A new technique for solving the multi-objective optimization problem using hybrid approach

Abstract

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.

Keywords

economic power dispatch (EPD) / firefly algorithm (FFA) / real genetic algorithm (RGA) / hybrid method

References

[1] Carpentier J. Contribution to the study of economic dispatch. Bulletin of the French Society of Electricians, 1962, 3: 431–447

[2] Vanderbei J R, Shanno F D. An interior-point algorithm for nonconvex nonlinear programming. Computational Optimization and Applications, 1999, 13(1–3): 231–252

[3] Bottero M H, Caliana E D, Fahmideh-Vojdani A R. Economic dispatch using the reduced hessian. IEEE Transactions on Power Apparatus and Systems, 1982, 101(10): 3679–3688

[4] Reid G E, Hasdorf L. Economic dispatch using quadratic programming. IEEE Transactions on Power Apparatus and Systems, 1973, 92(6): 2015–2023

[5] Stott B, Hobson E. Power system security control calculation using linear programming. IEEE Transactions on Power Apparatus and Systems, 1978, 97(5): 1713–1720

[6] Stott B, Hobson E. Power system security control calculation using linear programming. IEEE Transactions on Power Apparatus and Systems, 1978, 97(5): 1721–1731

[7] Momoh J A, Zhu J Z. Improved interior point method for OPF problems. IEEE Transactions on Power Systems, 1999, 14(3): 1114–1120

[8] Sun D I, Ashley B, Brewer B, Hughes A, Tinney W F. Optimal power flow by Newton approach. IEEE Transactions on Power Apparatus and Systems, 1984, PAS-103(10): 2864–2880

[9] Bahiense L, Oliveira G C, Pereira M, Granville S. A mixed integer disjunctive model for transmission network expansion. IEEE Transactions on Power Systems, 2001, 16(3): 560–565

[10] Dusonchet Y P, El-Abiad A H. Transmission planning using discrete dynamic optimization. IEEE Transactions on Power Apparatus and Systems, 1997, 92: 1358–1371

[11] Haffner S, Monticelli A, Garcia A, Romero R. Specialised branch and bound algorithm for transmission network expansion planning. IEE Proceedings-Generation, Transmission and Distribution, 2001, 148(5): 482–488

[12] Glover F. Tabu search—Part I. ORSA Journal on Computing, 1986, 1(3): 190–206

[13] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimisation by simulated annealing. Science, 1983, 220(4598): 671–680

[14] Lai L L, Ma J T, Yokoyama R, Zhao M. Improved genetic algorithms for optimal power flow under both normal and contingent operation states. International Journal of Electrical Power & Energy Systems, 1997, 19(5): 287–292

[15] Yuryevich J, Wong K P. Evolutionary programming based optimal power flow algorithm. IEEE Transactions on Power Systems, 1999, 14(4): 1245–1250

[16] Mousavian S, Valenzuela J, Wang J. Real-time data reassurance in electrical power systems based on artificial neural networks. Electric Power Systems Research, 2013, 96: 285–295

[17] Abido M A. Optimal power flow using particle swarm optimization. International Journal of Electrical Power & Energy Systems, 2002, 24(7): 563–571

[18] Song Y H, Chou C S, Stonham T J. Combined heat and power economic dispatch by improved ant colony search algorithm. Electric Power Systems Research, 1999, 52(2): 115–121

[19] Fesanghary M, Mahdavi M, Minary-Jolandan M, Alizadeh Y. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods Applied Mechanics and Engineering, 2008, 197(33–40): 3080–3091

[20] Abido M A. A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Electric Power Systems Research, 2003, 65(1): 71–81

[21] Abido M A. Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 315–329

[22] Yang X S. Review of meta-heuristic and generalized evolutionary walk algorithm. International Journal of Bio-Inspired Computation, 2011, 3(2): 77–84

[23] Amiri B, Hossain L, Crawford J W, Wigand R T. Community detection in complex networks: multi-objective enhanced firefly algorithm. Knowledge-Based Systems, 2013, 46: 1–11

[24] Gandomi A H, Yang X S, Talatahari S, Alavi A H. Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(1): 89–98

[25] Senapati M R, Dash P K. Local linear wavelet neural network based breast tumor classification using firefly algorithm. Neural Computing & Applications, 2013, 22(7–8): 1591–1598

[26] Fister I, Yang X S, Brest J, Fister I Jr. Modified firefly algorithm using quaternion representation. Expert Systems with Applications, 2013, 40(18): 7220–7230

[27] Kazem A, Sharifi E, Hussain F K, Saberi M, Hussain O K. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing, 2013, 13(2): 947–958

[28] Yang X S. Multiobjective firefly algorithm for continuous optimization. Engineering with Computers, 2013, 29(2): 175–184

[29] Yang X S. Nature-Inspired Metaheuristic Algorithms. Bristol: Luniver Press, 2008

[30] Yang X S. Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, 2010: 221–230

[31] Basu B, Mahanti G K. Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna. Progress in Electromagnetic Research B, 2011, 32: 169–190

[32] Yazdani A, Jayabarathi T, Ramesh V, Raghunathan T. Combined heat and power economic dispatch problem using firefly algorithm. Frontiers in Energy, 2013, 7(2): 133–139

[33] Holland J. Adaptation in Natural and Artificial Systems. Ann Arbor, USA: The University of Michigan Press, 1975

[34] Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Educational Publishers Inc, 1989

[35] Younes M, Rahli M, Koridak L A. Optimal power flow based on hybrid genetic algorithm. Journal of Information Science and Engineering, 2007, 23: 1801–1816

[36] Michalewicz Z. A survey of constraint handling techniques in evolutionary computation methods. In: Mcdonnell J R, Renolds R G, Fogel D B, eds. Proceedings of the 4th Annual Conference on Evolutionary Programming, MIT Press, 1995, 135–155

[37] Caorsi S, Massa A, Pastorino M. A computational technique based on a real-coded genetic algorithm for microwave imaging purposes. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(4): 1697–1708

[38] Oyama A, Obayashi S, Nakamura T. Real-coded adaptive range genetic algorithm applied to transonic wing optimization. Applied Soft Computing, 2001, 1(3): 179–187

[39] Niknam T, Narimani M R, Jabbari M, Malekpour A R. A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy, 2011, 36(11): 6420–6432

[40] Saini A, Chaturvedi D K, Saxena A K. Optimal power flow solution: a GA-fuzzy system approach. International Journal of Emerging Electric Power Systems, 2006, 5(2): 1–21

[41] Ongsakul W, Tantimaporn T. Optimal power flow by improved evolutionary programming. Electric Power Components and Systems, 2006, 34(1): 79–95

[42] Abido M A. Optimal power flow using tabu search algorithm. Electric Power Components and Systems, 2002, 30(5): 469–483

[43] Yuryevich J, Wong K P. Evolutionary programming based optimal power flow algorithm. IEEE Transactions on Power Systems, 1999, 14(4): 1245–1250

[44] Narimani M R, Azizipanah-Abarghooee R, Zoghdar-Moghadam-Shahrekohne B, Gholami K. A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type. Energy, 2013, 49: 119–136

[45] Sailaja Kunari M, Maheswarapu S. Enhanced genetic algorithm based computation technique for multi-objective optimal power flow. International Journal of Electrical Power & Energy Systems, 2010, 32(6): 736–742

[46] Vaisakh K, Srinivas L R. A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis. Energy, 2010, 35(8): 3155–3171

[47] Rezaei Adaryani M, Karami A. Artificial bee colony algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power & Energy Systems, 2013, 53: 219– 230

[48] Perez-Guerrero R E, Cedefio-Maldonado J R. Differential evolution based economic environmental power dispatch. In: Proceedings of the 37th Annual North American Power Symposium. Piscataway, USA: NJ IEEE Service Center, 2005, 191–197

[49] Wang L, Singh C. Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization. Electric Power Systems Research, 2008, 78(8): 1361–1368

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