Abstract
The energy consumption in the cold store is growing day by day, 70% of which is consumed by the refrigeration system. Meanwhile, a significant amount of electricity generated by power plants is wasted during off-peak periods. Demand-side management (DSM) provides a viable solution for addressing the problem of the time and space inconsistency between energy supply and consumption, hence improving overall system efficiency. In this paper, an artificial intelligence model is developed for accurate cooling load forecasting. On this basis, a peak shifting control strategy with two optional modes combining temperature setpoint control and operation mode control is then proposed to realize cost reductions. Taking a large-scale cold store as a case study, the cooling capacity supply and temperature variation within two typical working days are investigated to illustrate the feasibility and applicability of the strategy. Detailed thermodynamic and thermo-economic analyses of the proposed strategy are then carried out to demonstrate the control effect. The results show that both modes have good peaking performances and the average cost reduction rate of the two modes reaches 40% and 13.4%, respectively.
| Original language | English |
|---|---|
| Article number | 11855 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 13 |
| Issue number | 21 |
| DOIs | |
| State | Published - Nov 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- LSTM prediction
- cold store refrigeration system
- energy-saving operation
- intelligent control
- peak shifting
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