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Pre-implementation assessment for introducing direct load control strategies in the residential electricity sector

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Abstract

Demand response (DR) programs are getting wide acceptance among consumers in different parts of the world, and many utility establishments are looking for adopting such programs to meet the growing electricity demand. This paper discusses major focus areas to be considered while planning for introducing direct load control (DLC), one of the popular DR programs, by various stakeholders around the world. One of today's most extensively employed qualitative analytical tools, content analysis, was used in this study. The systematic software-based content analysis procedure followed in this study can be used as a reference for conducting similar studies in any frontier of science. The implementation strategy of DLC around the world was analyzed, and information related to different parameters, such as benefits, challenges, type of load, channels for awareness/marketing, implementation requirements, evaluation methods, and reasons for failure, was discussed in detail. The results of this research have far-fetching implications in effective policy designs for integrated energy planning to implement appropriate projects for sustainable developments. Based on the results, a conditional analysis was carried out to explore the feasibility of introducing DLC in Kuwait, which is one of the highest per capita electricity consuming countries in the world. Two widely used loads, such as air-conditioning units and water heaters, are identified as suitable loads for targeting DLC in Kuwait. These two loads account for a load share of 83% in the residential sector, which consumes 60% of electricity produced in the country. An action plan for implementing the suggested program for a pilot project also presented.

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References

  1. Strbac, G.: Demand side management: benefits and challenges. Energy Policy 36, 4419–4426 (2008). https://doi.org/10.1016/j.enpol.2008.09.030

    Article  Google Scholar 

  2. Yang, M.: Demand side management in Nepal. Energy 31, 2341–2362 (2006). https://doi.org/10.1016/j.energy.2005.12.008

    Article  Google Scholar 

  3. Saravanan, B.: DSM in an area consisting of residential, commercial and industrial load in smart grid. Front. Energy 9, 211–216 (2015). https://doi.org/10.1007/s11708-015-0351-0

    Article  Google Scholar 

  4. Warren, P.: A review of demand-side management policy in the UK. Renew. Sustain. Energy Rev. 29, 941–951 (2014). https://doi.org/10.1016/j.rser.2013.09.009

    Article  Google Scholar 

  5. Müller, D., Monti, A., Stinner, S., Schlösser, T., Schütz, T., Matthes, P., et al.: Demand side management for city districts. Build. Environ. 91, 283–293 (2015). https://doi.org/10.1016/j.buildenv.2015.03.026

    Article  Google Scholar 

  6. Energy Information Administration: U.S. Electric Utility Demand-Side Management 1994, vol. 0589 (1995)

  7. Smith, C., Parmenter, K.: Energy Management Principles Applications, Benefits, Savings, 2nd edn. Elsevier, Amsterdam (2015). https://doi.org/10.1115/1.3267640

    Book  Google Scholar 

  8. Energy Information Administration: Electric Utility Demand-Side Management 2000 2002;DOE/EIA-05:17

  9. Alasseri, R., Rao, T.J., Sreekanth, K.J.: Conceptual framework for introducing incentive-based demand response programs for retail electricity markets. Energy Strateg. Rev. 19, 44–62 (2018). https://doi.org/10.1016/j.esr.2017.12.001

    Article  Google Scholar 

  10. Behrangrad, M.: A review of demand side management business models in the electricity market. Renew. Sustain. Energy Rev. 47, 270–283 (2015). https://doi.org/10.1016/j.rser.2015.03.033

    Article  Google Scholar 

  11. Eissa, M.M.: Demand side management program evaluation based on industrial and commercial field data. Energy Policy 39, 5961–5969 (2011). https://doi.org/10.1016/j.enpol.2011.06.057

    Article  Google Scholar 

  12. Akinbulire, T.O., Oluseyi, P.O., Babatunde, O.M.: Techno-economic and environmental evaluation of demand side management techniques for rural electrification in Ibadan, Nigeria. Int. J. Energy Environ. Eng. 5, 375–385 (2014). https://doi.org/10.1007/s40095-014-0132-2

    Article  Google Scholar 

  13. Khan, I.: Energy-saving behaviour as a demand-side management strategy in the developing world: the case of Bangladesh. Int. J. Energy Environ. Eng. 10, 493–510 (2019). https://doi.org/10.1007/s40095-019-0302-3

    Article  Google Scholar 

  14. Kakran, S., Chanana, S.: Operation management of a renewable microgrid supplying to a residential community under the effect of incentive-based demand response program. Int. J. Energy Environ. Eng. 10, 121–135 (2019). https://doi.org/10.1007/s40095-018-0286-4

    Article  Google Scholar 

  15. Wood, M., Alsayegh, O.A.: Impact of oil prices, economic diversification policies and energy conservation programs on the electricity and water demands in Kuwait. Energy Policy 66, 144–156 (2014). https://doi.org/10.1016/j.enpol.2013.10.061

    Article  Google Scholar 

  16. Mehrara, M.: Energy consumption and economic growth: the case of oil exporting countries. Energy Policy 35, 2939–2945 (2007). https://doi.org/10.1016/j.enpol.2006.10.018

    Article  Google Scholar 

  17. Alasseri, R., Tripathi, A., Rao, T.J., Sreekanth, K.J.: A review on implementation strategies for demand side management (DSM ) in Kuwait through incentive-based demand response programs. Renew. Sustain. Energy Rev. 77, 617–635 (2017). https://doi.org/10.1016/j.rser.2017.04.023

    Article  Google Scholar 

  18. Alasseri, R., Rao, T.J., Sreekanth, K.J.: Institution of incentive-based demand response programs and prospective policy assessments for a subsidized electricity market. Renew. Sustain. Energy Rev. 117, 109490 (2020). https://doi.org/10.1016/j.rser.2019.109490

    Article  Google Scholar 

  19. Gils, H.C.: Assessment of the theoretical demand response potential in Europe. Energy 67, 1–18 (2014). https://doi.org/10.1016/j.energy.2014.02.019

    Article  Google Scholar 

  20. Hibbard, P.J., Okie, A.M., Darling, P.G.: Markets: reliability, dispatch. Electr. J. 25, 1–11 (2012). https://doi.org/10.1016/j.tej.2012.10.010

    Article  Google Scholar 

  21. Van, H.K., Gross, G.: Demand response resources are not all they’re made out to be: the payback effects severely reduce the reported DRR economic and emission benefit. Electr. J. 26, 86–97 (2013). https://doi.org/10.1016/j.tej.2013.07.008

    Article  Google Scholar 

  22. Earle, R., Kahn, E.P., Macan, E.: Measuring the capacity impacts of demand response. Electr. J. 22(6), 47–58 (2009). https://doi.org/10.1016/j.tej.2009.05.014

    Article  Google Scholar 

  23. Falk, J.: Paying for demand-side response at the wholesale level. Electr J 23, 13–18 (2010). https://doi.org/10.1016/j.tej.2010.10.001

    Article  Google Scholar 

  24. Walawalkar, R., Fernands, S., Thakur, N., Chevva, K.R.: Evolution and current status of demand response (DR) in electricity markets: insights from PJM and NYISO. Energy 35, 1553–1560 (2010). https://doi.org/10.1016/j.energy.2009.09.017

    Article  Google Scholar 

  25. Feuerriegel, S., Neumann, D.: Measuring the financial impact of demand response for electricity retailers. Energy Policy 65, 359–368 (2014). https://doi.org/10.1016/j.enpol.2013.10.012

    Article  Google Scholar 

  26. Faria, P., Vale, Z.: Demand response in electrical energy supply: an optimal real time pricing approach. Energy 36, 5374–5384 (2011). https://doi.org/10.1016/j.energy.2011.06.049

    Article  Google Scholar 

  27. Koliou, E., Bartusch, C., Picciariello, A., Eklund, T., Söder, L., Hakvoort, R.A.: Quantifying distribution-system operators’ economic incentives to promote residential demand response. Util. Policy 35, 28–40 (2015). https://doi.org/10.1016/j.jup.2015.07.001

    Article  Google Scholar 

  28. Kim, J.H., Shcherbakova, A.: Common failures of demand response. Energy 36, 873–880 (2011). https://doi.org/10.1016/j.energy.2010.12.027

    Article  Google Scholar 

  29. Lujano-Rojas, J.M., Monteiro, C., Dufo-López, R., Bernal-Agustín, J.L.: Optimum residential load management strategy for real time pricing (RTP) demand response programs. Energy Policy 45, 671–679 (2012). https://doi.org/10.1016/j.enpol.2012.03.019

    Article  Google Scholar 

  30. Li, X.H., Hong, S.H.: User-expected price-based demand response algorithm for a home-to-grid system. Energy 64, 437–449 (2014). https://doi.org/10.1016/j.energy.2013.11.049

    Article  Google Scholar 

  31. Barbato, A., Capone, A., Chen, L., Martignon, F., Paris, S.: A distributed demand-side management framework for the smart grid. Comput. Commun. 57, 13–24 (2015). https://doi.org/10.1016/j.comcom.2014.11.001

    Article  Google Scholar 

  32. Zibelman, A., Krapels, E.N.: Deployment of demand response as a real-time resource in organized markets. Electr. J. 21, 51–56 (2008)

    Article  Google Scholar 

  33. Hong, S.H., Yu, M., Huang, X.: A real-time demand response algorithm for heterogeneous devices in buildings and homes. Energy 80, 123–132 (2015). https://doi.org/10.1016/j.energy.2014.11.053

    Article  Google Scholar 

  34. Arun, S.L., Selvan, M.P.: Smart residential energy management system for demand response in buildings with energy storage devices. Front. Energy (2018). https://doi.org/10.1007/s11708-018-0538-2

    Article  Google Scholar 

  35. Oconnell, N., Pinson, P., Madsen, H.: Benefits and challenges of electrical demand response: a critical review. Renew. Sustain. Energy Rev. 39, 686–699 (2014). https://doi.org/10.1016/j.rser.2014.07.098

    Article  Google Scholar 

  36. Rejc, ŽB., Čepin, M.: Estimating the additional operating reserve in power systems with installed renewable energy sources. Int. J. Electr. Power Energy Syst. 62, 654–664 (2014). https://doi.org/10.1016/j.ijepes.2014.05.019

    Article  Google Scholar 

  37. Zhang, F., De Dear, R.: Thermal environments and thermal comfort impacts of direct load control air-conditioning strategies in university lecture theatres. Energy Build. 86, 233–242 (2015). https://doi.org/10.1016/j.enbuild.2014.10.008

    Article  Google Scholar 

  38. Zhang, F., de Dear, R.: Application of Taguchi method in optimising thermal comfort and cognitive performance during direct load control events. Build. Environ. 111, 160–168 (2017). https://doi.org/10.1016/j.buildenv.2016.11.012

    Article  Google Scholar 

  39. Zhang, F., de Dear, R., Candido, C.: Thermal comfort during temperature cycles induced by direct load control strategies of peak electricity demand management. Build. Environ. 103, 9–20 (2016). https://doi.org/10.1016/j.buildenv.2016.03.020

    Article  Google Scholar 

  40. Wang, S., Tang, R.: Supply-based feedback control strategy of air-conditioning systems for direct load control of buildings responding to urgent requests of smart grids. Appl. Energy 201, 419–432 (2017). https://doi.org/10.1016/j.apenergy.2016.10.067

    Article  Google Scholar 

  41. Tang, R., Wang, S., Yan, C.: A direct load control strategy of centralized air-conditioning systems for building fast demand response to urgent requests of smart grids. Autom. Constr. 87, 74–83 (2018). https://doi.org/10.1016/j.autcon.2017.12.012

    Article  Google Scholar 

  42. Goel, L., Wu, Q., Wang, P.: Fuzzy logic-based direct load control of air conditioning loads considering nodal reliability characteristics in restructured power systems. Electr Power Syst. Res. 80, 98–107 (2010). https://doi.org/10.1016/j.epsr.2009.08.009

    Article  Google Scholar 

  43. Shad, M., Momeni, A., Errouissi, R.: Identification and estimation for electric water heaters in direct load control programs. Smart Grid 8, 947–955 (2015)

    Google Scholar 

  44. Zhang, C., Xu, Y., Dong, Z.Y., Ma, J.: Robust operation of microgrids via two-stage coordinated energy storage and direct load control. IEEE Trans. Power Syst. 32, 2858–2868 (2017). https://doi.org/10.1109/TPWRS.2016.2627583

    Article  Google Scholar 

  45. Stenner, K., Frederiks, E.R., Hobman, E.V., Cook, S.: Willingness to participate in direct load control: the role of consumer distrust. Appl. Energy 189, 76–88 (2017). https://doi.org/10.1016/j.apenergy.2016.10.099

    Article  Google Scholar 

  46. Battegay, A., Hadj-Said, N., Roupioz, G., Lhote, F., Chambris, E., Boeda, D., et al.: Impacts of direct load control on reinforcement costs in distribution networks. Electr. Power Syst. Res. 120, 70–79 (2015). https://doi.org/10.1016/j.epsr.2014.09.012

    Article  Google Scholar 

  47. Lang, C., Okwelum, E.: The mitigating effect of strategic behavior on the net benefits of a direct load control program. Energy Econ. 49, 141–148 (2015). https://doi.org/10.1016/j.eneco.2015.01.025

    Article  Google Scholar 

  48. Evora, J., Hernandez, J.J., Hernandez, M.: A MOPSO method for direct load control in smart grid. Expert Syst. Appl. 42, 7456–7465 (2015). https://doi.org/10.1016/j.eswa.2015.05.056

    Article  Google Scholar 

  49. Hsieh, H.F., Shannon, S.E.: Three approaches to qualitative content analysis. Qual. Health Res. 15, 1277–1288 (2005). https://doi.org/10.1177/1049732305276687

    Article  Google Scholar 

  50. Krippendorff, K.H.: Content Analysis: An Introduction to Its Methodology, vol. 79, 2nd edn. Sage Publications, California (2012)

    Google Scholar 

  51. Ang, C.K., Embi, M.A., Yunus, M.M.: Enhancing the quality of the findings of a longitudinal case study: reviewing trustworthiness via ATLAS.ti enhancing the quality of the findings of a longitudinal case study. Qual. Rep. 21, 1855–1867 (2016)

    Google Scholar 

  52. Bowen, G.A.: Preparing a qualitative research-based dissertation: lessons learned. Qual. Rep. 10, 208–222 (2005)

    Google Scholar 

  53. Creswell, J.W.: Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 3rd edn. SAGE Publications, Inc, California (2009)

    Google Scholar 

  54. Franzosi, R., Doyle, S., McClelland, L.E., Putnam Rankin, C., Vicari, S.: Quantitative narrative analysis software options compared: PC-ACE and CAQDAS (ATLAS.ti, MAXqda, and NVivo). Qual. Quant. 47, 3219–3247 (2013). https://doi.org/10.1007/s11135-012-9714-3

    Article  Google Scholar 

  55. Smit, B.: Atlas.ti for qualitative data analysis. Perspect. Educ. 20, 65–76 (2002)

    Google Scholar 

  56. Woods, M., Macklin, R., Lewis, G.: How has computer-assisted qualitative data analysis software affected qualitative research? In: Qualitative Report Fourth Annual Conference (2013)

  57. Gibson, W., Callery, P., Campbell, M., Hall, A., Richards, D.: The digital revolution in qualitative research: working with digital audio data through Atlas.Ti. Sociol. Res. Online 10, 12 (2004). https://doi.org/10.5153/sro.1044

    Article  Google Scholar 

  58. Goulden, M., Bedwell, B., Rennick-Egglestone, S., Rodden, T., Spence, A.: Smart grids, smart users? The role of the user in demand side management. Energy Res. Soc. Sci. 2, 21–29 (2014). https://doi.org/10.1016/j.erss.2014.04.008

    Article  Google Scholar 

  59. Rahill, G.J., Joshi, M., Lescano, C., Holbert, D.: Symptoms of PTSD in a sample of female victims of sexual violence in post-earthquake Haiti. J. Affect. Disord. (2015). https://doi.org/10.1016/j.jad.2014.10.067

    Article  Google Scholar 

  60. Sanders, C., Rogers, A., Bowen, R., Bower, P., Hirani, S., Cartwright, M., et al.: Exploring barriers to participation and adoption of telehealth and telecare within the Whole System Demonstrator trial: a qualitative study. BMC Health Serv. Res. 12, 220 (2012). https://doi.org/10.1186/1472-6963-12-220

    Article  Google Scholar 

  61. Smit, B.: Atlas.ti for quality in qualitative research: a CAQDAS project. Educ. Chang. 6, 130–145 (2003)

    Google Scholar 

  62. Smith, J., Frith, J.: Qualitative data analysis: the framework approach. Nurse Res. 18, 52–63 (2011). https://doi.org/10.7748/nr2011.01.18.2.52.c8284

    Article  Google Scholar 

  63. Ducharme, D.: Using ATLAS.ti for content analysis. War Gaming. United States Naval War College (2014). Retrieved from https://www.scribd.com/document/358828621/Article-ATLASti-for-Content-Analysis

  64. Neuendorf, K.A.: The Content Analysis Guidebook, 2nd edn. SAGE Publications Inc, Thousand Oaks, CA (2017). https://doi.org/10.4135/9781071802878

    Book  Google Scholar 

  65. Friese, S.: Qualitative data analysis with ATLAS.ti, vol. 1. Sage (2012)

  66. Ellsworth-Krebs, K., Reid, L., Hunter, C.J.: Home -ing in on domestic energy research: “house”, “home”, and the importance of ontology. Energy Res. Soc. Sci. 6, 100–108 (2015). https://doi.org/10.1016/j.erss.2014.12.003

    Article  Google Scholar 

  67. Rockey Mountain Power: Air conditioner direct load control program (A/C-DLC) (Cool Keeper Program). https://www.rockymountainpower.net/content/dam/rocky_mountain_power/doc/About_Us/Rates_and_Regulation/Utah/Approved_Tariffs/Rate_Schedules/Air_Conditioner_Direct_Load_Control_Program_A_C_DLC_Cool_Keeper_Program.pdf (2014). Accessed 24 May 2016

  68. KEMA. Comparison of California investor-owned-utility (IOU) direct load control (DLC) programs. http://www.calmac.org/publications/Final_report_for_California_DLC_Program_Comparison.pdf (2010). Accessed 14 May 2016

  69. Faruqui, A.: Direct load control of residential air conditioners in Texas. 2012.

  70. RLW Analytics: Deemed savings estimates for legacy air conditioning and water heating direct load control programs in PJM region. 2007.

  71. Gomes, A., Antunes, C.H., Oliveira, E.: Direct load control in the perspective of an electricity retailer—a multi-objective evolutionary approach. Soft Comput. Ind. Appl. (2011). https://doi.org/10.1007/978-3-642-20505-7

    Article  Google Scholar 

  72. Crossley, D.: ETSA Utilities air conditioner direct load control program—Australia 1–16. http://www.ieadsm.org/article/etsa-utilities-air-conditioner-direct-load-control-programe/ (2016). Accessed 14 May 2016

  73. Hoeve, M.: Direct Load Control for Electricity Supply and Demand Matching: Increasing Reliability of Wind Energy? Lund University, Lund (2009)

    Google Scholar 

  74. Wisconsin Public Service. Contracted direct load control (CDLC) 2–3. http://www.wisconsinpublicservice.com/business/cdlc.aspx (2012). Accessed 15 May 2016

  75. Troutfetter, R.F.: Market potential study for water heater demand management. https://www.metering.com/market-potential-study-for-residential-water-heater-demand-management/ (2008). Accessed 24 May 2016

  76. Satchwell, A., Hledik, R.: Analytical frameworks to incorporate demand response in long-term resource planning. Util. Policy 28, 73–81 (2014). https://doi.org/10.1016/j.jup.2013.12.003

    Article  Google Scholar 

  77. Eid, C., Koliou, E., Valles, M., Reneses, J., Hakvoort, R.: Time-based pricing and electricity demand response: Existing barriers and next steps. Util. Policy 40, 15–25 (2016). https://doi.org/10.1016/j.jup.2016.04.001

    Article  Google Scholar 

  78. Mark Miner. Neural Energy Consulting: March 2011. http://www.neuralenergy.info/2011_03_01_archive.html (2011). Accessed 12 May 2016

  79. Fell, M.J., Shipworth, D., Huebner, G.M., Elwell, C.A.: Public acceptability of domestic demand-side response in Great Britain: the role of automation and direct load control. Energy Res. Soc. Sci. 9, 72–84 (2015). https://doi.org/10.1016/j.erss.2015.08.023

    Article  Google Scholar 

  80. Katz, J.: Linking meters and markets: roles and incentives to support a flexible demand side. Util. Policy 31, 74–84 (2014). https://doi.org/10.1016/j.jup.2014.08.003

    Article  Google Scholar 

  81. Wu, Q., Wang, P., Goel, L.: Direct load control (DLC) considering nodal interrupted energy assessment rate (NIEAR) in restructured power systems. IEEE Trans. Power Syst. 25, 1449–1456 (2010). https://doi.org/10.1109/TPWRS.2009.2038920

    Article  Google Scholar 

  82. Stadler, I.: Power grid balancing of energy systems with high renewable energy penetration by demand response. Util. Policy 16, 90–98 (2008). https://doi.org/10.1016/j.jup.2007.11.006

    Article  Google Scholar 

  83. Pathak, L., Shah, K.: Renewable energy resources, policies and gaps in BRICS countries and the global impact. Front. Energy (2019). https://doi.org/10.1007/s11708-018-0601-z

    Article  Google Scholar 

  84. Magazzino, C.: Electricity demand, GDP and employment: evidence from Italy. Front. Energy 8, 31–40 (2014). https://doi.org/10.1007/s11708-014-0296-8

    Article  Google Scholar 

  85. Kuzemko, C., Lockwood, M., Mitchell, C., Hoggett, R.: Governing for sustainable energy system change: politics, contexts and contingency. Energy Res. Soc. Sci. 12, 96–105 (2016). https://doi.org/10.1016/j.erss.2015.12.022

    Article  Google Scholar 

  86. Charlie, H.: Engaging utility customers with direct load control|intelligent utility. http://ec.ec.webenabled.net/blog/15/06/engaging-utility-customers-direct-load-control (2015). Accessed 25 May 2016

  87. Horowitz, S., Mauch, B., Sowell, F.: Forecasting For Direct Load Control In Energy Markets 1–29 (2013). Retrieved from http://fsowell.tepper.cmu.edu/Papers/Forecasting_DLC.pdf

  88. Molina, A., Gabaldon, A., Fuentes, J.A., Canovas, F.J.: Approach to multivariable predictive control applications in residential HVAC direct load control. In: 2000 IEEE Power Engineering Society Summer Meeting Conference Proceedings, vol. 1–4, pp. 1811–1816 (2000). https://doi.org/10.1109/PESS.2000.868809

  89. Heber Weller, G.: New wave of direct load control update on DLC systems, technology. http://www.elp.com/articles/powergrid_international/print/volume-16/issue-7/features/new-wave-of-direct-load-control-update-on-dlc-systems-technology.html (2011). Accessed 1 June 2016

  90. Warren, P.: The use of systematic reviews to analyse demand-side management policy. Energy Effic. (2013). https://doi.org/10.1007/s12053-013-9230-x

    Article  Google Scholar 

  91. Pedrasa, M.A.A., Oro, M.M., Reyes, N.C.R., Pedrasa, J.R.I.: Demonstration of direct load control of air conditioners in high density residential buildings. IEEE Innov. Smart Grid Technol. Asia (ISGT ASIA) 2014, 400–405 (2014). https://doi.org/10.1109/ISGT-Asia.2014.6873825

    Article  Google Scholar 

  92. Silver Spring Networks: The Business Case for DLC Replacement. http://www.silverspringnet.com/wp-content/uploads/SilverSpring-Whitepaper-BizCaseForReplacingLoadControlSwitches.pdf (2013). Accessed 26 May 2016

  93. Vesnic-Alujevic, L., Breitegger, M., Pereira, Â.G.: What smart grids tell about innovation narratives in the European Union: hopes, imaginaries and policy. Energy Res. Soc. Sci. 12, 16–26 (2016). https://doi.org/10.1016/j.erss.2015.11.011

    Article  Google Scholar 

  94. Cambini, C., Meletiou, A., Bompard, E., Masera, M.: Market and regulatory factors influencing smart-grid investment in Europe: evidence from pilot projects and implications for reform. Util. Policy 40, 36–47 (2016). https://doi.org/10.1016/j.jup.2016.03.003

    Article  Google Scholar 

  95. Popovic, Z.: Determination of optimal direct load control strategy using linear programming. In: Proceedings of CIRED (1999)

  96. Maki, S., Ashina, S., Fujii, M., Fujita, T., Yabe, N., Uchida, K., et al.: Employing electricity-consumption monitoring systems and integrative time-series analysis models: a case study in Bogor, Indonesia. Front. Energy 12, 426–439 (2018). https://doi.org/10.1007/s11708-018-0560-4

    Article  Google Scholar 

  97. Zhang, H., Zhang, C., Wen, F., Xu, Y.: A comprehensive energy solution for households employing a micro combined cooling, heating and power generation system. Front. Energy 12, 582–590 (2018). https://doi.org/10.1007/s11708-018-0592-9

    Article  Google Scholar 

  98. Southern California Edison: Domestic Summer Discount Plan 2014. https://www.sce.com/NR/sc3/tm2/pdf/ce342.pdf (2016). Accessed 11 June 2016

  99. He, X., Keyaerts, N., Azevedo, I., Meeus, L., Hancher, L., Glachant, J.M.: How to engage consumers in demand response: A contract perspective. Util. Policy 27, 108–122 (2013). https://doi.org/10.1016/j.jup.2013.10.001

    Article  Google Scholar 

  100. Lee, S.S., Lee, H.C., Yoo, T.H., Kwon, H.G., Park, J.K., Yoon, Y.T.: Demand response operation rules based on reliability for South Korean power system. IEEE Power Energy Soc. Gen. Meet. (2011). https://doi.org/10.1109/PES.2011.6039101

    Article  Google Scholar 

  101. Alsayegh, O., Saker, N., Alqattan, A.: Integrating sustainable energy strategy with the second development plan of Kuwait. Renew. Sustain. Energy Rev. 82, 3430–3440 (2018). https://doi.org/10.1016/j.rser.2017.10.048

    Article  Google Scholar 

  102. Borlick, R.: Paying for demand-side response at the wholesale level: the small consumers’ perspective. Electr. J. 24(9), 8–19 (2011). https://doi.org/10.1016/j.tej.2011.10.012

    Article  Google Scholar 

  103. Ministry of Electricity and Water (MEW): Statistical year book (electrical energy). Kuwait: 2013.

  104. Gelan, A.: Economic and environmental impacts of electricity subsidy reform in Kuwait: a general equilibrium analysis. Energy Policy 112, 381–398 (2018). https://doi.org/10.1016/J.ENPOL.2017.10.032

    Article  Google Scholar 

  105. Nanda, A.K., Panigrahi, C.K.: A state-of-the-art review of solar passive building system for heating or cooling purpose. Front. Energy 10, 347–354 (2016). https://doi.org/10.1007/s11708-016-0403-0

    Article  Google Scholar 

  106. Kuwait Institute for Scientific Research: KISR. Kuwait Energy Outlook, Kuwait (2019)

    Google Scholar 

  107. Ramadhan, M., Naseeb, A.: The cost benefit analysis of implementing photovoltaic solar system in the state of Kuwait. Renew. Energy 36, 1272–1276 (2011). https://doi.org/10.1016/j.renene.2010.10.004

    Article  Google Scholar 

  108. Mahmoud, M.A., Alajmi, A.F.: Quantitative assessment of energy conservation due to public awareness campaigns using neural networks. Appl. Energy 87, 220–228 (2010). https://doi.org/10.1016/j.apenergy.2009.03.020

    Article  Google Scholar 

  109. Ali, M., Iqbal, M.J., Sharif, M.: Relationship between extreme temperature and electricity demand in Pakistan. Int. J. Energy Environ. Eng. 4, 1–7 (2013). https://doi.org/10.1186/2251-6832-4-36

    Article  Google Scholar 

  110. Areas of Kuwait—Wikipedia n.d.: https://en.wikipedia.org/wiki/Areas_of_Kuwait (2020). Accessed 7 Dec 2020

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Authors and Affiliations

  1. School of Business, University of Petroleum and Energy Studies, Dehradun, 248007, India

    Rajeev Alasseri & T. Joji Rao

  2. Kuwait Institute for Scientific Research, P. B. No. 24885, 13109, Safat, Kuwait

    Rajeev Alasseri & K. J. Sreekanth

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  1. Rajeev Alasseri
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  2. T. Joji Rao
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  3. K. J. Sreekanth
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Correspondence to Rajeev Alasseri.

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Alasseri, R., Rao, T.J. & Sreekanth, K.J. Pre-implementation assessment for introducing direct load control strategies in the residential electricity sector. Int J Energy Environ Eng 12, 433–451 (2021). https://doi.org/10.1007/s40095-020-00378-6

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