Previous |  Up |  Next


economic dispatch; non-essential demand response; random wind power; bat algorithm; multi-subpopulation
In this paper, we propose a new economic dispatch model with random wind power, demand response and carbon tax. The specific feature of the demand response model is that the consumer's electricity demand is divided into two parts: necessary part and non-essential part. The part of the consumer's participation in the demand response is the non-essential part of the electricity consumption. The optimal dispatch objective is to obtain the minimum total cost (fuel cost, random wind power cost and emission cost) and the maximum consumer's non-essential demand response benefit while satisfying some given constraints. In order to solve the optimal dispatch objective, a multi-subpopulation bat optimization algorithm (MSPBA) is proposed by using different search strategies. Finally, a case of an economic dispatch model is given to verify the feasibility and effectiveness of the established mathematical model and proposed algorithm. The economic dispatch model includes three thermal generators, two wind turbines and two consumers. The simulation results show that the proposed model can reduce the consumer's electricity demand, reduce fuel cost and reduce the impact on the environment while considering random wind energy, non-essential demand response and carbon tax. In addition, the superiority of the proposed algorithm is verified by comparing with the optimization results of CPLEX+YALMIP toolbox for MATLAB, BA, DBA and ILSSIWBA.
[1] Abdelaziz, A. Y., Ali, E. S., Elazim, S. M. A.: Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems. Energy 101 (2016), 506-518. DOI 10.1016/
[2] Chakri, A., Khelif, R., Benouaret, M., al., et: New directional bat algorithm for continuous optimization problems. Expert Systems Appl. 69 (2017), 159-175. DOI 10.1016/j.eswa.2016.10.050
[3] Chen, C. L., Vempati, V. S., Aljaber, N.: An application of genetic algorithms for flow shop problems. Europ. J. Oper. Res. 80 (1995), 389-396. DOI 10.1016/0377-2217(93)e0228-p
[4] Cheng, C. T., Liao, S. L., Tang, Z. T., al., et: Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch. Energy Conversion Management 50 (2009), 3007-3014. DOI 10.1016/j.enconman.2009.07.020
[5] Chen, F., Zhou, J., Wang, C., al., et: A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching. Energy 121 (2017), 276-291. DOI 10.1016/
[6] Das, S., Suganthan, P. N.: Differential evolution: a Survey of the State-of-the-art. IEEE Trans. Evolutionary Comput. 15 (2011), 4-31. DOI 10.1109/tevc.2010.2059031 | MR 3032010
[7] Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Systems, Man, Cybernetics, Part B (Cybernetics) 26 (1996), 29-41. DOI 10.1109/3477.484436
[8] Fahrioglu, M., Alvarado, F. L.: Designing incentive compatible contracts for effective demand management. IEEE Trans. Power Systems 15 (2000), 1255-1260. DOI 10.1109/59.898098
[9] Fahrioglu, M., Alvarado, F. L.: Using utility information to calibrate customer demand management behavior models. IEEE Trans. Power Systems 16 (2001), 317-322. DOI 10.1109/59.918305
[10] Gan, C., Cao, W., Wu, M., al., et: A new bat algorithm based on iterative local search and stochastic inertia weight. Expert Systems Appl. 104 (2018), 202-212. DOI 10.1016/j.eswa.2018.03.015
[11] Gandomi, A. H., Yang, X. S.: Chaotic bat algorithm. J. Comput. Sci. 5 (2014), 224-232. DOI 10.1016/j.jocs.2013.10.002 | MR 3173261
[12] Gandomi, A. H., Yang, X. S., Alavi, A. H., al., et: Bat algorithm for constrained optimization tasks. Neural Computing Appl. 22 (2013), 1239-1255. DOI 10.1007/s00521-012-1028-9
[13] Ghasemi, M., Ghavidel, S., Ghanbarian, M. M., al., et: Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm. Energy 78 (2014), 276-289. DOI 10.1016/
[14] Guo, Y., Tong, L., Wu, W., al., et: Coordinated Multi-area Economic Dispatch via Critical Region Projection. IEEE Trans. Power Systems 32 (2017), 3736-3746. DOI 10.1109/tpwrs.2017.2655442
[15] Guo, F., Wen, C., Mao, J., al., et: Distributed economic dispatch for dmart grids with random wind power. IEEE Trans. Smart Grid 7 (2016), 1572-1583. DOI 10.1109/tsg.2015.2434831
[16] He, X. S., Ding, W. J., Yang, X. S.: Bat algorithm based on simulated annealing and Gaussian perturbations. Neural Comput. Appl. 25 (2014), 459-468. DOI 10.1007/s00521-013-1518-4
[17] Hetzer, J., Yu, D. C., Bhattarai, K.: An economic dispatch model incorporating wind power. IEEE Trans. Energy Conversion 23 (2008), 603-611. DOI 10.1109/tec.2007.914171
[18] Jabr, R., Coonick, A. H., Cory, B. J.: A homogeneous linear programming algorithm for the security constrained economic dispatch problem. IEEE Trans. Power Syst. 15 (2000), 930-936. DOI 10.1109/59.871715
[19] Jeddi, B., Vahidinasab, V.: A modified harmony search method for environmental/economic load dispatch of real-world power systems. Energy Conversion Management 78 (2014), 661-675. DOI 10.1016/j.enconman.2013.11.027
[20] Ji, M., Tang, H.: Application of chaos in simulated annealing. Chaos Solitons Fractals 21 (2004), 933-941. DOI 10.1016/j.chaos.2003.12.032 | MR 2076025
[21] Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. ICNN'95 - International Conference on Neural Networks, Perth 1995, 4, pp. 1942-1948. DOI 10.1109/icnn.1995.488968
[22] Lee, K. Y., Park, Y. M., Ortiz, J. L.: Fuel-cost minimisation for both real-and reactive-power dispatches. IEE Proceedings. Part C: Generation, Transmission and Distribution. 131 (1984), 85-93. DOI 10.1049/ip-c.1984.0012
[23] Li, M., Hou, J., Niu, Y., al., et: Economic dispatch of wind-thermal power system by using aggregated output characteristics of virtual power plants. In: International Conference on Control and Automation, IEEE 2016, pp. 830-835. DOI 10.1109/icca.2016.7505381
[24] Liang, H., Liu, Y., Shen, Y., al., et: A hybrid bat algorithm for economic dispatch with random wind power. IEEE Trans. Power Syst. 33 (2018), 5052-5061. DOI 10.1109/tpwrs.2018.2812711
[25] Liu, X., Xu, W.: Minimum emission dispatch constrained by stochastic wind power availability and cost. IEEE Trans. Power Systems 25 (2010), 1705-1713. DOI 10.1109/tpwrs.2010.2042085
[26] al., I. Mazhoud et: Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism. Engrg. Appl. Artif. Intell. 26 (2013), 1263-1273. DOI 10.1016/j.engappai.2013.02.002
[27] Nwulu, N. I., Fahrioglu, M.: A neural network model for optimal demand management contract design. In: International Conference on Environment and Electrical Engineering, IEEE 2011, pp. 1-4. DOI 10.1109/eeeic.2011.5874776
[28] Nwulu, N. I., Fahrioglu, M.: Power system demand management contract design: A comparison between game theory and artificial neural networks. Int. Rev. Modell. Simul. 4 (2011), 104-112.
[29] Nwulu, N. I., Xia, X.: Optimal dispatch for a microgrid incorporating renewables and demand response. Renewable Energy 101 (2017), 16-28. DOI 10.1016/j.renene.2016.08.026
[30] Park, J. B., Lee, K. S., Shin, J. R., al., et: A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Trans. Power Syst. 20 (2005), 34-42. DOI 10.1109/tpwrs.2004.831275
[31] Pavlyukevich, I.: Lévy flights, non-local search and simulated annealing. J. Comput. Physics 226 (2007), 1830-1844. DOI 10.1109/tpwrs.2004.831275 | MR 2356396
[32] Sen, T., Mathur, H. D.: A new approach to solve Economic Dispatch problem using a Hybrid ACO/ABC/HS optimization algorithm. Int. J. Electr. Power Energy Systems 78 (2016), 735-744. DOI 10.1016/j.ijepes.2015.11.121
[33] Walters, D. C., Sheble, G. B.: Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans. Power Systems 8 (1993), 1325-1332. DOI 10.1109/59.260861
[34] Wood, A. J., Wollenberg, B. F.: Power generation operation and control. Second edition. Fuel Energy Abstracts 37 (1996), 195. DOI 10.1016/0140-6701(96)88715-7
[35] Yang, X. S.: A new metaheuristic bat-inspired algorithm. Comput. Knowledge Technol. 284 (2010), 65-74. DOI 10.1007/978-3-642-12538-6_6
[36] Yang, X. S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Modell. Numer. Optim. 1 (2010), 330-343. DOI 10.1504/ijmmno.2010.035430
[37] Yang, X., Gandomi, A. H.: Bat algorithm: a novel approach for global engineering optimization. Engrg. Computations 29 (2012), 464-483. DOI 10.1108/02644401211235834 | MR 3206205
[38] Yang, H., Yi, J., Zhao, J., al., et: Extreme learning machine based genetic algorithm and its application in power system economic dispatch. Neurocomputing 102 (2013), 154-162. DOI 10.1016/j.neucom.2011.12.054
[39] Yao, F., Dong, Z. Y., Meng, K., al., et: Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia. IEEE Trans. Industr. Inform. 8 (2012), 880-888. DOI 10.1109/tii.2012.2210431
Partner of
EuDML logo