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ENERGY SAVING OPTIMIZATION OF THERMAL POWER CO-GENERATION AUTOMATION SYSTEM IN POWER PLANT

ABSTRACT
In order to establish a new optimal load distribution between CHP units and wind turbines and realize a new type of energy-saving dispatching, the authors proposed a dispatching system based on smart grid. The author proposed that the heat pump with electric drive air conditioning should share part of the heating load of the hot water radiator, accordingly, the supply of heating water is reduced, increase heating power load. This directly leads to the increase of the total power load in the grid and the decrease of the total heating and hot water load, changing the proportion of thermal power load. Based on the new heating water and power load constraints, a mathematical model of optimal scheduling is established, the energy-saving dispatching of cogeneration units and wind turbines is realized. The simulation results show that using the new energy-saving scheduling method, 342.4 MWh of fuel can be saved per hour, and the energy saving benefit is about 8.83%. If the calorific value of standard coal is 29271 kJ/kg, this means that the fuel consumption savings per hour is about 42.14 ton standard coal. The calculation results show that the higher the value is, the more economically feasible the feed-in price of wind power is. In conclusion based on the current electricity price and heating heat price, in order to ensure that the economic benefits of each participant are not changed, the feed-in price of wind power is discussed, and the economic feasibility of the method is proved.
KEYWORDS
PAPER SUBMITTED: 2022-09-18
PAPER REVISED: 2022-11-10
PAPER ACCEPTED: 2022-11-29
PUBLISHED ONLINE: 2023-03-25
DOI REFERENCE: https://doi.org/10.2298/TSCI2302249Z
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 2, PAGES [1249 - 1256]
REFERENCES
  1. Siniosoglou, I., et al., A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments, IEEE Transactions on Network and Service Management, 18 (2021), 2, pp. 1137-1151
  2. Massaoudi, M., et al., Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects, IEEE Access, 9 (2021), 1, pp. 54558-54578
  3. Zahedi, R.,et al., Investigation of the Load Management and Environmental Impact of the Hybrid Cogeneration of the Wind Power Plant and Fuel Cell, Energy Reports, 7(2021), 2, pp. 2930-2939
  4. Cao, Y., et al., Seasonal Design and Multi-Objective Optimization of a Novel Biogas-Fueled Cogeneration Application, International Journal of Hydrogen Energy, 46 (2021), 42, pp. 21822-21843
  5. Kallio, S., Siroux, M., Hybrid Renewable Energy Systems Based on Micro-Cogeneration, Energy Reports, 8 (2022), 3, pp. 762-769
  6. Sun, L., et al., Sustainable Residential Micro-Cogeneration System Based on a Fuel Cell Using Dynamic Programming-Based Economic Day-Ahead Scheduling, ACS Sustainable Chemistry and Engineering, 9 (2021), 8, pp. 3258-3266
  7. Latif, A., et al., A Review on Fractional Order (FO) Controllers' Optimization for Load Frequency Stabilization in Power Networks, Energy Reports, 7 (2021), 1, pp. 4009-4021
  8. Wen, G., et al., Recent Progress on the Study of Distributed Economic Dispatch in Smart Grid: An Overview, Frontiers of Information Technology and Electronic Engineering, 22 (2021), 1, pp. 25-39
  9. Lu, Q., et al., Achieving Acceleration for Distributed Economic Dispatch in Smart Grids over Directed Networks, IEEE Transactions on Network Science and Engineering, 7 (2020), 33, pp. 1988-1999
  10. Yang, Y., et al., Fast Economic Dispatch in Smart Grids Using Deep Learning: An Active Constraint Screening Approach, IEEE Internet of Things Journal, 7 (2020), 11, pp. 11030-11040
  11. Safdarian, F., Kargarian, A., Temporal Decomposition-Based Stochastic Economic Dispatch for Smart Grid Energy Management, IEEE Transactions on Smart Grid, 11 (2020), 5, pp. 4544-4554
  12. Sreenivasulu, G., Balakrishna, P., Optimal Dispatch of Renewable and Virtual Power Plants in Smart Grid Environment through Bilateral Transactions, Electric Power Components and Systems, 49 (2021), 4-5, pp. 488-503
  13. Fu, Y., et al., The Distributed Economic Dispatch of Smart Grid Based on Deep Reinforcement Learning, IET Generation, Transmission and Distribution, 15 (2021), 18, pp. 2645-2658
  14. Guo, F., et al., An Alternative Learning-Based Approach for Economic Dispatch in Smart Grid, IEEE Internet of Things Journal, 8 (2021), 19, pp. 15024-15036
  15. Liu, Y., et al., Evaluating Smart Grid Renewable Energy Accommodation Capability with Uncertain Generation Using Deep Reinforcement Learning, Future Generation Computer Systems, 110 (2020), 8, pp. 647-657

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence