基于改进粒子群算法的中央空调系统节能优化控制
CSTR:
作者:
作者单位:

(1.上海电力大学电气工程学院,上海 200090;2.国网上海市电力公司电力科学研究院,上海 200080)

通讯作者:

刘欣雨(1997—),女,硕士研究生,主要从事综合能源系统、需求侧响应等研究;E?mail:1355961333@qq.com

中图分类号:

TM925

基金项目:

国家自然科学基金项目(51807114)


Energy‑saving optimization control of central air‑conditioning system based on improved particle swarm algorithm
Author:
Affiliation:

(1.College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200082, China; 2.State Grid Shanghai Electrical Power Research Institute, Shanghai 200080, China)

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    摘要:

    建筑的中央空调系统等设备用电能源消耗大,导致建筑能耗持续增长。为解决上述问题,首先根据中央空调工作原理,建立中央空调系统各设备能耗的数学模型,并选取决策变量;然后以中央空调系统总能耗最小为目标,以各变量上下限、设备之间的耦合关系、能量守恒等作为约束条件,建立中央空调系统节能优化模型;其次,将室内实时所需冷负荷与室内设定温度作为已知量,各决策变量作为输入量,中央空调系统总能耗作为输出量,降低模型的复杂程度;接着提出一种基于动态权重系数与加速因子的改进粒子群算法(IPSO),对各决策变量进行协同优化控制,搜寻中央空调系统节能优化模型的最优解;最后,通过仿真分析对比IPSO与标准粒子群算法,IPSO结果更优、收敛性更好,优化后的空调系统总能耗较优化前显著降低,验证了所提模型与方法的有效性。

    Abstract:

    The central air conditioning system and other equipment in buildings consume a large amount of electrical energy, leading to the continuous increase in energy consumption in the construction sector in our country. To address the above?mentioned issue, firstly, a mathematical model is established for the energy consumption of each equipment in the central air conditioning system based on its working principle, and the decision variables are selected. Then, with the objective of minimizing the total energy consumption of the central air conditioning system, an energy?saving optimization model for the central air conditioning system is established, taking into account the constraints such as variable bounds, coupling relationships between equipment, and energy conservation principles. Next, the real?time indoor cooling load and the set temperature are taken as known values, while the decision variables are taken as input variables, and the total energy consumption of the central air conditioning system is taken as the output variable to reduce the complexity of the model. Subsequently, an improved particle swarm optimization (PSO) algorithm is proposed based on dynamic weight coefficients and acceleration factors. The method achieves collaborative optimal control of the decision variables to search for the optimal solution of the energy?saving optimization model for the central air conditioning system. Finally, the ISPO algorithm is compared with the standard particle swarm optimization algorithm through simulation analysis. The results show that IPSO achieves superior performance and better convergence. The optimized total energy consumption of the air conditioning system is significantly reduced compared to the pre?optimized state, thus validating the effectiveness of the proposed model and method.

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杨 秀,刘欣雨,孙改平,等.基于改进粒子群算法的中央空调系统节能优化控制[J].电力科学与技术学报,2023,38(3):65-75,93.
YANG Xiu, LIU Xinyu, SUN Gaiping, et al. Energy‑saving optimization control of central air‑conditioning system based on improved particle swarm algorithm[J]. Journal of Electric Power Science and Technology,2023,38(3):65-75,93.

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  • 在线发布日期: 2023-09-19
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