Siting and sizing of distributed generators via NSGA-II and max-min composition
Resumo
A Geração Distribuída (GD) pode proporcionar uma série de benefícios, sendo a redução das perdas o mais importante. Porém, mesmo com essa geração alcançando níveis crescentes de penetração, os custos associados ainda são elevados. Este trabalho apresenta um modelo de otimização multiobjetivo para o dimensionamento e posicionamento de geradores. Utilizou-se o NSGA-II como ferramenta de busca para minimizar as perdas de potência ativa e reduzir o custo anualizado. As simulações foram realizadas no MATPOWER em quatro versões do sistema de 69 barras. A composição max-min foi utilizada para trade-off e escolha final na fronteira de Pareto. As ferramentas utilizadas proporcionaram resultados superiores a outros trabalhos da literatura.
Referências
Acharya, N., Mahat, P., and Mithulananthan, N. (2006). An analytical approach for dg allocation in primary distribution network. International Journal of Electrical Power & Energy Systems, 28(10):669–678.
Ackermann, T., Andersson, G., and Söder, L. (2001). Distributed generation: a definition. Electric power systems research, 57(3):195–204.
Akella, A., Saini, R., and Sharma, M. P. (2009). Social, economical and environmental impacts of renewable energy systems. Renewable Energy, 34(2):390–396.
ANEEL (2012). Resolução Normativa no 482 de 17 de abril de 2012. Agência Nacional de Energia Elétrica, Brası́lia.
ANEEL (2015). Resolução Normativa no 687 de 24 de novembro de 2015. Agência Nacional de Energia Elétrica, Brası́lia.
ANEEL (2017). Resolução Normativa no 786 de 17 de outubro de 2017. Agência Nonal de Energia Elétrica, Brası́lia.
ANEEL (2018). Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional - Módulo 8. Agência Nacional de Energia Elétrica, Brası́lia.
Bansal, R. (2017). Handbook of distributed generation: electric power technologies, economics and environmental impacts. Springer.
Buayai, K., Ongsakul, W., and Mithulananthan, N. (2012). Multi-objective micro-grid planning by nsga-ii in primary distribution system. European Transactions on Electrical Power, 22(2):170–187.
Cano, E. B. (2007). Utilizing fuzzy optimization for distributed generation allocation. In TENCON 2007-2007 IEEE Region 10 Conference, pages 1–4. IEEE.
Das, D. (2008). Optimal placement of capacitors in radial distribution system using a fuzzy-ga method. International Journal of Electrical Power & Energy Systems, 30(67):361–367.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197.
EPE (2018). Plano Decenal de Expansão de Energia 2027. Empresa de Pesquisa Energética, Brası́lia.
Guedes, L. d. M. (2006). Localização e dimensionamento de unidades de geraçãotribuı́da em redes de distribuição radiais. Master’s thesis, Universidade de Brası́lia, Mestrado em Engenharia Elétrica.
Hung, D. Q., Mithulananthan, N., and Lee, K. Y. (2014). Determining pv penetration for distribution systems with time-varying load models. IEEE Transactions on Power Systems, 29(6):3048–3057.
Murty, V. and Kumar, A. (2015). Optimal placement of dg in radial distribution systems based on new voltage stability index under load growth. International Journal of Electrical Power & Energy Systems, 69:246–256.
Nawaz, S., Bansal, A., and Sharma, M. (2016). An analytical approach for dg placement in reconfigured distribution networks. Journal of Applied Power Engineering, 5(3):137–143.
Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., and D’haeseleer, W. (2005). Distributed generation: definition, benefits and issues. Energy policy, 33(6):787–798.
Prakash, D. and Lakshminarayana, C. (2016). Multiple dg placements in distribution system for power loss reduction using pso algorithm. Procedia Technology, 25:785– 792.
Rao, R. S., Ravindra, K., Satish, K., and Narasimham, S. (2012). Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE transactions on power systems, 28(1):317–325.
Raut, U. and Mishra, S. (2019). An improved elitist–jaya algorithm for simultaneous network reconfiguration and dg allocation in power distribution systems. Renewable Energy Focus, 30:92–106.
Zimmerman, R. D., Murillo-Sánchez, C. E., and Thomas, R. J. (2010). Matpower: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on power systems, 26(1):12–19.
Ackermann, T., Andersson, G., and Söder, L. (2001). Distributed generation: a definition. Electric power systems research, 57(3):195–204.
Akella, A., Saini, R., and Sharma, M. P. (2009). Social, economical and environmental impacts of renewable energy systems. Renewable Energy, 34(2):390–396.
ANEEL (2012). Resolução Normativa no 482 de 17 de abril de 2012. Agência Nacional de Energia Elétrica, Brası́lia.
ANEEL (2015). Resolução Normativa no 687 de 24 de novembro de 2015. Agência Nacional de Energia Elétrica, Brası́lia.
ANEEL (2017). Resolução Normativa no 786 de 17 de outubro de 2017. Agência Nonal de Energia Elétrica, Brası́lia.
ANEEL (2018). Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional - Módulo 8. Agência Nacional de Energia Elétrica, Brası́lia.
Bansal, R. (2017). Handbook of distributed generation: electric power technologies, economics and environmental impacts. Springer.
Buayai, K., Ongsakul, W., and Mithulananthan, N. (2012). Multi-objective micro-grid planning by nsga-ii in primary distribution system. European Transactions on Electrical Power, 22(2):170–187.
Cano, E. B. (2007). Utilizing fuzzy optimization for distributed generation allocation. In TENCON 2007-2007 IEEE Region 10 Conference, pages 1–4. IEEE.
Das, D. (2008). Optimal placement of capacitors in radial distribution system using a fuzzy-ga method. International Journal of Electrical Power & Energy Systems, 30(67):361–367.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197.
EPE (2018). Plano Decenal de Expansão de Energia 2027. Empresa de Pesquisa Energética, Brası́lia.
Guedes, L. d. M. (2006). Localização e dimensionamento de unidades de geraçãotribuı́da em redes de distribuição radiais. Master’s thesis, Universidade de Brası́lia, Mestrado em Engenharia Elétrica.
Hung, D. Q., Mithulananthan, N., and Lee, K. Y. (2014). Determining pv penetration for distribution systems with time-varying load models. IEEE Transactions on Power Systems, 29(6):3048–3057.
Murty, V. and Kumar, A. (2015). Optimal placement of dg in radial distribution systems based on new voltage stability index under load growth. International Journal of Electrical Power & Energy Systems, 69:246–256.
Nawaz, S., Bansal, A., and Sharma, M. (2016). An analytical approach for dg placement in reconfigured distribution networks. Journal of Applied Power Engineering, 5(3):137–143.
Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., and D’haeseleer, W. (2005). Distributed generation: definition, benefits and issues. Energy policy, 33(6):787–798.
Prakash, D. and Lakshminarayana, C. (2016). Multiple dg placements in distribution system for power loss reduction using pso algorithm. Procedia Technology, 25:785– 792.
Rao, R. S., Ravindra, K., Satish, K., and Narasimham, S. (2012). Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE transactions on power systems, 28(1):317–325.
Raut, U. and Mishra, S. (2019). An improved elitist–jaya algorithm for simultaneous network reconfiguration and dg allocation in power distribution systems. Renewable Energy Focus, 30:92–106.
Zimmerman, R. D., Murillo-Sánchez, C. E., and Thomas, R. J. (2010). Matpower: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on power systems, 26(1):12–19.
Publicado
15/10/2019
Como Citar
RIBEIRO, Andrei; LEMOS, Francisco; BARBOSA, Fábio.
Siting and sizing of distributed generators via NSGA-II and max-min composition. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1056-1067.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9357.
