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Research Article

Examination of optimum benefits of customer and LSE by incentive and dynamic price-based demand response

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References

  • Aalami, H. A., M. P. Moghaddam, and G. Yousefi. 2010. Demand response modelingconsidering Interruptible/Curtailable loads and capacity market programs. Applied Energy 87 (1):243–50. doi:10.1016/j.apenergy.2009.05.041.
  • Alasseri, R., A. Tripathi, T. Joji Rao, and K. J. Sreekanth. 2017. A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs. Renewable and Sustainable Energy Reviews 77:617–35. doi:10.1016/J.RSER.2017.04.023.
  • Albadi, M. H., and E. F. El-Saadany. 2008. A summary of demand response in electricity markets. Electric Power Systems Research 78 (11):1989–96. doi:10.1016/j.epsr.2008.04.002.
  • Allameh, G., M. S. Mehrabad, and S. J. Sadjadi. 2019. Pricing decisions in a decentralized biofuel supply chain with RIN mechanism. Energy Sources, Part B: Economics, Planning, and Policy 14 (6):254–73. doi:10.1080/15567249.2019.1668499.
  • Asadinejad, A., A. Rahimpour, K. Tomsovic, H. Qi, and C. F. Chen. 2018. Evaluation of residential customer elasticity for incentive based demand response programs ☆. Electric Power Systems Research 158:26–36. doi:10.1016/j.epsr.2017.12.017.
  • Asadinejad, A., K. Tomsovic, and C. F. Chen. 2016. Sensitivity of incentive based demand response program to residential customer elasticity. North American Power Symposium, Denver, CO, USA.
  • Asadinejad, A., K. Tomsovic, and M. G. Varzaneh. 2015. Examination of incentive based demand response in western connection reduced model. North American Power Symposium, Charlotte, NC, USA.
  • Asadinejad, A., and K. Tomsovic. 2017. Optimal use of incentive and price based demand response to reduce costs and price volatility. Electric Power Systems Research 144:215–23. doi:10.1016/j.epsr.2016.12.012.
  • Azeez, N. T., and U. Atikol. 2019. Utilizing demand-side management as tool for promoting solar water heaters in countries where electricity is highly subsidized. Energy Sources Part B Economics Planning and Policy 14:1–15.
  • Bassamzadeh, and Nastaran/Roger, G. 2013. Robust scheduling of smart appliances with uncertain electricity prices in a heterogeneous population. Energy & Buildings 84:537–47. doi:10.1016/j.enbuild.2014.08.035.
  • Campbell, A. 2018. Price and income elasticities of electricity demand: Evidence from Jamaica. Energy Economics 69:19–32. doi:10.1016/j.eneco.2017.10.040.
  • Cappers, P., C. Goldman, and D. Kathan. 2010. Demand response in U.S. electricity markets: Empirical evidence. Energy 35 (4):1526–35. doi:10.1016/j.energy.2009.06.029.
  • Chen, R. D.; Z. Y.; M.-Y. C.; J. 2011. Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial Electronics 11 (3):381-388.
  • Chow, R. D., and Z. Y. MY. 2015. A survey on demand response in smart grids: Mathematical models and approaches. IEEE Transactions on Industrial Informatics 11 (3):570–82.
  • Datchanamoorthy, S., S. Kumar, Y. Ozturk, and G. Lee (2011). Optimal time-of-use pricing for residential load control. 2011 IEEE International Conference on Smart Grid Communications, SmartGridComm 2011. doi:10.1109/SmartGridComm.2011.6102350
  • Espey, J. M. E. 2004. Turning on the lights: A meta analysis of residential electrical demand elasticity. Journal of Agricultural and Applied Economics 36 (1):65–81. doi:10.1017/S1074070800021866.
  • Ghazvini, M. A. F., P. Faria, S. Ramos, H. Morais, and Z. V. 2015a. Incentive-based demand re-sponse programs designed by asset-light retail electricity providers for the day-ahead market. Energy 88:786–99. doi:10.1016/j.energy.2015.01.090.
  • Green, R., and N. Vasilakos. 2010. Market behaviour with large amounts of intermittent generation. Energy Policy 38 (7):3211–20. doi:10.1016/j.enpol.2009.07.038.
  • Grünewald, P., and E. McKenna. 2015. Keep it simple: Time-of-use tariffs in high-wind scenarios. IET Renew. Power Gener 9 (2):176–83. doi:10.1049/iet-rpg.2014.0031.
  • Gyamfi, S., S. Krumdieck, and T. Urmee. 2013. Residential peak electricity demand response—Highlights of some behavioural issues. Renewable and Sustainable Energy Reviews 25:71–77. doi:10.1016/j.rser.2013.04.006.
  • Haider, H. T., O. H. See, and W. Elmenreich. 2016a. A review of residential demand response of smart grid. Renewable & Sustainable Energy Reviews 59:166–78. doi:10.1016/j.rser.2016.01.016.
  • Haider, H. T., O. H. See, and W. Elmenreich. 2016b. Residential demand response scheme based on adaptive consumption level pricing. Energy 113:301–08. doi:10.1016/j.energy.2016.07.052.
  • Haider, H. T., O. H. See, and W. Elmenreich. 2016c. Optimal residential load scheduling based on time varying pricing scheme. Research & Development 210-214.
  • Halford, C. K., and R. F. Boehm. 2007. Modeling of phase change material peak load shifting. Energy & Buildings 39 (3):298–305. doi:10.1016/j.enbuild.2006.07.005.
  • Herranz, R., A. M. S. Roque, J. Villar, and F. A. Campos. 2012. Optimal demand-side bidding strategies in electricity spot markets. IEEE Transactions on Power Systems 27 (3):1204–13. doi:10.1109/TPWRS.2012.2185960.
  • Jia, L., Q. Zhao, and T. Lang (2013). Retail pricing for stochastic demand with unknown parameters: An online machine learning approach. In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.
  • Katz, J., F. M. Andersen, and P. E. Morthorst. n.d. Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system. Energy, S036054421631009X.
  • Khodaei, A. 2011. SCUC with hourly demand response considering intertemporal load characteristics. IEEE Transactions on Smart Grid 2 (3):564–71. doi:10.1109/TSG.2011.2157181.
  • Kirschen, D. 2003a. Demand-side view of electricity markets. IEEE Transactions on Power Delivery 18 (2):520–27. doi:10.1109/TPWRS.2003.810692.
  • Kirschen, D. S. 2003b. Demand-side view of electricity markets. IEEE Transactions on Power Systems 18 (2):520–27. doi:10.1109/TPWRS.2003.810692.
  • Kuzlu, M., M. Pipattanasomporn, and S. Rahman. 2012. Hardware demonstration of a home energy management system for demand response applications. IEEE Transactions on Smart Grid 3 (4):1704–11. doi:10.1109/TSG.2012.2216295.
  • Li, C., X. Yu, W. Yu, G. Chen, and J. Wang. 2017. Efficient computation for sparse load shifting in demand side management. IEEE Transactions on Smart Grid 8 (1):1–12. doi:10.1109/TSG.2016.2521377.
  • Lu, R., and S. H. Hong. 2019. Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy 236:937–49. doi:10.1016/J.APENERGY.2018.12.061.
  • Luo, Z., S. Hong, and Y. Ding. 2019. A data mining-driven incentive-based demand response scheme for a virtual power plant. Applied Energy 239:549–59. doi:10.1016/j.apenergy.2019.01.142.
  • Mehdi Rahmani-andebili, H. S. 2017. Energy management of end users modeling their reaction from a GENCO’s point of view. International conference on computing, networking and communication Santa Clara, CA, USA.
  • Mohajeryami, S., I. N. Moghaddam, M. Doostan, B. Vatani, and P. Schwarz. 2016. A novel economic model for price-based demand response. Electric Power Systems Research 135:1–9. doi:10.1016/j.epsr.2016.03.026.
  • Ng, K. H., and G. B. Sheble. 1998. Direct load control-A profit-based load management using linear programming. Power Systems IEEE Transactions On 13 (2):688–94. doi:10.1109/59.667401.
  • Panapakidis, I. P., and A. Dagoumas. 2017. Modeling demand-price curve: A clustering approach to derive dynamic elasticity for demand response programs. Int. Association for Energy Economics, Singapore.
  • Pipattanasomporn, M., M. Kuzlu, and S. Rahman. 2012. An algorithm for intelligent home energy management and demand response analysis. IEEE Transactions on Smart Grid 3 (4):2166–73. doi:10.1109/TSG.2012.2201182.
  • Rahmani-andebili, M. 2016. Modeling nonlinear incentive-based and price-based demandresponse programs and implementing on real power markets. Electric Power Systems Research 132:115–24. doi:10.1016/j.epsr.2015.11.006.
  • Shafie-Khah, M., E. Heydarian-Forushani, G. J. Osório, F. A. S. Gil, J. Aghaei, M. Barani, and J. P. S. Catalão. 2016. Optimal behavior of electric vehicle parking lots as demand response aggregation agents. IEEE Transactions on Smart Grid 7 (6):2654–65. doi:10.1109/TSG.2015.2496796.
  • Shi,, and C. C. Qingxin. 2019. Estimating the profile of Incentive-based Demand Response (IBDR) by integrating technical models and social-behavioral factors. IEEE Transactions on Smart Grid, Demand response (DR) has been widely recognized as.
  • Strbac, G., and D. S. Kirschen. 1999. Assessing the competitiveness of demand-side bidding. IEEE Transactions on Power Systems 14 (1):120–25. doi:10.1109/59.744498.
  • Su, C. L., and D. Kirschen. 2009. Quantifying the effect of demand response on electricity markets. IEEE Transactions on Power Systems 24 (3):1199–207. doi:10.1109/TPWRS.2009.2023259.
  • Vassilis Stavrakas, A. F. 2020. A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector. Energy Conversion and Management, 205.
  • Vivekananthan, C., Y. Mishra, and G. Ledwich. 2013. A novel real time pricing scheme for demand response in residential distribution systems. Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE.
  • Vivekananthan, C., Y. Mishra, G. Ledwich, and L. F. 2014. Demand response for residential ap-pliances via customer reward scheme. IEEE Transactions on Smart Grid 5 (2):809–20. doi:10.1109/TSG.2014.2298514.
  • Vu, D. H., M. Kashem, A. P. Muttaqi, and A. B. Agalgaonkar. 2018. Customer reward-based demand response program to improve demand elasticity and minimise financial risk during price spikes. Iet Generation Transmission & Distribution 12 (15):3764–71. doi:10.1049/iet-gtd.2017.2037.
  • Wang, Y., W. Yang, and T. Liu. 2017. Appliances considered demand response optimisation for smart grid. Iet Generation Transmission & Distribution 11 (4):856–64. doi:10.1049/iet-gtd.2016.0750.
  • Wang, Z., and Y. He. 2016. Two-stage optimal demand response with battery energy storage systems. Iet Generation Transmission & Distribution 10 (5):1286–93. doi:10.1049/iet-gtd.2015.0401.
  • Wolak, F. A. 2007. Residential customer response to real-time pricing: The anaheim critical peak pricing experiment, Stanford University, ResearchGate.
  • Wu, Q., W. Peng, and L. Goel. 2010. Direct Load Control (DLC) considering Nodal Interrupted Energy Assessment Rate (NIEAR) in restructured power systems. IEEE Transactions on Power Systems 25 (3):1449–56. doi:10.1109/TPWRS.2009.2038920.
  • Xie, L., and H. Zheng. 2014. Demand elasticity analysis by least squares support vector machine. In International Congress on Image and Signal Processing 1085–89.
  • Yan, C. H., X. P. Lai, M. F. Yan, and M. H. Shan. 2014. Preliminary design of demand response management system. Applied Mechanics & Materials 448–453:2769–74.
  • Yu, M. 2019. Incentive-based demand response considering hierarchical electricity market: A stackelberg game approach. Ieee Transactions on Industrial Electronics 66 (2):1488–98. doi:10.1109/TIE.2018.2826454.
  • Zakariazadeh, A. 2014. Economic-environmental energy and reserve scheduling of smart distribution systems: Amultiobjective mathematical programming approach. Energy Conversion and Management 78:151–64. doi:10.1016/j.enconman.2013.10.051.
  • Zhong, H., L. Xie, and Q. Xia. 2013. Coupon incentive-based demand response: Theory and case study. IEEE Transactions on Power Systems 28 (2):1266–76. doi:10.1109/TPWRS.2012.2218665.

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