Optimization of Deep Peak Shaving Methods for Fossil Fuel-Based Power Units Using the Improved Energy Consumption Framework
DOI:
https://doi.org/10.4108/ew.9811Keywords:
Fox Optimization Algorithm, Fossil Fuel-Based Power Units, Demand Response, Thermal Storage, Load Shifting, Peak Demand ManagementAbstract
The design optimisation of Fossil Fuel-Based Power Plants is critical for improving energy efficiency and minimising environmental impact, particularly amid the increasing global demand for electricity. Fossil fuel plants are vital for supplying energy needs, but are hindered by fuel inefficiency and emissions. The main aim of this research is to improve the performance of such power plants during peak demand hours and to reduce fuel consumption and emissions. The emphasis is placed on maximizing energy generation, enhancing operational effectiveness, and sustainability. The suggested work combines two advanced optimization methods.
Downloads
References
[1] Bin Abu Sofian ADA, Lim HR, Munawaroh HSH, Ma Z, Chew KW, Show PL. Machine learning and the renewable energy revolution: exploring solar and wind energy solutions for a sustainable future including innovations in energy storage. Sustainable Development. 2024;32(4):3953–3978. doi:10.1002/sd.2885.
[2] Nnabuife SG, Darko CK, Obiako PC, Kuang B, Sun X, Jenkins K. A comparative analysis of different hydrogen production methods and their environmental impact. Clean Technologies. 2023;5(4):1344–1380. doi:10.3390/cleantechnol5040067.
[3] Abdul Latif SN, Chiong MS, Rajoo S, Takada A, Chun YY, Tahara K, Ikegami Y. The trend and status of energy resources and greenhouse gas emissions in the Malaysia power generation mix. Energies. 2021;14(8):2200. doi:10.3390/en14082200.
[4] Popoola O, Chipango M. Improved peak load management control technique for nonlinear and dynamic residential energy consumption pattern. Building Simulation. 2021;14(1):195–208. doi:10.1007/s12273-020-0601-x.
[5] Wang D, Gryshova I, Balian A, Kyzym M, Salashenko T, Khaustova V, Davidyuk O. Assessment of power system sustainability and compromises between the development goals. Sustainability. 2022;14(4):2236. doi:10.3390/su14042236.
[6] Abo-Khalil AG, Alobaid M. A guide to the integration and utilization of energy storage systems with a focus on demand resource management and power quality enhancement. Sustainability. 2023;15(20):14680. doi:10.3390/su152014680.
[7] Liu J, Hu H, Yu SS, Trinh H. Virtual power plant with renewable energy sources and energy storage systems for sustainable power grid—formation, control techniques and demand response. Energies. 2023;16(9):3705. doi:10.3390/en16093705.
[8] Jia K, Yang X, Peng Z. Design of an integrated network order system for main distribution network considering power dispatch efficiency. Energy Informatics. 2024;7(1):69. doi:10.1186/s42162-024-00369-5.
[9] Capone M, Guelpa E, Mancò G, Verda V. Integration of storage and thermal demand response to unlock flexibility in district multi-energy systems. Energy. 2021;237:121601. doi:10.1016/j.energy.2021.121601.
[10] Zhang J. Energy access challenge and the role of fossil fuels in meeting electricity demand: promoting renewable energy capacity for sustainable development. Geoscience Frontiers. 2024;15(5):101873. doi:10.1016/j.gsf.2024.101873.
[11] Eslaminia N, Mashhadi HR. Enhancing peak shaving through nonlinear incentive-based demand response: a consumer-centric utility optimization approach. Int Trans Electr Energy Syst. 2023;2023:4650539. doi:10.1155/2023/4650539.
[12] Zhu J, Cui X, Ni W. Model predictive control-based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand. J Energy Storage. 2022;46:103811. doi:10.1016/j.est.2022.103811.
[13] Yu S, et al. Demand side management full-season optimal operation potential analysis for coupled hybrid photovoltaic/thermal, heat pump, and thermal energy storage systems. J Energy Storage. 2024;80:110375. doi:10.1016/j.est.2024.110375.
[14] Xie T, Chang H, Zhang G, Zhang K, Qing S. Deep power peak regulation of thermal power-energy storage under a high proportion of renewable energy access based on the cooperative game method. Appl Therm Eng. 2025;127464. doi:10.1016/j.applthermaleng.2025.127464.
[15] Ahmadian A, Ponnambalam K, Almansoori A, Elkamel A. Optimal management of a virtual power plant consisting of renewable energy resources and electric vehicles using mixed-integer linear programming and deep learning. Energies. 2023;16(2):1000. doi:10.3390/en16021000.
[16] Wang D, Gao N, Liu D, Li J, Lewis FL. Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications. IEEE/CAA J Autom Sin. 2024;11(1):18–36. doi:10.1109/JAS.2023.123843.
[17] Rana MM, Atef M, Sarkar MR, Uddin M, Shafiullah GM. A review on peak load shaving in microgrid—potential benefits, challenges, and future trend. Energies. 2022;15(6):2278. doi:10.3390/en15062278.
[18] Gan G, Li J, Ma G, Yang G. Optimized strategies for peak shaving and BESS efficiency enhancement through cycle-based control and cluster-level power allocation. arXiv. 2025. arXiv:2502.10268.
[19] Jayaprakassam BS, Hemnath R. Optimized microgrid energy management with cloud-based data analytics and predictive modelling. Int J Microelectron Comput Eng. 2018;6(3):1–9.
[20] Ding Y, Chen X, Wang J. Deep reinforcement learning-based method for joint optimization of mobile energy storage systems and power grid with high renewable energy sources. Batteries. 2023;9(4):219. doi:10.3390/batteries9040219.
[21] Coccato S, Barhmi K, Lampropoulos I, Golroodbari S, van Sark W. A review of battery energy storage optimization in the built environment. Batteries. 2025;11(5):179. doi:10.3390/batteries11050179.
[22] Khan MR, Haider ZM, Malik FH, Almasoudi FM, Alatawi KSS, Bhutta MS. A comprehensive review of microgrid energy management strategies considering electric vehicles, energy storage systems, and AI techniques. Processes. 2024;12(2):270. doi:10.3390/pr12020270.
[23] Kwon K, Lee HB, Kim N, Park S, Joshua SR. Integrated battery and hydrogen energy storage for enhanced grid power savings and green hydrogen utilization. Applied Sciences. 2024;14(17):7631. doi:10.3390/app14177631.
[24] Yang Y, Lu Q, Yu Z, Wang W, Hu Q. Multi-type energy storage collaborative planning in power system based on stochastic optimization method. Processes. 2024;12(10):2079. doi:10.3390/pr12102079.
[25] Giuzio GF, Russo G, Forzano C, Del Papa G, Buonomano A. Evaluating the cost of energy flexibility strategies to design sustainable building clusters: modelling and multi-domain analysis. Energy Reports. 2024;12:656–672. doi:10.1016/j.egyr.2024.06.047.
[26] Yao M, Da D, Lu X, Wang Y. A review of capacity allocation and control strategies for electric vehicle charging stations with integrated photovoltaic and energy storage systems. World Electric Vehicle Journal. 2024;15(3):101. doi:10.3390/wevj150300101.
[27] Nyangon J. Smart grid strategies for tackling the duck curve: a qualitative assessment of digitalization, battery energy storage, and managed rebound effects benefits. Energies. 2025;18(15):3988. doi:10.3390/en18153988.
[28] Cholidis D, Sifakis N, Savvakis N, Tsinarakis G, Kartalidis A, Arampatzis G. Enhancing port energy autonomy through hybrid renewables and optimized energy storage management. Energies. 2025;18(8):1941. doi:10.3390/en18081941.
[29] Xiang M, Zhang Y, Li C, Qi C. Peak-shaving cost of power system in the key scenarios of renewable energy development in China: Ningxia case study. Journal of Energy Storage. 2024;91:112133. doi:10.1016/j.est.2024.112133.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Xing Zhang, Yunlong Zhou
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
