Seleção Adaptativa de Operadores Aplicada ao Problema do Despacho Econômico de Energia Elétrica
Resumo
O Despacho Econômico de Energia Elétrica é um dos mais importantes problemas na área de geração e distribuição de energia elétrica. A Evolução Diferencial é um algoritmo evolutivo eficiente para otimização contínua. Diferentes operadores da Evolução Diferencial são adequados para a resolução de problemas com características diferentes, contudo a escolha do operador mais adequado é uma tarefa complexa. Neste trabalho são investigadas duas técnicas de seleção adaptativa de operadores (Adaptive Pursuit e Probability Matching) para escolher em tempo de execução qual o operador mais eficiente para a resolução do Despacho Econômico de Energia Elétrica. Os algoritmos propostos são validados em problemas de teste que consideram 13 e 40 geradores térmicos e levam em consideração efeitos de ponto de válvula. Os métodos propostos superam os resultados reportados na literatura obtidos por metaheurísticas modernas, sendo capazes de encontrar o melhor valor de custo mínimo conhecido para todos os sistemas de teste considerados.
Referências
Balamurugan, R. and Subramanian, S. (2007). Self-adaptive differential evolution based power economic dispatch of generators with valve-point effects and multiple fuel options. International Journal of Electrical, Electronic Science and Engineering.
Banerjee, S., Maity, D., and Kumar, C. (2015). Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. International Journal of Electrical Power and Energy Systems, 73:456–464.
Basu, M. (2002). Fuel constrained economic emission load dispatch using hopfield neural networks. Electric Power Systems Research.
Bhattacharya, A. and Chattopadhyay, P. K. (2010). Biogeography-based optimization for different economic load dispatch problems. IEEE Transactions on Power Systems.
Coelho, L. and Mariani, V. (2008). Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects. Energy Conversion and Management, 49(11):1373–1378.
Coelho, L. S. and Mariani, V. C. (2006). Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Transactions on Power Systems, 21(2):989–996.
Coelho, L. S., Souza, R. T. T., and Mariani, V. C. (2009). Improved differential evolution approach based on cultural algorithms and diversisty measure applied to solve economic load dispatch problems. Mathematics and Computers in Simulation, 79:3136–3147.
Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. Wiley Publishing, 2nd edition.
Ghasemi, M., Taghizadeh, M., Ghavidel, S., and Abbasian, A. (2015). Colonial competitive differential evolution: An experimental study for optimal economic load dispatch. Applied Soft Computing, 40:342–363.
Ghorbani, N. and Babaei, E. (2016). Exchange market algorithm for economic load dispatch. International Journal of Electrical Power and Energy Systems, 75:19–27.
Goldberg, D. E. (1990). Probability matching, the magnitude of reinforcement, and classifier system bidding. Mach. Learn., 5:407–425.
Gong, W., A´ lvaro Fialho, Cai, Z., and Li, H. (2011). Adaptive strategy selection in differential evolution for numerical optimization: An empirical study. Inform. Sciences, 181(24):5364 – 5386.
Gong, W., Fialho, A., and Cai, Z. (2010). Adaptive strategy selection in differential evolution. In Conference on Genetic and Evolutionary Computation, pages 409–416.
Han, L., Romero, C. E., and Yao, Z. (2015). Economic dispatch optimization algorithm based on particle diffusion. Energy Conversion and Management, 105:1251–1260.
Hosseinnezhad, V. and Babaei, E. (2013). Economic load dispatch using -pso. Energy Conversion and Management, 49:160 – 169.
Hosseinnezhad, V., Rafiee, M., Ahmadian, M., and Ameli, M. T. (2014). Species-based Quantum Particle Swarm Optimization for economic load dispatch. International Journal of Electrical Power & Energy Systems, 63:311–322.
Khamsawang, S., Pothiya, S., and Boonseng, S. (2004). Distributed tabu search algorithm for solving the economic dispatch problem. In TENCON, pages 484–487, Chiang Mai, Thailand. TENCON.
Li, K., Fialho, A., and Kwong, S. (2011). Multi-objective differential evolution with adaptive control of parameters and operators. In International Conference on Learning and Intelligent Optimization, pages 473–487.
Lin,W.-M., Gow, H.-J., and Tsai, M.-T. (2011). Combining of direct search and signal-tonoise ratio for economic dispatch optimization. Energy Conversion and Management, 52(1):487 – 493.
Mallipeddi, R., Suganthan, P., Pan, Q., and Tasgetiren, M. (2011). Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput., 11(2):1679–1696.
Meng, K., Wang, H. G., Dong, Z., and Wong, K. P. (2010). Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Transactions on Power Systems, 25(1):215–222.
Mohammadi-Ivatloo, B., Rabiee, A., Soroudi, A., and Ehsan, M. (2012). Iteration {PSO} with time varying acceleration coefficients for solving non-convex economic dispatch problems. International Journal of Electrical Power & Energy Systems, 42(1):508 –516.
Moradi-Dalvand, M., Mohammadi-Ivatloo, B., Najafi, A., and Rabiee, A. (2012). Continuous quick group search optimizer for solving non-convex economic dispatch problems. Electric Power Systems Research, 93(0):93 – 105.
Nguyen, T. T. and Vo, D. N. (2015). The application of one rank cuckoo search algorithm for solving economic load dispatch problems. Applied Soft Computing Journal, 37:763–773.
Noman, N. and Iba, H. (2008). Differential evolution for economic load dispatch problems. Electric Power Systems Research, 78(3):1322–1331.
Park, J., Jeong, Y., and Lee,W. (2006). An improved particle swarm optimization for economic dispatch problems with non-smooth cost functions. In IEEE Power Engineering Society General Meeting, Montreal, Que.
Qin, A. K., Huang, V. L., and Suganthan, P. N. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput., 13:398–417.
Qin, A. K. and Suganthan, P. N. (2005). Self-adaptive differential evolution algorithm for numerical optimization. In IEEE Congress on Evolutionary Computation, pages 1785–1791.
Roy, P. K., Bhui, S., and Paul, C. (2014). Solution of economic load dispatch using hybrid chemical reaction optimization approach. Applied Soft Computing, 24:109–125.
Sayah, S. and Hamouda, A. (2013). A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Applied Soft Computing, 13(4):1608 – 1619.
Selvakumar, A. and Thanushkodi, K. (2009). Optimization using civilized swarm: solution to economic dispatch with multiple minima. Electric Power Systems Research, 79(1):8–16.
Selvakumar, A. I. and Thanushkodi, K. (2007). New particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Transactions on Power Systems, 22(1):42–51.
Sinha, N. and Purkayastha, B. (2004). Pso embedded evolutionary programming technique for nonconvex economic load dispatch. In IEEE PES - Power Systems Conference and Exposition, pages 66–71, Brasil. IEEE PES.
Storn, R. and Price, K. (1995). Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces.
Storn, R. and Price, K. (1997). Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim., 11(4):341–359.
Swain, R., Sahu, N., and Hota, P. (2012). Gravitational search algorithm for optimal economic dispatch. Procedia Technology, 6(0):411 – 419.
Thierens, D. (2005). An adaptive pursuit strategy for allocating operator probabilities. In Conference on Genetic and Evolutionary Computation, pages 1539–1546.
Victoire, T. and Jeyakumar, A. (2004). Hybrid pso-sqp for economic dispatch with valvepoint effect. Electric Power Systems Research, 71(1):51–59.
Vlachogiannis, J. G. and Lee, K. Y. (2009). Economic load dispatch - a comparative study on heuristic optimization techniques with an improved coordinated aggregation-based pso. IEEE Transactions on Power Systems, 24(2):991–1001.
Wang, S. K., Chiou, J. P., and Liu, C. W. (2007). Non-smooth/nonconvex economic dispatch by a novel hybrid differential evolution algorithm. IET Generation, Transmission & Distribution, 1(5):793–803.
Yang, X.-S., Hosseini, S. S. S., and Gandomi, A. H. (2012). Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing, 12(3):1180 – 1186.
Yu, J. J. Q. and Li, V. O. K. (2015). A social spider algorithm for solving the non-convex economic load dispatch problem. Neurocomputing, 171:1–11.
Zhang, J. and Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput., 13:945–958.
Zhao, S.-Z., Suganthan, P. N., and Zhang, Q. (2012). Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans. Evol. Comput., 16(3):442–446.
Banerjee, S., Maity, D., and Kumar, C. (2015). Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. International Journal of Electrical Power and Energy Systems, 73:456–464.
Basu, M. (2002). Fuel constrained economic emission load dispatch using hopfield neural networks. Electric Power Systems Research.
Bhattacharya, A. and Chattopadhyay, P. K. (2010). Biogeography-based optimization for different economic load dispatch problems. IEEE Transactions on Power Systems.
Coelho, L. and Mariani, V. (2008). Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects. Energy Conversion and Management, 49(11):1373–1378.
Coelho, L. S. and Mariani, V. C. (2006). Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Transactions on Power Systems, 21(2):989–996.
Coelho, L. S., Souza, R. T. T., and Mariani, V. C. (2009). Improved differential evolution approach based on cultural algorithms and diversisty measure applied to solve economic load dispatch problems. Mathematics and Computers in Simulation, 79:3136–3147.
Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. Wiley Publishing, 2nd edition.
Ghasemi, M., Taghizadeh, M., Ghavidel, S., and Abbasian, A. (2015). Colonial competitive differential evolution: An experimental study for optimal economic load dispatch. Applied Soft Computing, 40:342–363.
Ghorbani, N. and Babaei, E. (2016). Exchange market algorithm for economic load dispatch. International Journal of Electrical Power and Energy Systems, 75:19–27.
Goldberg, D. E. (1990). Probability matching, the magnitude of reinforcement, and classifier system bidding. Mach. Learn., 5:407–425.
Gong, W., A´ lvaro Fialho, Cai, Z., and Li, H. (2011). Adaptive strategy selection in differential evolution for numerical optimization: An empirical study. Inform. Sciences, 181(24):5364 – 5386.
Gong, W., Fialho, A., and Cai, Z. (2010). Adaptive strategy selection in differential evolution. In Conference on Genetic and Evolutionary Computation, pages 409–416.
Han, L., Romero, C. E., and Yao, Z. (2015). Economic dispatch optimization algorithm based on particle diffusion. Energy Conversion and Management, 105:1251–1260.
Hosseinnezhad, V. and Babaei, E. (2013). Economic load dispatch using -pso. Energy Conversion and Management, 49:160 – 169.
Hosseinnezhad, V., Rafiee, M., Ahmadian, M., and Ameli, M. T. (2014). Species-based Quantum Particle Swarm Optimization for economic load dispatch. International Journal of Electrical Power & Energy Systems, 63:311–322.
Khamsawang, S., Pothiya, S., and Boonseng, S. (2004). Distributed tabu search algorithm for solving the economic dispatch problem. In TENCON, pages 484–487, Chiang Mai, Thailand. TENCON.
Li, K., Fialho, A., and Kwong, S. (2011). Multi-objective differential evolution with adaptive control of parameters and operators. In International Conference on Learning and Intelligent Optimization, pages 473–487.
Lin,W.-M., Gow, H.-J., and Tsai, M.-T. (2011). Combining of direct search and signal-tonoise ratio for economic dispatch optimization. Energy Conversion and Management, 52(1):487 – 493.
Mallipeddi, R., Suganthan, P., Pan, Q., and Tasgetiren, M. (2011). Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput., 11(2):1679–1696.
Meng, K., Wang, H. G., Dong, Z., and Wong, K. P. (2010). Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Transactions on Power Systems, 25(1):215–222.
Mohammadi-Ivatloo, B., Rabiee, A., Soroudi, A., and Ehsan, M. (2012). Iteration {PSO} with time varying acceleration coefficients for solving non-convex economic dispatch problems. International Journal of Electrical Power & Energy Systems, 42(1):508 –516.
Moradi-Dalvand, M., Mohammadi-Ivatloo, B., Najafi, A., and Rabiee, A. (2012). Continuous quick group search optimizer for solving non-convex economic dispatch problems. Electric Power Systems Research, 93(0):93 – 105.
Nguyen, T. T. and Vo, D. N. (2015). The application of one rank cuckoo search algorithm for solving economic load dispatch problems. Applied Soft Computing Journal, 37:763–773.
Noman, N. and Iba, H. (2008). Differential evolution for economic load dispatch problems. Electric Power Systems Research, 78(3):1322–1331.
Park, J., Jeong, Y., and Lee,W. (2006). An improved particle swarm optimization for economic dispatch problems with non-smooth cost functions. In IEEE Power Engineering Society General Meeting, Montreal, Que.
Qin, A. K., Huang, V. L., and Suganthan, P. N. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput., 13:398–417.
Qin, A. K. and Suganthan, P. N. (2005). Self-adaptive differential evolution algorithm for numerical optimization. In IEEE Congress on Evolutionary Computation, pages 1785–1791.
Roy, P. K., Bhui, S., and Paul, C. (2014). Solution of economic load dispatch using hybrid chemical reaction optimization approach. Applied Soft Computing, 24:109–125.
Sayah, S. and Hamouda, A. (2013). A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Applied Soft Computing, 13(4):1608 – 1619.
Selvakumar, A. and Thanushkodi, K. (2009). Optimization using civilized swarm: solution to economic dispatch with multiple minima. Electric Power Systems Research, 79(1):8–16.
Selvakumar, A. I. and Thanushkodi, K. (2007). New particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Transactions on Power Systems, 22(1):42–51.
Sinha, N. and Purkayastha, B. (2004). Pso embedded evolutionary programming technique for nonconvex economic load dispatch. In IEEE PES - Power Systems Conference and Exposition, pages 66–71, Brasil. IEEE PES.
Storn, R. and Price, K. (1995). Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces.
Storn, R. and Price, K. (1997). Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim., 11(4):341–359.
Swain, R., Sahu, N., and Hota, P. (2012). Gravitational search algorithm for optimal economic dispatch. Procedia Technology, 6(0):411 – 419.
Thierens, D. (2005). An adaptive pursuit strategy for allocating operator probabilities. In Conference on Genetic and Evolutionary Computation, pages 1539–1546.
Victoire, T. and Jeyakumar, A. (2004). Hybrid pso-sqp for economic dispatch with valvepoint effect. Electric Power Systems Research, 71(1):51–59.
Vlachogiannis, J. G. and Lee, K. Y. (2009). Economic load dispatch - a comparative study on heuristic optimization techniques with an improved coordinated aggregation-based pso. IEEE Transactions on Power Systems, 24(2):991–1001.
Wang, S. K., Chiou, J. P., and Liu, C. W. (2007). Non-smooth/nonconvex economic dispatch by a novel hybrid differential evolution algorithm. IET Generation, Transmission & Distribution, 1(5):793–803.
Yang, X.-S., Hosseini, S. S. S., and Gandomi, A. H. (2012). Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing, 12(3):1180 – 1186.
Yu, J. J. Q. and Li, V. O. K. (2015). A social spider algorithm for solving the non-convex economic load dispatch problem. Neurocomputing, 171:1–11.
Zhang, J. and Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput., 13:945–958.
Zhao, S.-Z., Suganthan, P. N., and Zhang, Q. (2012). Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans. Evol. Comput., 16(3):442–446.
Publicado
04/07/2016
Como Citar
GONÇALVES, Richard Aderbal; DE ALMEIDA, Carolina Paula; VENSKE, Sandra Mara G. S.; KUK, Josiel N.; PAVELSKI, Lucas M..
Seleção Adaptativa de Operadores Aplicada ao Problema do Despacho Econômico de Energia Elétrica. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 43. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 1807-1818.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2016.9529.
