Using Dynamic Thermal Rating and Energy Storage Systems Technologies Simultaneously for Optimal Integration and Utilization of Renewable Energy Sources

Document Type : Original Article


1 Department of Electrical Engineering, Urmia University, Urmia, Iran

2 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran


Nowadays, optimal integration and utilization of renewable energy sources (RES) are of the most challenging issues in power systems. The wind and solar generation units' maximum production may or may not occur at peak consumption times resulting in non-optimal utilization of these resources. As a solution to this problem, energy storage systems (ESS) are embedded in networks. However, the power transfer from RES to ESS may lead to network congestion. In this paper, the simultaneous application of dynamic thermal rating (DTR) technology and ESS devices is proposed. The DTR is used to overcome the problem of transmission lines limited capacity and ESS is responsible for mitigating curtailment of RESs energy production by saving their generated energy in non-peak hours. The RESs generation and lines’ ratings are calculated based on hourly actual weather elements. For evaluating the proposed method, a linearized formulation of DC-OPF is used in the problem definition and also simulated on a modified IEEE 30-bus test system including a wind farm, solar park, and ESS devices by using MATLAB software. In addition, different comparisons are performed demonstrating the remarkable and better performance of the proposed method compared to previously introduced methods.


1. Xu, B., Ulbig, A. and Andersson, G., "Impacts of dynamic line
rating on power dispatch performance and grid integration of
renewable energy sources", in IEEE PES ISGT Europe 2013,
IEEE., (2013), 1-5. 
2. Gholami, M., "Islanding detection method of distributed
generation based on wavenet", International Journal of
Engineering,  Vol. 32, No. 2, (2019), 242-248. 
3. BARFOROSHI, T. and Adabi, J., "Distributed generation
expansion planning considering load growth uncertainty: A
novel multi-period stochastic model", International Journal of
Engineering,  Vol. 31, No. 3, (2018), 405-414. 
4. Nikkhah, S., Jalilpoor, K., Kianmehr, E. and Gharehpetian,
G.B., "Optimal wind turbine allocation and network
reconfiguration for enhancing resiliency of system after major
faults caused by natural disaster considering uncertainty", IET
Renewable Power Generation,  Vol. 12, No. 12, (2018), 14131423.
5. Movahedi, A., Niasar, A.H. and Gharehpetian, G., "Designing
sssc, tcsc, and statcom controllers using avurpso, gsa, and ga for
transient stability improvement of a multi-machine power
system with pv and wind farms", International Journal of
Electrical Power & Energy Systems,  Vol. 106, (2019), 455466.
6. Tavakkoli-Moghaddam, R. and Mousavi, M., "Group decision
making based on a new evaluation method and hesitant fuzzy
setting with an application to an energy planning problem",
International Journal of Engineering-Transactions C:
Aspects,  Vol. 28, No. 9, (2015), 1303-1311. 
7. Heckenbergerová, J. and Hošek, J., "Dynamic thermal rating of
power transmission lines related to wind energy integration", in
2012 11th International Conference on Environment and
Electrical Engineering, IEEE., (2012), 798-801. 
8. Carreras, B.A., Newman, D.E., Dobson, I. and Poole, A.B.,
"Evidence for self-organized criticality in a time series of
electric power system blackouts", IEEE Transactions on
Circuits and Systems I: Regular Papers,  Vol. 51, No. 9,
(2004), 1733-1740. 
9. Roberts, D., Taylor, P. and Michiorri, A., "Dynamic thermal
rating for increasing network capacity and delaying network
reinforcements", CIRED Seminar: SmartGrids for
Distribution, (2008), 1-4. 
10. Gungor, V.C., Sahin, D., Kocak, T., Ergut, S., Buccella, C.,
Cecati, C. and Hancke, G.P., "Smart grid technologies:
Communication technologies and standards", IEEE
Transactions on Industrial Informatics,  Vol. 7, No. 4, (2011),
11. Abdelkader, S.M., John Morrow, D., Fu, J. and Abbot, S.,
"Partial least squares model for dynamic rating of overhead lines
in wind intensive areas based on field measurements", Journal
of Renewable and Sustainable Energy,  Vol. 5, No. 6, (2013),
12. Kazerooni, A., Mutale, J., Perry, M., Venkatesan, S. and
Morrice, D., "Dynamic thermal rating application to facilitate
wind energy integration", in 2011 IEEE Trondheim PowerTech,
IEEE., (2011), 1-7. 
13. Nick, M., Alizadeh-Mousavi, O., Cherkaoui, R. and Paolone,
M., "Security constrained unit commitment with dynamic
thermal line rating", IEEE Transactions on Power Systems, 
Vol. 31, No. 3, (2015), 2014-2025. 
14. Cigré, T., "353: Guidelines for increased utilization of existing
overhead transmission lines", Working Group B,  Vol. 2, No.,
(2008), 13. 
15. Oh, H., "Optimal planning to include storage devices in power
systems", IEEE Transactions on Power Systems,  Vol. 26, No.
3, (2010), 1118-1128. 
16. Xiong, P. and Singh, C., "Optimal planning of storage in power
systems integrated with wind power generation", IEEE
Transactions on Sustainable Energy,  Vol. 7, No. 1, (2015),
17. Safdarian, A., Degefa, M.Z., Fotuhi-Firuzabad, M. and
Lehtonen, M., "Benefits of real-time monitoring to distribution 

systems: Dynamic thermal rating", IEEE Transactions on
Smart Grid,  Vol. 6, No. 4, (2015), 2023-2031. 
18. "738-2012 - ieee standard for calculating the current-temperature
relationship of bare overhead conductors", in ISBN: 978-07381-8887-4.,
19. Gao, Y. and Billinton, R., "Adequacy assessment of generating
systems containing wind power considering wind speed
correlation", IET Renewable Power Generation,  Vol. 3, No. 2,
(2009), 217-226. 
20. Giorsetto, P. and Utsurogi, K.F., "Development of a new
procedure for reliability modeling of wind turbine generators",
IEEE Transactions on Power Apparatus and Systems,  Vol.,
No. 1, (1983), 134-143. 
21. Hung, D.Q., Mithulananthan, N. and Lee, K.Y., "Determining
pv penetration for distribution systems with time-varying load
models", IEEE Transactions on Power Systems,  Vol. 29, No.
6, (2014), 3048-3057. 
22. Coffrin, C. and Van Hentenryck, P., "A linear-programming
approximation of ac power flows", Informs Journal on
Computing,  Vol. 26, No. 4, (2014), 718-734. 
23. Carrión, M. and Arroyo, J.M., "A computationally efficient
mixed-integer linear formulation for the thermal unit
commitment problem", IEEE Transactions on Power Systems, 
Vol. 21, No. 3, (2006), 1371-1378. 
24. ApS, M., "Mosek optimization toolbox for matlab", User’s
Guide and Reference Manual, Version,  Vol. 4, (2001). 
25. Löfberg, J., "Yalmip: A toolbox for modeling and optimization
in matlab", in Proceedings of the CACSD Conference, Taipei,
Taiwan. Vol. 3, (2004). 
26. Zimmerman, R.D., Murillo-Sánchez, C.E. and Gan, D.,
"Matpower: A matlab power system simulation package",
Manual, Power Systems Engineering Research Center, Ithaca
NY,  Vol. 1, (1997). 
27. Teh, J. and Cotton, I., "Reliability impact of dynamic thermal
rating system in wind power integrated network", IEEE
Transactions on Reliability,  Vol. 65, No. 2, (2016), 1081-1089. 
28. Chu, R., "On selecting transmission lines for dynamic thermal
line rating system implementation", IEEE Transactions on
Power Systems,  Vol. 7, No. 2, (1992), 612-619.