آمار

تعداد نشریات30
تعداد شماره‌ها426
تعداد مقالات3,749
تعداد مشاهده مقاله5,895,318
تعداد دریافت فایل اصل مقاله4,088,444
Goyal, G.R., Vadhera, S.. (1403). Solution to Objectives of Supply Side Energy Management by Integrating Enhanced Demand Response Strategy. سامانه مدیریت نشریات علمی, 12(4), 269-279. doi: 10.22098/joape.2023.11565.1861
G.R. Goyal; S. Vadhera. "Solution to Objectives of Supply Side Energy Management by Integrating Enhanced Demand Response Strategy". سامانه مدیریت نشریات علمی, 12, 4, 1403, 269-279. doi: 10.22098/joape.2023.11565.1861
Goyal, G.R., Vadhera, S.. (1403). 'Solution to Objectives of Supply Side Energy Management by Integrating Enhanced Demand Response Strategy', سامانه مدیریت نشریات علمی, 12(4), pp. 269-279. doi: 10.22098/joape.2023.11565.1861
Goyal, G.R., Vadhera, S.. Solution to Objectives of Supply Side Energy Management by Integrating Enhanced Demand Response Strategy. سامانه مدیریت نشریات علمی, 1403; 12(4): 269-279. doi: 10.22098/joape.2023.11565.1861

Solution to Objectives of Supply Side Energy Management by Integrating Enhanced Demand Response Strategy

Journal of Operation and Automation in Power Engineering
مقاله 1، دوره 12، شماره 4، اسفند 2024، صفحه 269-279    اصل مقاله (829.22 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.11565.1861
نویسندگان
G.R. Goyal* 1؛ S. Vadhera2
1University of Engineering & Management, Jaipur, India
2National Institute of Technology, Kurukshetra, India
چکیده
Supply-side energy management (SSEM) aims to improve efficiency in operations and strategic planning. Both the cost of generating electricity and the amount of emissions from that generation are minimized in SSEM. It is required to formulate an optimization problem with these two competing goals in order to come up with a compromise. Resolving problems with network reliability caused by peak demand on the electricity system is another goal of SSEM. The ultimate goal of this study is to reduce energy use during peak hours while also cutting down on power losses, generation costs, and pollution from power plants. In this paper all goals of the smart grid system are satisfied and addressed optimally through the use of optimal generator scheduling and an improved demand response technique. To formulate this problem standard IEEE 30-bus system is considered as test boat. The suggested system employs the Cuckoo search method and its most recent variant, adaptive Cuckoo search, to solve a stochastic non-linear optimization problem. The adaptive Cuckoo search approach, when combined with the proposed demand side management strategy, reduces fuel costs by 7.84%, emission dispatch by 16.35%, power losses by 10.31%, and peak hour demand by 15.6%.
کلیدواژه‌ها
Demand response؛ Dynamic pricing؛ Energy management system؛ Peak load management
مراجع
  1. Rajaei, M. Haddadi, and N. Nord, “A new approach of optimal appliance scheduling for peak load reduction of an off-grid residential building,” Journal of Building Performance Simulation, vol. 16, no. 2, pp. 131–143, 2023.
  2. Makhdoomi and J. Moshtagh, “Optimal scheduling of electrical storage system and flexible loads to participate in energy and flexible ramping product markets,” Journal of Operation and Automation in Power Engineering, vol. 11, no. 3, pp. 203–212, 2023.
  3. R. Goyal and S. Vadhera, “Smart home appliances’ scheduling by two-stage optimization with real-time price model,” Electric Power Components and Systems, vol. 51, no. 6, pp. 604–618, 2023.
  4. Mellouk, M. Boulmalf, A. Aaroud, K. Zine-Dine, and D. Benhaddou, “Genetic algorithm to solve demand side management and economic dispatch problem,” Procedia computer science, vol. 130, pp. 611–618, 2018.
  5. Jalilian, A. Rastgou, S. Kharrati, and S. Hosseini-Hemati, “Frequency stability of hybrid power system in the presence of superconducting magnetic energy storage and uncertainties,” Journal of Operation and Automation in Power Engineering, vol. 11, no. 4, pp. 230–239, 2023.
  6. Shewale, A. Mokhade, N. Funde, and N. D. Bokde, “An overview of demand response in smart grid and optimization techniques for efficient residential appliance scheduling problem,” Energies, vol. 13, no. 16, p. 4266, 2020.
  7. Goyal and S. Vadhera, “Solution of combined economic emission dispatch with demand side management using meta-heuristic algorithms,” Journal Européen des Systemes Automatisés, vol. 52, no. 2, pp. 143–148, 2019.
  8. R. Goyal and S. Vadhera, “Challenges of implementing demand side management in developing countries,” Journal of Power Technologies, vol. 100, no. 1, p. 43, 2020.
  9. Dutta and K. Mitra, “A literature review on dynamic pricing of electricity,” Journal of the Operational Research Society, vol. 68, no. 10, pp. 1131–1145, 2017.
  10. Mohebbi-Gharavanlou, S. Nojavan, and K. Zareh, “Energy management of virtual power plant to participate in the electricity market using robust optimization,” Journal of Operation and Automation in Power Engineering, vol. 8, no. 1, pp. 43–56, 2020.
  11. R. Goyal and S. Vadhera, “Multi-interval programming based scheduling of appliances with user preferences and dynamic pricing in residential area,” Sustainable Energy, Grids and Networks, vol. 27, p. 100511, 2021.
  12. Afraz, B. Rezaeealam, S. SeyedShenava, and M. Doostizadeh, “Generation scheduling of active distribution network with renewable energy resources considering demand response management,” Journal of Operation and Automation in Power Engineering, vol. 9, no. 2, pp. 132–143, 2021.
  13. Mohammad and Y. Mishra, “Demand-side management and demand response for smart grid,” Smart grids and their communication systems, pp. 197–231, 2019.
  14. Chandra, S. Banerjee, K. K. Radhakrishnan, and S. K. Panda, “Transactive energy market framework for decentralized coordination of demand side management within a cluster of buildings,” IEEE Transactions on Industry Applications, vol. 57, no. 4, pp. 3385–3395, 2021.
  15. Shewale, A. Mokhade, N. Funde, and N. D. Bokde, “An overview of demand response in smart grid and optimization techniques for efficient residential appliance scheduling problem,” Energies, vol. 13, no. 16, p. 4266, 2020.
  16. Jo and J. Park, “Demand-side management with shared energy storage system in smart grid,” IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 4466–4476, 2020.
  17. Poudel and R. Gokaraju, “Optimal operation of smr-res hybrid energy system for electricity & district heating,” IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3146–3155, 2021.
  18. Basu, “Fuel constrained dynamic economic dispatch with demand side management,” Energy, vol. 223, p. 120068, 2021.
  19. Samuel, S. Javaid, N. Javaid, S. H. Ahmed, M. K. Afzal, and F. Ishmanov, “An efficient power scheduling in smart homes using jaya based optimization with time-of-use and critical peak pricing schemes,” Energies, vol. 11, no. 11, p. 3155, 2018.
  20. D. Sen, J. Sharma, G. R. Goyal, and A. K. Singh, “A multiobjective pso (mopso) algorithm for optimal active power dispatch with pollution control,” Mathematical Modelling of Engineering Problems, vol. 4, no. 3, pp. 113–119, 2017.
  21. Lokeshgupta and S. Sivasubramani, “Multi-objective dynamic economic and emission dispatch with demand side management,” International Journal of Electrical Power & Energy Systems, vol. 97, pp. 334–343, 2018.
  22. Liu and J. Li, “A bi-level energy-saving dispatch in smart grid considering interaction between generation and load,” IEEE Transactions on Smart Grid, vol. 6, no. 3, pp. 1443–1452, 2015.
  23. P. Mathur, A. Arya, and M. Dubey, “Impact of emission trading on optimal bidding of price takers in a competitive energy market,” in Harmony Search and Nature Inspired Optimization Algorithms: Theory and Applications, ICHSA 2018, pp. 171–180, Springer, 2019.
  24. Shayeghi and M. Alilou, “Multi-objective demand side management to improve economic and environmental issues of a smart microgrid,” Journal of Operation and Automation in Power Engineering, vol. 9, no. 3, pp. 182–192, 2021.
  25. Derakhshan, H. A. Shayanfar, and A. Kazemi, “The optimization of demand response programs in smart grids,” Energy Policy, vol. 94, pp. 295–306, 2016.
  26. Hasan and M. El-Hawary, “Economic dispatch at peak load using load reduction for smart grid network,” in 2016 IEEE Electrical Power and Energy Conference (EPEC), pp. 1–5, IEEE, 2016.
  27. Tariq, S. Alelyani, G. Abbas, A. Qahmash, and M. R. Hussain, “Solving renewables-integrated economic load dispatch problem by variant of metaheuristic batinspired algorithm,” Energies, vol. 13, no. 23, p. 6225, 2020.
  28. Kakran and S. Chanana, “Operation management of a renewable microgrid supplying to a residential community under the effect of incentive-based demand response program,” International Journal of Energy and Environmental Engineering, vol. 10, pp. 121–135, 2019.
  29. P. Biswas, P. N. Suganthan, B. Y. Qu, and G. A. Amaratunga, “Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power,” Energy, vol. 150, pp. 1039–1057, 2018.
  30. Suresh, S. Sreejith, S. K. Sudabattula, and V. K. Kamboj, “Demand response-integrated economic dispatch incorporating renewable energy sources using ameliorated dragonfly algorithm,” Electrical Engineering, vol. 101, pp. 421–442, 2019.
  31. -S. Ryu and M.-K. Kim, “Combined economic emission dispatch with environment-based demand response using wu-abc algorithm,” Energies, vol. 13, no. 23, p. 6450, 2020.
  32. Guerrero, O. Castillo, and M. García, “Cuckoo search via lévy flights and a comparison with genetic algorithms,” Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics: Theory and Applications, pp. 91–103, 2015.
  33. Cheng, J. Wang, M. Zhang, H. Song, T. Chang, Y. Bi, and K. Sun, “Improvement and application of adaptive hybrid cuckoo search algorithm,” IEEE Access, vol. 7, pp. 145489–145515, 2019.
  34. Kaushik, M. Goswami, M. Manuja, S. Indu, and D. Gupta, “A binary pso approach for improving the performance of wireless sensor networks,” Wireless Personal Communications, vol. 113, pp. 263–297, 2020.
  35. R. Chinda and R. D. Rao, “A binary particle swarm optimization approach for power system security enhancement,” International Journal of Electrical & Computer Engineering, vol. 12, no. 2, pp. 1929–1936, 2022.
آمار
تعداد مشاهده مقاله: 925
تعداد دریافت فایل اصل مقاله: 898


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب