Electric buses play a vital role in future sustainable transportation and estimating the energy consumption of electric buses is important for reducing range anxiety and optimizing charging schedules. However, due to ...
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Electric buses play a vital role in future sustainable transportation and estimating the energy consumption of electric buses is important for reducing range anxiety and optimizing charging schedules. However, due to the numerous unobserved factors in real-world driving, there are naturally significant uncertainties in the energy consumption of electric vehicles. Therefore, besides focusing on improving model accuracy, the estimating probabilistic distribution of the uncertainties can also help to better characterize electric bus energy consumption and increase the confidence in the estimation results. In this paper, two probabilistic models, based on Bayesian regression and quantile regression, are proposed to estimate the probabilistic distribution of electric bus energy consumption; the probabilistic models are trained and validated using real-world driving data from 10 electric buses over a year. The results show that the proposed methods both capture the probabilistic characteristics well; however, the variations of uncertainties are better adapted in quantile regression.
Hydrogen energy, a type of green secondary energy, has attracted much attention in recent years. The production of hydrogen by water electrolysis is less harmful to the environment and makes it possible to obtain hydr...
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Hydrogen energy, a type of green secondary energy, has attracted much attention in recent years. The production of hydrogen by water electrolysis is less harmful to the environment and makes it possible to obtain hydrogen with a high degree of purity. But it requires a lot of electricity and is expensive. Installed capacity and the share of electricity generation from new energy sources have increased recently. The "double high" trend in the power system is becoming apparent. The combination of new energy power generation and hydrogen production by water electrolysis can not only promote the absorption of new energy power generation, but also reduce the cost of hydrogen production. The conventional thyristor power supply system for hydrogen production has low power factor and poor harmonic performance, which cannot meet the requirements of the new power system. Hydrogen production power supply based on fully controlled devices is gradually becoming mainstream. In this paper, the low voltage ride through capability and the reactive power support function for the power grid of hydrogen production power supply based on fully controlled devices are verified by simulation. For both symmetrical and asymmetrical faults, the hydrogen production power supply can ride through the fault.
As COVID-19 sweeps through the whole world,human activities have been changed *** such circumstances,the electricity sector is deeply affected and faced with great *** paper provides a comprehensive review of the impa...
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As COVID-19 sweeps through the whole world,human activities have been changed *** such circumstances,the electricity sector is deeply affected and faced with great *** paper provides a comprehensive review of the impacts that the pandemic has caused on the electricity *** demand has dropped sharply as governments around the world executed lockdown restrictions,while the load composition and daily load profile have also *** share of renewable generation has increased against the decline of the total electricity *** power balance situation and increased uncertainty of demand have posed higher pressure on system operators,along with voltage violation issue and challenges for system maintenance and *** electricity market is also substantially influenced,while longterm investment in clean energy is expected to be *** externality such as emission reduction is also discussed.
With the construction of Global Energy Internet, Intelligent distribution network has more and more characteristics of multi scene, multi service and changeable measurement and control objects. In order to realize dat...
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Introduce the IoT platform technology based on SDN (Software Defined Network), cloud-based, flexible management and configuration of smart terminals, and support access to millions of terminal devices and smart operat...
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Medium and long-term weather sequence forecast becomes unreliable beyond two weeks since the weather is a chaotic system. Using values of same months for electricity prediction of wind power is the usual method. This ...
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Cleanliness of energy system requires both NOx reduction due to urgent air pollution control and CO2 cutting due to long-term climate change pressure. In the urban heating sector, quick-win plans use to be popular tha...
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Accurate assessment of the power generation capacity of the wind farm is the key basis for incorporating it into power system scheduling and other optimizing operation activities. Traditional evaluation methods, such ...
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ISBN:
(纸本)9781665414401
Accurate assessment of the power generation capacity of the wind farm is the key basis for incorporating it into power system scheduling and other optimizing operation activities. Traditional evaluation methods, such as benchmarking wind turbine method, have higher requirements for input data, but many wind farms in operation for many years are difficult to meet such data conditions. A new power generation capacity evaluation method based on correlation analysis of adjacent wind farms is proposed in this paper. Meteorological data and power of adjacent wind farms are used as replaced input data based on correlation analysis between the target farm with its adjacent farms. First, anticipate wind speed data and then construct the wind power curve with three different neural networks to evaluate power generation capacity. Afterwards, adjacent wind farms are used as data sources to fit the power data of the target wind farm with the application of influence radius method, inverse square distance method and bilinear interpolation method. Finally, combined with data in the actual case, investigate the advantages and disadvantages of different methods in the evaluation of power generation capacity.
With the large-scale integration of distributed generations (DGs), a centralized control approach is being challenged concerning communication efficiency, resiliency to communication failure, privacy, and scalability....
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ISBN:
(纸本)9798350399677
With the large-scale integration of distributed generations (DGs), a centralized control approach is being challenged concerning communication efficiency, resiliency to communication failure, privacy, and scalability. An autonomous decentralized control for DGs in distribution networks is proposed to ensure privacy while also reducing the computational burden. Meanwhile,a multi-agent reinforcement learning method considering privacy protection constraints is developed to solve the autonomous decentralized control problem. The effectiveness of the proposed control method is verified by the numerical example of the modified 141-bus distribution system test_feeder.
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