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With the rapid development of new energy sources and the increasing proportion of electric vehicles (EVs) connected to the power grid in China, peak load regulation of power systems will face severe challenges. Therefore, in this study, we analyzed the relationship between the electricity consumption characteristics of EVs and the peak load regulation (PLR) mechanism of power systems, and we proposed an operation mode for virtual power plants with EVs to participate in the auxiliary service market and facilitate deep peak load regulation in the thermal power units. Based on the electricity demand‐side management theory and cost‐benefit analysis method, we constructed a decision model for economic deep peak load regulated operation (DPLR) of the auxiliary thermal power units in a virtual power plant with EVs, aiming to optimize the operation efficiency. The case study showed that a virtual power plant that included EVs can effectively reduce the total PLR cost of the system and the peak valley difference of the net load as well as improve the economic benefits of the thermal power units. The results indicated that the virtual power plant had improved economic efficiency. Therefore, the results of this research can help improve the PLR capacity of the grid and significantly promote the consumption of intermittent renewable energy.
Xiaolong Yang; Dongxiao Niu; Lijie Sun; Keke Wang; Gejirifu De. Participation of electric vehicles in auxiliary service market to promote renewable energy power consumption: Case study on deep peak load regulation of auxiliary thermal power by electric vehicles. Energy Science & Engineering 2021, 1 .
AMA StyleXiaolong Yang, Dongxiao Niu, Lijie Sun, Keke Wang, Gejirifu De. Participation of electric vehicles in auxiliary service market to promote renewable energy power consumption: Case study on deep peak load regulation of auxiliary thermal power by electric vehicles. Energy Science & Engineering. 2021; ():1.
Chicago/Turabian StyleXiaolong Yang; Dongxiao Niu; Lijie Sun; Keke Wang; Gejirifu De. 2021. "Participation of electric vehicles in auxiliary service market to promote renewable energy power consumption: Case study on deep peak load regulation of auxiliary thermal power by electric vehicles." Energy Science & Engineering , no. : 1.
As a low-cost, low-carbon, and clean renewable energy, hydropower is crucial to carbon emissions reduction and climate change mitigation. Compared with non-steady and highly volatile renewable energy, flexible hydropower can help secure the reliable operation of the grid. However, development cost increase, resettlement and environmental issues have recently hindered China’s hydropower development. This paper first reviews the history and current status of hydropower development in China, integrates the economy-energy-environment system, the water-energy-food nexus, and socio-hydrology theory to analyze the complex influence mechanism of hydropower development, and identifies driving factors of hydropower development through a cost-benefit analysis. Then, the quantitative system dynamics (SD) model of hydropower development is constructed based on the interdisciplinary qualitative analysis. Combining incentive policies and technological progress, four scenarios are set. The multi-scenario prediction results and sensitivity analysis results of the SD model indicate that incentive policies can enhance the attractiveness of hydropower investment and significantly promote long-term stable hydropower development, while technological progress can improve the efficiency and carbon emissions reduction benefits of hydropower. Based on the comparative analysis of these results, specific policy recommendations for promoting sustainable development of green hydropower in China are proposed in terms of four aspects: the hydropower development concept, cooperation mechanism, policy mechanism, and technological progress.
Lijie Sun; Dongxiao Niu; Keke Wang; Xiaomin Xu. Sustainable development pathways of hydropower in China: Interdisciplinary qualitative analysis and scenario-based system dynamics quantitative modeling. Journal of Cleaner Production 2020, 287, 125528 .
AMA StyleLijie Sun, Dongxiao Niu, Keke Wang, Xiaomin Xu. Sustainable development pathways of hydropower in China: Interdisciplinary qualitative analysis and scenario-based system dynamics quantitative modeling. Journal of Cleaner Production. 2020; 287 ():125528.
Chicago/Turabian StyleLijie Sun; Dongxiao Niu; Keke Wang; Xiaomin Xu. 2020. "Sustainable development pathways of hydropower in China: Interdisciplinary qualitative analysis and scenario-based system dynamics quantitative modeling." Journal of Cleaner Production 287, no. : 125528.
With the continuous increase in new energy installed capacity, the slowdown in the growth of social power consumption, the pressure created by high coal prices, and the reduction in on-grid electricity tariffs, the challenges facing the survival and development of thermal power generation enterprises are becoming more severe. Hence, based on the cost–benefit analysis method, this paper proposes a diversified operating benefit analysis and decision model for thermal power generation enterprises that includes four profit models: power sales, peak load regulation (without oil), peak load regulation (with oil), and generation right trading. The opportunity cost of peak load regulation and generation rights trading was considered, and six scenarios were designed. An empirical analysis was conducted by selecting a thermal power enterprise in Ningxia, Northwest China, as an example, using scenario and sensitivity analyses. The results show that under the diversified business model, thermal power generation enterprises can more effectively avoid the risks when the external environment changes and significantly improve its economic benefits. The consumption of new energy can be promoted, and positive social effects will be achieved. Therefore, the findings will help the thermal power generation enterprises to face these challenges.
Xiaolong Yang; Dongxiao Niu; Meng Chen; Keke Wang; Qian Wang; Xiaomin Xu. An Operation Benefit Analysis and Decision Model of Thermal Power Enterprises in China against the Background of Large-Scale New Energy Consumption. Sustainability 2020, 12, 4642 .
AMA StyleXiaolong Yang, Dongxiao Niu, Meng Chen, Keke Wang, Qian Wang, Xiaomin Xu. An Operation Benefit Analysis and Decision Model of Thermal Power Enterprises in China against the Background of Large-Scale New Energy Consumption. Sustainability. 2020; 12 (11):4642.
Chicago/Turabian StyleXiaolong Yang; Dongxiao Niu; Meng Chen; Keke Wang; Qian Wang; Xiaomin Xu. 2020. "An Operation Benefit Analysis and Decision Model of Thermal Power Enterprises in China against the Background of Large-Scale New Energy Consumption." Sustainability 12, no. 11: 4642.
To mitigate solar curtailment caused by large-scale development of photovoltaic (PV) power generation, accurate forecasting of PV power generation is important. A hybrid forecasting model was constructed that combines random forest (RF), improved grey ideal value approximation (IGIVA), complementary ensemble empirical mode decomposition (CEEMD), the particle swarm optimization algorithm based on dynamic inertia factor (DIFPSO), and backpropagation neural network (BPNN), called RF-CEEMD-DIFPSO-BPNN. PV power generation is affected by many factors. The RF method is used to calculate the importance degree and rank the factors, then eliminate the less important factors. Then, the importance degree calculated by RF is transferred as the weight values to the IGIVA model to screen the similar days of different weather types to improve the data quality of the training sets. Then, the original power sequence is decomposed into intrinsic mode functions (IMFs) at different frequencies and a residual component by CEEMD to weaken the fluctuation of the original sequence. We empirically analyzed a PV power plant to verify the effectiveness of the hybrid model, which proved that the RF-CEEMD-DIFPSO-BPNN is a promising approach in terms of PV power generation forecasting.
Dongxiao Niu; Keke Wang; Lijie Sun; Jing Wu; Xiaomin Xu. Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study. Applied Soft Computing 2020, 93, 106389 .
AMA StyleDongxiao Niu, Keke Wang, Lijie Sun, Jing Wu, Xiaomin Xu. Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study. Applied Soft Computing. 2020; 93 ():106389.
Chicago/Turabian StyleDongxiao Niu; Keke Wang; Lijie Sun; Jing Wu; Xiaomin Xu. 2020. "Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study." Applied Soft Computing 93, no. : 106389.
To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040. Random forests (RF) method is used to select the important carbon emissions influencing factors to reduce data redundancy. The superiority of IFWA-GRNN forecasting model is verified by historical data from 1990 to 2015. The basic as usual (BAU), policy tightening (PT) and market allocation (ML) scenarios are set to forecast the TCE and CEI. The results show that under the BAU scenario, China’s CEI reduction commitments in 2020 (40%–45%) can be achieved, but the commitment in 2030 (60%–65%) cannot be achieved. Under the PT and ML scenarios, China can achieve its CEI commitments in 2030, and the TCE will decrease gradually after reaching its peak in 2030. Under the existing macro development planning and policy intensity in China, there are still certain pressures to achieve CEI reduction targets. It is necessary to implement policy adjustment and market mechanism incentives for both energy supply and consumption, optimize power supply structure, promote electric energy substitution, and accelerate the construction of a unified national electricity market, carbon market, etc.
Dongxiao Niu; Keke Wang; Jing Wu; Lijie Sun; Yi Liang; Xiaomin Xu; Xiaolong Yang. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network. Journal of Cleaner Production 2019, 243, 118558 .
AMA StyleDongxiao Niu, Keke Wang, Jing Wu, Lijie Sun, Yi Liang, Xiaomin Xu, Xiaolong Yang. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network. Journal of Cleaner Production. 2019; 243 ():118558.
Chicago/Turabian StyleDongxiao Niu; Keke Wang; Jing Wu; Lijie Sun; Yi Liang; Xiaomin Xu; Xiaolong Yang. 2019. "Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network." Journal of Cleaner Production 243, no. : 118558.