Energy-saving retrofit and thermal economy optimization of peak-shaving for coal-fired power plants utilizing molten salt thermal storage

Does climate change significantly impact the benefits of existing building energy-saving retrofit? evidence from a parametric study

Analysis of the application effect of energy-saving retrofit with additional evaporative cooling condenser for outdoor units of VRF air conditioning systems

With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system.

Thermal comfort and energy-saving retrofits: An empirical study in high-altitude regions

Analysis of the evolutionary game of energy-saving retrofit of residential buildings in urban villages under the background of urban renewal

In the global low-carbon era, building energy conservation has achieved significant success. However, especially in the culture and tourism industry, there are many brick–wood buildings that imitate ancient styles. As their appearance authenticity and structural safety must be maintained, energy-saving retrofits face multiple constraints. For such buildings, regulating building energy consumption through the renovation of the enclosure structure has practical value in supporting the achievement of carbon peaking and carbon neutrality goals. This study addresses the contradiction between the preserving architectural forms and improving energy efficiency in the energy-saving renovation of brick–wood buildings that imitate ancient styles. It presents a “Three-Micro” technical system grounded in the minimum-intervention principle, integrating micro-intervention implantation, micro-realignment regulation, and micro-renewal iteration. Through modular node design, it combines traditional construction with modern energy-saving techniques and systematically devises an energy-saving retrofit plan for such existing buildings. Through simulation and verification using the case of the Northwest Corner Tower in the Imperial City of Shengjing, the results show that the energy-saving rate of the building itself is 58.47%, while the comprehensive energy-saving rate is 87.56%. Both meet the evaluation criteria for ultra-low energy consumption buildings under the relevant standards, which proves the feasibility of the “Three-Micro” technical system. It provides solutions for the energy-saving renovation of similar buildings, especially those brick–wood buildings that imitate ancient styles and have a high degree of completion (a high level of imitation of ancient architecture). At the same time, it also holds important reference value for the energy-saving renovation of some non-core ancient buildings that are commonly used in everyday life, such as those serving as ticket offices, exhibition halls, administrative offices, etc.

The energy-saving retrofit (ESR) of existing buildings under the energy performance contracting (EPC) mode depends on the effective risk early warnings of energy service companies (ESCOs); therefore, this paper constructs an ESCO risk early warning model for energy-saving retrofit projects of existing buildings based on cloud matter element theory (CMET). The ESCO risk early warning indicator system is established according to the essential characteristics of ESR projects of existing buildings. The subjective weighting method (G1 method) and the objective weighting method (entropy weight method) are introduced to determine the comprehensive weights of ESCO risk early warning indicators. The ESCO risk warning level of ESR projects of existing buildings is evaluated based on the cloud matter element model concerning the randomness and ambiguity of the ESCO risk early warning indicators. Finally, the risk early warning model is applied to the ESCO risk management practice of an existing building ESR project in Tianjin. By comparing the actual project and the risk early warning model constructed in this paper, it is concluded that the model has high levels of feasibility, reasonableness, and efficiency. This model has scientific guidance value for ESCO enterprise risk control.

With the increasing demand for information services, the number of data centers has surged, leading to a significant rise in energy consumption. Traditional air-cooling systems can no longer meet the cooling requirements of high-density cabinets. To address the thermal management challenges in high-heat-density data centers, this paper proposes a thermal management system that couples flat-plate liquid cooling with air cooling for cabinet-level cooling. The cooling performance of the coupled thermal management system is compared with that of a traditional air-cooling system, and experimental studies are conducted to investigate the cooling performance and variation patterns of the flat-plate liquid-cooling and air-cooling coupled system. The results indicate that the coupled cooling system improves the average cooling efficiency by 59.77% compared to traditional air-cooling cabinets, with a maximum heat dissipation of 19 378.35 kJ, a maximum cooling efficiency of 88.96%, and a maximum coefficient of performance of 1.54. The residual heat after heat exchange can be used for domestic water heating and regenerating desiccants, offering potential for heat recovery. Based on experimental data and theoretical calculations, a computational model for the cooling system was established. The results of the model were in good agreement with experimental measurements, indicating that the model can predict the cooling performance of the coupled system under different operating conditions. The findings of this study contribute to improving the cooling efficiency of high-density cabinets, reducing cooling energy consumption, and facilitating energy-saving retrofits for existing cabinets.

This study addresses the issue of energy efficiency evaluation for rural residential buildings and proposes a method for facade recognition based on an improved Mask R-CNN network model. By introducing the Coordinate Attention (CA) mechanism module, the quality of feature extraction and detection accuracy is enhanced. Experimental results demonstrate that this method effectively recognizes and segments windows, doors, and other components on building facades, accurately extracting key information, such as their dimensions and positions. For energy consumption simulation, this study utilized the Ladybug Tool in the Grasshopper plugin, combined with actual collected facade data, to assess and simulate the energy consumption of rural residences. By setting building envelope parameters and air conditioning operating parameters, detailed calculations of energy consumption for different orientations, window-to-wall ratios, and sunshade lengths were performed. The results show that the improved Mask R-CNN network model plays a crucial role in quickly and accurately extracting building parameters, providing reliable data support for energy consumption evaluation. Finally, through case studies, specific energy-saving retrofit suggestions were proposed, offering robust technical support and practical guidance for energy optimization in rural residences.

Under the background of dual carbon, the retrofitting of the equipment operation system of a refrigeration station and the optimization combination of its control system are significant for its efficient operation and energy saving. The single-direction variable flow technology is often used in the chilled water system in refrigeration stations nowadays. However, the single-direction variable flow technology cannot achieve both thermal balance and flow balance for the chiller system, which is unfavorable for improving energy efficiency and reliability. To improve the reliability and energy efficiency of the refrigeration station equipment, the bidirectional variable flow technology of primary and secondary chilled water pumps was presented. Meanwhile, the feasibility of fuzzy neural networks in bidirectional variable flow systems and their energy-saving effect were studied. Before the energy saving retrofit, the refrigeration station used traditional PID (proportional-integral-derivative) controllers, and the chilled water system used single-direction variable flow technology; After the energy-saving retrofit, the refrigeration station adopted a fuzzy neural network control algorithm to optimize the PID controller parameters, and at the same time, the chilled water system used bidirectional variable flow technology. Through a large number of trial calculations of the established neural network model, it was found that 2 hidden layers and 25 hidden layer nodes can achieve higher accuracy. Specifically, the controller of the central refrigeration station consists of a training neural network and a predictive neural network working in parallel. The task of training neural networks is to learn the relationship between different input parameters and the whole energy consumption. Then it serves as the excitation function of the prediction network. The function of the predictive neural network is to find the control parameters that minimize energy consumption. The application results showed that before and after the retrofit annual power consumption and energy-saving effects were very Significant. After the energy-saving retrofit of the refrigeration station, the energy saving is 422,775 KWh every year, the energy-saving rate is 11.67%, and the annual saving cost is about 0.3382 million yuan. The results demonstrated that bidirectional variable flow technology and its control methods were feasible, reasonable, and worthy of promotion.

In the energy-saving retrofit of existing buildings, investors are particularly concerned about the energy-saving performance of exterior windows and the payback period of additional costs. This study evaluates representative cities in four different climate zones in China to simulate the energy consumption of large office buildings after replacing different glass windows and conducting energy-saving and economic feasibility assessments. The research method includes the following steps: First, a baseline model of large office buildings in four cities was established using AutoBPS and OpenStudio. Then, the baseline and retrofit models of replacing glass windows were simulated using the EnergyPlus V9.3.0 to obtain multiple hourly energy consumption results. The commercial electricity and gas prices in the four cities were adjusted to calculate the total cost within 20 years after replacing different types of windows. Using the discounted payback period (DPP), net present value (NPV), and profitability index (PI) as evaluation indicators, a feasibility analysis was conducted in the four regions to evaluate the economic feasibility of replacing building windows. The simulation results show that considering economic feasibility and meeting energy-saving standards, it is more economical to choose windows with moderate U-value and SHGC value in the four regions than to choose windows with the smallest U-value and SHGC value, and that both energy savings and economic benefits are closely related to building age, with older buildings (especially those in Changsha and Shenzhen) showing greater benefits. Furthermore, the optimal window types in the four cities determined in this study can recover the investment cost within the window life, with Harbin (SC), Beijing (C), Changsha (HC), and Shenzhen (HW) with the payback period of 6.60, 15.66, 10.16, and 11.42 years, respectively. The research model established in this study provides a useful evaluation path for selecting windows for the energy-saving retrofit of large office buildings in cities in different climate zones and provides data support for the decision making of energy-saving retrofit investors.

The thermal performance of a typical prefab container house

Abstract The air-conditioning system in ultra-high-voltage direct current (UHVDC) converter stations uses hydrofluorocarbons (HFCs) as refrigerants currently, which pose a certain environmental hazard. Moreover, the cooling system of the converter valves contains a great deal of low-grade thermal energy, with waste heat being directly discharged into the air, resulting in significant energy wastage and air pollution. In light of the functional characteristics and energy wastage situation of current converter station buildings, a green energy-saving retrofit scheme for the air-conditioning system in UHVDC converter stations is proposed. It adopts a transcritical CO2 cycle system for building cooling and introduces an ejector into the system to improve its performance. Simultaneously, the heat pump technology is used to elevate the grade of waste heat from the converter valves, which is then utilized for heating in the control building. Simulation results demonstrate that this scheme is effective in enhancing ecological and environmental benefits while promoting green development of the converter station.

Life cycle carbon emission assessment and carbon payback period analysis for the regeneration of old residential areas in cold regions: Case study in Qingdao, China

Research on energy-saving renovation of old oceanarium based on energy consumption monitoring technology

Energy-saving and economic analysis of the data center cooling system using magnetic bearing chillers under different climate conditions

ABSTRACT The combination of conventional thermal insulation materials and phase change materials (PCMs) was proposed without damaging the appearance of REHBs, the internal insulation measures of four types of expanded polystyrene (EPS) and six kinds of PCM boards combinations were investigated, and 19 retrofit cases were obtained. By conducting detailed simulations of energy consumption and indoor temperature for different cases at different locations, and comprehensively comparing multi-objects (energy-saving, economic and low-carbon), the energy consumption, cost recovery period and carbon reduction of these cases are compared and analyzed. The results show that Case I (only EPS) has the best energy-saving performance, economy and carbon emission reduction, Case III (EPS and PCM) is the second and Case II (only PCM) is the worst. The energy-saving rate of Case I and Case III (16 ℃) differs by 5.65%, while the cost, incremental benefit and payback period differ by 9.34%, 10.2%, and 1.57 years, respectively. Case III is a new and better passive retrofit measure of the REHB. Its energy-saving, cost and carbon reduction are only about 10% lower than that of Case I, but it has better indoor comfort. The outcomes can provide a reference for the innovative energy-saving retrofit cases for REHBs.

The number of colleges and universities in China has been increasing year by year. University buildings have tremendous energy-saving potential due to their high personnel density and energy consumption demand. However, there is a lack of research and regulations focusing on such buildings and taking functional requirements, operating patterns, and climate conditions into account. In the HSCW zone of China, the overlap of energy consumption peak and universities’ winter and summer vacations will lead to improper or excessive implementation of energy-saving measures in practice. This research study on a university teaching building in Shanghai simulated the energy consumption with EnergyPlus (Version 22.1.0) to compare the variation trend of the building’s energy consumption (heating, cooling and annual energy consumption) under different design parameter settings. The influence of orientation and window–wall ratio on the energy consumption intensity of classrooms of various sizes was analyzed, and design strategies were proposed. The research indicates that the annual energy consumption of educational buildings in hot summer and cold winter areas can be reduced by approximately 44.4% during vacations. However, cooling energy consumption remains 18.0–19.4% greater than heating energy consumption. The energy intensity of classrooms decreases as the space size increases. Medium-sized classrooms, with an energy intensity ranging from 44.2–47.6 kWh/m2, require priority in energy-efficient design owing to their considerable quantity and high utilization. The findings offer design suggestions for the optimal orientation and window-to-wall ratios of classrooms of different scales, which can be used as a reference for the design of university teaching buildings and the energy-saving retrofit of existing campus buildings.

Energy and environmental challenges are a major concern across the world and the urban residential building sector, being one of the main stakeholders in energy consumption and greenhouse gas emissions, needs to be more energy efficient and reduce carbon emissions. While it is easier to design net zero energy homes, existing home stocks are a major challenge for energy retrofitting. Two key challenges are determining the extent of retrofitting required, and developing knowledge-based effective policies that can be applied en-masse to housing stocks and neighborhoods. To overcome these challenges, it is essential to gather critical data about qualities of existing buildings including their age, geo-location, construction type, as well as electro-mechanical and occupancy parameters of each dwelling. The objective of this study was to develop a GIS-based model embedded with critical data of residential buildings to facilitate evidence-based retrofit programs for urban neighborhoods. A model based on a bottom-up approach was proposed in which information gathered from all stakeholders was inputted into one database that can be used for decision-making. A geo-located case study to validate a proposed GIS-based residential retrofitting model sample size of 74 residential buildings in the city of Riyadh was statistically analyzed and used. The results indicate behavior-based patterns, with a strong positive correlation (r = 0.606) between the number of occupants and number of household appliances, while regression analysis showed high occupancy rates do not necessarily result in high utility costs at the end of the month, and there is no statistical difference in the average monthly cost of gas between partial and fully occupied houses. Furthermore, neither the type of building, height, age, nor occupancy status play a significant role in the average energy consumed. Additionally, the GIS-based model was validated and found to be effective for energy-use mapping and gathering critical data for analyzing energy consumption patterns at neighborhood scale, making it useful for municipalities to develop effective policies aimed at energy efficient and smart neighborhoods, based on a recommended list of most effective energy-saving retrofit measures.