A smart home energy management approach incorporating an enhanced northern goshawk optimizer to enhance user comfort, minimize costs, and promote efficient energy consumption

   Hydrogen plays a crucial role in the quest for sustainable and clean energy solutions, and its effect on smart home energy management is of particular interest. With the rapid advancements in smart home technologies, energy optimization has become essential, aiming to achieve efficient energy consumption, cost reduction, and enhanced user comfort. Green hydrogen, produced through the electrolysis of water using renewable energy sources, emerges as a promising solution for sustainable energy. It offers numerous benefits, including zero greenhouse gas emissions, high energy density, and versatile applications. In the context of this study, the enhanced northern goshawk optimization (ENGO) algorithm and the original northern goshawk optimization (NGO) algorithm are investigated for optimizing smart home energy management. By employing a two-stage approach based on high and low-velocity ratios, ENGO overcomes the limitations of NGO, such as low exploitation capability and being trapped in local optima. The study demonstrates that ENGO outperforms NGO in achieving multiple objectives simultaneously, including reducing the peak-to-average ratio (PAR), lowering electricity costs, and ensuring user comfort. Furthermore, ENGO proves to be more robust, capable of handling complex smart home energy management problems with multiple constraints. Thus, the integration of hydrogen solutions, such as green hydrogen, with advanced optimization techniques like ENGO, can significantly contribute to the effective management of energy resources in smart homes, promoting sustainability and user satisfaction.

1. S.Z. Zhiznin, N.N. Shvets, V.M. Timokhov, A.L. Gusev. Economics of hydrogen energy of green transition in the world and Russia. Part I, International Journal of Hydrogen Energy, 2023, 48(57), p. 21544-21567.

2. S.Z. Zhiznin, S. Vassilev, A.L. Gusev. Economics of secondary renewable energy sources with hydrogen generation, International Journal of Hydrogen Energy, 2019, 44(23), 11385-11393.

3. Valverde-Isorna Luis, Rosa Felipe, Bordons Carlos, Guerra J. Energy management strategies in hydrogen smart-grids: a laboratory experience. Int J Hydrogen Energy 2016;41(31):13715-25.

4. Zhang Xiongwen, Chan Siew Hwa, Kwee Ho Hiang, Siew-ChongTan, Li Mengyu, Li Guojun, Li Jun, Feng Zhenping. Towards a smart energy network: The roles of fuel/electrolysis cells and technological perspective. Int J Hydrogen Energy 2015;40(21):6866-919.

5. Tabanjat Abdulkader, Mohamed Becherif, Hissel Daniel, Ramadan HS. Energy management hypothesis for hybrid power system of H2/2/PV/GMT via AI techniques. Int J Hydrogen Energy 2018;43(6):3527-41.

6. Arabul Fatma Keskin, Arabul Ahmet Yigit, Kumru Celal Fadil, Boynuegri Ali Rifat. Providing energy management of a fuel cellebatteryewind turbineesolar panel hybrid off grid smart home system. Int J Hydrogen Energy, 2017;42(43):26906-13.

7. Asanova, S., et al., Optimization of the structure of autonomous distributed hybrid power complexes and energy balance management in them. International Journal of Hydrogen Energy, 2021. 46(70): p. 34542-34549.

8. Asanov, M., et al., Design methodology of intelligent autonomous distributed hybrid power complexes with renewable energy sources. International Journal of Hydrogen Energy, 2023.

9. Hashmi, M., S. Hänninen, and K. Mäki. Survey of smart grid concepts, architectures, and technological demonstrations worldwide. in 2011 IEEE PES conference on innovative smart grid technologies Latin America (ISGT LA). 2011. IEEE.

10. Rahimi, F. and A. Ipakchi, Demand response as a market resource under the smart grid paradigm. IEEE Transactions on smart grid, 2010. 1(1): p. 82-88.

11. Ozturk, Y., et al., An intelligent home energy management system to improve demand response. IEEE Transactions on smart Grid, 2013. 4(2): p. 694-701.

12. Yi, P., et al., Real-time opportunistic scheduling for residential demand response. IEEE Transactions on smart grid, 2013. 4(1): p. 227-234.

13. Molderink, A., et al. Domestic energy management methodology for optimizing efficiency in smart grids. in 2009 IEEE Bucharest PowerTech. 2009. IEEE.

14. Tsui, K.M. and S.-C. Chan, Demand response optimization for smart home scheduling under real-time pricing. IEEE Transactions on Smart Grid, 2012. 3(4): p. 1812-1821.

15. Logenthiran, T., D. Srinivasan, and T.Z. Shun, Demand side management in smart grid using heuristic optimization. IEEE transactions on smart grid, 2012. 3(3): p. 1244-1252.

16. Muralitharan, K., R. Sakthivel, and Y. Shi, Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing, 2016. 177: p. 110-119.

17. Motevasel, M. and A.R. Seifi, Expert energy management of a micro-grid considering wind energy uncertainty. Energy Conversion and Management, 2014. 83: p. 58-72.

18. Mary, G.A. and R. Rajarajeswari, Smart grid cost optimization using genetic algorithm. Int. J. Res. Eng. Technol, 2014. 3(07): p. 282-287.

19. Zhang, J., et al., A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Applied energy, 2016. 183: p. 791-804.

20. Siano, P., et al., Designing and testing decision support and energy management systems for smart homes. Journal of Ambient Intelligence and Humanized Computing, 2013. 4: p. 651-661.

21. Miao, H., X. Huang, and G. Chen, A genetic evolutionary task scheduling method for energy efficiency in smart homes. International Review of Electrical Engineering (IREE), 2012. 7(5): p. 5897-5904.

22. Agnetis, A., et al., Load scheduling for house-hold energy consumption optimization. IEEE Transactions on Smart Grid, 2013. 4(4): p. 2364-2373.

23. Moenik, J., et al., A concept to optimize power consumption in smart homes based on demand-side management and using smart switches. Electrotechnical Review, 2013. 80(5): p. 217-221.

24. Ogwumike, C., M. Short, and M. Denai. Near-optimal scheduling of residential smart home appliances using heuristic approach. in 2015 IEEE International Conference on Industrial Technology (ICIT). 2015. IEEE.

25. Ahmad, A., et al., A modified feature selection and artificial neural network-based day-ahead load fore-casting model for a smart grid. Applied Sciences, 2015. 5(4): p. 1756-1772.

26. Rastegar, M., M. Fotuhi-Firuzabad, and H. Zareipour, Home energy management incorporating operational priority of appliances. International Journal of Electrical Power & Energy Systems, 2016. 74: p. 286-292.

27. Rasheed, M.B., et al., Priority and delay constrained demand side management in real‐time price environment with renewable energy source. International Journal of Energy Research, 2016. 40(14): p. 2002-2021.

28. Khan, M.A., et al., A generic demand‐side management model for smart grid. International Journal of Energy Research, 2015. 39(7): p. 954-964.

29. Surender Reddy, S., J.Y. Park, and C.M. Jung, Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm. Frontiers in Energy, 2016. 10(3): p. 355-362.

30. Ma, K., et al., Residential power scheduling for demand response in smart grid. International Journal of Electrical Power & Energy Systems, 2016. 78: p. 320-325.

31. Rasheed, M.B., et al., Real time information based energy management using customer preferences and dynamic pricing in smart homes. Energies, 2016. 9(7): p. 542.

32. Geem, Z.W. and Y. Yoon, Harmony search optimization of renewable energy charging with energy storage system. International Journal of Electrical Power & Energy Systems, 2017. 86: p. 120-126.

33. Wu, Z., et al., Energy-efficiency-oriented scheduling in smart manufacturing. Journal of Ambient Intelligence and Humanized Computing, 2019. 10: p. 969-978.

34. Moon, S. and J.-W. Lee, Multi-residential demand response scheduling with multi-class appliances in smart grid. IEEE transactions on smart grid, 2016. 9(4): p. 2518-2528.

35. Jalili, H., et al., Modeling of demand response programs based on market elasticity concept. Journal of Ambient Intelligence and Humanized Computing, 2019. 10: p. 2265-2276.

36. Khan, A., et al., A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. Journal of Ambient Intelligence and Humanized Computing, 2019. 10: p. 1655-1678.

37. Liu, B., et al., Cost control of the transmission congestion management in electricity systems based on ant colony algorithm. Energy and Power Engineering, 2011. 3(01): p. 17.

38. Tang, L., Y. Zhao, and J. Liu, An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Transactions on Evolutionary Computation, 2013. 18(2): p. 209-225.

39. Arabali, A., et al., Genetic-algorithm-based optimization approach for energy management. IEEE Transactions on Power Delivery, 2012. 28(1): p. 162-170.

40. Zhao, Z., et al., An optimal power scheduling method for demand response in home energy management system. IEEE transactions on smart grid, 2013. 4(3): p. 1391-1400.

41. Mehrjerdi Hasan, Bornapour Mosayeb, Hemmati Reza, Ghiasi Seyyed Mohammad Sadegh. Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes. Energy 2019;168:919-30.

42. Nechaev, Y.S., Gusev, A.L., Gupta, B.K., Srivastava, O.N. and Veziroglu, T.N., 2005, January. On the experimental and theoretical basis developing a “super” hydrogen adsorbent. In Transactions of International conference" Solid State Hydrogen Storage-Materials and Applications", January 31-February 1, 2005.

43. Nechaev, Yu S., A. L. Gusev, B. K. Gupta, O. N. Srivastava, and T. N. Veziroglu. On using graphite nanofibers for hydrogen on-board storage. In Transactions of International conference “Solid State Hydrogen Storage–Materials and Applications”. 2005.

44. Nechaev, Yu. S., Alexeeva, O.K., Filippov, G.A., Gusev, A.L. On the experimental and theoretical basis of developing a super hydrogen carbonaceous adsorbent for fuel-cell-powered vehicles // Alternative Energy and Ecology. 2005.

45. Gusev, A.L., Zolotukhin, I.V., Kalinin, Yu.E., Sitnikov, A.V. Sensors for hydrogen and hydrogen-containing molecules //Alternative Energy and Ecology. 2005. № 5.

46. Gusev, A.L. Thermosensor for hydrogen leakage control based on palladed manganese dioxide in superinsulation of a cryogenic hydrogen reservoir and pipelines // Physics fundamental and applied problems of Physics. – 2017. – P. 254.

47. Gusev, A.L., Zababurkin, D.I. Multi-channel leak detectors for monitoring the level of combustible, toxic and explosive gases // Alternative Energy and Ecology. 2010. No. 10. P. 10-15.

48. Zababurkin, D.I., Gusev, A.L., Nemyshev, V.I. Hydrogen leak detectors and leak indicators // International Scientific Journal Alternative Energy and Ecology. 2010. No. 6. pp. 33-42.

49. Rahim, S., et al., Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy and Buildings, 2016. 129: p. 452-470.

50. Soares, J., et al. An optimal scheduling problem in distribution networks considering V2G. in 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG). 2011. IEEE.

51. Khan, Z.A., et al., Hybrid meta-heuristic optimization based home energy management system in smart grid. Journal of Ambient Intelligence and Humanized Computing, 2019. 10: p. 4837-4853.

52. Dehghani, M., Š. Hubálovský, and P. Trojovský, Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems. IEEE Access, 2021. 9: p. 162059-162080.

53. Hassan, M.H., et al., A modified Marine predators algorithm for solving single-and multi-objective combined economic emission dispatch problems. Computers & Industrial Engineering, 2022. 164: p. 107906.