Skip to main content
SpringerLink
Log in

Demand Response in Future Power Networks: Panorama and State-of-the-art

  • Chapter
  • First Online:
Sustainable Interdependent Networks II

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 186))

Abstract

One of the key features of future power networks, referred to as smart grids, is deploying demand-side resources in order to reduce the stress at the supply side. This implies active participation of electricity customers, as a societal network, in the power networks, as a physical network, which increases the interdependencies of these two networks due to the effect of demand response programs on power systems. Furthermore, in the future smart cities there is a crucial need to take advantage of demand-side resources to supply electricity in a sustainable manner. In this context, demand response programs play a pivotal role in electricity market in order to achieve supply-demand balance by taking advantage of the load flexibility.

In this chapter, we provide a thorough review of the state-of-the-art approaches to implement demand response programs in smart grid environment. To this end, we first introduce the available methods to model load participation in terms of demand response programs, such as game theoretic frameworks, price elasticity, and direct load control. We then review the methods for integrating demand-side resources into power systems. Several aspects of demand response programs are reviewed in this chapter. Finally, an overview of the recent advances in demand response literature is presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Parvania, M., & FotuhiFioruzabad, M. (2010). Demand response scheduling by stochastic SCUC. IEEE Transactions on Smart Grid, 1, 89–98.

    Article  Google Scholar 

  2. Crossley, D. (2006). Worldwide survey of network-driven demand-side management projects (1st ed.). Paris: IEA Press.

    Google Scholar 

  3. Arasteh, H., SadeghSepasian, M., & Vahidinasab, V. (2015). Toward a smart distribution system expansion planning by considering demand response resources. Journal of Operation and Automation in Power Engineering (JOAPE), 3(2), 116–130.

    Google Scholar 

  4. Bompard, E., Ma, Y., Napoli, R., & Abrate, G. (2007). The demand elasticity impacts on the strategic bidding behavior of the electricity producers. IEEE Transactions on Power Systems., 22(1), 188–197.

    Article  Google Scholar 

  5. Goel, L., Wu, Q., & Wang, P. (2007). Reliability enhancement and nodal price volatility reduction of restructured power systems with stochastic demand side load shift. IEEE Power Engineering Society General Meeting Conference (pp. 1–8).

    Google Scholar 

  6. Yu, N., & Yu, J. L. (2006). Optimal TOU decision considering demand response model. International Conference on Power System Technology (pp. 1–5).

    Google Scholar 

  7. Goel, L., Qiuwei, W., & Peng, W. (2006). Reliability enhancement of a deregulated power system considering demand response. IEEE Power Engineering Society General Meeting Conference (pp. 1–6).

    Google Scholar 

  8. Su, C. L., & Kirschen, D. (2009). Quantifying the effect of demand response on electricity markets. IEEE Transactions on Power Systems., 24(3), 1199–1207.

    Article  Google Scholar 

  9. Schweppe, F. C., Caramanis, M. C., Tabors, R. D., & Bohn, R. E. (2013). Spot pricing of electricity. Berlin: Springer Science & Business Media.

    Google Scholar 

  10. Yousefi, S., Moghaddam, M. P., & Majd, V. J. (2011). Optimal real time pricing in an agent-based retail market using a comprehensive demand response model. Energy, 36(9), 5716–5727.

    Article  Google Scholar 

  11. Conejo, A. J., Morales, M., & Baringo, L. (2010). Real-time demand response model. IEEE Transactions on Smart Grid, 1(3), 236–242.

    Article  Google Scholar 

  12. Mahmoudi-Kohan, N., Parsa Moghaddam, M., & Sheikh-El-Eslami, M. K. (2010). An annual framework for clustering-based pricing for an electricity retailer. Electric Power Systems Research, 80(9), 1042–1048.

    Article  Google Scholar 

  13. Hatami, A. R., Seifi, H., & Sheikh-El-Eslami, M. K. (2009). Optimal selling price and energy procurement strategies for a retailer in an electricity market. Electric Power Systems Research, 79(1), 246–254.

    Article  Google Scholar 

  14. Alcázar-Ortega, M., Escrivá-Escrivá, G., & Segura-Heras, I. (2011). Methodology for validating technical tools to assess customer demand response: Application to a commercial customer. Energy Conversion and Management, 52(2), 1507–1511.

    Article  Google Scholar 

  15. Chao, H. (2011). Demand response in wholesale electricity markets: The choice of customer baseline. Journal of Regulatory Economics, 39(1), 68–88.

    Article  Google Scholar 

  16. Ferreira, R. S., Barroso, L. A., & Carvalho, M. M. (2012). Demand response models with correlated price data: A robust optimization approach. Applied Energy, 96, 133–149.

    Article  Google Scholar 

  17. Lecocq, S., & Robin, M. (2006). Estimating demand response with panel data. Empirical Economics, 31(4), 1043–1060.

    Article  Google Scholar 

  18. Chen, L., Li, N., Low, S. H., & Doyle, J. C. (2010). Two market models for demand response in power networks. First IEEE International Conference on Smart Grid Communications (pp. 397–402).

    Google Scholar 

  19. Kirschen, D. S., Strbac, G., Cumperayot, P., & DdP, M. (2000). Factoring the elasticity of demand in electricity prices. IEEE Transactions on Power Systems, 15(2), 612–617.

    Article  Google Scholar 

  20. Khodaei, A., Shahidehpour, M., & Bahramirad, S. (2011). SCUC with hourly demand response considering intertemporal load characteristics. IEEE Transactions on Smart Grid, 2(3), 564–571.

    Article  Google Scholar 

  21. Aghaei, J., & Alizadeh, M. I. (2014). Robust n-k contingency constrained unit commitment with ancillary service demand response program. IET Generation, Transmission and Distribution, 8(12), 1928–1936.

    Article  Google Scholar 

  22. Abdollahi, A., Moghaddam, M. P., Rashidinejad, M., & Sheikh-el-Eslami, M. K. (2012). Investigation of economic and environmental-driven demand response measures incorporating UC. IEEE Transactions on Smart Grid, 3(1), 12–25.

    Article  Google Scholar 

  23. Aalami, H., Yousefi, G. R., & Moghadam, M. P. (2008). A MADM-based support system for DR programs. 43rd International Universities Power Engineering Conference (pp. 1–7).

    Google Scholar 

  24. Aalami, H., Yousefi, G. R., & Moghadam, M. P. (2008). Demand response model considering EDRP and TOU programs. IEEE/PES Transmission and Distribution Conference and Exposition, 1–6.

    Google Scholar 

  25. Aalami, H. A., Moghaddam, M. P., & Yousefi, G. R. (2010). Demand response modeling considering interruptible/curtailable loads and capacity market programs. Applied Energy, 87(1), 243–250.

    Article  Google Scholar 

  26. Aalami, H. A., Moghaddam, M. P., & Yousefi, G. R. (2010). Modeling and prioritizing demand response programs in power markets. Electric Power Systems Research, 80(4), 426–435.

    Article  Google Scholar 

  27. Hajebrahimi, A., Abdollahi, A., & Rashidinejad, M. (2017). Probabilistic multiobjective transmission expansion planning incorporating demand response resources and large-scale distant wind farms. IEEE Systems Journal, 11(2), 1170–1181.

    Article  Google Scholar 

  28. Aghaei, J., Alizadeh, M. I., Abdollahi, A., & Barani, M. (2016). Allocation of demand response resources: Towards an effective contribution to power system voltage stability. IET Generation, Transmission and Distribution, 10(16), 4169–4177.

    Article  Google Scholar 

  29. Mollahassani-pour, M., Abdollahi, A., & Rashidinejad, M. (2015). Investigation of market-based demand response impacts on security-constrained preventive maintenance scheduling. IEEE Systems Journal, 9(4), 1496–1506.

    Article  Google Scholar 

  30. Mollahassani-Pour, M., Rashidinejad, M., Abdollahi, A., & Forghani, M. A. (2017). Demand response resources’ allocation in security-constrained preventive maintenance scheduling via MODM method. IEEE Systems Journal, 11(2), 1196–1207.

    Article  Google Scholar 

  31. Aghaei, J., & Alizadeh, M. I. (2013). Critical peak pricing with load control demand response program in unit commitment problem. IET Generation, Transmission and Distribution, 7(7), 681–690.

    Article  Google Scholar 

  32. Arasteh, H. R., Moghaddam, M. P., Sheikh-El-Eslami, M. K., & Abdollahi, A. (2013). Integrating commercial demand response resources with unit commitment. Electrical Power and Energy Systems, 51, 153–161.

    Article  Google Scholar 

  33. Moghaddam, M. P., Abdollahi, A., & Rashidinejad, M. (2011). Flexible demand response programs modeling in competitive electricity markets. Applied Energy, 88(9), 3257–3269.

    Article  Google Scholar 

  34. Tabandeh, A., Abdollahi, A., & Rashidinejad, M. (2016). Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures. Energy, 104(1), 1 213-228.

    Google Scholar 

  35. Amini, M. H., Nabi B., & Haghifam M.-R. (2013). Load management using multi-agent systems in smart distribution network. Power and Energy Society General Meeting (PES), 2013 IEEE. IEEE, 2013.

    Google Scholar 

  36. Amini, M. H., Frye, J., Ilić, M. D., & Karabasoglu, O. (2015, October). Smart residential energy scheduling utilizing two stage mixed integer linear programming. In North American power symposium (NAPS) (pp. 1–6).

    Google Scholar 

  37. Amini, M. H., Nabi, B., Moghaddam, M. P., & Mortazavi, S. A. (2012, May). Evaluating the effect of demand response programs and fuel cost on PHEV owners behavior, a mathematical approach. In Smart grids (ICSG), 2nd Iranian conference on IEEE (pp. 1–6).

    Google Scholar 

  38. Boroojeni, K. G., Amini, M. H., & Iyengar, S. S. (2017). End-user data privacy. Smart grids: Security and privacy issues (pp. 85–92). Berlin: Springer International Publishing.

    Book  Google Scholar 

  39. Parvania, M., Fotuhi-Firuzabad, M., & Shahidehpour, M. (2014, November). ISO’s optimal strategies for scheduling the hourly demand response in day-ahead markets. IEEE Transactions on Power Apparatus and Systems, 29(6), 2636–2645.

    Article  Google Scholar 

  40. Parvania, M., Fotuhi-Firuzabad, M., & Shahidehpour, M. (2013, December). Optimal demand response aggregation in wholesale electricity markets. IEEE Transactions on Smart Grid, 4(4), 1957–1965.

    Article  Google Scholar 

  41. Mahmoudi, N., Heydarian-Forushani, E., Shafie-khah, M., Saha, T. K., Golshan, M. E. H., & Siano, P. (2017, February). A bottom-up approach for demand response aggregators’ participation in electricity markets. Electric Power Systems Research, 143, 121–129.

    Article  Google Scholar 

  42. Nguyen, D. T., Nguyen, H. T., & Le, L. B. (2016, September). Dynamic pricing design for demand response integration in power distribution networks. IEEE Transactions on Power Apparatus and Systems, 31(5), 3457–3472.

    Article  MathSciNet  Google Scholar 

  43. Wu, H., Shahidehpour, M., & Khodayar, M. E. (2013, August). Hourly demand response in day-ahead scheduling considering generating unit ramping cost. IEEE Transactions on Power Apparatus and Systems, 28(3), 2446–2454.

    Article  Google Scholar 

  44. Papavasiliou, A., & Oren, S. S. (2014, January). Large-scale integration of deferrable demand and renewable energy sources. IEEE Transactions on Power Apparatus and Systems, 29(1), 489–499.

    Article  Google Scholar 

  45. Knudsen, J., Hansen, J., & Annaswamy, A. M. (2016, May). A dynamic market mechanism for the integration of renewables and demand response. IEEE Transactions on Control Systems Technology, 24(3), 940–955.

    Article  Google Scholar 

  46. Shafie-khah, M., Heydarian-Forushani, E., Osório, G. J., Gil, F. A. S., Aghaei, J., Barani, M., & Catalão, J. P. S. (2016, November). Optimal behavior of electric vehicle parking lots as demand response aggregation agents. IEEE Transactions on Smart Grid, 7(6), 2654–2665.

    Article  Google Scholar 

  47. Amini, M. H., Parsa Moghaddam, M., & Heydarian Forushani, E. (2013). Forecasting the PEV owner reaction to the electricity price based on the customer acceptance index. In Smart Grid Conference (SGC). IEEE.

    Google Scholar 

  48. Shao, S., Pipattanasomporn, M., & Rahman, S. (2011). Demand response as a load shaping tool in an intelligent grid with electric vehicles. IEEE Transactions on Smart Grid, 2(4), 624–631.

    Article  Google Scholar 

  49. Fan, Z. (2012). A distributed demand response algorithm and its application to PHEV charging in smart grids. IEEE Transactions on Smart Grid, 3(3), 1280–1290.

    Article  Google Scholar 

  50. Tan, Z., Yang, P., & Nehorai, A. (2014). An optimal and distributed demand response strategy with electric vehicles in the smart grid. IEEE Transactions on Smart Grid, 5(2), 861–869.

    Article  Google Scholar 

  51. Rassaei, F., Soh, W. S., & Chua, K. C. (2015). A statistical modelling and analysis of residential electric vehicles’ charging demand in smart grids. 2015 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1–5).

    Google Scholar 

  52. Rassaei, F., Soh, W. S., & Chua, K. C. (2015, October). Demand response for residential electric vehicles with random usage patterns in smart grids. IEEE Transactions on Sustainable Energy, 6(4), 1367–1376.

    Article  Google Scholar 

  53. Rassaei, F., Soh, W. S., & Chua, K. C. (2016). Distributed scalable autonomous market-based demand response via residential plug-in electric vehicles in smart grids (Vol. 9, p. 3281). IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2016.2629515.

  54. Rassaei, F., Soh, W. S., & Chua, K. C. (2015). Joint shaping and altering the demand profile by residential plug-in electric vehicles for forward and spot markets in smart grids. 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA) (pp. 1–6).

    Google Scholar 

  55. Rassaei, F., Soh, W. S., & Chua, K. C., & Modarresi, M. S. (2017). Environmentally-friendly demand response for residential plug-in electric vehicles. 2017 IEEE Texas Power and Energy Conference (TPEC) (pp. 1–6).

    Google Scholar 

  56. Fei, W., Hanchen, X., Xu, T., Li, K., Shafie-khah, M., & Catalão, J. P. S. (2017). The values of market-based demand response on improving power system reliability under extreme circumstances. Applied Energy.

    Google Scholar 

  57. Talari, S., Yazdaninejad, M., & Haghifam, M.-R. (2015, April). Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads. IET Generation Transmission and Distribution, 9(12), 1498–1509.

    Article  Google Scholar 

  58. Kazemi, M., Mohammadi-Ivatloo, B., & Ehsan, M. (2015). Risk-constrained strategic bidding of Gencos considering demand response. IEEE Transactions on Power Apparatus and Systems, 30(1), 376–384.

    Article  Google Scholar 

  59. Kazemi, M., Mohammadi-Ivatloo, B., & Ehsan, M. (2014). Risk-based bidding of large electric utilities using information gap decision theory considering demand response. Electric Power Systems Research, 114, 86–92.

    Article  Google Scholar 

  60. Kazemi, M., Zareipour, H., Ehsan, M., & Rosehart, W. D. (2016). A robust linear approach for offering strategy of a hybrid electric energy company. IEEE Transactions on Power Systems, 32, 1949–1959.

    Article  Google Scholar 

  61. Kharrati, S., Kazemi, M., & Ehsan, M. (2016). Equilibria in the competitive retail electricity market considering uncertainty and risk management. Energy, 106, 315–328.

    Article  Google Scholar 

  62. Kharrati, S., Kazemi, M., & Ehsan, M. (2015). Medium-term retailer's planning and participation strategy considering electricity market uncertainties. International Transactions on Electrical Energy Systems, 26(5), 920–933.

    Article  Google Scholar 

  63. Kazemi, M., Mohammadi-Ivatloo, B., & Ehsan, M. (2013). IGDT based risk-constrained strategic bidding of Gencos considering bilateral contracts. In 2013 21st Iranian Conference on Electrical Engineering (ICEE). IEEE, 2013 (pp. 1–6).

    Google Scholar 

  64. Hatami, A., Seifi, H., & Sheikh-El-Eslami, M. K. (2011). A stochastic-based decision-making framework for an electricity retailer: Time-of-use pricing and electricity portfolio optimization. IEEE Transactions on Power Systems, 26(4), 1808–1816.

    Article  Google Scholar 

  65. Kirschen, D. S. (2003). Demand-side view of electricity markets. IEEE Transactions on Power Apparatus and Systems, 18(2), 520–527.

    Article  Google Scholar 

  66. Arasteh, H., Sepasian, M. S., Vahidinasab, V., & Siano, P. (2016, December). SoS-based multiobjective distribution system expansion planning. Electric Power Systems Research (EPSR), 141, 392–406.

    Article  Google Scholar 

  67. Arasteh, H., Sepasian, M. S., & Vahidinasab, V. (2016, January 1). An aggregated model for coordinated planning and reconfiguration of electric distribution networks. Energy, 94, 786–798.

    Article  Google Scholar 

  68. Mahmoudi, N. (2015). New demand response and its applications for electricity markets. PhD, School of ITEE, University of Queensland, University of Queensland.

    Google Scholar 

  69. Mahmoudi, N., Eghbal, M., & Saha, T. K. (2014). Employing demand response in energy procurement plans of electricity retailers. International Journal of Electrical Power & Energy Systems, 63, 455–460.

    Article  Google Scholar 

  70. Mahmoudi, N., Saha, T. K., & Eghbal, M. (2014). A new demand response scheme for electricity retailers. Electric Power Systems Research, 108, 144–152.

    Article  Google Scholar 

  71. Arasteh, H. R., Parsa Moghaddam, M., & Sheikh-El-Eslami, M. K. (2013, March). A comprehensive framework for retailer’s financial policy. Journal of Electrical Systems and Signals, 1(1), 7–18.

    Google Scholar 

  72. Nguyen, D. T., Negnevitsky, M., & Groot, M. D. (2011). Pool-based demand response exchange—Concept and modeling. IEEE Transactions on Power Apparatus and Systems, 26, 1677–1685.

    Article  Google Scholar 

  73. Arasteh, H. R., Parsa Moghaddam, M., & Sheikh-El-Eslami, M. K. (2012). Bidding strategy in demand response exchange market. 2nd Iranian Conference on Smart Grid, Tehran, Iran, May 23–24, 2012.

    Google Scholar 

  74. Mahmoudi, N., Saha, T. K., & Eghbal, M. (2015). Wind offering strategy in the Australian National Electricity Market: A two-step plan considering demand response. Electric Power Systems Research, 119, 187–198.

    Article  Google Scholar 

  75. Mahmoudi, N., Saha, T. K., & Eghbal, M. (2015). Wind power offering strategy in day-ahead markets: Employing demand response in a two-stage plan. IEEE Transactions on Power Systems, 30(4), 1888–1896.

    Article  Google Scholar 

  76. Mahmoudi, N., Saha, T. K., & Eghbal, M. (2014, November 15). Modelling demand response aggregator behavior in wind power offering strategies. Applied Energy, 133, 347–355.

    Article  Google Scholar 

  77. Mahmoudi, N., Saha, T. K., & Eghbal, M. (2016). Demand response application by strategic wind power producers. IEEE Transactions on Power Systems, 31(2), 1227–1237.

    Article  Google Scholar 

  78. Bahrami, S., & Amini, M. H. (2018). A decentralized trading algorithm for an electricity market with generation uncertainty. Applied Energy, 218, 520–532.

    Article  Google Scholar 

  79. Mohammadi, A., Mehrtash, M., & Kargarian, A. (2018). Diagonal quadratic approximation for decentralized collaborative TSO+DSO optimal power flow. IEEE Transactions on Smart Grid, 1. https://doi.org/10.1109/TSG.2018.2796034.

  80. Siano, P. (2014). Demand response and smart grids—A survey. Renewable and Sustainable Energy Reviews, 30, 461–478.

    Article  Google Scholar 

  81. Bhattacharjee, V., & Khan, I. (2018). A non-linear convex cost model for economic dispatch in microgrids. Applied Energy, 222, 637–648.

    Article  Google Scholar 

  82. Behrens, D., Schoormann, T., & Knackstedt, R. (2018). Developing an algorithm to consider multiple demand response objectives. Engineering, Technology and Applied Science Research, 8(1), 2621–2626.

    Google Scholar 

  83. Rabiee, A., Masood Mohseni-Bonab, S., Parniani, M., & Kamwa, I. (2018). Optimal cost of voltage security control using voltage dependent load models in presence of demand response. IEEE Transactions on Smart Grid, 1–12.

    Google Scholar 

  84. Darby, S. J. (2018). Smart electric storage heating and potential for residential demand response. Energy Efficiency, 11(1), 67–77.

    Article  Google Scholar 

  85. Vahid-Ghavidel, M., Mahmoudi, N., & Mohammadi-ivatloo, B. (2018). Self-scheduling of demand response aggregators in short-term markets based on information gap decision theory. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2017.2788890.

  86. do Prado, J. C., & Qiao, W. (2018). A stochastic decision-making model for an electricity retailer with intermittent renewable energy and short-term demand response. IEEE Transactions on Smart Grid, 1–12.

    Google Scholar 

  87. Salah, F., Henríquez, R., Wenzel, G., Olivares, D., Negrete-Pincetic, M., & Weinhardt, C. (2018). Portfolio design of a demand response aggregator with satisficing consumers. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2018.2799822.

  88. Hurtado, L. A., Mocanu, E., Nguyen, P. H., Gibescu, M., & Kamphuis, R. I. G. (2018, January). Enabling cooperative behavior for building demand response based on extended joint action learning. IEEE Transactions on Industrial Informatics, 14(1), 127–136.

    Article  Google Scholar 

  89. Alipour, M., Zare, K., & Abapour, M. (2018, January). MINLP probabilistic scheduling model for demand response programs integrated energy hubs. IEEE Transactions on Industrial Informatics, 14(1), 79–88.

    Article  Google Scholar 

  90. Bitaraf, H., & Rahman, S. (2018, January). Reducing curtailed wind energy through energy storage and demand response. IEEE Transactions on Sustainable Energy, 9(1), 228–236.

    Article  Google Scholar 

  91. Behboodi, S., Chassin, D. P., Djilali, N., & Crawford, C. (2018). Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets. Applied Energy, 210, 1310–1320.

    Article  Google Scholar 

  92. Lu, T., Wang, Z., Wang, J., Ai, Q., & Wang, C. A data-driven Stackelberg market strategy for demand response-enabled distribution systems. IEEE Transactions on Smart Grid.

    Google Scholar 

  93. Rahimi, F., & Ipakchi, A. (2010). Demand response as a market resource under the smart grid paradigm. IEEE Transactions on Smart Grid, 1(1), 82–88.

    Article  Google Scholar 

  94. Massrur, H. R., Niknam, T., & Fotuhi-Firuzabad, M. (2018). Investigation of carrier demand response uncertainty on energy flow of renewable-based integrated electricity-gas-heat systems. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2018.2798820.

  95. Srivastava, A., Van Passel, S., & Laes, E. (2018). Assessing the success of electricity demand response programs: A meta-analysis. Energy Research and Social Science, 40, 110–117.

    Article  Google Scholar 

  96. Crosbie, T., Broderick, J., Short, M., Charlesworth, R., & Dawood, M. (2018). Demand response technology readiness levels for energy management in Blocks of buildings. Buildings, 8(2), 13.

    Article  Google Scholar 

  97. Viana, M. S., Manassero, G., & Udaeta, M. E. M. (2018). Analysis of demand response and photovoltaic distributed generation as resources for power utility planning. Applied Energy, 217, 456–466.

    Article  Google Scholar 

  98. Thornton, M., Motalleb, M., Smidt, H., Branigan, J., & Ghorbani, R. (2018). Demo abstract: Testbed for distributed demand response devices—internet of things. Computer Science-Research and Development, 33(1–2), 277–278.

    Article  Google Scholar 

  99. Shinde, P., & Swarup, K. S. (2018, September). Stackelberg game-based demand response in multiple utility environments for electric vehicle charging. IET Electrical Systems in Transportation, 8(3), 167–174.

    Article  Google Scholar 

  100. Motalleb, M., Branigan, J., & Ghorbani, R. (2018). Demand response market considering dynamic pricing. Computer Science-Research and Development, 33(1–2), 257–258.

    Article  Google Scholar 

Download references

Acknowledgements

J.P.S. Catalão acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICTPAC/0004/2015—POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013, and 02/SAICT/2017—POCI-01-0145-FEDER-029803, and also funding from the EU 7th Framework Programme FP7/2007-2013 under GA no. 309048.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    M. Hadi Amini & Vikram Bhattacharjee

  2. C-MAST, University of Beira Interior, Covilha, Portugal

    Saber Talari, Miadreza Shafie-Khah & João P. S. Catalão

  3. Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

    Hamidreza Arasteh

  4. Niroo Research Institute, Tehran, Iran

    Hamidreza Arasteh

  5. School of ITEE, University of Queensland, Brisbane, QLD, Australia

    Nadali Mahmoudi

  6. Faculty of Electrical Engineering, University of Shahreza, Shahreza, Iran

    Mostafa Kazemi

  7. Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Amir Abdollahi

  8. Department of Industrial Engineering, University of Salerno, Salerno, Italy

    Pierluigi Siano

  9. INESC TEC and the Faculty of Engineering of the University of Porto, Porto, Portugal

    João P. S. Catalão

  10. INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    João P. S. Catalão

Authors
  1. M. Hadi Amini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saber Talari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamidreza Arasteh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nadali Mahmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mostafa Kazemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amir Abdollahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vikram Bhattacharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Miadreza Shafie-Khah
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Pierluigi Siano
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. João P. S. Catalão
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Hadi Amini .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    M. Hadi Amini

  2. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    Kianoosh G. Boroojeni

  3. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    S. S. Iyengar

  4. Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  5. Department of Energy Technology, Aalborg University, Aalborg, Denmark

    Frede Blaabjerg

  6. Department of Electrical & Computer Engineering, University of California Los Angeles, Los Angeles, CA, USA

    Asad M. Madni

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Amini, M.H. et al. (2019). Demand Response in Future Power Networks: Panorama and State-of-the-art. In: Amini, M., Boroojeni, K., Iyengar, S., Pardalos, P., Blaabjerg, F., Madni, A. (eds) Sustainable Interdependent Networks II. Studies in Systems, Decision and Control, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-319-98923-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98923-5_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98922-8

  • Online ISBN: 978-3-319-98923-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation