An Optimal Hybrid Control Method for Energy-Saving of Chilled Water System in Central Air Conditioning

Abstract

Chilled water system of central air conditioning is a typical hybrid system; variable frequency behavior with amplitude limited of pumps reflects continuous and discrete dynamic characteristics. The way of energy-saving is variable water volume, via variable frequency behavior of pumps to gain adjustment of power consumption. Facing the situation of the variable frequency pumps with parallel operation, single continuous or discrete modeling cannot reflect the hybrid features. Thus, the control method will have some questions, such as bad energy-saving effect, difficult accurate adjustment of cold capacity, and low running energy efficiency. However, hybrid system modeling can reflect hybrid dynamic behavior of pumps, which is combining continuous and discrete features. The questions of nonlinear and multiparameters can be solved by control method based on hybrid system. Here, an optimum control method is proposed with the principle of the minimum, by setting the minimum power consumption as the performance function in fixed time, which realizes variable control of pumps and accurate adjustment of temperature inside room. At last, it shows the system characteristics and energy-saving affection by hybrid system modeling and the optimum control method.

A Type of Mixed Logical System Model Using in Laboratory Optimal Energy-Saving Management

“…Let the lab cost function be P E H S M P P P P P P      (6) where P P is the cost of electricity, which can be subdivided into the cost of lighting P L , the cost of air conditioning/heating P CM , and the cost of instrument and equipment P C . Once the laboratory is opened, the lighting cost is the fixed value, which is always P L =C L ; the proportion between the cost of air conditioning and heating and the number of people is calculated according to the power of central air conditioning, which is about the square relationship [25]. It is simply estimated that P CM =C CM v 2 , where v is the count of students in the laboratory, C CM is the cost coefficient of air conditioning / heating; and the cost function of instrument and equipment is P C =C C v, which is the same as that of students in the laboratory The quantity is in direct proportion; P E is the use loss of equipment.…”

Energy-saving optimization of refrigeration system in a data centre based on Nelder-Mead method

“…Literature [4] uses Genetic Algorithm to control parallel pumps, and changes the frequency of each pump in real time according to the load to achieve the energy saving result. Literature [5] proposes an optimal control method based on the principle of minimum, which realizes the variable control strategy on pump's and high-precised adjustment of indoor temperature. Literature [6] proposed a distributed probability estimation algorithm to configure the number of water pumps and optimize the speed ratio.…”

Optimization of Metro Central Air Conditioning Cold Source System Based on PCA-ANN Data Model

“…The metro central air conditioning system is a typical nonlinear system with a high inertia, significant hysteresis, time variable behavior, and strong coupling. The refrigeration and cooling systems of the metro central air conditioning unit are intricately connected and subject to distinct limitations (Zhang, 2016). Additionally, the central air conditioner's working parameters vary dynamically in response to variations in load rate and outside climatic circumstances.…”

“…The literature (Ning et al, 2017) developed a multi objective optimization methodology dubbed TOPSIS method for evaluating the task/ ambient conditioning system's integrated performance and the optimal functioning was attained. According to the literature (Zhang et al, 2016), an energy consumption model for each component of the refrigeration system was presented, and the model was solved using methods such as surrogate lagrangian relaxation (SLR) and sequential quadratic programming (SQP). When compared to a baseline utilizing existing methodologies, numerical testing demonstrates that our approach results in considerable power consumption reduction, with the largest savings being roughly 18 percent in a partial load state at the maximum efficiency.…”