The emergence of photovoltaic/thermal (PV/T) technology has effectively solved the problem of high temperature and low electrical efficiency of photovoltaic cells, and significantly improved the utilization rate of solar energy. At present, improving the thermoelectric performance of PV/T systems is a research hotspot. The effects of operating parameters such as inlet temperature, solar radiation, ambient temperature, and coolant mass flow rate, are investigated through numerical simulations. An experimental platform is built to verify the effectiveness of the three-dimensional numerical model. It is found that when the solar radiation changes from 800 W/m² to 1000 W/m², the increase rate in thermal efficiency will obviously slow down. When the coolant mass flow rate is increased from 60 to 320 L/h, the thermal efficiency is raised by 8.24%. For each 40 L/h increase in mass flow rate, the electrical efficiency increased by 0.047%. However, when the mass flow rate is too large, the increase in electrical and thermal efficiency gradually decreases. Orthogonal experiments and analysis of variance (ANOVA) are used to study the effects of each parameter and parameter combination on overall efficiency. The results show that ambient temperature has the greatest effect, followed by inlet temperature. Finally, the mathematical model of overall efficiency is established, and the coolant mass flow control formula is proposed. This formula can determine the optimal flow rate according to different environmental conditions, so that the system can operate under the optimal flow rate at all times and maximize the thermoelectric efficiency. Experimental results show that flow control increases the overall energy gain by 2.5% compared with the optimal constant mass flow.

Flexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large‐scale energy storage technique, the advanced adiabatic compressed air energy storage (AA‐CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA‐CAES has immense potential in multi‐energy supply systems, CCHP dispatch with AA‐CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA‐CAES system equipped with WPG is addressed. The AA‐CAES system is comprehensively modelled by considering its thermal characteristics, air‐temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA‐CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA‐CAES system is minimised. The numerical results demonstrate that the participation of AA‐CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA‐CAES are also discussed.

The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Firstly, an objective function is designed based on the constraint, and a base load forecasting model is established when aggregating renewable generation and non-deferrable load into a power system, which aims to transform the problem of deferrable loads scheduling into a distributed optimal control problem. Then, to optimize the objective function, a real-time scheduling algorithm is presented to solve the proposed control problem. At every time step, the purpose is to minimize the variance of differences between power supply and aggregate load, which can thus ensure the effective utilization of power resources. Finally, simulation examples are provided to illustrate the effectiveness of the proposed algorithm.

Compressed air energy storage (CAES) can balance the fluctuation of renewable generation. In this study, the CAES is used as an energy storage system in the distribution network with wind power generation (WPG). Considering the forecasting error of WPG and load, a robust day‐ahead optimal dispatch scheme for the CAES is developed in this study. The characteristics of the CAES are modelled in detail for the expansion/compression operation during the robust optimal dispatch (ROD). The aim is to minimise the fluctuation of the net load in the distribution network. Robust dynamic programming is employed to solve the optimisation problem in this study. The developed scheme is tested with the IEEE‐RBTS load profile and real WPG data. The deterministic and ROD‐ahead dispatch scheme are simulated and compared. The fluctuation of the net load is compared with different weights of the variation and uncertainty set.

How to decide the location and the capacity of distributed generators (DGs) in the distribution network is a complex and hard work. In this paper, several technical indexes involving energy loss, voltage quality and stability, and line loadability are considered to optimize DG capacity and location. It is an optimization problem wherein both discrete and continuous variables are involved, and cloud theory adapted genetic algorithm (CAGA) is employed to avoid search exploration and long time search. The weights of the indexes are decided by judgment matrix. DGs in a 28-node distributed network are employed to be optimized quickly to improve the indexes aforementioned to demonstrate the CAGA developed in this paper. Meanwhile, the optimization results are compared with traditional GA. Copyright © 2013 John Wiley & Sons, Ltd.

In recent years, the optimal scheduling of multienergy has become the focus of the research. Against this background, this paper builds a model of a multienergy flow system of cooling, heating, and power, and advanced adiabatic compressed air energy storage (AA-CAES) is introduced to smooth wind power generation (WPG) and supply heating/cooling energy. Simulated annealing algorithm (SAA) is employed to energy-saving scheduling of the system with “exergy assessment” method. The energy-saving index and the exergy efficiency are compared in different cases. SAA is compared with the particle swarm optimization (PSO) algorithm in solving the optimal scheduling strategy. The cooling, heating, and power demands of an industrial park and WPG in typical days are employed to the simulation. The scheduling results of exergy input of SAA are far less than those of PSO in typical days of different seasons. The multienergy system without AA-CAES is also modeled, and energy-saving economic scheduling is carried out. The exergy efficiency of the system with AA-CAES is between 38% and 58% while the exergy efficiency of the system without AA-CAES is merely between 27% and 48%.