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Sensitivity Analysis of Priority-Based Demand Response Metrics with Continuous Real-Time Pricing Scheme Using Swap-Based Butterfly Particle Swarm Optimization

  • Research Article-Electrical Engineering
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Abstract

Energy prices are booming to high levels with the ongoing global events and massive energy demand post-COVID, triggering panic among-st the general public. In this regard, energy demand management can play an important role here by letting both the entities (utility and customer) take control over the consumption pattern long enough until renewables take over the energy supply from the conventional resources using real-time device scheduling algorithms. To achieve this phenomenon, both participating entities need proper prioritization and their interests to be properly taken care of. In this paper sensitivity analysis of varying priorities to the objectives (customer cost and peak-to-average power ratio) representing both the entities has been carried out. A real-time device scheduling algorithm is implemented to improve the consumption pattern of appliances consisting of multiple objectives. Proper way of normalization is developed to retain the original essence of these objectives. The analysis embeds novel swap operations that are incorporated with standard particle swarm optimization (PSO) and its variants under varying pricing schemes. In order to demonstrate the proposed DR-based real-time optimization, a residence in Chicago, Illinois, USA is taken as a case study for this work. The results demonstrate the quickness and proper optimizations of the PSO variants with butterfly-PSO performing the best among-st the rest. Continuous real-time pricing obtained the best solution with apt quickness among-st its counterparts for each time-slot and for cumulative time of the day. An overall reduction in each entity’s metrics is obtained in this work.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Mehmet, Y.; Tayfun, D.: Multi-objective time-cost trade-off optimization for the construction scheduling with Rao algorithms. Structures 48, 798–808 (2023)

    Article  Google Scholar 

  2. Ghole, M. S.; Paliwal, P.; Thakur, T.: A nowcasting central controller with continuous RTP for residential device scheduling using swap-based BF-PSO. Arab. J. Sci. Eng. pp. 1–17 (2023).

  3. Celik, B.; Robin, R.; David, B.; Abdellatif, M.: Decentralized neighborhood energy management with coordinated smart home energy sharing. IEEE Trans. Smart Grid 9(6), 6387–6397 (2017)

    Article  Google Scholar 

  4. Anees, A.; Tharam, D.; Steve, W.: Optimization of day-ahead and real-time prices for smart home community. Int. J. Electric. Power Energy Syst. 124(2020), 106403 (2021)

    Article  Google Scholar 

  5. Hansen, T.M.; Edwin, K.P.C.; Siddharth, S.: A partially observable Markov decision process approach to residential home energy management. IEEE Trans. Smart Grid 9(2), 1271–1281 (2018)

    Article  Google Scholar 

  6. Fernandez, E.; Hossain, M.J.; Khizir, M.; Nizami, M.S.H.: A Bi-level optimization-based community energy management system for optimal energy sharing and trading among peers. J. Clean. Prod. 279, 123254 (2021)

    Article  Google Scholar 

  7. Zhou, S.; Fenghua, Z.; Zhi, Wu.: A smart community energy management scheme considering user dominated demand side response and P2P trading. Int. J. Electric. Power Energy Syst. 114, 105378 (2020)

    Article  Google Scholar 

  8. Mediwaththe, C.P.; Blackhall, L.: Network-aware demand-side management framework with a community energy storage system considering voltage constraints. IEEE Trans. Power Syst. 36(2), 1229–1238 (2020)

    Article  Google Scholar 

  9. Mahmud, K.; Nizami, M.S.H.; Ravishankar, J.; Hossain, M.J.; Siano, P.: Multiple home-to-home energy transactions for peak load shaving. IEEE Trans. Ind. Appl. 56(2), 1074–1085 (2020)

    Article  Google Scholar 

  10. Hachem, C.; Singh, K.: Developing an optimization methodology for urban energy resources mix. Appl. Energy 269, 115066 (2020)

    Article  Google Scholar 

  11. Rehman, Z.; Mahmood, A.; Razzaq, S.; Ali, W.; Naeem, U.; Shehzad, K.: Prosumer based energy management and sharing in smart grid. Renew. Sustain. Energy Rev. 82, 1675–1684 (2018)

    Article  Google Scholar 

  12. Zhou, S.; Hu, Z.; Gu, W.; Jiang, M.; Zhang, X.P.: Artificial intelligence based smart energy community management: a reinforcement learning approach. CSEE J. Power Energy Syst. 5(1), 1–10 (2019)

    Google Scholar 

  13. Liu, G.; Tao, J.; Ollis, T.B.; Zhang, X.; Tomsovic, K.: Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics. Appl. Energy 239, 83–95 (2019)

    Article  Google Scholar 

  14. Benoit, D.; Davigny, A.; Kazmierczak, S.; Barry, H.; Saudemont, C.; Benoît, R.: Decentralized neighbourhood energy management considering residential profiles and welfare for grid load smoothing. Sustain. Cities Soc. 63, 102464 (2020)

    Article  Google Scholar 

  15. Ziadeh, A.; Abualigah, L.; Elaziz, M.A.: Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes. Multimed. Tools Appl. 80(21), 31569–31597 (2021)

    Article  Google Scholar 

  16. Abderraouf, B.; Mena, A.J.G.; Haddad, S.; Ferrari, M.L.: Efficient energy scheduling considering cost reduction and energy saving in hybrid energy system with energy storage. J. Energy Storage 33, 101887 (2021)

    Article  Google Scholar 

  17. Siano, Pierluigi; Hosseinnezhad, Vahid; Shafie, Miadreza: An optimal home energy management paradigm with an adaptive neuro-fuzzy regulation. IEEE Access 8, 19614–19628 (2020)

    Article  Google Scholar 

  18. Li, Shenglin; Yang, Junjie; Song, Wenzhan; Chen, An.: A real-time electricity scheduling for residential home energy management. IEEE Internet Things J. 6(2), 2602–2611 (2018)

  19. Lu, Renzhi; Hong, Seung Ho; Mengmeng, Yu.: Demand response for home energy management using reinforcement learning and artificial neural network. IEEE Trans. Smart Grid 10(6), 6629–6639 (2019)

  20. Waseem, M.; Waqas, A. B.; Ali, Y.; Khan, D.; Faheem, Z. B.; Manan, A.; Shabbir, U. (2021). Home energy management strategy for dr accomplishment considering pv uncertainties and battery energy storage system. In: 2021 International Conference on Emerging Power Technologies (ICEPT) (pp. 1–5). IEEE.

  21. Xu, Xu.; Jia, Youwei; Yan, Xu.; Zhao, Xu.; Chai, Songjian; Lai, Chun Sing: A multi-agent reinforcement learning-based data-driven method for home energy management. IEEE Trans. Smart Grid 11(4), 3201–3211 (2020)

  22. Luo, Fengji; Kong, Weicong; Ranzi, Gianluca; Dong, Zhao Yang: Optimal home energy management system with demand charge tariff and appliance operational dependencies. IEEE Trans. Smart Grid 11(1), 4–14 (2019)

    Article  Google Scholar 

  23. Duman, A Can; Erden, Hamza Salih; Gönül, Ömer.; Güler, Önder.: A home energy management system with an integrated smart thermostat for demand response in smart grids. Sustain. Cities Soc. 65, 102639 (2021)

  24. Nezhad, A.E.; Rahimnejad, A.; Andrew Gadsden, S.: Home energy management system for smart buildings with inverter-based air conditioning system. Int. J. Electric. Power Energy Syst. 133, 107230 (2021)

    Article  Google Scholar 

  25. Luo, Fengji; Ranzi, Gianluca; Wan, Can: A multistage home energy management system with residential photovoltaic penetration. IEEE Trans. Ind. Inf. 15(1), 116–126 (2018)

    Article  Google Scholar 

  26. Samadi, Mikhak; Fattahi, Javad; Schriemer, Henry: Demand management for optimized energy usage and consumer comfort using sequential optimization. Sensors 21(1), 130 (2020)

    Article  Google Scholar 

  27. Wang, Xiuwang; Mao, Xinna; Khodaei, Hossein: A multi-objective home energy management system based on internet of things and optimization algorithms. J. Build. Eng. 33, 101603 (2021)

  28. Alfaverh, Fayiz; Denai, Mouloud; Sun, Yichuang: Demand response strategy based on reinforcement learning and fuzzy reasoning for home energy management. IEEE Access 8, 39310–39321 (2020)

    Article  Google Scholar 

  29. Arcos, Diego; Pascual, Julio; Guinjoan, Francesc; Marroyo, Luis: An energy management system design using fuzzy logic control: Smoothing the grid power profile of a residential electro-thermal microgrid. IEEE Access 9, 25172–25188 (2021)

    Article  Google Scholar 

  30. Ali, Saqib; Malik, Tahir; Nadeem; Raza, Aamir,: Risk-averse home energy management system. IEEE Access 8, 91779–91798 (2020)

  31. Ghosh, S.; Chatterjee, D.: Artificial bee colony optimization based non-intrusive appliances load monitoring technique in a smart home. IEEE Trans. Consum. Electron. 67(1), 77–86 (2021)

    Article  Google Scholar 

  32. Hammou, I.; Ouassaid, M.; Maaroufi, M.: Dynamic time-and load-based preference toward optimal appliance scheduling in a smart home. Math. Probl, Eng (2021)

    Book  Google Scholar 

  33. Yousefi, Mojtaba; Hajizadeh, Amin; Soltani, Mohsen N.; Hredzak, Branislav: Predictive home energy management system with photovoltaic array, heat pump, and plug-in electric vehicle. IEEE Trans. Industr. Inf. 17(1), 430–440 (2020)

    Article  Google Scholar 

  34. Dinh, Huy Truong; Yun, Jaeseok; Kim, Dong Min; Lee, Kyu-Haeng.; Kim, Daehee: A home energy management system with renewable energy and energy storage utilizing main grid and electricity selling. IEEE Access 8, 49436–49450 (2020)

  35. Thilker, Christian Ankerstjerne; Madsen, Henrik; Jørgensen, John Bagterp: Advanced forecasting and disturbance modelling for model predictive control of smart energy systems. Appl. Energy 292, 116889 (2021)

  36. Ghole, M. S.; Ghosh, A.; and Ray, A. K.: Multi-agent task assignment using swap-based particle swarm optimization for surveillance and disaster management. In: 2023 Robotics, Control and Computer Vision: Select Proceedings of ICRCCV (2022), Springer 1: 127–138.

  37. Shami, Tareq M.; El-Saleh, Ayman A.; Alswaitti, Mohammed: Particle swarm optimization: a comprehensive survey. IEEE Access 10, 10031–10061 (2022)

    Article  Google Scholar 

  38. Mukherjee, Debanjan; Mallick, Sourav; Rajan, Abhishek: A Levy Flight motivated meta-heuristic approach for enhancing maximum loadability limit in practical power system. Appl. Soft Comput. 114, 108146 (2022)

  39. Ghole, M. S.; Tiwari, A.; Thakkar, N.; Paliwal, P.; Thakur, T.; Arya, A.: Optimal Placement of Distributed Generator using BFPSO. In: 2022 2nd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), vol. 1: 1–6 (2022). IEEE.

  40. Barbaros, A.; Rafiullah, G.; Tayfun, D.; Şevket, A.: The effect of post-tensioning force and different cable arrangements on the behavior of cable-stayed bridge. Structures 44, 1824–1843 (2022)

    Article  Google Scholar 

  41. Tayfun, D.; Barbaros, A.; Maksym, G.; Venkata, R.R.: Optimal design of dome structures with recently developed algorithm: Rao series. Structures 42, 65–79 (2022)

    Article  Google Scholar 

  42. “[online] Available: https://hourlypricing.comed.com/live-prices/.” .

  43. Asley, S.E.; Tayfun, D.: 3D cost optimization of 3 story RC constructional building using Jaya algorithm. Structures 40, 803–811 (2022)

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Authors and Affiliations

  1. Electrical Engineering Department, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, 462003, India

    Mukund Subhash Ghole, Priyanka Paliwal & Tripta Thakur

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  1. Mukund Subhash Ghole
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  2. Priyanka Paliwal
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  3. Tripta Thakur
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Correspondence to Mukund Subhash Ghole.

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Ghole, M.S., Paliwal, P. & Thakur, T. Sensitivity Analysis of Priority-Based Demand Response Metrics with Continuous Real-Time Pricing Scheme Using Swap-Based Butterfly Particle Swarm Optimization. Arab J Sci Eng 49, 6923–6940 (2024). https://doi.org/10.1007/s13369-023-08556-4

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