Return on Investment in Transmission Network Expansion Planning Considering Wind Generation Uncertainties Applying Non-dominated Sorting Genetic Algorithm

Document Type : Research paper

Authors

1 Razi University

2 Razi University (Kermanshah)

Abstract

Although significant private investment is absorbed in different sectors of power systems, transmission sector is still suffering from appropriate private investment. This is because of the pricing policies of transmission services, tariffs, and especially for investment risks. Investment risks are due to the uncertain behaviour of power systems that discourage investors to invest in the transmission sectors. In uncertain environment of power systems, a proper method is needed to find investment attractive transmission lines with high investment return and low risk. Nowadays, wind power generation has a significant portion in total generation of most power systems. However, its uncontrollable and variable nature has turned it as a main source of uncertainty in power systems. Accordingly, the wind power generation can play a fundamental role in increasing investment risk in the transmission networks. In this paper, impact of this type of generation on investment risk and returned investment cost in transmission network is investigated. With different levels of wind power penetration, the recovered values of investment cost and risk cost in transmission network are calculated and compared. This is a simple method to find investment attractive lines in presence of uncertainties. Wherein, transmission network expansion planning (TNEP) is formulated as a multi-objective optimization problem with objectives of minimizing the investment cost, maximizing the recovered investment cost and network reliability. The point estimation method (PEM) is used to address wind speed variations at wind farms sites in the optimization problem, and the NSGA II algorithm is applied to determine the trade-off regions between the TNEP objective functions. The fuzzy satisfying method is used to decide about the final optimal plan. The proposed methodology is applied on the IEEE 24-bus RTS and simplified Iran 400 kV network.

Keywords

Main Subjects

References

[1]          P. Maghouli, S. H. Hosseini, M. O. Buygi, and M. Shahidehpour, “A multi-objective framework for transmission expansion planning in deregulated environments,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 1051-1061, 2009.
[2]          Y. Wang et al., “Pareto optimality-based multi-objective transmission planning considering transmission congestion,” Electr. Power Syst. Res., vol. 78, pp. 1619-1626, 2008.
[3]          H. A. Gil, F. D. Galiana, and A. J. Conejo, “Multiarea transmission network cost allocation,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1293-1301, 2005.
[4]          H. A. Gil, F. D. Galiana, E. L. Silva, “Nodal price control : a mechanism for transmission network cost allocation,” IEEE Trans. Power Syst., vol. 21, no. 1, pp. 3-10, 2006.
[5]          J. M. Zolezzi and H. Rudnick, “Transmission cost allocation by cooperative games and coalition formation,” IEEE Trans. Power Syst., vol. 17, no. 4, pp. 1008-1015, 2002.
[6]          M. Rahmani, R. A. Romero, M. J. Rider, and M. Rahmani, “Risk/investment-driven transmission expansion planning with multiple scenarios,” IET Gener. Transm. Distrib., vol. 7, no. 2, pp. 154-165, 2013.
[7]          I. I. Skoteinos, G. A. Orfanos, P. S. Georgilakis, and N. D. Hatziargyriou, “Methodology for assessing transmission investments in deregulated electricity markets”, Proc. IEEE Power Tech. Conf., 2011, Trondheim, Norway, pp. 1-6, 2011.
[8]          J. D. Molina, J. Contreras, H. Rudnick, “A risk-constrained project portfolio in centralized transmission expansion planning,” IEEE Trans. Power Syst., vol. 11, no. 3, pp. 1653-1661, 2017.
[9]          J. Qiu, Z. Y. Dong, J. Zhao, Y. Xu, F. Luo, and J. Yang, “A risk-based approach to multi-stage probabilistic,” IEEE Trans. Power Syst., pp. 1-10, 2015.
[10]        A. Arabali, M. Ghofrani, “A multi-objective transmission expansion planning framework in deregulated power systems with wind generation,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 3003-3011, 2014.
[11]        H. Salazar, C. Liu, R. F. Chu, “Decision analysis of merchant transmission investment by perpetual options theory,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1194-1201, 2007.
[12]        F. F. Wu, F. L. Zheng, and F. S. Wen, “Transmission investment and expansion planning in a restructured electricity market,” Energy, vol. 31, no. 6-7, pp. 954-966, 2006.
[13]        M. Moeini-Aghtaie, A. Abbaspour, and M. Fotuhi-Firuzabad, “Incorporating large-scale distant wind farms in probabilistic transmission expansion Planning; Part I: Theory and Algorithm,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1594-1601, 2012.
[14]        M. O. Buygi, G. Balzer, H. M. Shanechi, and M. Shahidehpour, “Market-based transmission expansion planning,” IEEE Trans. Power Syst., vol. 19, no. 4, pp. 2060-2067, 2004.
[15]        “World Wind Energy Association.” [Online]. Available: http://www.wwindea.org/home/index.php.
[16]        V. Hamidi, F. Li, and L. Yao, “Value of wind power at different locations in the grid,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 526-537, 2011.
[17]        G. A. Orfanos, P. S. Georgilakis, and N. D. Hatziargyriou, “Transmission expansion planning of systems with increasingwind power integration,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1355-1362, 2013.
[18]        C. Munoz, E. Sauma, J. Contreras, J. Aguado, and S. De La Torre, “Impact of high wind power penetration on transmission network expansion planning,” IET Gener. Transm. Distrib, vol. 6, no. 12, pp. 1281-1291, 2012.
[19]        F. Ugranli and E. Karatepe, “Multi-objective transmission expansion planning considering minimization of curtailed wind energy,” Int. J. Electr. Power Energy Syst., vol. 65, pp. 348-356, 2015.
[20]        K. Zou, A. P. Agalgaonkar, K. M. Muttaqi, and S. Perera, “Distribution system planning with incorporating DG reactive capability and system uncertainties,” IEEE Trans. Sustain. Energy, vol. 3, no. 1, pp. 112-123, 2012.
[21]        E. Rosenblueth, “Point estimates for probability moments,” Proc. Natl. Acad. Sci., vol. 72, no. 10, pp. 3812-3814, 1975.
[22]        G. Verbic, A. Claudio, and A. Canizares, “Probabilistic optimal power flow in electricity markets based on a two point estimate method,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1883-1894, 2006.
[23]        S. Abbasi and H. Abdi, “Multiobjective transmission expansion planning problem based on ACOPF considering load and wind power generation uncertainties,” Int. Trans. Electr. Energy Syst, pp. 1-15, 2016.
[24]        A. Najafi, R. Aboli, H. Falaghi, and M. Ramezani, “Capacitor placement in distorted distribution network subject to wind and load uncertainty,” J. Oper. Autom. Power Eng., vol. 4, no. 2, pp. 153-164, 2016.
[25]        K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm :,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, 2002.
[26]        P. K. Shukla and K. Deb, “On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods,” Eur. J. Oper. Res., vol. 181, pp. 1630-1652, 2007.
[27]        P. Maghouli, S. H. Hosseini, M. Oloomi Buygi, and M. Shahidehpour, “A scenario-based multi-objective model for multi-stage transmission expansion planning,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 470-478, 2011.
[28]        J. Moshtagh and S. Ghasemi, “Optimal distribution system reconfiguration using non- dominated sorting genetic algorithm ( NSGA-II ),” J. Oper. Autom. Power Eng., vol. 1, no. 1, pp. 12-21, 2013.
[29]        J. Choi et al., “A method for transmission system expansion planning considering probabilistic reliability criteria,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1606-1615, 2005.
[30]        S. López, A. Aguilera, and G. Blanco, “Transmission expansion planning under uncertainty: An approach based on real option and game theory against nature,” IEEE Lat. Am. Trans., vol. 11, no. 1, pp. 566-571, 2013.
[31]        J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind energy explained: theory, design and application. John Wiley & Sons, 2010.
[32]        R. Fang and D. J. Hill, “A new strategy for transmission expansion in competitive electricity markets,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 374-380, 2003.
[33]        M. Shahidehpour, H. Yamin, and Z. Li, “Market operations in electric power systems : forecasting, scheduling, and risk management,” John Wiley & Sons, 2002.
[34]        M. Moeini-Aghtaie, A. Abbaspour, and M. Fotuhi-Firuzabad, “Incorporating large-scale distant wind farms in probabilistic transmission expansion planning; part ii: case studies,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1585-1593, 2012.
[35]        M. Sakawa and Y. Hitoshi, “An interactive fuzzy satisficing method for multiobjective nonlinear programming problems with fuzzy parameters,” Fuzzy Sets Syst., vol. 30, pp. 221-238, 1989.
[36]        R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 12-19, 2011.
[37]        P. M. Subcommittee, “IEEE reliability test system,” IEEE Trans. Power Appl. Syst., no. 6, pp. 2047-2054, 1979.
[38]        “TAVANIR Annual Reports.” [Online]. Available: http://www.tavanir.org.ir.
[39]        “Renewable Energy Organization of Iran,” 2016. [Online]. Available: http://www.suna.org.ir/fa/wind /perspective.
Journal of Operation and Automation in Power Engineering
Volume 6, Issue 1
June 2018
Pages 89-100

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History

  • Receive Date: 26 July 2017
  • Revise Date: 22 November 2017
  • Accept Date: 03 February 2018
  • First Publish Date: 01 June 2018

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