Search Results (3)

An air-conditioning system (ACS), which consumes large amounts of high-grade energy, is essential for maintaining the indoor thermal environment of modern buildings. However, an ACS consumes almost half of the total energy of the building. Therefore, it is necessary to reduce the energy consumption of the ACS to promote energy conservation and emission reduction in the building sector. In fact, there is an abundance of waste heat and low-grade energies with the potential to be utilized in ACS in nature, but many of them are not utilized efficiently or cannot be utilized at all due to the low efficiency of thermal energy conversion. Known as a passive thermal transfer device, the application of a heat pipe (HP) in the ACS has shown explosive growth in recent years. HPs have been demonstrated to be an effective method for reducing building cooling and heating demands and energy consumption in ACS with experimental and simulation methods. This paper summarizes the different HP types applied in the ACS and provides brief insight into the performance enhancement of the ACS integrated with HP. Four types of HPs, namely tubular HP (THP), loop HP (LHP), pulsating HP (PHP) and flat HP (FHP), are presented. Their working principles and scope of applications are reviewed. Then, HPs used in natural cooling system, split air conditioner (SAC), centralized ACS (CACS) and cooling terminal devices are comprehensively reviewed. Finally, the heat transfer characteristics and energy savings of the above systems are critically analyzed. The results show that the performance of the HP is greatly affected by its own structure, working fluid and external environmental conditions. The energy saving of ACS coupled with HP is 3–40.9%. The payback period of this system ranges from 1.9–10 years. It demonstrates that the HP plays a significant role in reducing ACS energy consumption and improving indoor thermal comfort. Full article

The present study establishes a stochastic adaptive robust dispatch model for virtual power plants (VPPs) to address the risks associated with uncertainties in electricity market prices and photovoltaic (PV) power outputs. The model consists of distributed components, such as the central air-conditioning system (CACS) and PV power plant, aggregated by the VPP. The uncertainty in the electricity market price is addressed using a stochastic programming approach, and the uncertainty in PV output is addressed using an adaptive robust approach. The model is decomposed into a master problem and a sub-problem using the binding scenario identification approach. The binding scenario subset is identified in the sub-problem, which greatly reduces the number of iterations required for solving the model, and thereby increases the computational efficiency. Finally, the validity of the VPP model and the solution algorithm is verified using a simulated case study. The simulation results demonstrate that the operating profit of a VPP with a CACS and other aggregated units can be increased effectively by participating in multiple market transactions. In addition, the results demonstrate that the binding scenario identification algorithm is accurate, and its computation time increases slowly with increasing scenario set size, so the approach is adaptable to large-scale scenarios. Full article

As the energy-saving control of central air conditioning has been widely applied in modern architecture, research of real-time optimal control based on historical data and identification of its optimal control strategies are of great importance for reducing energy wasting of buildings. However, due to the property of easily falling into local optimum, conventional k-means approach cannot achieve the goal of real-time optimal control, we therefore propose an innovative binary k-means clustering algorithm which is used to adjust the target value of temperature difference (TD) in the control system of chilled water and cooling water of central air conditioning system (CACS). Thanks to the clustering control, among the 304 test data, the coefficient of performance (COP) of 211 sets of data, which accounted for 69.41%, are higher than those of the traditional control method. In the simulation system, the COP of 191 sets of data, which accounted for 62.83%, are higher than those of traditional control methods, achieving better energy efficiency. To achieve the goal of identify potential energy-saving control strategies, the Apriori algorithm is proposed to correlate the key parameters and energy consumption efficiency of the CACS. The results show when the chilled water temperature difference (CWTD) > 2.0 °C and the cooling water temperature difference (COWTD) > 2.4 °C, some rules are discovered as follows: 1. The probability of a larger system COP will increase if the CWTD is set lower than the third quartile value or the COWTD is set lower than the first quartile value. 2. The probability of a larger system COP will also increase if the CTWD is set lower than the first quartile and the COWTD is set between the first and the third quartile. These underlying regularity is useful for technicians to adjust the control parameters of the equipment, to improve energy efficiency and to reduce energy consumption. Full article