Growing concerns about energy conservation and the environmental impacts of greenhouse gas emissions over the world have promoted the development of the electric vehicles (EVs) market. However, one ...of the biggest barriers in the development of the EV market is the lack of the public charging infrastructure. This paper reviews the factors that can directly and indirectly influence the economics of the public charging infrastructure. The knowledge gaps, barriers and opportunities in the development of the charging infrastructure have been identified and analyzed. In order to promote the development of the public charging infrastructure, more research efforts should be paid on the impacts of psychological factors of customers and the technical development of charging infrastructures and EV batteries. The government support has been proved to play an important role, so that how the government policy can be tailored for the development of the charging infrastructure market should receive more attentions. In addition, the charging price as an endogenous factor should be considered more carefully in modelling the charging infrastructure market. New business models are also urgently needed to accelerate the future development of the public charging infrastructure.
•This paper reviews the factors that can influence the economics of public charging infrastructures.•Knowledge gaps, barriers and opportunities in the development of charging infrastructures have been identified.•More research efforts should be focused on customer psychological factors.•The government policy should be tailored according to the status of charging infrastructure market.•Developing new business models of charging infrastructures should receive more attention.
At present, China's economic and social development is restricted by many factors, such as environmental pollution and the supply of energy, land resources and water resources. Compared with ...traditional terrestrial photovoltaic (PV) systems, floating PV systems can save a lot of land and water resources and obtain higher power generation efficiency. Although the academics have reached a general consensus about the advantages of floating systems, very few in-depth studies focus on the specifications of floating PV systems. Therefore, this study first discusses the development of PV technology, then studies the power generation efficiency of floating PV systems, and finally comprehensively analyzes the advantages and potential of floating PV systems in China.
The significant increase in energy consumption and the rapid development of renewable energy, such as solar power and wind power, have brought huge challenges to energy security and the environment, ...which, in the meantime, stimulate the development of energy networks toward a more intelligent direction. Smart meters are the most fundamental components in the intelligent energy networks (IENs). In addition to measuring energy flows, smart energy meters can exchange the information on energy consumption and the status of energy networks between utility companies and consumers. Furthermore, smart energy meters can also be used to monitor and control home appliances and other devices according to the individual consumer's instruction. This paper systematically reviews the development and deployment of smart energy meters, including smart electricity meters, smart heat meters, and smart gas meters. By examining various functions and applications of smart energy meters, as well as associated benefits and costs, this paper provides insights and guidelines regarding the future development of smart meters.
Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The ...installation of smart energy meters has provided a huge volume of data at different time resolutions, suggesting data analysis is required for clustering, demand forecasting, energy generation optimization, energy pricing, monitoring and diagnostics. The currently adopted data analysis technologies for IENs include pattern recognition, machine learning, data mining, statistics methods, and so on. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by IENs, therefore more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.
Based on life cycle assessment (LCA), this paper compares five different methods for food waste treatment. To achieve it, the environmental performance of food waste in Shandong University as a case ...study is evaluated. The case study indicates that saving food is the ultimate way to reduce environment impact and the distance for transportation also affects a lot. The result of comparison shows that landfill contributes most to climate change, which is about 10 times larger than others. For acidification and eutrophication, incineration shows the worst result 7.77 PAF*m2yr. And composting has the largest impact on carcinogens with the result 3.49E-05 DALY. On the whole, the waste treatment technologies are recommended in proper sequence from anaerobic digestion, heat-moisture reaction, composting and incineration to landfill.
•An agent-based model is developed to simulate the energy consumption of offices.•Different pricing mechanisms and people’s maximum-saving behavior are simulated.•The detailed features of various ...types of appliances are carefully analyzed.•Offices have huge energy saving potential, i.e. 24.5%, without technical investment.
This paper developed an agent-based model (ABM) to explore the energy saving potentials (ESPs) of various types of appliances in offices under different pricing mechanisms. The model included four types of commonly used appliances in office buildings: an air conditioner (AC), computers, lights and a basic load. The total ESPs of the entire office are 6.7% and 17.4% on the second and the third price tier of the tiered pricing mechanism (TEP), while the ESPs are 11.8% and 14.2% under the peak-valley pricing (PVP) and critical peak pricing (CPP), respectively. Within different types of appliances, AC consumes the largest amount of electricity, over 50%, while the ESPs of the AC under different pricing mechanisms are only 6.9–12.1%. In contrast, the lights have the biggest ESP, i.e. 14.1–53.4%, under various pricing levels. Both the pricing mechanisms of PVP and CPP only have the effect of peak clipping and do not have a significant effect of valley filling, since there is no people working in the office during the valley price period. The maximum ESP, which is based on people’s maximum-saving behavior, is much larger than the ESPs on the basis of people’s ordinary consumption patterns. This implies the importance of improving people’s awareness of energy saving and refining their behaviors. Lastly, the model developed in this study provides a generic platform for simulating many types of energy systems and is very effective for handling the complicated relations between different types of technology and the way how they are used and interacted with each other. ABMs have very good adaptability and capacity in simulating energy systems.
The integration of an energy storage system into an integrated energy system (IES) enhances renewable energy penetration while catering to diverse energy loads. In previous studies, the adoption of a ...battery energy storage (BES) system posed challenges related to installation capacity and capacity loss, impacting the technical and economic performance of the IES. To overcome these challenges, this study introduces a novel design incorporating a compressed CO2 energy storage (CCES) system into an IES. This integration mitigates the capacity loss issues associated with BES systems and offers advantages for configuring large-scale IESs. A mixed integer linear programming problem was formulated to optimize the configuration and operation of the IES. With an energy storage capacity of 267 MWh, the IES integrated with a CCES (IES–CCES) system incurred an investment cost of MUSD 161.9, slightly higher by MUSD 0.5 compared to the IES integrated with a BES (IES–BES) system. When not considering the capacity loss of the BES system, the annual operation cost of the IES–BES system was 0.5 MUSD lower than that of the IES–CCES system, amounting to MUSD 766.6. However, considering the capacity loss of the BES system, this study reveals that the operation cost of the IES–BES system surpassed that of the IES–CCES system beyond the sixth year. Over the 30-year lifespan of the IES, the total cost of the IES–CCES system was MUSD 4.4 lower than the minimum total cost of the IES–BES system.
Lithium-ion batteries are used in a wide range of applications. However, their cycle life suffers from the problem of capacity fade, which includes calendar and cycle aging. The effects of storage ...time, temperature and partial charge-discharge cycling on the capacity fade of Li-ion batteries are investigated in this study. The calendar aging and cycle aging are presented based on the storage and cycling experiment on LiCoO
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/graphite cells under different storage temperature and different ranges of state of charge (SOC). Based on the measurement data, a one-component and a double-component aging model are presented to respectively describe the capacity fade caused by calendar and cycle aging. The calendar aging of LiCoO
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/graphite batteries is mainly affected by temperature and SOC during the storage. Mean SOC and change in SOC (ASOC) are the main factors affecting battery degradation during cycling operation.
To reduce the electricity grid’s valley—peak difference, thereby resulting in a smoother electricity load, this study employs a compressed CO2 energy storage system to facilitate load shifting. Load ...shifting by the CCES system not only enhances the energy flexibility of the electricity load but also creates energy arbitrage from variations in the electricity prices. An optimization model is developed to optimize the operation of the CCES system to minimize the standard deviation of the electricity load. Thereby, load shifting by the CCES system can be achieved. Based on the real electricity loads and prices, results indicate that, with an energy storage capacity of 267 MWh, the CCES system can provide 3845 MWh, 4052 MWh, and 3816 MWh of upward flexible energy and 3846 MWh, 3180 MWh, and 3735 MWh of downward flexible energy during a week in summer, winter, and the transition season, respectively. With a lifespan of 35 years, the CCES system can attain a net present value (NPV) of MUSD 239.9 and a payback time of 2 years. The sensitivity analysis shows that increasing the energy storage capacity of the CCES system augments both the upward and downward flexible energy of the electricity load but reduces the NPV of the CCES system.
Accurate prediction of photovoltaic (PV) power is the prerequisite for the safe and stable operation of the power grid with high penetration of PV. Despite various machine learning models for ...forecasting PV power have been developed, their accuracies are generally unstable. Toward this end, this study proposes a novel Stacking ensemble forecast model to improve the precision of day-ahead PV power forecasts. Different from the traditional Stacking model that uses the original training dataset to train the base learners, the proposed model creates multiple sub-training sets from the original training dataset to train the base learners, so as to enhance the diversity of base models and further improve the prediction accuracy. Specifically, in the proposed Stacking ensemble model, four machine learning learners, i.e., generalized regression neural network (GRNN), extreme learning machine (ELM), Elman neural network (ElmanNN), and Long shot-term memory (LSTM) neural network are incorporated, which are trained with the diverse sub-training datasets, and a variety of candidate base models are generated. For those candidate base models, the ones with the best performance are selected and integrated through a meta-model, namely the back-propagation network work (BPNN), to produce the final PV power prediction. The proposed model is evaluated using measured data from a 15kW PV power station in Ashland, Oregon, USA. Results indicate that across three weather scenarios, the performance of the novel Stacking ensemble model consistently outperforms single models and the traditional Stacking ensemble model in terms of the errors for out-of-sample forecasting, which proves the effectiveness of the developed procedure in improving PV power forecasting accuracy.
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