Impact of Demand Response Technique on Hybrid Transmission expansion planning and Reactive Power planning

Document Type: Research paper


1 Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran

2 Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran.

3 Tehran Area Operating Center (TAOC), Tehran Regional Electric Company (TREC), Tehran, Iran.


In this paper, a model for hybrid transmission expansion planning (TEP) and reactive power planning (RPP) considering demand response (DR) model has been presented. In this study RPP considered by TEP for its effects on lines capacity and reduction of system expansion costs. On the other hand the expansion of the transmission system is an important subject, especially dealing with the new issues of smart networks like as demand response. Demand response program can change the network expansion planning by shifting elasticity loads and reducing of peak load to improve conditions and decrease the costs. To combine demand response model into the transmission expansion planning and reactive power planning, nonlinear mixed integer meta-heuristic optimization algorithm is used. To evaluate the impact of the proposed expansion planning, this model is exerted to the 30-bus test system. Simulation outcomes display the proposed technique considering demand response model reduces the overall cost of the hybrid TEP-RPP.


Main Subjects

[1]   R. Hemmati, R.-A. Hooshmand, and A. Khodabakhshian, “State-of-the-art of transmission expansion planning: Comprehensive review,” Renew. Sustain. Energy Rev., vol. 23, pp. 312-319, 2013.

[2]   S. N. Jahromi, A. Askarzadeh, and A. Abdollahi, “Modelling probabilistic transmission expansion planning in the presence of plug-in electric vehicles uncertainty by multi-state Markov model,” IET Gener. Transm. Distrib., vol. 11, pp. 1716-1725, 2017.

[3]   S. Dehghan, N. Amjady, and A. J. Conejo, “Adaptive robust transmission expansion planning using linear decision rules,” IEEE Trans. Power Syst, vol. 32, pp. 4024-4034, 2017.

[4]   M. Rahmani and M. Rashidinejad, “Integrated AC transmission network expansion and reactive power planning,” 2011.

[5]   H. Zhang, G. T. Heydt, V. Vittal, and J. Quintero, “An Improved Network Model for Transmission Expansion Planning Considering Reactive Power and Network Losses,” IEEE Trans. Power Syst., vol. 28, pp. 3471-3479, 2013.

[6]   A. Mahmoudabadi, M. Rashidinejad, and M. Zeinaddini-Maymand, “A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment,” Eng., vol. 04, pp. 119-125, 2012.

[7]   R.-A. Hooshmand, R. Hemmati, and M. Parastegari, “Combination of AC Transmission Expansion Planning and Reactive Power Planning in the restructured power system,” Energy Convers. Manag., vol. 55, pp. 26-35, 2012.

[8]   R. Hemmati, R.-A. Hooshmand, and A. Khodabakhshian, “Market based transmission expansion and reactive power planning with consideration of wind and load uncertainties,” Renew. Sustain. Energy Rev., vol. 29, pp. 1-10, 2014.

[9]   H. Shayeghi and Y. Hashemi, “Incorporating large-size photovoltaic units in extension plans of power grids,” J. Renew. Sustain. Energy, vol. 8, p. 063501, 2016.

[10] H. Shayeghi and Y. Hashemi, “Technical–economic analysis of including wind farms and HFC to solve hybrid TNEM–RPM problem in the deregulated environment,” Energy Convers. and Management, vol. 80, pp. 477-490, 2014.

[11] X. Zhang, K. Tomsovic, and A. Dimitrovski, “Security constrained multi-stage transmission expansion planning considering a continuously variable series reactor,” IEEE Trans. Power Syst., vol. 32, pp. 4442-4450, 2017.

[12] R. Atia and N. Yamada, “Sizing and analysis of renewable energy and battery systems in residential microgrids,” IEEE Trans. Smart Grid, vol. 7, pp. 1204-1213, 2016.

[13] J. A. Merrigan, “Sunlight to electricity: prospects for solar energy conversion by photovoltaics,” Cambridge, Mass., MIT Press, 1975. 172 p., 1975.

[14] A. K. Kazerooni and J. Mutale, “Network investment planning for high penetration of wind energy under demand response program,” Probabilistic Methods Appl. Power Syst. (PMAPS), 2010 IEEE 11th Int. Conf., 2010, pp. 238-243.

[15] A. Hajebrahimi, A. Abdollahi, and M. Rashidinejad, “Probabilistic Multiobjective Transmission Expansion Planning Incorporating Demand Response Resources and Large-Scale Distant Wind Farms,” IEEE Syst. J., pp. 1-1, 2015.

[16] N. Zhang, Z. Hu, C. Springer, Y. Li, and B. Shen, “A bi-level integrated generation-transmission planning model incorporating the impacts of demand response by operation simulation,” Energy Convers. Manag., vol. 123, pp. 84-94, 2016.

[17] R. Shigenobu, O. B. Adewuyi, A. Yona, and T. Senjyu, “Demand response strategy management with active and reactive power incentive in the smart grid: a two-level optimization approach” AIMS Energy, vol. 5, pp. 482-505, 2017.

[18] Y. Hashemi, H. Shayeghi, and B. Hashemi, “Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment,” Frontiers in Energy, vol. 9, pp. 282-296, 2015.

[19] M. Esmaili and A. Vedadi, “Interaction of demand response and voltage stability in smart grids,” Electr. Power Energy Conf. (EPEC), 2016 IEEE, 2016, pp. 1-6.

[20] M. Nojavan and H. Seyedi, “Preventive voltage control scheme considering demand response, correlated wind and load uncertainties,” J. Energy Manag. Technol., vol. 1, pp. 43-52, 2017.

[21] M. Kazemi, B. Mohammadi-Ivatloo, and M. Ehsan, “Risk-based bidding of large electric utilities using information gap decision theory considering demand response,” Electr. Power Syst.Research, vol. 114, pp. 86-92, 2014.

[22] C. Li, Z. Dong, G. Chen, F. Luo, and J. Liu, “Flexible transmission expansion planning associated with large-scale wind farms integration considering demand response,” IET Gener. Transm. Distrib., vol. 9, pp. 2276-2283, 2015.

[23] S. Nojavan, K. Zare, and B. Mohammadi-Ivatloo, “Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program,” Appl.Energy, vol. 187, pp. 449-464, 2017.

[24] Y.-P. Chang, “Optimal the tilt angles for photovoltaic modules in Taiwan,” Int. J. Electr. Power Energy Syst., vol. 32, pp. 956-964, 2010.

[25] J. Wang, H. Zhong, Q. Xia, and C. Kang, “Transmission network expansion planning with embedded constraints of short circuit currents and N-1 security,” J. Mod. Power Syst. Clean Energy, vol. 3, pp. 312-320, 2015.

[26] Y.-L. Chen and Y. Ke, “Multi-objective VAr planning for large-scale power systems using projection-based two-layer simulated annealing algorithms,” IEE Proc.-Gener. Transm. Distrib., vol. 151, pp. 555-560, 2004.

[27] C. Rathore and R. Roy, “Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning,” Int. J. Electr. Power Energy Syst., vol. 75, pp. 59-73, 2016.

[28] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Demand response modeling considering Interruptible/Curtailable loads and capacity market programs,” Appl. Energy, vol. 87, pp. 243-250, 2010.

[29] A. Yousefi, T. T. Nguyen, H. Zareipour, and O. P. Malik, “Congestion management using demand response and FACTS devices,” Int. J. Electr.Power Energy Syst., vol. 37, pp. 78-85, 2012.