181, pp. 375-390, Nov. 2016 (doi: 10.1016/j.apenergy.2016.08.067).

[8]     Manoharan PS, Kannan PS, Baskar S, Willjuice Iruthayarajan M, “Evolutionary algorithm solution and KKT based optimality verification to multi-area economic dispatch”, International Journal of Electrical Power and Energy Systems, vol. 31, pp. 365-373, Sep. 2009 (doi: 10.1016/j.ijepes.2009.03.010).

[9]     H. Abdi, E. Dehnavi, F. Mohammadi, “Dynamic economic dispatch problem integrated with demand response (DEDDR) considering non-linear responsive load models”, IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2586–2595, Nov. 2016 (doi: 10.1109/TSG.2015.2508779).

[10] Rajan A, Malakar T, “Optimum economic and emission dispatch using exchange market algorithm”, International Journal of Electrical Power and Energy Systems, vol. 82, pp. 545–560, Nov. 2016 (doi: 10.1016/j.ijepes.2016.04.022 ).

[11] F. Zaman, S.M. Elsayed, T. Ray, R.A. Sarker, “Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems”, Engineering Applications of Artificial Intelligence,Vol. 53, pp.105–25, Aug. 2016 (doi: 10.1016/j.engappai.2016.04.001).

[12] M. Alham, M. Elshahed, D.K. Ibrahim, EEDA El Zahab, “A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management”, Renewable Energy, vol. 96, pp. 800–811, Oct. 2016 (doi: 10.1016/j.renene.2016.05.012).

[13] M. Arnold, S. Knöpfli, G. Andersson, “Improvement of OPF decomposition methods applied to multi-area power systems”, Accessed, May 2017. [Online]. Available: http://www.eeh.ee.eth­z.ch/uploa­ds/tx_et­hpubl­ications/Arnold_

[14] Z. Li, W. Wu, B. Zhang, B. Wang, “Decentralized multi-area dynamic economic dispatch using modified generalized Benders decomposition”, IEEE Trans. on Power Syst, vol. 31, no. 1, pp. 526–538, Jan. 2016 (doi: 10.1109/TPWRS.2015.2399474).

[15] Y. Guo, L. Tong, W. Wu, B. Zhang and H. Sun, “Coordinated multi-area economic dispatch via critical region projection”, IEEE Trans. on Power Systems, vol. 32, no. 5, pp. 3736-3746, Sept. 2017 (doi: 10.11­0­9­/­­TPWR­S.2017.2655442).

[16] W. Zheng, W. Wu, “Distributed multi-area load flow for multi-microgrid systems”, IET Generation, Transmission and Distribution, vol. 13, no. 3, pp. 327-336, Dec. 2019 (doi: 10.1049/iet-gtd.2018.6220).

[17] R. Azizipanah-Abarghooee, P. Dehghanian and V. Terzija, “Practical multi-area bi-objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm”, IET Generation, Transmission and Distribution, vol. 10, no. 14, pp. 3580-3596, Nov. 2016 (doi: 10.1049/iet-gtd.2016.0333).

[18] S. Boyd, L. Vandenberghe, Convex optimization, New York, NY, USA: Cambridge Univ. Press, 2004.

[19] M. Yazdani-Damavandi, N. Neyestani, M. Shafie-khah, J. Contreras, J. P. S. Catalão, “Strategic beha­vio­r of multi-energy players in electricity markets as aggregators of demand side resources using a bi-level approach”, IEEE Trans. on Power Systems, vol. 33, no. 1, pp. 397-411, Jan. 2018 (doi: 10.1109­/T­PW­RS.2­01­7.2688344).

[20] H. Narimani, S.E. Razavi, A. Azizivahed, E. Naderi, M. Fathi, M.H. Ataei, M.R. Narimani, “A multi-objective framework for multi-area economic emission dispatch”, Energy, vol. 154, Pages 126-142, July. 2018 (doi: 10.1016/j.energy.2018.04.080).

[21] J. Lin, Z.J. Wang, “Multi-area economic dispatch using an improved stochastic fractal search algorithm”, Energy, vol. 166, Pages 47-58, 2019 (doi: 10.1016/j.energy.2018.10.065).