Multi-Objective Operation of the Distribution System Including Wind Turbines, Taking Into Account the Minimization of Environmental Pollution in the Network
Subject Areas : Energy and environment
Reza
Sedaghati
1
(Department of Electrical Engineering, Beyza Branch, Islamic Azad University, Beyza, Iran.)
Keywords: Distribution System, Environmental Pollution, Wind Turbine, Reconfiguration, Firefly Algorithm.,
Abstract :
Introduction: The ever-increasing growth of consumption loads and the necessity of proper, timely and reliable supply of power networks require a new attitude in the optimal operation of power systems and lines more than ever. On the other hand, in recent years, there has been a lot of support for distributed generation sources based on renewable energies, especially wind turbines. One of the main problems of wind turbines is the problem of extreme wind fluctuations and the dependence of output power on wind speed. Parallel to this problem, in the discussion of network management, the error caused by forecasting the consumption load in the future can also lead to the problem becoming more and more difficult. One of the suitable techniques without initial cost is the method of network topology reconfiguration with the objective of improving the network situation. Materials and Methods: Therefore, in this research, in order to investigate the problem of reconfiguration of the distribution network with the presence of wind turbine sources, a new method for their simultaneous management has been presented. A multi-objective function is considered to reduce the active losses of the network, reduce the overall costs of the network, improve the voltage profile of the existing buses, and reduce the total emissions generated by the network, which uses the firefly optimization algorithm to minimize it. Results and Discussion: Solving the problem of renewing the structure by considering the uncertainty caused by wind turbines is considered. The presence of wind resources in the network has been able to significantly reduce the objective functions. Conclusion: The results of this research showed that the American land reclamation method is better than the other mentioned methods because it has estimated more flow in flood calculation. An important result of flood zoning resulting from the breaking of Tangab dam is that the urban area of Firozabad is safe from this flood and the villages are not flooded as far as the studied area is concerned. Based on the obtained results, it can be concluded that the result of the possible failure of the dam, based on this research, the flood caused by the failure of the dam, except for 1 hectare of the industrial sector, which is a very small area, will cause damage only to agricultural lands.
[1] Liu, T., Xu, G., Cai, P.,Tian, L., Q. Huang, Q., “Development forecast of renewable energy power generation in China and its influence on the GHG control strategy of the country, ” Renew. Energy, 2018; 36:1284-92. DOI: 10.1016/j.renene.2010.09.020.
[2] Braun, R.J., Klein, S.A., Reindl, D.T., “Evaluation of system configurations for solid oxide fuel cell-based micro-combined heat and power generators in residential applications,” J Power Sources, 2016; 158:1290 – 305,. DOI: 10.1016/j.jpowsour.2005.10.064.
[3] Dalton, G.J., Lockington, D.A., Baldock, T.E., “Feasibility analysis of renewable energy supply options for a grid-connected large hotel,” Renew. Energy, 2009; 34: 955-64. DOI: 10.1016/j.renene.2008.08.012.
[4] Kim, H., Ko, Y., Jung, K.H., “Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems,” IEEE Trans Power Deliv., 1993; 8(3):1356-66. DOI: 10.1109/61.252662.
[5] Taylor, T., Lubkeman, D., “Implementation of heuristic search strategies for distribution feeder reconfiguration,” IEEE Trans Power Deliv.,2015; 5(3): 239-245. DOI: 10.1109/61.107279.
[6] Wu, W.C., Tsai , M.S., “Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration”, IEEE Trans. on Power Syst., 2011; 26(3): 1591 – 1599. DOI: 10.1109/TPWRS.2010.2094212.
[7] Singh, D., Misra, R.K., “Multi-objective feeder reconfiguration in different tariff structures,” IET Gener. Trans & Distri. , 2017; 4(8): 974 – 988. DOI: 10.1049/iet-gtd.2010.0128.
[8] Chung-Fu, C., “Reconfiguration and capacitor placement for loss reduction of distribution systems by ant colony search algorithm,” IEEE Trans. on Power Syst., 2018; 23(4): 1747 – 1755. DOI: 10.1109/TPWRS.2008.2002169.
[9] Salazar , H., Gallego, R., Romero, R., “Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems, ” IEEE Trans. on Power Deliv. , 2016; 21(3): 1735 – 1742. DOI: 10.1109/TPWRD.2006.875854.
[10] Debaprya, D., “A fuzzy multi-objective approach for network reconfiguration of distribution systems,” IEEE Trans. Power Del., 2016; 21(1): 202-209. DOI: 10.1109/TPWRD.2005.852335.
[11] Ahuja, A., Das, S., and Pahwa, A., , “An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration,” IEEE Trans. on Power Syst., 2019; 22(3):1101-1111. DOI: 10.1109/TPWRS.2007.901286.
[12] Haffner, S., Pereira, L.F.A., Pereira, L.A., and Barreto, L.S., “Multistage model for distribution expansion planning with distributed generation—Part I: problem formulation, ” IEEE Trans. on Power Deliv., 2018; 23(2): 915-923. DOI: 10.1109/TPWRD.2008.917916.
[13] Porkar, S., Poure, P., Abbaspour-Tehrani-fard, A., Saadate, S., “A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity market, ” Electr Power Syst Res., 2020; 80(7): 828-837 . DOI: 10.1016/j.epsr.2009.12.008.
[14] Bie, Z., Li, G., Liu, H., Wang, H., “Studies on voltage fluctuation in the integration of wind power plants using probabilistic load flow, ” IEEE Power and Energy Soc. Gen, 2018; 1-7. DOI: 10.1109/PES.2008.4596131.
[15] Li, X., Pei, J., and Zhang, S., “A probabilistic wind farm model for probabilistic load flow calculation,” Asia-Pac. Power and Energy Engineering, 2015. DOI: 10.1109/APPEEC.2010.5448689.
[16] Chiang, H.D., and Jean-Jameau, R.M., “Optimal network reconfiguration in distribution systems, part 1: A new formulation and a solution methodology,” IEEE Trans. Power Del., 2019; 5(4):1902–1909. DOI: 10.1109/61.103687.
[17] Shirmohammadi, D., and Hong, H.W., “Reconfiguration of electric distribution networks for resistive line loss reduction,” IEEE Trans. Power Del., 2018; 4(1): 1492–1498. DOI: 10.1109/61.25637.
[18] Agalgaonkar, P., Kulkarni, S.V., Khaparde, Soman, S.A.,“. Placement and penetration of distributed generation under standard market design,”. Int J Emerg Electr Power Syst 2021; 1(1). DOI: 10.2202/1553-779X.1003.
[19] Niknam, T., “Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators,” J Zhejiang Univ Sci, 2021;9(12):1753–64. DOI: 10.1631/jzus.A0820047.
[20] Olamaie, J., Niknam, T., Gharehpetion, G., “Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators,” Appl Math Comput , 2022; 20(1):575–86. DOI: 10.1016/j.amc.2007.12.053.
[21] Apostolopoulos, T., Vlachos, A., “Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem,” Int. J. Comb., 2020:1-23. DOI: 10.1155/2011/523806.
[22] Yang, X.S., “Nature-Inspired Metaheuristic Algorithms,” Frome: Luniver Press. (2019), ISBN 1905986106. [23] Sauer, P., Pai, M., “Power System Steady-State Stability and the Load Flow Jacobian”, IEEE Trans. Pow. Syst., 2018; 5(4): 1-65. DOI: 10.1109/59.99389.
[24] Chen, T.H., Chen, M.S., Hwang, K.J., Kotas, P., Chebli, E.A., “Distribution System Power Flow Analysis – A Rigid Approach”, IEEE Trans. Pow. Deliv., 2022; 6(3):1146-1152. DOI: 10.1109/61.85860.
[25] Chen, H., Chen, J., Shi, D., Duan, X., “Power Flow Study and Voltage Stability Analysis for Distribution Systems with Distributed Generation”, IEEE Pow. Eng. Society General, 2016. DOI: 10.1109/PES.2006.1709198.
[26] Eminoghlu, U., Hocaoglu, M.H., “A new power flow method for radial distribution systems including voltage dependent load models”, Electric Pow Syst Res., 2015; 76: 106-114. DOI: 10.1016/j.epsr.2005.05.008.
[27] Augugliaro, A., Dusonchet, L., Favuzza, S., Ippolito, M.G., Sanseverino, E.R., “A new backward/forward method for solving radial distribution networks with PV nodes”, Electric Pow Syst Res., 2018; 78: 330-336, 2018. DOI: 10.1016/j.epsr.2007.03.001.
[28] Yang, X.S.,“ Nature-Inspired Metaheuristic Algorithms,” Frome: Luniver Press., 2019, ISBN 1905986106.
[29] Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E., Wind Eng. Handbook, John Wiley & Sons, Ltd, 2016.