THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

online first only

The simulation and operation control strategy of ground source thermal energy management system by cold and heat auxiliary technology

ABSTRACT
To explore the performance of the ground source thermal energy management system under the cold and heat sources, based on the cold and heat auxiliary technology, a ground source thermal energy composite management system is constructed and simulated. The constructed ground source heat pump-refrigeration unit-hybrid heating management system of urban heating networks, as well as the simple system, are analyzed and investigated in terms of power consumption and underground temperature control. The research results show that the constructed ground source heat pump-refrigeration unit-hybrid heating management system of the urban heating network has lower power and energy consumption than a simple system during the same period, which meets the economic requirements and guarantees the system with relatively low energy consumption. For underground temperature control, the constructed system is more stable than a simple system without excessive temperature fluctuations. The operation control strategy of the constructed system is mainly for chiller units, heat pump units, cooling towers, source side, and side circulation water pump modules. In summary, the constructed ground source heat pump-refrigeration unit-hybrid heating management system of an urban heating network based on the ground source heat pump meets the requirements for energy consumption and temperature control and can operate the control strategy normally. The results are significant for subsequent researches on the ground source thermal energy management system based on cold and heat auxiliary technology.
KEYWORDS
PAPER SUBMITTED: 2019-11-10
PAPER REVISED: 2019-12-18
PAPER ACCEPTED: 2020-01-20
PUBLISHED ONLINE: 2020-03-15
DOI REFERENCE: https://doi.org/10.2298/TSCI191110106X
REFERENCES
  1. Sun H, et al. Energy management for multi-energy flow: challenges and prospects, Automation of Electric Power Systems, 40(2016), 15, pp. 1-8.
  2. Shaofei Wu. Construction of visual 3-d fabric reinforced composite thermal performance prediction system, Thermal Science, 23(2019), 5, pp.2857-2865.
  3. Allouche Y, et al. Dynamic simulation of an integrated solar-driven ejector based air conditioning system with PCM cold storage, Applied energy, 190(2017), 8, pp. 600-611.
  4. Wu C, et al. Combined economic dispatch considering the time-delay of district heating network and multi-regional indoor temperature control, IEEE Transactions on Sustainable Energy, 9(2017), 1, pp. 118-127.
  5. Bejarano G, et al. Efficient simulation strategy for PCM-based cold-energy storage systems, Applied Thermal Engineering, 139(2018), 4, pp. 419-431.
  6. Alimohammadisagvand B, et al. Influence of energy demand response actions on thermal comfort and energy cost in electrically heated residential houses, Indoor and Built Environment, 26(2017), 3, pp. 298-316.
  7. Shaofei Wu,A Traffic Motion Object Extraction Algorithm,International Journal of Bifurcation and Chaos, 25(2015),14,Article Number 1540039.
  8. Zhao M, et al. Experimental investigation and feasibility analysis on a capillary radiant heating system based on solar and air source heat pump dual heat source, Applied energy, 185(2017), 6, pp. 2094-2105.
  9. Luo X, et al. Modelling study, efficiency analysis and optimisation of large-scale Adiabatic Compressed Air Energy Storage systems with low-temperature thermal storage, Applied energy, 162(2016), 8, pp. 589-600.
  10. Žandeckis A, et al. Performance simulation of a solar-and pellet-based thermal system with low temperature heating solutions, Energy Efficiency, 10(2017), 3, pp. 729-741.
  11. Máša V, et al. Using a utility system grey-box model as a support tool for progressive energy management and automation of buildings, Clean Technologies and Environmental Policy, 18(2016), 1, pp. 195-208.
  12. Yin Z, et al. Optimal scheduling strategy for domestic electric water heaters based on the temperature state priority list, Energies, 10(2017), 9, pp. 1425.
  13. Ye Y, et al. Performance assessment and optimization of a heat pipe thermal management system for fast charging lithium ion battery packs, International Journal of Heat and Mass Transfer, 92(2016), 8, pp. 893-903.
  14. Alamin Y, et al. An economic model-based predictive control to manage the users' thermal comfort in a building, Energies, 10(2017), 3, pp. 321.
  15. Wang J, et al. Off-design performance analysis of a transcritical CO2 Rankine cycle with LNG as cold source, International Journal of Green Energy, 14(2017), 9, pp. 774-783.
  16. Li Y, et al. District heating and cooling optimization and enhancement-Towards integration of renewables, storage and smart grid, Renewable and Sustainable Energy Reviews, 72(2017), 6, pp. 281-294.