A Multi-Objective VAR Planning Approach Considering Seasonal Variations of Wind Power and Solar PV
Abstract
Keywords
Full Text:
PDFReferences
N. Yorino, E. E. El-Araby, H. Sasaki and S. Harada, “A new formulation for FACTS allocation for security enhancement against voltage collapse”, IEEE Trans. Power Systems, vol. 18, no. 1, pp. 3-10, Feb. 2003.
N. Karmakar and B. Bhattacharyya, “Optimal reactive power planning in power transmission network using sensitivity based bi-level strategy”, Sustainable Energy, Grids and Networks, vol. 23, 2020.
Q. Nguyena, X. Kea, N. Samaana, J. Holzera, M.Elizondoa , H. Zhoua, Z. Houa, R. Huanga, M. Vallema, B. Vyakaranama, M. Ghosala, and Y. V. Makarova, “Transmission-distribution long-term volt-var planning considering reactive power support capability of distributed PV”, International Journal of Electrical Power & Energy Systems, vol. 138, June 2022, 107955.
R. Syah, P. Khorshidian Mianaei, M. Elveny, N. Ahmadian, D. Ramdan, R. Abibifar, and A. Davarpanah, “A new hybrid algorithm for multi-objective reactive power planning via facts devices and renewable wind resources”, Sensors, 21(15), 2021, 5246. doi:10.3390/s21155246.
T.A. Costa, W.G. Zvietcovich, F.R.A.C. Baracho, and L. J. S. Damião, “Optimal allocation of capacitors banks in radial distribution systems using clonal algorithm”, in 2018 6th International Conference on Smart Grid (icSmartGrid), pp. 92-97,2018.
N. Yang, C.W. Yu, F.S. Wen and C.Y Chung, “An investigation of reactive power planning based on chance constrained programming”, International Journal of Electrical Power & Energy Systems. vol. 29, pp. 650–656, 2007.
J. López, J. Contreras, and J. R. S. Mantovani, “Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimization”, IET Gener. Transm. Distrib., vol.9, no. 3, pp. 231-240, 2014.
Y. Hong and K. Pen, “Optimal VAR planning considering intermittent wind power using Markove model and quantum evolutionary algorithm”, IEEE Trans. Power Syst., vol. 25, no.4, pp. 2987–2996, 2010.
M. Dadkhah and B. Venkatesh, “Cumulant based stochastic reactive power planning method for distribution systems with wind generators”, IEEE Trans. Power Syst., vol 27, no. 4, pp. 2351–2359, 2012.
H. Amaris and M. Alonso, “Coordinated reactive power management in power networks with wind turbines and FACTS devices”, Energy Convers. Manag, vol 52, pp. 2575–2586, 2011.
M. Alonso, H. Amaris, and C. Alvarez-Ortega, “A multiobjective approach for reactive power planning in networks with wind power generation”, Renew. Energy, vol 37, pp. 180–191, 2012.
M. Niu and Z.Y. Dong, “DE-based two-stage reactive power planning with wind power penetration”, in 9th IET International Conference on Advances in Power System Control, Operation and Management, 2012. doi:10.1049/cp.2012.2151.
A.E. and A. Eladl, “Planning of multi-type FACTS devices in restructured power systems with wind generation”, International Journal of Electrical Power & Energy Systems, vol. 77, pp. 33–42, 2016. doi:10.1016/j.ijepes.2015.11.023.
M. Niu and Z. Xu, “Reactive power planning for transmission grids with wind power penetration”, Proc. Of IEEE PES ISGT Asia, May, 2012.
X. Fang, F. Li, Y. Wei, R. Azim and Y. Xu, “Reactive power planning under high penetration of wind energy using Benders decomposition, “ IET Gener. Transm. Distrib., vol. 9, no. 14, pp. 1835-1844, Nov. 2015.
F. Ugranli and E. Karatepe, “Coordinated TCSC allocation and network reinforcements planning with wind power”, IEEE Trans. Sustain. Energy, vol. 8, no. 4, pp. 1694-1705, Oct. 2017.
N. Gupta, “Stochastic optimal reactive power planning and active power dispatch with large penetration of wind generation”, Journal of Renewable and Sustainable Energy, vol. 10, no.2,2018, 025902. doi:10.1063/1.5010301.
N. Savvopoulos, C.Y. Evrenosoglu, A. Marinakis, A. Oudalov, and N. Hatziargyriou, “A long-term reactive power planning framework for transmission grids with high shares of variable renewable generation”, in IEEE Milan PowerTech, 2019. doi:10.1109/ptc.2019.8810680.
N. Gupta, “Probabilistic optimal reactive power planning with onshore and offshore wind generation, EV, and PV uncertainties”, IEEE Trans. Ind. Appl., vol.56, no. 4, pp. 4200-4213, 2020.
J. Lopez, D. Pozo, J. Contreras, and J.R.S. Mantovani, “A multiobjective minimax regret robust VAr planning model”, IEEE Transactions on Power Systems, vol. 32, no. 3, pp. 1761–1771, 2017. doi:10.1109/tpwrs.2016.2613544.
Y. Chi, Y. Xu, and T. Ding, “Coordinated Var planning for voltage stability enhancement of a wind-energy power system considering multiple resilience indices”, IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 2367-2379,2020.
A.H. Shojaei, A.A. Ghadimi, M.R. Miveh, F. Mohammadi, and F. Jurado, “Multi-objective optimal reactive power planning under load demand and wind power generation uncertainties using ?-constraint method”, Applied Sciences, vol. 10, no. 8, 2020,2859.
A.H. Shojaei, A.A. Ghadimi, M.R. Miveh., F.H. Gandoman, and A. Ahmadi”, Multiobjective reactive power planning considering the uncertainties of wind farms and loads using Information Gap Decision Theory”, Renewable Energy vol. 163, pp. 1427-1443, January 2021. doi:10.1016/j.renene.2020.06.129.
Y. Chi, Y. Xu, and R. Zhang, “Many-objective robust optimization for dynamic var planning to enhance voltage stability of a wind-energy power system”, IEEE Transactions on Power Delivery, vol. 36, no.1, pp. 30-42, Feb. 2021. doi:10.1109/tpwrd.2020.2982471.
Y.M. Atwa, E.F. El-Saadany, M.M.A Salama, and R. Seethapathy, “Optimal renewable resources mix for distribution system energy loss minimization”, IEEE Transactions on Power Systems, vol 25, no. 1, pp. 360–370, 2010 . doi:10.1109/tpwrs.2009.2030276.
P. Kayal, and C.K. Chanda, “Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network”, Renewable Energy, vol. 75, pp. 173–186, 2015. doi:10.1016/j.renene.2014.10.003.
N.S. Ghiasi, S.M.S. Ghiasi, and R. Hadidi, “Stochastic seasonal planning of DG-based smart grid and energy hub by considering demand response program and environmental impacts”, in 2023 11th International Conference on Smart Grid (icSmartGrid), 2023.
A. Ova and S. Demirbas, “Estimating wind power plant outputs in transmission system planning studies based on probability approaches”, in 2021 10th International Conference on Renewable Energy Research and Applications (ICRERA), pp. 180-183, 2021.
S. Sannigrahi, S.R. Ghatak, and P. Acharjee, “Multi-scenario based Bi-level coordinated planning of active distribution system under uncertain environment”, IEEE Transactions on Industry Applications, vol. 56, no. 1., pp. 850-863, 2020.
S.N.V.S.K. Chaitanya, R.A. Bakkiyaraj, B. V. Rao, and K. Jayanthi, “Scenario-based method to solve optimal reactive power dispatch using modified Ant Lion optimizer considering uncertainties in load, solar, and wind power”, International Journal of Renewable Energy Research, vol 13, no.4, pp 279-291, 2023.
M. Eghbal, N. Yorino, E. E. El-Araby and Y. Zoka, “Multi load level reactive power planning considering slow and fast var devices by means of particle swarm optimization”, Proc. of IET Transaction on Generation, Transmission and Distribution, Vol. 2, no. 5, pp.743-751, 2008.
E. E El-Araby and N. Yorino, “Reactive power reserve management tool for voltage stability enhancement”, IET Generation, Transmission & Distribution, vol 12, no. 8, pp.1879–1888, 2018.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, Apr. 2002.
M.K. Heris, NSGA-II in MATLAB (URL: https://yarpiz.com/56/ypea120-nsga2), Yarpiz, 2015. [Accessed:13 Oct.-2023]
M.A. Kamarposhti, I. Colak, H. Shokouhandeh, C. Iwendi, S. Padmanaban, and S. S. Band, “Optimum operation management of microgrids with cost and environment pollution reduction approach considering uncertainty using multi-objective NSGAII algorithm”, IET Renewable Power Generation, pp. 1-13, August 2022, doi.org/10.1049/rpg2.12579.
P. Stackhouse, “Nasa power”, Nasa.gov. [Online]. Available: http://power.larc.nasa.gov/data-access-viewer/. [Accessed:23 Oct.-2023]
DOI (PDF): https://doi.org/10.20508/ijrer.v15i1.14578.g9021
Refbacks
- There are currently no refbacks.
Online ISSN: 1309-0127
Publisher: Gazi University
IJRER is indexed in EI Compendex, SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics)and CrossRef.
IJRER has been indexed in Emerging Sources Citation Index from 2016 in web of science.
WEB of SCIENCE in 2025;
h=35,
Average citation per item=6.59
Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43
Category Quartile:Q4