Search

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

Most of the crude oil produced in China is highly viscous and easy to condense, and the pipeline transportation method is mostly heated. At present, frequency conversion technology is gradually applied to oil pump units. In order to find the best pipeline operation scheme, this paper is based on MATLAB programming software and established a corresponding mathematical model of oil pipeline energy consumption. This model innovatively takes the rotational speed of the variable frequency pump as one of the model optimization variables, calculating the pressure change and temperature change between stations of the pipeline, predicting the energy consumption of the pipeline and can guide the optimized operation of the pipeline, which has a high popularization and application value. An example of Qingtie Fourth‐line crude oil pipeline shows that among the genetic algorithm, particle swarm optimization, and simulated annealing algorithms for solving the optimization model, the particle swarm optimization algorithm has fast convergence speed, short optimization time, obtains the most optimal scheme, and can reduce energy consumption by 7.84%, which proves the practicability and creativity of the optimization model. [ABSTRACT FROM AUTHOR]

The application of a photovoltaic (PV) system for zero energy buildings is crucial to balance energy consumption. This paper investigated the potential of PV systems for buildings in different climate zones of China. The tilt angle, orientation, plot ratio, PV conversion efficiency and location were selected to study their influences on electricity production. The performance of the PV system was analyzed based on a residential building, and employed RETScreen in combination with TRNSYS. The results indicate that southwest China is the best place to develop zero energy buildings. Low-rise residential buildings can realize zero energy in China when the PV conversion efficiency is higher than 20%. However, an energy balance only occurs in the southwest when the PV conversion efficiency is 10%. For different building heights, with a high conversion efficiency (20%), the energy consumptions rates are less than 15 kW h/m2a all over China. In the southeast, the effect of the building shape on energy generation can be ignored due to the high solar elevation angle in the summer. Although the optimal tilt angle varies for different cities, the energy deviations are less than 10% when tilt angle is within 10° of the optimal angle. This study proposed a formula to evaluate the potential of a building PV system. The maximum difference between the simulated and calculated results is approximately 10%, which is reasonable and acceptable. This paper provides some references for the application of PV systems in zero energy buildings. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. [ABSTRACT FROM AUTHOR]

Surface water environment in China was degraded rapidly in the last two decades, resulting in increasingly tighten criteria issued for municipal wastewater treatment plants (WWTPs). This paper reviewed the recent advances of process design and operational optimization for nutrients removal. Three major processes, as anaerobic-anoxic-oxic (AAO) process, oxidation ditch (OD), and sequencing batch reactor (SBR) occupied 65% of WWTPs amounts and 54% of treatment volumes of China in 2006. However conservative process designs and operational faults often impaired the process performances and energy efficiency. Therefore, typical processes were modified, combined, and innovated to meet the requirements of the diverse influent characteristics and lower energy consumptions. Furthermore, operational optimization techniques by modeling, simulation, and real-time control were also developed and applied in China to improve the process operation. Although great efforts had been contributed to improve the WWTPs performances in China, attentions should be continuously paid to the introduction, instruction, and implementation of advanced techniques. At last, the technical demands and appropriated techniques of WWTPs in China were briefly discussed. [ABSTRACT FROM AUTHOR]

The rural residences of Northwest China are characterized by a state of high energy consumption and low comfort due to the limited economic level and awareness of energy-saving compared with the urban residences. To remedy this, appropriate passive design strategies should be adopted first, in order to provide a design mode with low energy consumption and low cost for rural residences under the premise of thermal comfort. In this paper, taking Hanzhong region (Shaanxi Province, China) as an example, we establish a benchmark model based on a field survey and develop an optimization process by combining EnergyPlus simulation software, the MOBO optimization engine, and weighted sum method. The action mechanisms of passive design parameters, including the building orientation, length–width ratio, building envelope parameters, external shading system, and window–wall ratio, on heating, cooling, and total energy consumption are analyzed, and the quantitative relationships between single-parameter and energy consumption are established. Then, the mutually restricted indices of total energy consumption and initial investment cost are taken as optimization objectives, and 17 design parameters are selected as the optimization variables. The NSGA-II algorithm is adopted to conduct the multi-parameter, multi-objective optimization design for rural houses in Hanzhong area, through coupling of the EnergyPlus and MOBO software. In this way, Pareto solutions are obtained and the value distributions of the multi-objectives and design parameters are analyzed. Based on the actual requirements of decision-makers and using the weight method, three design schemes focusing on different performance tendencies are proposed. The results indicate that by using the proposed optimization process, the building energy consumption can be significantly reduced while taking initial investment costs into account, where the energy-saving rate is in the range of 31.9–61.5%. When the EC/IC ratio is 1:1, 2:1, and 1:2, the energy-saving rate can reach 51.5%, 57.8%, and 43.5%, respectively. It can provide a beneficial pattern for the energy-saving design and renovation of rural residences in Hanzhong area of China. [ABSTRACT FROM AUTHOR]

Demand for electricity in China kept accelerating in recent years; moreover, there exist serious mismatches among the distribution of power demand, energy resources, and power generation infrastructure across different regions in China, both of which indicate a necessity of a holistic and integrated approach to the strategic planning and development of its power industry. Material benefits could be realized by ensuring that the long term development of the power system is optimized by taking into consideration the different regional dynamics and characteristics. This paper proposes a multi-region optimization model that can deliver insights into how planning of the long term development of China’s power sector could minimize the total cost of China’s power sector by considering regional variations in availabilities of resources and inter-region power transmission line capacity. A case study considered how investment decisions to expand and alter the existing generation mix could be optimized across a timeframe from 2011 to 2050. By comparing results between single and multi-region optimizations, it was possible to show the likely impact on how investment decisions would differ when regional differences were taken into account. The multi-region optimization arguably better reflects and considers conditions and challenges in the real world. [ABSTRACT FROM AUTHOR]

Under the background of carbon peaking and carbon neutralization proposed by China, the development of energy storage technology is imperative. The heat consumption of building central heating is large, which can be used as an important means of load side energy storage. The mathematical model of the central heating system is established, the optimal simulation of the regenerative temperature stratified water tank in the heating system is carried out, and the optimal structure of the temperature stratified water tank is obtained. The heating power of the fully mixed water tank is compared with the indoor maintenance temperature, and the power consumption of the primary pump is reduced by 8.5%. The energy utilization rate of the heating system is high and has high energy-saving potential. [ABSTRACT FROM AUTHOR]

Abstract: Current energy designs for a building in China focus on the energy efficiency of the whole building and thus often neglect the energy difference between different housing units in apartment buildings. The best design technique in terms of energy savings is not only to decrease whole building energy demand, but also to reduce the energy difference between different housing units to a relatively low level. This paper introduces an important index “energy performance difference between housing units” (EDH) to evaluate the drawbacks of conventional designs on a typical residential building in hot summer and cold winter zone. Then nine improved design options were considered as the possible strategies to diminish the EDH, based on a number of building simulations with the program DeST-h. Results show that the option 9 (add movable shading for the east facing windows and west facing windows, and reduce the U-values of the east walls and west facing windows), an improved design strategy according to the EDH of the current design, reduces the EDH to less than 4% that is much lower than other options and meanwhile ensures a decrease in whole building energy demands. A case study was carried out to validate the effectiveness of this index in optimization. As a conclusion, EDH is a very useful index for optimizing energy designs of apartment buildings and can be used in China to improve conventional building designs. [Copyright &y& Elsevier]

Abstract: Along with high-speed economic development and increasing energy consumption, the Chinese Government faces a growing pressure to maintain the balance between energy supply and demand. In 2009, China has become both the largest energy consumer and CO2 emitting country in the world. In this case, the inappropriate energy consumption structure should be changed. As an alternative, a suitable infrastructure for the implementation of renewable energy may serve as a long-term sustainable solution. The perspective of a 100% renewable energy system has been analyzed and discussed in some countries previously. In this process, assessment of domestic renewable energy sources is the first step. Then appropriate methodologies are needed to perform energy system analyses involving the integration of more sustainable strategies. Denmark may serve as an example of how sustainable strategies can be implemented. The Danish system has demonstrated the possibility of converting into a 100% renewable energy system. This paper discusses the perspective of renewable energy in China firstly, and then analyses whether it is suitable to adopt similar methodologies applied in other countries as China approaches a renewable energy system. The conclusion is that China’s domestic renewable energy sources are abundant and show the possibility to cover future energy demand; the methodologies used to analyse a 100% renewable energy system are applicable in China. Therefore, proposing an analysis of a 100% renewable energy system in China is not unreasonable. [ABSTRACT FROM AUTHOR]

The low-carbon economy, as a major trend of global economic development, has been a widespread concern, which is a rare opportunity to realize the transformation of the economic way in China. The realization of a low-carbon economy requires improved resource utilization efficiency and reduced carbon emissions. The reasonable location of logistics nodes is of great significance in the optimization of a logistics network. This study formulates a double objective function optimization model of reverse logistics facility location considering the balance between the functional objectives of the carbon emissions and the benefits. A hybrid multi-objective optimization algorithm that combines a gravitation algorithm and a particle swarm optimization algorithm is proposed to solve this reverse logistics facility location model. The mobile phone recycling logistics network in Jilin Province is applied as the case study to verify the feasibility of the proposed reverse logistics facility location model and solution method. Analysis and discussion are conducted to monitor the robustness of the results. The results prove that this approach provides an effective tool to solve the multi-objective optimization problem of reverse logistics location. [ABSTRACT FROM AUTHOR]

China's "dual carbon" goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level 2. The optimized data fluctuation range is controlled within the range of ±1. The fault detection accuracy rate of the model optimized based on the MA method is increased from 32.51% to 83.96% when the evaporation temperature sensor fault is a 5 °C deviation. However, the data will deviate from the original data trend when the smoothing parameter is set too large. Therefore, the smoothness of the data should not be too large. The approach proposed in this study is of great significance to the energy saving and emission reduction of the energy system. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)