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Mr. Shijian Liu
North China Electric Power University, Beijing China.

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0 Energy Management
0 Energy Planning
0 Hydrogen
0 Fuel cell
0 evaluation method

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Journal article
Published: 18 September 2020 in IEEE Access
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Clean and sustainable hydrogen production is key to the establishment of zero-carbon hydrogen energy system in response to the global warming challenge. This paper is aimed to analyze China’s potential of hydrogen production from wind and solar power. To achieve this goal, this paper firstly used the SBM model with undesirable output to measure the efficiency of green hydrogen production at the provincial level. Then, the efficiency of green hydrogen production and the installed capacity of wind and solar power are integrated to construct a comprehensive indicator. Lastly, China’s potential of green hydrogen production from both provincial and regional perspectives are simulated by entropy method and the dynamic change of the potential from 2017 to 2030 is also studied. From the results, we can find that: (1) the efficiency of the hydrogen production from wind power is significantly higher than that of the hydrogen production from solar power; (2) the efficiency and potential of green hydrogen production for each province of China in 2030 are improved compared with that in 2017; (3) the potential of green hydrogen production in the Northwest and North China is significantly higher than other regions; (4) there is a certain inconsistency in the development of the supply side and demand side of hydrogen energy, specially for the Southern China and northwest. Finally, based on the results above, some policy implications are provided to facilitate the high-quality development of the hydrogen energy industry.

ACS Style

Yuan-Sheng Huang; Shi-Jian Liu. Chinese Green Hydrogen Production Potential Development: A Provincial Case Study. IEEE Access 2020, 8, 171968 -171976.

AMA Style

Yuan-Sheng Huang, Shi-Jian Liu. Chinese Green Hydrogen Production Potential Development: A Provincial Case Study. IEEE Access. 2020; 8 (99):171968-171976.

Chicago/Turabian Style

Yuan-Sheng Huang; Shi-Jian Liu. 2020. "Chinese Green Hydrogen Production Potential Development: A Provincial Case Study." IEEE Access 8, no. 99: 171968-171976.

Journal article
Published: 08 February 2020 in Journal of Cleaner Production
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The hydrogen production transition from coal to renewable energy is key to realize low-carbon in the whole hydrogen energy system. To accelerate the transition, this study proposes a sustainable hydrogen production scheme combining coal-based hydrogen production with renewable hydrogen production. Specifically, the excess alkalinity generated by water electrolysis can capture the CO2 from coal-based hydrogen production. Additionally, the oxygen generated by water electrolysis can be provided for coal-based hydrogen production. Different from previous studies, we use the super efficiency SBM model with undesirable outputs to calculate the efficiency of coal-based hydrogen production, renewable hydrogen production and the integrated scheme. Results show that the efficiency of the integrated scheme with the reasonable configuration is 2.01. And it has the highest efficiency ranking, followed by coal-based hydrogen production and hydrogen production from wind energy, which are 1.07 and 0.84, respectively. On the other hand, the hydrogen production from solar energy is 0.32, which has the lowest ranking. Therefore, the integrated scheme can provide the low-carbon hydrogen by the inexpensive cost. Popularizing the integrated scheme should promote the large-scale of renewable hydrogen production, thereby guiding the green transformation of hydrogen production.

ACS Style

Yuansheng Huang; Shijian Liu. Efficiency evaluation of a sustainable hydrogen production scheme based on super efficiency SBM model. Journal of Cleaner Production 2020, 256, 120447 .

AMA Style

Yuansheng Huang, Shijian Liu. Efficiency evaluation of a sustainable hydrogen production scheme based on super efficiency SBM model. Journal of Cleaner Production. 2020; 256 ():120447.

Chicago/Turabian Style

Yuansheng Huang; Shijian Liu. 2020. "Efficiency evaluation of a sustainable hydrogen production scheme based on super efficiency SBM model." Journal of Cleaner Production 256, no. : 120447.

Journal article
Published: 16 January 2020 in Polish Journal of Environmental Studies
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Generation expansion planning for more renewable energy is of great significance to the implementation of low-carbon economy and energy transition in the power sector. This paper introduces two widely used renewable energy incentives (such as carbon trading mechanism and green certificate transaction mechanism) into traditional generation expansion planning, and establishes a low-carbon generation expansion planning model. Then the brain storm optimization algorithm was employed to solve the model. Finally, for the comparison between the two mechanisms, this paper sets four scenarios for case simulation. The results show that both carbon trading mechanisms and green certificate transaction mechanisms can increase the installed capacity of renewable energy and reduce carbon emissions, and the optimization effect of green certificate transaction mechanism on planning results is better than that of the carbon trading mechanism. When both mechanisms are introduced, the installed proportion of renewable energy will be the highest and carbon emissions will achieve the minimum. Moreover, with the increase of carbon price or green certificate price, and the strengthening of carbon emission constraint or renewable energy quota constraint, the proportion of coal-fired units in the power supply structure is gradually decreasing, and the carbon emissions of the system are gradually reduced.

ACS Style

Yuansheng Huang; Jianjun Hu; Yingqi Yang; Lei Yang; Shijian Liu. A Low-Carbon Generation Expansion Planning Model Considering Carbon Trading and Green Certificate Transaction Mechanisms. Polish Journal of Environmental Studies 2020, 29, 1169 -1183.

AMA Style

Yuansheng Huang, Jianjun Hu, Yingqi Yang, Lei Yang, Shijian Liu. A Low-Carbon Generation Expansion Planning Model Considering Carbon Trading and Green Certificate Transaction Mechanisms. Polish Journal of Environmental Studies. 2020; 29 (2):1169-1183.

Chicago/Turabian Style

Yuansheng Huang; Jianjun Hu; Yingqi Yang; Lei Yang; Shijian Liu. 2020. "A Low-Carbon Generation Expansion Planning Model Considering Carbon Trading and Green Certificate Transaction Mechanisms." Polish Journal of Environmental Studies 29, no. 2: 1169-1183.

Articles
Published: 01 June 2019 in Systems Science & Control Engineering
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The forecasting of carbon emissions trading market price is the basis for improving risk management in the carbon trading market and strengthening the enthusiasm of market participants. This paper will apply machine learning methods to forecast the price of China's carbon trading market. First, the daily average transaction prices of the carbon trading market in Hubei and Shenzhen are collected, and these data are preprocessed by PCAF approach to choose the input variables. Second, a prediction model based on Radical Basis Function (RBF) neural network is established and the parameters of the neural network are optimized by Particle Swarm Optimization (PSO). Finally, the PSO-RBF model is validated by the actual data and proved that the PSO-RBF model has better prediction effect than BP and RBF neural network in China's carbon prices prediction. It is indicated that the prediction model has more significant applicability and deserves further popularization.

ACS Style

Yuansheng Huang; Jianjun Hu; Hui Liu; Shijian Liu. Research on price forecasting method of China's carbon trading market based on PSO-RBF algorithm. Systems Science & Control Engineering 2019, 7, 40 -47.

AMA Style

Yuansheng Huang, Jianjun Hu, Hui Liu, Shijian Liu. Research on price forecasting method of China's carbon trading market based on PSO-RBF algorithm. Systems Science & Control Engineering. 2019; 7 (2):40-47.

Chicago/Turabian Style

Yuansheng Huang; Jianjun Hu; Hui Liu; Shijian Liu. 2019. "Research on price forecasting method of China's carbon trading market based on PSO-RBF algorithm." Systems Science & Control Engineering 7, no. 2: 40-47.

Articles
Published: 28 May 2019 in Systems Science & Control Engineering
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With the development of China's economy, more and more energy consumption has led to serious environmental problems. Faced with the enormous pressure of large amounts of carbon dioxide (CO2) emissions, China is now actively implementing the development strategy of low-carbon and emission reduction. Through the analysis of the influencing factors of CO2 emissions in China, five key influencing factors are selected: urbanization level, gross domestic product (GDP) of secondary industry, thermal power generation, real GDP per capital and energy consumption per unit of GDP. This paper applies the Elman neural network optimized by the Firefly Algorithm (FA) to forecast the CO2 emissions in China. And the results show that the performance of the FA–Elman is better than the Elman neural network and Back Propagation Neural Network (BPNN), verifying the effectiveness of the FA–Elman model for the CO2 emissions prediction. Finally, we make some suggestions for low-carbon and emission reduction in China by analysing key influencing factors and forecasting CO2 emissions using the FA–Elman model from 2017 to 2020.

ACS Style

Yuansheng Huang; Hongwei Wang; Hui Liu; Shijian Liu. Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions. Systems Science & Control Engineering 2019, 7, 8 -15.

AMA Style

Yuansheng Huang, Hongwei Wang, Hui Liu, Shijian Liu. Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions. Systems Science & Control Engineering. 2019; 7 (2):8-15.

Chicago/Turabian Style

Yuansheng Huang; Hongwei Wang; Hui Liu; Shijian Liu. 2019. "Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions." Systems Science & Control Engineering 7, no. 2: 8-15.

Articles
Published: 28 May 2019 in Systems Science & Control Engineering
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As air pollution becomes increasingly critical, ‘near-zero emission’ technological innovation in coal-fired plants are needed for the government and public consumers. The aim of this paper is to build the evolutionary game for analysing ‘near-zero emission’ technological innovation diffusion in coal-fired plants. According to bionics research of evolution, this paper introduces the co-evolutionary algorithm to simulate the diffusion. By modelling the evolutionary gaming behaviour of coal-fired plants, the simulation can capture the dynamics of coal-fired plants' strategy, which is adopting ‘near-zero emission’ technological innovation or not. It is key to model the diffusion under the electricity market and government regulation because it can provide some suggestions for promoting the diffusion. Simulations show that with no government regulations, the coal-fired plant fails to adopt the ‘near-zero emission’ technological innovation. However, the coal-fired plant for most profit should adopt independent R&D for ‘near-zero emission’ technology when the government provides subsidy incentives for the low-pollution coal-fired plant. With the promotion of subsidy incentives, all coal-fired plants will adopt ‘near-zero emission’ technology. Moreover, increasing the subsidy intensity has a significant role in promoting the diffusion.

ACS Style

Yuansheng Huang; Hongwei Wang; Shijian Liu. Research on ‘near-zero emission’ technological innovation diffusion based on co-evolutionary game approach. Systems Science & Control Engineering 2019, 7, 23 -31.

AMA Style

Yuansheng Huang, Hongwei Wang, Shijian Liu. Research on ‘near-zero emission’ technological innovation diffusion based on co-evolutionary game approach. Systems Science & Control Engineering. 2019; 7 (2):23-31.

Chicago/Turabian Style

Yuansheng Huang; Hongwei Wang; Shijian Liu. 2019. "Research on ‘near-zero emission’ technological innovation diffusion based on co-evolutionary game approach." Systems Science & Control Engineering 7, no. 2: 23-31.

Journal article
Published: 14 May 2019 in Energies
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It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models were applied on three different wind speed sequences. The calculation outcomes reveal that: (1) the EEMD is able to boost the wind speed prediction capacity and robustness of the LSTM approach effectively; (2) the BSO based parameter optimization method is effective in finding the optimal parameter for GRNN and improving the forecasting performance for the EEMD-LSTM-GRNN model; (3) the error correction method based on the optimized GRNN promotes the forecasting accuracy of the EEMD-LSTM model significantly; and (4) compared with all models involved, the proposed EEMD-LSTM-GRNN-BSO model is proved to have the best performance in predicting the short-term wind speed sequence.

ACS Style

Yuansheng Huang; Lei Yang; Shijian Liu; Guangli Wang. Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy. Energies 2019, 12, 1822 .

AMA Style

Yuansheng Huang, Lei Yang, Shijian Liu, Guangli Wang. Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy. Energies. 2019; 12 (10):1822.

Chicago/Turabian Style

Yuansheng Huang; Lei Yang; Shijian Liu; Guangli Wang. 2019. "Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy." Energies 12, no. 10: 1822.

Journal article
Published: 14 November 2018 in Sustainability
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Optimal sizing of single micro-grid faces problems such as high life cycle cost, low self-consumption of power generated by renewable energy, and disturbances of intermittent renewable energy. Interconnecting single micro-grids as a cooperative system to reach a proper size of renewable energy generations and batteries is a credible method to promote performance in reliability and economy. However, to guarantee the optimal collaborative sizing of two micro-grids is a challenging task, particularly with power exchange. In this paper, the optimal sizing of economic and collaborative for two micro-grids and the tie line is modelled as a unit commitment problem to express the influence of power exchange between micro-grids on each life cycle cost, meanwhile guaranteeing certain degree of power supply reliability, which is calculated by Loss of Power Supply Probability in the simulation. A specified collaborative operation of power exchange between two micro-grids is constructed as the scheduling scheme to optimize the life cycle cost of two micro-grids using genetic algorithm. The case study verifies the validity of the method proposed and reveal the advantages of power exchange in the two micro-grids system. The results demonstrate that the proposed optimal sizing means based on collaborative operation can minimize the life cycle cost of two micro-grids respectively considering different renewable energy sources. Compared to the sizing of single micro-grid, the suggested method can not only improve the economic performance for each micro-grid but also form a strong support between interconnected micro-grids. In addition, a proper price of power exchanges will balance the cost saving between micro-grids, making the corresponding stake-holders prefer to be interconnected.

ACS Style

Yuansheng Huang; Lei Yang; Shijian Liu; Guangli Wang. Cooperation between Two Micro-Grids Considering Power Exchange: An Optimal Sizing Approach Based on Collaborative Operation. Sustainability 2018, 10, 4198 .

AMA Style

Yuansheng Huang, Lei Yang, Shijian Liu, Guangli Wang. Cooperation between Two Micro-Grids Considering Power Exchange: An Optimal Sizing Approach Based on Collaborative Operation. Sustainability. 2018; 10 (11):4198.

Chicago/Turabian Style

Yuansheng Huang; Lei Yang; Shijian Liu; Guangli Wang. 2018. "Cooperation between Two Micro-Grids Considering Power Exchange: An Optimal Sizing Approach Based on Collaborative Operation." Sustainability 10, no. 11: 4198.

Conference paper
Published: 07 October 2018 in Programmieren für Ingenieure und Naturwissenschaftler
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As air pollution becomes increasingly critical, “near-zero emission” technological innovation in coal-fired plants are needed for the government and public consumers. The aim of this paper is to built the evolutionary game for analysing “near-zero emission” technological innovation diffusion in coal-fired plants. According to bionics research of evolution, this paper introduces the co-evolutionary algorithm to simulate the diffusion. By modeling the evolutionary gaming behavior of coal-fired plants, the simulation can capture the dynamics of coal-fired plants’ strategy, which is adopting “near-zero emission” technological innovation or not. It is key to model the diffusion under electricity market and government regulation because it can provide some suggestions for promoting the diffusion. Simulations show that the coal-fired plant for most profit should adopt independent R&D for “near-zero emission” technology and increasing the subsidy intensity has a significant role in promoting the diffusion.

ACS Style

Yuansheng Huang; Hongwei Wang; Shijian Liu. Research on “Near-Zero Emission” Technological Innovation Diffusion Based on Co-evolutionary Game Approach. Programmieren für Ingenieure und Naturwissenschaftler 2018, 48 -59.

AMA Style

Yuansheng Huang, Hongwei Wang, Shijian Liu. Research on “Near-Zero Emission” Technological Innovation Diffusion Based on Co-evolutionary Game Approach. Programmieren für Ingenieure und Naturwissenschaftler. 2018; ():48-59.

Chicago/Turabian Style

Yuansheng Huang; Hongwei Wang; Shijian Liu. 2018. "Research on “Near-Zero Emission” Technological Innovation Diffusion Based on Co-evolutionary Game Approach." Programmieren für Ingenieure und Naturwissenschaftler , no. : 48-59.

Conference paper
Published: 07 October 2018 in Programmieren für Ingenieure und Naturwissenschaftler
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Medium and long-term power load forecasting is the basis for power system planning and construction. This paper builds a prediction model based on SaDE-SVM algorithm. In order to reduce its selection problem of excessive large-scale hyperplane parameters, improve global optimization ability of traditional SVM, and further improve the prediction accuracy of SVM, the SaDE-SVM optimization algorithm is proposed. This algorithm optimizes the training process of traditional SVM based on adaptive differential evolution algorithm. The results of the medium and long-term forecasting for China’s power load show that the improved SaDE-SVM algorithm has good adaptability, robustness, fast convergence rate, and high accuracy for multi-influencing factors prediction model with less data volume, and is applicable to relevant medium and long-term forecasts.

ACS Style

Yuansheng Huang; Lijun Zhang; Mengshu Shi; Shijian Liu; Siyuan Xu. Medium and Long-Term Forecasting Method of China’s Power Load Based on SaDE-SVM Algorithm. Programmieren für Ingenieure und Naturwissenschaftler 2018, 484 -495.

AMA Style

Yuansheng Huang, Lijun Zhang, Mengshu Shi, Shijian Liu, Siyuan Xu. Medium and Long-Term Forecasting Method of China’s Power Load Based on SaDE-SVM Algorithm. Programmieren für Ingenieure und Naturwissenschaftler. 2018; ():484-495.

Chicago/Turabian Style

Yuansheng Huang; Lijun Zhang; Mengshu Shi; Shijian Liu; Siyuan Xu. 2018. "Medium and Long-Term Forecasting Method of China’s Power Load Based on SaDE-SVM Algorithm." Programmieren für Ingenieure und Naturwissenschaftler , no. : 484-495.