Óptima ubicación de los sistemas de cogeneración en una red de distribución del tipo industrial, considerando información georreferenciada / Localização ótima dos sistemas de cogeração em uma rede de distribuição do tipo industrial, considerando informações georreferenciadas

Darwin Paredes, Alex Valenzuela

Abstract


Los sistemas de cogeneración permiten aprovechar el recurso energético en aplicaciones donde se genera electricidad y calor, el cual puede ser utilizado en distintas aplicaciones tanto residenciales como industriales. Este documento se enfoca en la determinación de la ubicación de sistemas de cogeneración tomando información georreferenciada; es así, que mediante archivos de Open Street Map se extrae la información de la ubicación de los usuarios industriales, así como de la red eléctrica. Adicionalmente, utilizando el algoritmo GWO ("Grey Wolf Optimizer") se realiza la ubicación de los sistemas de generación para la minimización de pérdidas del sistema considerando restricciones técnicas. El trazado de la red de calefacción para el aprovechamiento de la producción se realiza en base a técnicas de Minimum Spanning Tree (MST) para determinar el camino más corto entre los centros de producción de calor y los usuarios industriales.


Keywords


sistemas de cogeneración, sistemas georreferenciados, grey wolf optmizar, técnicas de minimum spanning tree, ubicación de generación.

References


Lozano, M.A. Optimización de sistemas de cogeneración para calefacción y refrigeración de distrito 2004. 15, 9.

Hawkes, A.; Staffell, I.; Brett, D.; Brandon, N. Fuel cells for micro-combined heat and power generation. Energy & Environmental Science 2009, 2, 729–744. doi:10.1039/B902222H.

Department for Bussiness Energy, .I.S. Combined Heat and Power-Technologies A detailed guide for CHP developers-Part 2. OGL.

Mehigan, L.; Deane, J.P.; Gallachóir, B.Ó.; Bertsch, V. A review of the role of distributed generation (DG) in future electricity systems. Energy 2018, 163, 822–836. doi:10.1016/J.ENERGY.2018.08.022.

Tarraq, A.; Elmariami, F.; Belfqih, A.; Haidi, T. Meta-heuristic optimization methods applied to renewable distributed generation planning: A review. E3SWeb of Conferences. EDP Sciences, 2021, Vol. 234. doi:10.1051/E3SCONF/202123400086.

Katagiri, H.; Hayashida, T.; Nishizaki, I.; Guo, Q. A hybrid algorithm based on tabu search and ant colony optimization for k-minimum spanning tree problems. Expert Systems with Applications 2012, 39, 5681–5686. doi:10.1016/J.ESWA.2011.11.103.

Katal, F.; Fazelpour, F. Multi-criteria evaluation and priority analysis of different types of existing power plants in Iran: An optimized energy planning system. Renewable Energy 2018, 120, 163–177. doi:10.1016/J.RENENE.2017.12.061.

Volkova, A.; Latõšov, E.; Siirde, A. Use of multi-criteria decision analysis for choosing an optimal location for a wood fuel based cogeneration plant: a case study in Estonia. Selected Topics in Energy, Environment, Sustainable Development and Landscaping 2010, 4, 116–122. doi:10.2478/V10145-010-0026-3.

Buoro, D.; De Nardi, A.; Pinamonti, P.; Reini, M. Optimization of an industrial area energy supply system with distributed cogeneration and solar district heating. Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2012, Vol. 44694, pp. 949–960. doi:10.1115/GT2012-68988.

Moretti, L.; Astolfi, M.; Vergara, C.; Macchi, E.; Pérez-Arriaga, J.I.; Manzolini, G. A design and dispatch optimization algorithm based on mixed integer linear programming for rural electrification. Applied energy 2019, 233, 1104–1121. doi:10.1016/J.APENERGY.2018.09.194.

Jayakumar, N.; Subramanian, S.; Ganesan, S.; Elanchezhian, E. Grey wolf optimization for combined heat and power dispatch with cogeneration systems. International Journal of Electrical Power & Energy Systems 2016, 74, 252–264. doi:10.1016/J.IJEPES.2015.07.031.

Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey wolf optimizer. Advances in engineering software 2014, 69, 46–61. doi:10.1016/J.ADVENG-SOFT.2013.12.007.

Velamuri, S.; Sreejith, S.; Ponnambalam, P. Static economic dispatch incorporating wind farm using flower pollination algorithm. Perspectives in Science 2016, 8, 260–262.

Bayón, L.; Grau, J.M.; Ruiz, M.M.; Suárez, P.M. The exact solution of the environmental/economic dispatch problem. IEEE transactions on power systems 2012, 27, 723–731. doi:10.1109/TPWRS.2011.2179952.

Xia, X.; Elaiw, A. Optimal dynamic economic dispatch of generation: A review. Electric power systems research 2010, 80, 975–986. doi:10.1016/J.EPSR.2009.12.012.

Sarker, B.R.; Wu, B.; Paudel, K.P. Modeling and optimization of a supply chain of renewable biomass and biogas: Processing plant location. Applied Energy 2019, 239, 343–355. doi:10.1016/J.APENERGY.2019.01.216.

Celli, G.; Ghiani, E.; Loddo, M.; Pilo, F.; Pani, S. Optimal location of biogas and biomass generation plants. 2008 43rd international universities power engineering conference. IEEE, 2008, pp. 1–6. doi:10.1109/UPEC.2008.4651490.

Moysiadis, I. Optimal lay-out and operation of combined heat & power distributed generation systems in urban areas. 2020.

Sameti, M.; Haghighat, F. Optimization of 4th generation distributed district heating system: Design and planning of combined heat and power. Renewable Energy 2019, 130, 371–387. doi:10.1016/J.RENENE.2018.06.068.

Karschin, I.; Geldermann, J. Efficient cogeneration and district heating systems in bioenergy villages: an optimization approach. Journal of Cleaner Production 2015, 104, 305–314. doi:10.1016/J.JCLEPRO.2015.03.086.

Uhlemair, H.; Karschin, I.; Geldermann, J. Optimizing the production and distribution system of bioenergy villages. International Journal of Production Economics 2014, 147, 62–72. doi:10.1016/J.IJPE.2012.10.003.

Prato, A.P.; Strobino, F.; Broccardo, M.; Giusino, L.P. Integrated management of cogeneration plants and district heating networks. Applied Energy 2012, 97, 590–600. doi:10.1016/J.APENERGY.2012.02.038.

Beiron, J.; Montañés, R.M.; Normann, F.; Johnsson, F. Dynamic modeling for assessment of steam cycle operation in waste-fired combined heat and power plants. Energy Conversion and Management 2019, 198, 111926. doi:10.1016/J.ENCONMAN.2019.111926.

Girardin, L.; Marechal, F.; Dubuis, M.; Calame-Darbellay, N.; Favrat, D. EnerGis: A geographical information based system for the evaluation of integrated energy conversion systems in urban areas. Energy 2010, 35, 830–840. doi:10.1016/J.ENERGY.2009.08.018.

Sultana, A.; Kumar, A. Optimal siting and size of bioenergy facilities using geographic information system. Applied Energy 2012, 94, 192–201. doi:10.1016/J.APENERGY.2012.01.052.

Valenzuela, A.; Inga, E.; Simani, S. Planning of a Resilient Underground Distribution Network Using Georeferenced Data 2019. pp. 1–19. doi:10.3390/en12040644.

de cogeneración, a.d.e.p. Guía Técnica para la medida y determianción del calor útil, de la electricidad y del ahorro de energía primaria de cogeenración de alta eficiencia 2008.

Bianchi, L.; Dorigo, M.; Gambardella, L.M.; Gutjahr,W.J. A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing 2009, 8, 239–287. doi:10.1007/S11047-008-9098-4.

Faris, H.; Aljarah, I.; Al-Betar, M.A.; Mirjalili, S. Grey wolf optimizer: a review of recent variants and applications. Neural computing and applications 2018, 30, 413–435. doi:10.1007/S00521-017-3272-5.

Ni,W.; Collings, I.; Lipman, J.;Wang, X.; Tao, M.; Abolhasan, M. Graph theory and its applications to future network planning: Software-defined online small cell management. IEEE Wireless Communications 2015, 22, 52–60. doi:10.1109/MWC.2015.7054719.

Bajpai, P.; Chanda, S.; Srivastava, A.K. A Novel Metric to Quantify and Enable Resilient Distribution System using Graph Theory and Choquet Integral. IEEE Transactions on Smart Grid 2016, 3053, 1–1. doi:10.1109/TSG.2016.2623818.

Li, H.; Mao, W.; Zhang, A.; Li, C. An improved distribution network reconfiguration method based on minimum spanning tree algorithm and heuristic rules. International Journal of Electrical Power and Energy Systems 2016, 82, 466–473. doi:10.1016/j.ijepes.2016.04.017

Mosbah, M.; Arif, S.; Mohammedi, R.D.; Hellal, A. Optimum dynamic distribution network reconfiguration using minimum spanning tree algorithm. 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B) 2017, pp. 1–6. doi:10.1109/ICEE-B.2017.8192170.

Gnana Swathika, O.V.; Hemamalini, S. Prims-Aided Dijkstra Algorithm for Adaptive Protection in Microgrids. IEEE Journal of Emerging and Selected Topics in Power Electronics 2016, 4, 1279–1286. doi:10.1109/JESTPE.2016.2581986.

Moradijoz, M.; Moghaddam, M.P.; Haghifam, M.R. A flexible active distribution system expansion planning model: A risk-based approach. Energy 2018, 145, 442–457. doi:10.1016/j.energy.2017.12.160.

Wang, G.W.; Zhang, C.X.; Zhuang, J. Clustering with Prim’s sequential representation of minimum spanning tree. Applied Mathematics and Computation 2014, 247, 521–534. doi:10.1016/j.amc.2014.09.026.




DOI: https://doi.org/10.34115/basrv6n3-022

Refbacks

  • There are currently no refbacks.