基于双负荷预测的温湿独控蓄冷空调系统优化控制策略

Optimal Control Strategy of Temperature and Humidity Independent Control Cold Storage Air-conditioning System Based on Dual-load Prediction

近年来冰蓄冷作为一种关键的空调需求响应(DR)技术,可以有效实现建筑的柔性用能,增强电网稳定性。对于带冰蓄冷的温湿度独立控制系统,由于存在高、低温2种冷源,因此,其控制策略的优化也更为复杂,常规策略难以适用。为此,提出了1种基于高温和低温双负荷预测的温湿独控冰蓄冷空调系统优化控制策略。该策略采用径向基函数(RBF)神经网络算法分别预测逐时的高温和低温空调冷负荷,同时结合空调设备特性和当地分时电价,建立了冷机和蓄冰槽的逐时冷量分配模型。以深圳市某办公建筑冰蓄冷空调系统为例,展开模拟和实验研究,以测试和验证所提策略。结果表明,在保证室内热舒适的前提下,相较于传统控制策略,所提优化控制策略可实现32.3%的运行费用节省率和88.7%的峰值负荷削减率,更好地兼顾了用户侧经济效益和电网侧经济效益。

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 airconditioning 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.