Constrained Fuzzy Power Flow Applied to Transmission Congestion
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The deregulation of the electricity industry, the increasing penetration of renewable energy resources, and the presence of new stakeholders, such as Electric Vehicles (EV), make managing transmission system congestion critical. In this context, load flow methodologies that consider non-probabilistic uncertainty due to a lack of data (particularly concerning EV) seem to be a handy tool to analyze network congestion situations and to support electrical network planning. The congestion of the branches is related to the load and generation of the network buses. Still, the congestion of a specific branch may not be related to the load and generation on the neighbouring buses. In this line, Fuzzy Power Flow (FPF) is suitable for identifying congestion situations in the transmission system and the branches responsible for such cases when probabilistic models may not describe uncertainty. This paper uses an FPF model, a non-linearized Constrained Fuzzy Power Flow (CFPF), to characterize congestion situations in transmission network planning. The application of the CFPF to analyze congestion situations is a novelty. The results of that application may be used to define the reinforcement of the most appropriate branches to achieve specific suitability of the network in the face of a diverse set of uncertainties. Furthermore, the dual values provided by Lagrange multipliers resulting from the CFPF optimization problem are used to identify the most promising options to mitigate congestion situations.
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R. Hemmati, R.-A. Hooshmand, and A. Khodabakhshian, Comprehensive review of generation and transmission expansion planning, IET, Gener. Transm. Distrib., vol. 7, no. 9, pp. 955-964, Sep. 2013.
S. Güner and B. Bilir, Identification of congestion in transmission networks by power-flow solutions, National Conference on Electrical, Electronics and Computer Engineering, 2010, pp. 103-107.
E. Ela et al., Future Electricity Markets: Designing for Massive Amounts of Zero-Variable-Cost Renewable Resources, in IEEE Power and Energy Magazine, vol. 17, no. 6, pp. 58-66, Nov.-Dec. 2019.
K. Wook-Won, P. Jong-Keun, Y. Yong-Tae and K. Mun-Kyeom, Transmission Expansion Planning under Uncertainty for Investment Options with Various Lead-Times, Energies MDPI Journal, 2018.
P. Bresesti, A. Capasso, M. C. Falvo and S. Lauria, Power system planning under uncertainty conditions. Criteria for transmission network flexibility evaluation,2003 IEEE Bologna Power Tech Conference Proceedings, 2003, pp. 6 pp. Vol.2.
L. Zhang, Q. Zhou, T. Yan, H. Cheng and S. Zhang, A Transmission System Planning Method Considering Fuzzy Model of Load and Interval Model of Renewable Power, 2018 International Conference on Power System Technology (POWERCON), 2018, pp. 1892-1898.
R. Billinton, W. Zhang, Cost related reliability evaluation of bulk power systems, International Journal of Electrical Power & Energy Systems, Volume 23, Issue 2, 2001, Pages 99-112, ISSN 0142-0615.
European Commission, DIRECTIVE 2009/72/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 13 July 2009 concerning common rules for the internal market in electricity and repealing Directive 2003/54/EC, Official Journal of European Union, August 2009.
C. Linnemann, D. Echternacht, C. Breuer, A. Moser, Modeling optimal redispatch for the European Transmission grid, 2011 IEEE Trondheim PowerTech, 2011, pp. 1-8.
S. Bhattacharya, B. R. Kuanr, A. Routray, A. Dash, Transmission congestion management in restructured power system by rescheduling of generators using TLBO, 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), 2017, pp. 1-7.
F.Capitanescu, T.V.Cutsem, A unified management of congestions due to voltage instability and thermal overload. Electric Power Systems Research, Vol. 77(10), pp. 1274-1283, 2007.
S. Balaraman, N. Kamaraj, Congestion management in deregulated power system using real coded genetic algorithm, International Journal of Engineering Science and Technology, vol. 2, no. 11, pp. 6681-6690, 2010.
R. S. Fang, A. K. David, Transmission congestion management in an electricity market, IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 877-883, Aug. 1999.
V. Sharma, P. Walde, RK Saket, S. Mekhilef, Optimization of distributed generation size based on line sensitivity using transmission congestion cost, Int Trans Electr Energ Syst. 2021; 31 : e12695.
A. Kumar, S. C. Srivastava, S. N. Singh, A zonal congestion management approach using real and reactive power rescheduling, in IEEE Transactions on Power Systems, vol. 19, no. 1, pp. 554-562, Feb. 2004.
D. Sudipta, S. Singh, Optimal rescheduling of generators for congestion management based on particle swarm optimization. IEEE Transactions on Power Systems 23:1560-1569, 2008.
Hosseini SA, Amjady N, Shafie-khah M, Catalao JPS, A new multi-objective solution approach to solve transmission congestion management problem of energy markets Applied Energy,Volume 165, 2016, Pages 462-471,ISSN 0306-2619.
N. K. Patel, B. N. Suthar, J. Thakkar, Transmission congestion management considering voltage stability margin. SN Appl. Sci. 3,261 (2021).
T. Niimura and Y. Niu, Transmission congestion relief by economic load management, IEEE Power Engineering Society Summer Meeting, 2002, pp. 1645-1649 vol. 3.
Prashant and A. S. Siddiqui, Congestion Management Based on Minimization of TCC and Optimal Placement & Sizing of DG, 2021 IEEE Bombay Section Signature Conference (IBSSC), 2021, pp. 1-6.
Prashant, M. Sarwar, A. S. Siddiqui, S. S. M. Ghoneim, K. Mahmoud and M. M. F. Darwish, Effective Transmission Congestion Management via Optimal DG Capacity Using Hybrid Swarm Optimization for Contemporary Power System Operations, in IEEE Access, vol. 10, pp. 71091-71106, 2022.
S. D. Carlo, A. Genna, F. Massaro, F. Montana and E. R. Sanseverino, Optimizing Renewable Power Management in Transmission Congestion. An Energy Hub Model Using Hydrogen Storage, 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2021, pp. 1-5.
A. Narain, S.K. Srivastava, S.N. Singh, Congestion management approaches in restructured power system: Key issues and challenges, The Electricity Journal, Volume 33, Issue 3, 2020, 106715, ISSN 1040-6190.
M. A. Matos and E. M. Gouveia, The Fuzzy Power Flow Revisited, IEEE Trans. Power Syst., vol. 23, no. 1, pp. 213-218, 2008.
E. M. Gouveia, M. A. Matos, Symmetric AC Fuzzy Power Flow Model, European Journal of Operational Research., vol 197. Issue 3, pp. 1012-1018, 2009.
V. Miranda, M. Matos, Distribution System Planning with Fuzzy Models and Techniques, Proceedings of CIRED 89, Brighton, pp 472 - 476, 1989.
A. Dimitrovski, K. Tomsovic, Slack bus treatment in load flow solutions with uncertain nodal powers, 2004 International Conference on Probabilistic Methods Applied to Power Systems, 2004, pp. 532-537.
L. Zadeh, Fuzzy Sets, Information and Control, no. 8, pp. 338-353, Aug. 1965.
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