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

Journal of Operation and Automation in Power Engineering | ||

مقاله 3، دوره 4، شماره 1، تابستان 2016، صفحه 29-41
اصل مقاله (424 K)
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نوع مقاله: Research paper | ||

نویسندگان | ||

Ehsan Dehnavi^{1}؛ Hamdi Abdi, ^{} ^{2}؛ Farid Mohammadi^{1}
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^{1}Electrical Engineering Departments, Engineering Faculty, Razi University, Kermanshah, Iran. | ||

^{2}Razi University (Kermanshah) | ||

چکیده | ||

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. | ||

کلیدواژهها | ||

Emergency demand response program؛ Dynamic economic emission dispatch؛ Imperialist competitive algorithm؛ Optimal incentive؛ Strategy success indices | ||

عنوان مقاله [English] | ||

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

مراجع | ||

[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,” [2] M. Joung and J. Kim, “Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability,” [3] A. K. David and Y. C. Lee, “Dynamic tariffs theory of utility-consumer interaction,” [4] A. K. David and Y. Z. Li, “Effect of inter-temporal factors on the real time pricing of electricity,” [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,” [6] M. Parvania, M. Fotuhi-Firuzabad and M. Shahidehpour, “Optimal demand response aggregation in wholesale electricity markets,” [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,” [8] M. Kazemi, B. Mohammadi-IvatlooandM. Ehsan, “Risk constrained strategic bidding of Gencos considering demand response,” [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,” [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,” [11] M. Parvania and M. Fotuhi Firuzabad, “Demand response scheduling by stochastic SCUC,” [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,” [13] H. R. Arasteh, M.Parsa Moghaddam, M.K.Sheikh-El-Eslami and A. Abdollahi, “Integrating commercial demand response resources with unit commitment,” [14] J. Aghaei and M.I. Alizadeh. “Robust n-k contingency constrained unit commitment with ancillary service demand response program,” [15] Ch. Zhao, J. Wang, J. P. Watson and Y. Guan, “Multi-stage robust unit commitment considering wind and demand response uncertainties,” [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,” [17] A. Ashfaq, S. Yingyun and A. Zia Khan, “Optimization of economic dispatch problem integrated with stochastic demand side response,” in Proceedings of the [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,” [19] H. Khorramdel, B. Khorramdel, M. T. Khorrami and H. Rastegar, “A multi-objective economic load dispatch considering accessibility of wind power with here-and-now (hn) approach,” [20] Sh. Jiang, Zh. Ji and Y. Shen, “A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints,” [21] D. C. Secui, “A new modified artificial bee colony algorithm for the economic dispatch problem,” [22] L. Wang and L.P. Li, “An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems,” [23] A. Hatefi and R. Kazemzadeh, “Intelligent tuned harmony search for solving economic dispatch problem with valve-point effects and prohibited operating zones,” [24] L. Benasla, A. Belmadani and M. Rahli, “Spiral optimization algorithm for solving combined economic and emission dispatch,” [25] A. Gargari, “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition,” in Proceedings of the [26] B. Mohammadi-ivatloo, A. Rabiee, A. Soroudi and M. Ehsan, “Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch,” [27] R. Roche, L. Idoumghar, B. Blunier, and A. Miraoui. “Imperialist competitive algorithm for dynamic optimization of economic dispatch in power systems,” [28] H. Aalami, M. Parsa Moghadam and G. R. Yousefi, “Modeling and prioritizing demand response programs in power markets,” [29] N. Pandita, A. Tripathia, Sh. Tapaswia and M. Panditb, “An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch,” [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,” [31] R. Zhang, J. Zhou, L. Mo, Sh. Ouyang and X. Liao, “Economic environmental dispatch using an enhanced multi-objective cultural algorithm,” [32] Staff Report, “Assessment of demand response and advanced metering,” | ||

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