Dynamic negawatt demand response resource modeling and prioritizing in power markets

Authors

1 Department of Economics, Shahid Bahonar University of Kerman, Kerman, Iran.; Shakhes Pajouh Research Institute, Isfahan, Iran.

2 Faculty of Engineering, Imam Khomeini International University of Qazvin, Iran.; Shakhes Pajouh Research Institute, Isfahan, Iran.

3 Department of Engineering, Shahid Bahonar University, Kerman, Iran.

Abstract

    In recent years, integrated use of demand- and supply-side resources has been performed by electric utilities, because of its potential attractiveness, both at operation and economic levels. Demand Response Resources (DRRs) can be used as demand side options which are the consequence of implementing Demand Response Programs (DRPs). DRPs comprise the actions taken by end-use customers to reduce their electricity consumption in response to electricity market’s high prices; and/or reliability problems on the electricity network. In this paper, a dynamic economic model of DRPs is derived based upon the concept of flexible elasticity of demand and the customer benefit function. Precise modeling of these virtual negawatt resources helps system operators to investigate the impact of responsive loads on power system studies. This paper also aims to prioritize multifarious DRPs by means of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy methods. Performance of the proposed model is investigated through numerical studies using a standard IEEE test system.

Keywords

Main Subjects


1. IEA, Strategic Plan for the IEA Demand-Side Management Program 2008-2012, [Online]. Available: www.iea.org; [accessed 2015-11-12]. 2. Goransson, L., Goop, J., Unger, T., Odenberger, M., and Johnsson, F. Linkages between demandside management and congestion in the European electricity transmission system", Energy, 69, pp. 860- 872 (2014). 3. Parvania, M. and Fotuhi-Firuzabad, M. Demand response scheduling by stochastic SCUC", IEEE Transactions on Smart Grid, 1(1), pp. 89-98 (2010). 4. Rahmani-andebili, M. Modeling nonlinear incentivebased and price-based demand response programs and implementing on real power markets", Electric Power Systems Research, 132, pp. 115-124 (2016). 5. FERC, Sta_ Report, Assessment of demand response and advanced metering [Online]. Available: www.FERC.gov (August 2006). 6. http://en.wikipedia.org/wiki/Negawatt power [accessed 2015-10-27]. 7. Rahmani-andebili, M. Nonlinear demand response programs for residential customers with nonlinear behavioral models", Energy and Buildings, 119, pp. 352- 362 (2016). 8. Bompard, E., Ma, Y., Napoli, R., and Abrate, G. The demand elasticity impacts on the strategic bidding behavior of the electricity producers", IEEE Transactions on Power Systems, 22(1), pp. 188-197 (2007). 9. Goel, L., Wu, Q., and Wang, P. Reliability enhancement and nodal price volatility reduction of restructured power systems with stochastic demand side load shift", IEEE Power Engineering Society General Meeting Conference, Florida, USA, pp. 1-8 (2007). 10. Yu, N. and Yu, J.l. Optimal TOU decision considering demand response model", International Conference on Power System Technology, Chongqing, China, pp. 1-5 (2006). 11. Goel, L., Qiuwei, W., and Peng, W. Reliability enhancement of a deregulated power system considering demand response", IEEE Power Engineering Society General Meeting Conference, Montreal, Que., Canada, pp. 1-6 (2006). 12. Su, C.L. and Kirschen, D. Quantifying the e_ect of demand response on electricity markets", IEEE Transactions on Power Systems, 24(3), pp. 1199-1207 (2009). 13. Schweppe, F.C., Caramanis, M.C., Tabors, R.D., and Bohn, R.E. Spot pricing of electricity", In Power Electronics and Power Systems, Springer Science & Business Media (2013). 14. Youse_, S., Moghaddam, M.P., and Majd, V.J. Optimal real time pricing in an agent-based retail market using a comprehensive demand response model", Energy, 36(9), pp. 5716-5727 (2011). 15. Conejo, A.J., Morales, M., and Baringo, L. Realtime demand response model", IEEE Transactions on Smart Grid, 1(3), pp. 236-242 (2010). 16. Mahmoudi-Kohan, N., Moghaddam, M.P., and Sheikh-El-Eslami, M.K. An annual framework for clustering-based pricing for an electricity retailer", Electric Power Systems Research, 80(9), pp. 1042-1048 (2010). 17. Hatami, A.R., Sei_, H., and Sheikh-El-Eslami, M.K. Optimal selling price and energy procurement strategies for a retailer in an electricity market", Electric Power Systems Research, 79(1), pp. 246-254 (2009). 18. Alc_azar-Ortega, M., Escriv_a-Escriv_a, G., and Segura- Heras, I. Methodology for validating technical tools to assess customer demand response: application to a commercial customer", Energy Conversion and Management, 52(2), pp. 1507-1511 (2011). 19. Chao, H. Demand response in wholesale electricity markets: the choice of customer baseline", Journal of Regulatory Economics, 39(1), pp. 68-88 (2011). 20. Ferreira, R.S., Barroso, L.A., and Carvalho, M.M. Demand response models with correlated price data: A robust optimization approach", Appllied Energy, 96, pp.133-149 (2012). 21. Lecocq, S. and Robin, J.M. Estimating demand response with panel data", Empirical Economics, 31(4), pp. 1043-1060 (2006). 22. Chen, L., Li, N., Low, S.H., and Doyle, J.C. Two market models for demand response in power networks", First IEEE International Conference on Smart Grid Communications, Gaithersburg, USA, pp.397- 402 (2010). 1372 A. Abdollahi et al./Scientia Iranica, Transactions D: Computer Science & ... 27 (2020) 1361{1372 23. Kirschen, D.S., Strbac, G., Cumperayot, P., and Mendes, D.P. Factoring the elasticity of demand in electricity prices", IEEE Transactions on Power Systems, 15(2), pp. 612-617 (2000). 24. Khodaei, A., Shahidehpour, M., and Bahramirad, S. SCUC with hourly demand response considering intertemporal load characteristics", IEEE Transactions on Smart Grid, 2(3), pp. 564-571 (2011). 25. Aghaei, J. and Alizadeh, M.I. Robust n-k contingency constrained unit commitment with ancillary service demand response program", IET Generation, Transmission and Distribution, 8(12), pp. 1928-1936 (2014). 26. Abdollahi, A., Moghaddam, M.P., Rashidinejad, M., and Sheikh-el-Eslami, M.K. Investigation of economic and environmental-driven demand response measures incorporating UC", IEEE Transactions on Smart Grid, 3(1), pp. 12-25 (2012). 27. Aalami, H., Youse_, G.R., and Moghadam, M.P. A MADM-based support system for DR programs", 43th International Universities Power Engineering Conference, Padova, Italy, pp. 1-7 (2008). 28. Aalami, H., Youse_, G.R., and Moghadam, M.P. Demand response model considering EDRP and TOU programs", IEEE/PES Transmission and Distribution Conference and Exposition, Bogota, Colombia, pp. 1-6 (2008). 29. Aalami, H.A., Moghaddam, M.P., and Youse_, G.R. Demand response modeling considering interruptible/ curtailable loads and capacity market programs", Applied Energy, 87(1), pp. 243-250 (2010). 30. Aalami, H.A., Moghaddam, M.P., and Youse_, G.R. Modeling and prioritizing demand response programs in power markets", Electric Power Systems Research, 80(4), pp. 426-435 (2010). 31. Moghaddam, M.P., Abdollahi, A., and Rashidinejad, M. Flexible demand response programs modeling in competitive electricity markets", Applied Energy, 88(9), pp. 3257-3269 (2011). 32. Aghaei, J. and Alizadeh, M.I. Critical peak pricing with load control demand response program in unit commitment problem", IET Generation, Transmission and Distribution, 7(7), pp. 681-690 (2013). 33. Thimmapuram, P.R. and Kim, J. Consumers' price elasticity of demand modeling with economic e_ects on electricity markets using an agent-based model", IEEE Transactions on Smart Grid, 4(1), pp. 390-397 (2013). 34. Tzeng, G.H. and Huang, J.J., Multiple Attribute Decision Making Methods and Applications, Taylor & Francis Group, LLC (2011). 35. Aghaei, J. and Alizadeh, M.I. Demand response in smart electricity grids equipped with renewable energy sources: a review", Renewable and Sustainable Energy Reviews, 18, pp. 64-72 (2013). 36. Mozafari, B., Amraee, T., Ranjbar, A.M., and Mirjafari, M. Particle swarm optimization method for optimal reactive power procurement considering voltage stability", Scientia Iranica, 14(6), pp. 534-545 (2007). 37. Kirschen, D. and Strbac, G., Fundamentals of Power System Economics, John Wiley & Sons, Ltd (2004). 38. Tabandeh, A., Abdollahi, A., and Rashidinejad, M. Reliability constrained congestion management with uncertain negawatt demand response _rms considering repairable advanced metering infrastructures", Energy, 104, pp. 213-228 (2016). 39. Nigim, K., Munier, N., and Green, J. Pre-feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources", Renewable Energy, 29(11), pp. 1775-1791 (2004). 40. Shanian, A. and Savadogo, O. TOPSIS multiplecriteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell", Journal of Power Sources, 159(2), pp. 1095-1104 (2006). 41. Barforoushi, T., Moghaddam, M.P., Javidi, M.H., and Sheikh-El-Eslami, M.K. Evaluation of regulatory impacts on dynamic behavior of investments in electricity markets: a new hybrid dp/game framework", IEEE Transactions on Power Systems, 25(4), pp. 1978-1986 (2010).