Dehnavi, E., Abdi,, H., Mohammadi, F. (2016). Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem. Journal of Operation and Automation in Power Engineering, 4(1), 29-41.

Ehsan Dehnavi; Hamdi Abdi,; Farid Mohammadi. "Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem". Journal of Operation and Automation in Power Engineering, 4, 1, 2016, 29-41.

Dehnavi, E., Abdi,, H., Mohammadi, F. (2016). 'Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem', Journal of Operation and Automation in Power Engineering, 4(1), pp. 29-41.

Dehnavi, E., Abdi,, H., Mohammadi, F. Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem. Journal of Operation and Automation in Power Engineering, 2016; 4(1): 29-41.

Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem

Nowadays, demand response programs (DRPs) play an important role in price reduction and reliability improvement. In this paper, an optimal integrated model for the emergency demand response program (EDRP) and dynamic economic emission dispatch (DEED) problem has been developed. Customer’s behavior is modeled based on the price elasticity matrix (PEM) by which the level of DRP is determined for a given type of customer. Valve-point loading effect, prohibited operating zones (POZs), and the other non-linear constraints make the DEED problem into a non-convex and non-smooth multi-objective optimization problem. In the proposed model, the fuel cost and emission are minimized and the optimal incentive is determined simultaneously. The imperialist competitive algorithm (ICA) has solved the combined problem. The proposed model is applied on a ten units test system and results indicate the practical benefits of the proposed model. Finally, depending on different policies, DRPs are prioritized by using strategy success indices.

[1] H. Falsafi, A. Zakariazadeh and Sh. Jadid, “The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming,” Energy, vol. 64, pp. 853-867, 2013.

[2] M. Joung and J. Kim, “Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability,” Applied Energy, vol. 101, pp. 441-448, 2013.

[3] A. K. David and Y. C. Lee, “Dynamic tariffs theory of utility-consumer interaction,” IEEE Transactions on Power System, vol. 4, pp. 904-911, 1989.

[4] A. K. David and Y. Z. Li, “Effect of inter-temporal factors on the real time pricing of electricity,” IEEE Transactions on Power System, vol. 1, pp. 44-52, 1993.

[5] N. Venkatesan, J. Solanki and S. Kh. Solanki, “Residential demand response model and impact on voltage profile and losses of an electric distribution network,” Applied Energy, vol. 96, pp. 84-91, 2012.

[6] M. Parvania, M. Fotuhi-Firuzabad and M. Shahidehpour, “Optimal demand response aggregation in wholesale electricity markets,” IEEE Transactions on Smart Grid, vol. 4, pp. 1957-1965, 2013.

[7] M. Alipour, K. Zare and B. Mohammadi-Ivatloo, “Short term scheduling of combined heat and power generation units in the presence of demand response programs,” Energy, vol. 71, pp. 289-301, 2014.

[8] M. Kazemi, B. Mohammadi-IvatlooandM. Ehsan, “Risk constrained strategic bidding of Gencos considering demand response,” IEEE Transa-ctions onPower Systems, vol. 30.1, pp. 376-384, 2015.

[9] M. M. Sahebi, E.A. Duki, M. Kia, A. Soroudi and M. Ehsan, “Simultaneous emergency dem-and response programming and unit commit-ent programming in comparison with interrup-tible load contracts,” IET Generation, Transmi-ssion & Distribution, vol. 6.7, pp. 605-611, 2012.

[10] S. Nojavan, B. Mohammadi-Ivatloo and K. Zare, “Optimal bidding strategy of electricity retailers using robust optimization approach considering time of use rate demand response programs under market price uncertainties,” IET Generation, Transmission & Distribution, vol. 9.4, pp. 328-338, 2015.

[11] M. Parvania and M. Fotuhi Firuzabad, “Demand response scheduling by stochastic SCUC,” IEEE Transactions on Smart Grid, vol. 1, pp. 89-98, 2010.

[12] F. H. Magnago, J. Alemany and J. Lin, “Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market,” International Journal of Electrical Power and Energy Systems, vol. 68, pp. 142-149, 2015.

[13] H. R. Arasteh, M.Parsa Moghaddam, M.K.Sheikh-El-Eslami and A. Abdollahi, “Integrating commercial demand response resources with unit commitment,” Electrical Power and Energy Systems, vol. 51, pp. 153-161, 2013.

[14] J. Aghaei and M.I. Alizadeh. “Robust n-k contingency constrained unit commitment with ancillary service demand response program,” IET Generation, Transmission & Distribution, vol. 8, pp. 1928-1936, 2014.

[15] Ch. Zhao, J. Wang, J. P. Watson and Y. Guan, “Multi-stage robust unit commitment considering wind and demand response uncertainties,” IEEE Transactions on Power Systems, vol. 28, pp. 2708-2717, 2013.

[16] Y. Chen and J. Li. “Comparison of security constrained economic dispatch formulations to incorporate reliability standards on demand response resources into Midwest ISO co-optimized energy and ancillary service market,” Electric Power Systems Research, vol. 81, pp. 1786-1795, 2011.

[17] A. Ashfaq, S. Yingyun and A. Zia Khan, “Optimization of economic dispatch problem integrated with stochastic demand side response,” in Proceedings of the IEEE International Conference on Intelligent Energy and Power Systems, pp. 116-121, 2014.

[18] N. I. Nwulu and X. Xia, “Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs,” Energy Conversion and Management, vol. 89, pp. 963-974, 2015.

[25] A. Gargari, “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition,” in Proceedings of the IEEE Congress on Evolutionary Computation,pp. 4661-4667, 2007.

[26] B. Mohammadi-ivatloo, A. Rabiee, A. Soroudi and M. Ehsan, “Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch,” Energy, vol. 44, pp. 228-240, 2012.

[27] R. Roche, L. Idoumghar, B. Blunier, and A. Miraoui. “Imperialist competitive algorithm for dynamic optimization of economic dispatch in power systems,” Springer-Verlag Berlin Heidelberg, vol. 7401, pp. 217-228, 2012,

[28] H. Aalami, M. Parsa Moghadam and G. R. Yousefi, “Modeling and prioritizing demand response programs in power markets,” Electric Power System Research, vol. 80, pp. 426-435, 2010.

[29] N. Pandita, A. Tripathia, Sh. Tapaswia and M. Panditb, “An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch,” Applied Soft Computing, vol. 12, pp. 3500-3513, 2012.

[30] A. Abdollahi, M. Parsa Moghaddam, M. Rashidinejad and M. K. Sheikh-El-Eslami, “Investigation of economic and environmental-driven demand response measures incorporating UC,” IEEE Transactions on Smart Grid, vol. 3, pp. 12-25, 2012.

[31] R. Zhang, J. Zhou, L. Mo, Sh. Ouyang and X. Liao, “Economic environmental dispatch using an enhanced multi-objective cultural algorithm,” Electric Power Systems Research, vol. 99, pp. 18-29, 2013.

[32] Staff Report, “Assessment of demand response and advanced metering,” FERC, Available: http://www.FERC.gov Dec. 2008.