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

  • M. Hadi Amini
  • Saber Talari
  • Hamidreza Arasteh
  • Nadali Mahmoudi
  • Mostafa Kazemi
  • Amir Abdollahi
  • Vikram Bhattacharjee
  • Miadreza Shafie-Khah
  • Pierluigi Siano
  • João P. S. Catalão
Chapter
Part of the Studies in Systems, Decision and Control book series (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.

Keywords

Demand response Smart grid Electricity market Load management Bidding strategy Decision-making frameworks Power systems Demand-side management Responsive load DR aggregator Load serving entity Electricity retailer Real time pricing Sustainable energy Interdependent networks 

Notes

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.

References

  1. 1.
    Parvania, M., & FotuhiFioruzabad, M. (2010). Demand response scheduling by stochastic SCUC. IEEE Transactions on Smart Grid, 1, 89–98.Google Scholar
  2. 2.
    Crossley, D. (2006). Worldwide survey of network-driven demand-side management projects (1st ed.). Paris: IEA Press.Google Scholar
  3. 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. 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.Google Scholar
  5. 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. 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. 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. 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.Google Scholar
  9. 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. 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.Google Scholar
  11. 11.
    Conejo, A. J., Morales, M., & Baringo, L. (2010). Real-time demand response model. IEEE Transactions on Smart Grid, 1(3), 236–242.Google Scholar
  12. 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.Google Scholar
  13. 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.Google Scholar
  14. 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.Google Scholar
  15. 15.
    Chao, H. (2011). Demand response in wholesale electricity markets: The choice of customer baseline. Journal of Regulatory Economics, 39(1), 68–88.Google Scholar
  16. 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.Google Scholar
  17. 17.
    Lecocq, S., & Robin, M. (2006). Estimating demand response with panel data. Empirical Economics, 31(4), 1043–1060.Google Scholar
  18. 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. 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.Google Scholar
  20. 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.Google Scholar
  21. 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.Google Scholar
  22. 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.Google Scholar
  23. 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. 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. 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.Google Scholar
  26. 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.Google Scholar
  27. 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.Google Scholar
  28. 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.Google Scholar
  29. 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.Google Scholar
  30. 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.Google Scholar
  31. 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.Google Scholar
  32. 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.Google Scholar
  33. 33.
    Moghaddam, M. P., Abdollahi, A., & Rashidinejad, M. (2011). Flexible demand response programs modeling in competitive electricity markets. Applied Energy, 88(9), 3257–3269.Google Scholar
  34. 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. 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. 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. 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. 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.Google Scholar
  39. 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.Google Scholar
  40. 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.Google Scholar
  41. 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.Google Scholar
  42. 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.Google Scholar
  43. 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.Google Scholar
  44. 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.Google Scholar
  45. 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.Google Scholar
  46. 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.Google Scholar
  47. 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. 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.Google Scholar
  49. 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.Google Scholar
  50. 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.Google Scholar
  51. 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. 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.Google Scholar
  53. 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. 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. 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. 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. 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.Google Scholar
  58. 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.Google Scholar
  59. 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.Google Scholar
  60. 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.Google Scholar
  61. 61.
    Kharrati, S., Kazemi, M., & Ehsan, M. (2016). Equilibria in the competitive retail electricity market considering uncertainty and risk management. Energy, 106, 315–328.Google Scholar
  62. 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.Google Scholar
  63. 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. 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.Google Scholar
  65. 65.
    Kirschen, D. S. (2003). Demand-side view of electricity markets. IEEE Transactions on Power Apparatus and Systems, 18(2), 520–527.Google Scholar
  66. 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.Google Scholar
  67. 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.Google Scholar
  68. 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. 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.Google Scholar
  70. 70.
    Mahmoudi, N., Saha, T. K., & Eghbal, M. (2014). A new demand response scheme for electricity retailers. Electric Power Systems Research, 108, 144–152.Google Scholar
  71. 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. 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.Google Scholar
  73. 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. 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.Google Scholar
  75. 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.Google Scholar
  76. 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.Google Scholar
  77. 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.Google Scholar
  78. 78.
    Bahrami, S., & Amini, M. H. (2018). A decentralized trading algorithm for an electricity market with generation uncertainty. Applied Energy, 218, 520–532.Google Scholar
  79. 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. 80.
    Siano, P. (2014). Demand response and smart grids—A survey. Renewable and Sustainable Energy Reviews, 30, 461–478.Google Scholar
  81. 81.
    Bhattacharjee, V., & Khan, I. (2018). A non-linear convex cost model for economic dispatch in microgrids. Applied Energy, 222, 637–648.Google Scholar
  82. 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. 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. 84.
    Darby, S. J. (2018). Smart electric storage heating and potential for residential demand response. Energy Efficiency, 11(1), 67–77.Google Scholar
  85. 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. 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. 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. 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.Google Scholar
  89. 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.Google Scholar
  90. 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.Google Scholar
  91. 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.Google Scholar
  92. 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. 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.Google Scholar
  94. 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. 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.Google Scholar
  96. 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.Google Scholar
  97. 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.Google Scholar
  98. 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.Google Scholar
  99. 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.Google Scholar
  100. 100.
    Motalleb, M., Branigan, J., & Ghorbani, R. (2018). Demand response market considering dynamic pricing. Computer Science-Research and Development, 33(1–2), 257–258.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. Hadi Amini
    • 1
  • Saber Talari
    • 2
  • Hamidreza Arasteh
    • 3
    • 4
  • Nadali Mahmoudi
    • 5
  • Mostafa Kazemi
    • 6
  • Amir Abdollahi
    • 7
  • Vikram Bhattacharjee
    • 1
  • Miadreza Shafie-Khah
    • 2
  • Pierluigi Siano
    • 8
  • João P. S. Catalão
    • 2
    • 9
    • 10
  1. 1.Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA
  2. 2.C-MAST, University of Beira InteriorCovilhaPortugal
  3. 3.Department of Electrical EngineeringShahid Beheshti UniversityTehranIran
  4. 4.Niroo Research InstituteTehranIran
  5. 5.School of ITEEUniversity of QueenslandBrisbaneAustralia
  6. 6.Faculty of Electrical EngineeringUniversity of ShahrezaShahrezaIran
  7. 7.Department of Electrical EngineeringShahid Bahonar University of KermanKermanIran
  8. 8.Department of Industrial EngineeringUniversity of SalernoSalernoItaly
  9. 9.INESC TEC and the Faculty of Engineering of the University of PortoPortoPortugal
  10. 10.INESC-ID, Instituto Superior TécnicoUniversity of LisbonLisbonPortugal

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