Document Type : Research paper


Shahid Rajaee University


In this paper, a heuristic mathematical model for optimal decision-making of a Distribution Company (DisCo) is proposed that employs demand response (DR) programs in order to participate in a day-ahead market, taking into account elastic and inelastic load models. The proposed model is an extended responsive load modeling that is based on price elasticity and customers’ incentives in which they participate in demand response program, voluntarily and would be paid according to their declared load curtailment amounts. It is supposed that DisCo has the ability to trade with the wholesale market and it can also use its own distributed generation (DG), while decision making process. In this regard, at first, DisCo’s optimization frameworks in two cases, with and without elastic load modelings are acquired. Subsequently, utilizing Hessian matrix and mathematical optimality conditions, optimal aggregated load curtailment amounts are obtained and accordingly, individual customer’s load reductions are calculated. Furthermore, effects of DG contributions and wholesale electricity market are investigated. An IEEE 18 bus test system is employed to obtain the results and show the accuracy of the proposed model.


Main Subjects

[1]     S. Yousefi, M. P. Moghaddam and V. JohariMajd “Optimal real time pricing in an agent-based retail market using a comprehensive demand response model,” Energy, vol. 36, no. 9, pp. 16-27, 2011
[2]     H.A. Aalami, M.P. Moghaddam and G.R. Yousefi “Modeling and prioritizing demand response programs in power markets,” Electr. Power Syst. Res., vol. 80, no. 4, pp.426-435, 2011.
[3]     M. Kazemi, A. Zangeneh and A. Badri “Prioritization of demand response programs in electricity power markets using TOPSIS,” Proc.  Smart Grid Conf., pp. 327-333, 2013.
[4]     H. Arasteh M.P. Moghaddam M, Sheikh El Eslami, and et al “Integrating commercial demand response resources with unit commitment,” Electr. Power Energy Syst., vol. 51, no. 1, pp. 153-161, 2013.
[5]     A. Abdollahi, M.P. Moghaddam, M, Rashidinejad and et al “Investigation of economic & environmental driven demand response measures incorporating UC,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 12–25, 2012.
[6]     C. S. Kirschen and D. Quantifying “The effect of demand response on electricity markets,” IEEE Trans. Power Syst., vol. 24, no. 1, pp. 199-207, 2009.
[7]     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, no. 1, pp. 243-250, 2010.
[8]     D. Nguen and H. Nguen and L. Le “Dynamic pricing design for demand response integration in power distribution networks ,” IEEE Trans. Power Syst., vol. 31, no. 5, pp. 3457-3472, 2016.
[9]     F Meng and X. Zend , “A profit maximization approach to demand response management with customers behavior learning in smart grid,” IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1516-1529, 2016.
[10]  M. R. Sahebi, E. AbediniDuki, M. Kia, A. Soroudi and M. Ehsan, “Simultanous EDRP and unit commitment programming in comparison with interruptible load contracts”, IET Gener. Trans.  Distrib., vol.6, no.7, pp. 605–611, 2012.
[11]  H. Aalami, M. P. Moghadam, and G. R. Yousefi, “Determination of optimal demand response incentives using DR programs”, Proc. 22nd Int. Power Syst. Conf., pp. 132-136, 2007.
[12]  N. Zareen, M. W. Mustafa, U. Sultana and etal, “Optimal real time cost benefit based demand response with intermittent resources”, Energy, vol. 90, no.2, pp.1695-1706, 2015.
[13]  Y. Wang, and M. Li, Lin, “Time of use based electricity demand response for sustainable manufacturing systems”, Energy, vol. 63, no. 15, pp.233-244, 2013.
[14]  M. Sarker, M. Vazquez and D.S. Kirschen “Optimal coordination and scheduling of demand response via monetary incentives,” IEEE Trans. Power Syst., vol. 6, no. 5, pp. 1341-1352, 2015.
[15]  A. Badri, and K. Hosseinpour, “A stochastic multi period decision making framework of an electricity retailer considering aggregated optimal charging and discharging of electric vehicles”, J. Autom. Oper.  Power Eng., vol. 3, no. 1, pp. 34-46, 2015.
[16]  E. Bompard, R. Napoli, and B. Wan, “The effect of programs for demand response incentives in competitive electricity markets”, Eur. Trans. Electr. Power, vol. 19, no. 1, pp.127-139, 2009.
[17]  N. Ruiz, B. Claessens, J. Jimeno, and etal, “Residential load forecasting under a demand response program based on economic incentives,” Int. Trans. Electr. Energy Syst., vol. 25, no. 8, pp.1436-1451, 2015.
[18]  H. Arasteh, M. Sepasian, and V. Vahidinasab, “Toward a smart distribution system expansion planning by considering demand response resources”, J. Autom. Oper.  Power Eng., vol. 3, no. 2, pp. 116-130, 2015.
[19]  M.Peik-Herfeh, H. Seifi and M.K. Sheikh-El-Eslami, “Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method”, Electr. Power Energy Syst., vol. 44, no. 1, pp. 88-98, 2013.
[20]  H. Kwag and J. Kim, “Optimal combined scheduling of generation and demand response with demand resource constraints”, Appl. Energy, vol. 96, no. 2, pp.161-170, 2012.
[21]  H. Aalami, G. R. Yousefi and M. P. Moghadam, “Demand response model considering EDRP and TOU programs”, Proc. IEEE/PES Transm. Distrib. Conf. Exhibition, 2008.
[22]  F. C. Schweppe, M. C. Caramanis, R. D. Tabors and R. E. Bohn, “Spot Pricing of Electricity”, Boston, MA: Kluwer Academic Publishers, 1998.