Optimal Control Strategy of Temperature and Humidity Independent Control Cold Storage Air-conditioning System Based on Dual-load Prediction
In recent years,ice storage as a key air-conditioning demand response(DR)technology can effectively realize flexible energy use in buildings and enhance grid stability.For the temperature and humidity independent control system with ice storage,the optimization of its control strategy is also more complicated and conventional strategies are difficult to apply due to the existence of two cold sources,high and low temperature.To this end,a temperature and humidity independent control system with ice storage air-conditioning system optimization control strategy based on the dual load prediction of high and low temperatures was proposed.The strategy adopted the radial basis function(RBF)neural network algorithm to predict the high-temperature and low-temperature air-conditioning cold loads,and at the same time,combined the characteristics of air-conditioning equipment and local time-sharing tariffs,to establish a time-by-time cold capacity allocation model for chillers and ice storage tanks.The ice storage air-conditioning system of an office building in Shenzhen was selected as a case study for simulation and experimental studies to test and validate the proposed strategies.The results show that under the premise of ensuring indoor thermal comfort,the proposed optimized control strategy can achieve 32.3％of operating cost savings and 88.7％of peak load reduction compared with the traditional control strategy,which is a better balance between user-side and grid-side economic benefits.
ice storage air-conditioning systemtemperature and humidity independent controldemand responsecold load predictionoptimal control
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