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Energy-saving technology
Global economic growth translates to a large amount of resources and high environmental costs. Only by persisting in economic development and sustainable development can we maintain rapid and good development. At the same time, greenhouse gas emissions have caused global warming and have received extensive attention from the international community. Therefore, further strengthening energy conservation and emission reduction is also an urgent need to address global climate change. Zhaohui Zhang et al. used a distributed learning method to coordinate a sliding window game of building temperature control to enable each building to achieve a globally optimal portion through state transition learning and thereby minimize energy costs or energy usage during peak hours. Benachir Medjdoub et al. investigated the impact of UK household transformation on household energy consumption patterns by studying social behavior and then developed an urban energy model called “EvoEnergy” to demonstrate the importance of this model in achieving an effective sustainable energy decision making. Patrick X.W. et al. reviewed and drew lessons from energy efficiency policies of five Australian states. They analyzed key factors for the successful implementation of government building energy transformation programs to ultimately reduce energy consumption in government buildings. Jiming Wa and Ng et al. summarized and reviewed 12 articles from the issue on operational analysis and optimization of manufacturing, logistics and energy systems. Shengwei Wang et al. introduced general methods for the design and control optimization of building energy systems in a life cycle to improve the energy efficiency of building energy systems and promote the development of smart buildings and smart cities.
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  • RESEARCH ARTICLE
    Benefit-based cost allocation for residentially distributed photovoltaic systems in China: A cooperative game theory approach
    Xi LUO, Xiaojun LIU, Yanfeng LIU, Jiaping LIU, Yaxing WANG
    Frontiers of Engineering Management, 2021, 8(2): 271-283. https://doi.org/10.1007/s42524-019-0083-7
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    Distributed photovoltaic (PV) systems have constantly been the key to achieve a low-carbon economy in China. However, the development of Chinese distributed PV systems has failed to meet expectations because of their irrational profit and cost allocations. In this study, the methodology for calculating the levelized cost of energy (LCOE) for PV is thoroughly discussed to address this issue. A mixed-integer linear programming model is built to determine the optimal system operation strategy with a benefit analysis. An externality-corrected mathematical model based on Shapley value is established to allocate the cost of distributed PV systems in 15 Chinese cities between the government, utility grid and residents. Results show that (i) an inverse relationship exists between the LCOEs and solar radiation levels; (ii) the government and residents gain extra benefits from the utility grid through net metering policies, and the utility grid should be the highly subsidized participant; (iii) the percentage of cost assigned to the utility grid and government should increase with the expansion of battery bank to weaken the impact of demand response on increasing theoretical subsidies; and (iv) apart from the LCOE, the local residential electricity prices remarkably impact the subsidy calculation results.

  • REVIEW ARTICLE
    Energy-saving operation approaches for urban rail transit systems
    Ziyou GAO, Lixing YANG
    Frontiers of Engineering Management, 2019, 6(2): 139-151. https://doi.org/10.1007/s42524-019-0030-7
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    With the accelerated urbanization in China, passenger demand has dramatically increased in large cities, and traffic congestion has become serious in recent years. Developing public urban rail transit systems is an indispensable approach to overcome these problems. However, the high energy consumption of daily operations is an emerging issue due to increased rail transit networks and passenger demands. Thus, reducing the energy consumption and operational cost by using advanced optimization methodologies is an urgent task for operation managers. This work systematically introduces energy-saving approaches for urban rail transit systems in three aspects, namely, train speed profile optimization, utilization of regenerative energy, and integrated optimization of train timetable and speed profile. Future research directions in this field are also proposed to meet increasing passenger demands and network-based urban rail transit systems.