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Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models

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  1. Du, Y.F. & Jiang, L. & Li, Y.Z. & Counsell, J. & Smith, J.S., 2016. "Multi-objective demand side scheduling considering the operational safety of appliances," Applied Energy, Elsevier, vol. 179(C), pages 864-874.
  2. El-Baz, Wessam & Tzscheutschler, Peter, 2015. "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Elsevier, vol. 147(C), pages 10-19.
  3. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
  4. Madia Safdar & Ghulam Amjad Hussain & Matti Lehtonen, 2019. "Costs of Demand Response from Residential Customers’ Perspective," Energies, MDPI, vol. 12(9), pages 1-16, April.
  5. Matthew Gough & Sérgio F. Santos & Mohammed Javadi & Rui Castro & João P. S. Catalão, 2020. "Prosumer Flexibility: A Comprehensive State-of-the-Art Review and Scientometric Analysis," Energies, MDPI, vol. 13(11), pages 1-32, May.
  6. Abdulaal, Ahmed & Moghaddass, Ramin & Asfour, Shihab, 2017. "Two-stage discrete-continuous multi-objective load optimization: An industrial consumer utility approach to demand response," Applied Energy, Elsevier, vol. 206(C), pages 206-221.
  7. Chmielewski, Adrian & Gumiński, Robert & Mączak, Jędrzej & Radkowski, Stanisław & Szulim, Przemysław, 2016. "Aspects of balanced development of RES and distributed micro-cogeneration use in Poland: Case study of a µCHP with Stirling engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 930-952.
  8. Wakui, Tetsuya & Kawayoshi, Hiroki & Yokoyama, Ryohei & Aki, Hirohisa, 2016. "Operation management of residential energy-supplying networks based on optimization approaches," Applied Energy, Elsevier, vol. 183(C), pages 340-357.
  9. Loganthurai, P. & Rajasekaran, V. & Gnanambal, K., 2016. "Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand," Energy, Elsevier, vol. 107(C), pages 419-430.
  10. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
  11. Leiva, Javier & Palacios, Alfonso & Aguado, José A., 2016. "Smart metering trends, implications and necessities: A policy review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 227-233.
  12. Si, Fangyuan & Wang, Jinkuan & Han, Yinghua & Zhao, Qiang & Han, Peng & Li, Yan, 2018. "Cost-efficient multi-energy management with flexible complementarity strategy for energy internet," Applied Energy, Elsevier, vol. 231(C), pages 803-815.
  13. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
  14. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
  15. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
  16. Upton, J. & Murphy, M. & Shalloo, L. & Groot Koerkamp, P.W.G. & De Boer, I.J.M., 2015. "Assessing the impact of changes in the electricity price structure on dairy farm energy costs," Applied Energy, Elsevier, vol. 137(C), pages 1-8.
  17. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
  18. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
  19. Alam, Muhammad Raisul & St-Hilaire, Marc & Kunz, Thomas, 2019. "Peer-to-peer energy trading among smart homes," Applied Energy, Elsevier, vol. 238(C), pages 1434-1443.
  20. P, Balakumar & Ramu, Senthil Kumar & T, Vinopraba, 2024. "Optimizing electric vehicle charging in distribution networks: A dynamic pricing approach using internet of things and Bi-directional LSTM model," Energy, Elsevier, vol. 294(C).
  21. Rieger, Alexander & Thummert, Robert & Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang, 2016. "Estimating the benefits of cooperation in a residential microgrid: A data-driven approach," Applied Energy, Elsevier, vol. 180(C), pages 130-141.
  22. Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
  23. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
  24. Blaschke, Maximilian J., 2022. "Dynamic pricing of electricity: Enabling demand response in domestic households," Energy Policy, Elsevier, vol. 164(C).
  25. Brange, Lisa & Englund, Jessica & Lauenburg, Patrick, 2016. "Prosumers in district heating networks – A Swedish case study," Applied Energy, Elsevier, vol. 164(C), pages 492-500.
  26. C. Ruiz & F. J. Nogales & F. J. Prieto, 2018. "Retail Equilibrium with Switching Consumers in Electricity Markets," Networks and Spatial Economics, Springer, vol. 18(1), pages 145-180, March.
  27. Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
  28. Hua, Weiqi & Chen, Ying & Qadrdan, Meysam & Jiang, Jing & Sun, Hongjian & Wu, Jianzhong, 2022. "Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  29. Francesco Liberati & Alessandro Di Giorgio, 2017. "Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node," Energies, MDPI, vol. 11(1), pages 1-23, December.
  30. Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang & Rieger, Alexander & Thimmel, Markus, 2018. "One rate does not fit all: An empirical analysis of electricity tariffs for residential microgrids," Applied Energy, Elsevier, vol. 210(C), pages 800-814.
  31. Dong, Bing & Li, Zhaoxuan & Taha, Ahmad & Gatsis, Nikolaos, 2018. "Occupancy-based buildings-to-grid integration framework for smart and connected communities," Applied Energy, Elsevier, vol. 219(C), pages 123-137.
  32. Dufo-López, Rodolfo, 2015. "Optimisation of size and control of grid-connected storage under real time electricity pricing conditions," Applied Energy, Elsevier, vol. 140(C), pages 395-408.
  33. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
  34. Wei Li & Jiekai Shi & Hanyun Zhou, 2024. "Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid," Energies, MDPI, vol. 17(13), pages 1-17, June.
  35. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2016. "Power demand control scenarios for smart grid applications with finite number of appliances," Applied Energy, Elsevier, vol. 162(C), pages 83-98.
  36. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
  37. Kobus, Charlotte B.A. & Klaassen, Elke A.M. & Mugge, Ruth & Schoormans, Jan P.L., 2015. "A real-life assessment on the effect of smart appliances for shifting households’ electricity demand," Applied Energy, Elsevier, vol. 147(C), pages 335-343.
  38. Fiorentini, Massimo & Wall, Josh & Ma, Zhenjun & Braslavsky, Julio H. & Cooper, Paul, 2017. "Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage," Applied Energy, Elsevier, vol. 187(C), pages 465-479.
  39. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
  40. Parra, David & Swierczynski, Maciej & Stroe, Daniel I. & Norman, Stuart.A. & Abdon, Andreas & Worlitschek, Jörg & O’Doherty, Travis & Rodrigues, Lucelia & Gillott, Mark & Zhang, Xiaojin & Bauer, Chris, 2017. "An interdisciplinary review of energy storage for communities: Challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 730-749.
  41. Drysdale, Brian & Wu, Jianzhong & Jenkins, Nick, 2015. "Flexible demand in the GB domestic electricity sector in 2030," Applied Energy, Elsevier, vol. 139(C), pages 281-290.
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