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[Display omitted] • It analyzes the feasibility of achieving net zero emission in the Chinese power sector by 2050 and the required policy measures. • It is essential to foster the concurrent development of renewable energy, energy storage, and demand response technologies. • The carbon pricing mechanism with exponential growth is more suitable. • Referring to the European offshore wind power investment model is beneficial for the development of offshore wind power in China. The low-carbon transition in China's power sector plays a crucial role in the mitigation of climate change worldwide, and increasing renewable energy penetration rate is widely recognized as the key approach to decarbonize this sector. This paper proposes a renewable energy penetration model to examine the feasibility for net zero emission by 2050 in China's power sector taking into account of economic and social development. It also evaluates the impacts of carbon pricing and financial incentives on renewable energy penetration and carbon emissions. The results indicate that along with economic development, China's electricity demand will continue to grow, but at a gradually slowing rate. It is possible for the power sector to achieve net zero emission by 2050 while meeting this growing electricity demand, with a renewable energy penetration rate of 75%. To ensure the achievement of the goal, we recommend setting the carbon price with the current market price as the baseline and increasing it annually by a relative increment index of 10%. Furthermore, given the high initial cost and late start of offshore wind power, we suggest incorporating associated grid connection cost into the transmission and distribution price to ensure the comprehensive and healthy development of renewable energy. [ABSTRACT FROM AUTHOR]

• A PV/wind power prediction model based on Time2Vec, WDCNN and BiLSTM is proposed. • WDCNN model is adopted to expand receptive field and improve feature extraction. • Case study validates the effectiveness and superiority of the proposed method. • The proposed model shows significance in processing data with strong volatility. Accurate prediction of photovoltaic (PV)/wind power is an effective solution for the grid stability, reasonable dispatching and power supply reliability. Nowadays, various deep learning prediction methods are developed, most of which are recurrent neural network (RNN)-based and convolutional neural network (CNN)-based. To further improve the prediction performance, a novel hybrid method based on deep CNN with wide first-layer kernels (WDCNN) and bidirection long short-term (BiLSTM) is presented in this paper, in which WDCNN is introduced for large receptive field and useful information extraction, and stacked BiLSTM layers is incorporated to extract temporal correlations of past and future datasets. Besides, Time2Vec is adopted for better feature extraction through decomposing the time series data into non-periodic and periodic components. Several ablation and comparison experiments are carried out with a case study in Yongxing Island, China, and the performance metrics including normalized mean absolute error (NMAE), normalized mean square error (NMSE), normalized root mean square error (NRMSE), and mean absolute scaled error (MASE) confirm the effectiveness and superiority of the proposed model. Compared with individual WDCNN and BiLSTM, the performance of the combined WDCNN-BiLSTM are improved by 5.79%, 6.61%, 3.36%, 5.79% and 2.03%, 3.54%, 1.78%, 2.03% for PV prediction, and 20.38%, 29.37%, 15.96%, 20.38% and 39.12%, 59.68%, 36.50%, 39.12% for wind prediction, respectively. The adoption of Time2Vec further improves the prediction performance by 5.96%, 8.10%, 4.14%, 5.96% for PV prediction, and 4.86%, 9.19%, 4.70%, 4.86% for wind prediction, respectively. The proposed model yields most accurate prediction compared with other competing models. The replacement of CNN with WDCNN improves the prediction accuracy by 6.64%, 8.47%, 4.33%, 6.64% for PV prediction, and 30.59%, 42.78%, 24.35%, 30.59% for wind prediction, respectively. Moreover, the proposed model significantly outperforms compared models in prediction with more random fluctuations, which demonstrates the superiority of the proposed model in mining complex relationships. [ABSTRACT FROM AUTHOR]

• Province-level grid parity potential of wind and solar PV power plants in China. • Techno-economic feasibility of baseload supply in hybrid renewable energy system. • Correlation analysis of system cost and resource potential and complementarity. • Projected Grid parity potential of renewable energy system for baseload supply. To promote the large-scale deployment and grid integration of renewable-based power system, this paper investigates the province-level techno-economic feasibility of wind-photovoltaic-battery hybrid renewable energy system (HRES) for baseload supply application around China. Firstly, the power generation cost and grid parity potential of province-level standalone wind and photovoltaic power plants are assessed. Then, the optimal sizing of province-level HRESs aims to minimize the net present cost and meet the reliability constraint for baseload supply, which is solved by teaching and learning-based optimization. Finally, the techno-economic comparison between different system configurations, the correlation between HRES cost-effectiveness and resource potential as well as resources complementarity, and the grid parity potential of HRES for baseload supply are quantitatively analyzed. The results reveal that: (i) 84.4% of regions in China can achieve solar photovoltaic plant-side grid parity in 2022, while only 15.6% of regions can achieve wind power plant-side grid parity; (ii) HRESs in all regions are technically feasible but not cost-effective for baseload supply; (iii) wind-photovoltaic-battery HRES is the optimal configuration for most regions, indicating that the wind-solar hybridization is more reliable and cost-effective than standalone power plants; (iv) the economic performance of HRES depends mainly on the wind-solar joint resource potentials and the supply-demand relative deviation; (v) investment cost reduction and utilization of energy curtailment are promising to help HRES achieve plant-side grid parity for baseload supply. The findings of this paper can help policy-makers and investors optimally determine the national pathway of renewable energy transition. [ABSTRACT FROM AUTHOR]

• An advanced forecasting system is proposed for short-term wind speed prediction. • The data pre-processing technique can extract effective information from the raw sequences and eliminate the volatility and uncertainty of the data. • Choose the appropriate benchmark models in different situations based on optimal benchmark model selection strategy. • A multi-objective optimizer is applied to find the optimal weights and a theoretical proof indicates that the weights assigned by this optimizer are Pareto optimal solutions. • Our proposed forecasting system can quantify the uncertainty of the wind speed sequences. Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Temperature feedback is essential to discuss the district heating networks' potential. • Developing integrated heat and power dispatch model considering temperature feedback. • Establishing the theoretical model of room temperature feedback mechanism. • Applying thermal-electrical analogy model into the integrated heat and power dispatch. • Considering temperature feedback causes wind power curtailment to increase by 4.1%. The wind curtailment has been serious in China, reaching 1.66e + 10 kW·h in 2020, since the "heat-led" operation mode of the combined heat and power unit restrains the flexibility of the power systems. The district heating networks, due to thermal storage ability, have a great potential in providing flexibility of the integrated electricity and heat energy systems as well as improving wind power accommodation. However, this paper finds that analysis deviations of evaluating the district heating networks' real potential may exist in the previous literature since they neglect the temperature feedback mechanism. The mechanism can compensate for the heat supply if the district heating networks' model is inaccurate, which is a basic method to guarantee the users' thermal comfort. Ignorance of the compensation results in serious analysis deviations of the systems modelling and optimization. In order to correct the deviations, this paper offers a reliable integrated heat and power dispatch model that simultaneously considers the temperature feedback mechanism and the thermal inertia of the district heating networks, to investigate the actual influences of the district heating networks' thermal inertia on wind power promotion. Results show that the consideration of the temperature feedback mechanism causes the wind power curtailment to increase by 3.4% to 4.1% in comparison with that when the mechanism is not considered. [ABSTRACT FROM AUTHOR]

• A screening method for capacity configuration is proposed. • The interaction between transmission and feasibility constraints is explored. • The bundling system solves renewable consumption problems and supply–demand imbalance. • The integrated feed-in tariff is exponential with the transmission guarantee rate. The hydro-wind-solar-storage bundling system plays a critical role in solving spatial and temporal mismatch problems between renewable energy resources and the electric load in China. An efficient bundling system capacity configuration can improve the consumption level and reduce the renewable energy transmission cost. However, the restriction between economic feasibility and technical transmission constraints of the bundling system is not well understood. This restriction is becoming more and more obvious with the decrease of renewable energy price compensation year by year. This study compares the role of technical and economic indicators of capacity configuration, as well as the constraint relationship between electricity price and transmission constraints. Three screening principles of capacity configuration are proposed to reveal the techno-economic interaction. This paper explores a practical engineering case of Northwest China using a bundling system capacity configuration model. The internal rate of return is assumed as 8% for the bundling system. The results show that reducing the wind curtailment rate can effectively reduce the cost per MWh when the transmission guarantee rate is larger than 90%. Moreover, an integrated feed-in tariff based on market competitiveness is obtained if the wind curtailment rate is controlled by 5%. For example, the integrated feed-in tariff is 463.7 RMB/MWh when the wind curtailment rate is less than 5% and the transmission guarantee rate is larger than 95%. These results verify that the techno-economic interaction of the bundling system offers important theoretical support for selecting techno-economic indicators and capacity configuration. [ABSTRACT FROM AUTHOR]

• Analysis of the techno-economic pathways to net-zero emission attainment in China. • Analysis of peak carbon emission and carbon neutrality by year 2030 and 2050. • Solar PV, biomass, hydro, and wind power are considered for 100 % decarbonization. • River hydro is constructed considering dry, normal, and wet years. • Carbon emission is reduced by 13.26% to 15.3% between 2030 and 2050. With the imminent acute problems and the harsh reality of climate change, many countries/groups of countries have developed policies to reduce their carbon emission. At the end of 2020, the Chinese government announced a revised policy that will see the country attain carbon neutrality by 2060 and peak emission by 2030. This study aims to determine the techno-economic requirements for net-zero emission attainment in China. The models created are used to explicate the 100% integration of renewable energy into the energy systems in a Province in China considering the years 2030 and 2050. This study is novel as it aggregates the detailed requirements of all the energy sectors and the government policies for the case study. While the approach presented in this paper is applied specifically to Sichuan Province, China, the application of this method is limitless as it is vast and viable for other countries. This study can also serve as a template for many high carbon-emitting countries with sizeable RE potential. The total energy demand in the case study is summarized under three sectors; electricity, industry, and transport. Following the government policies, renewable energy sources such as wind power, solar PV, river hydro, and biomass are considered for 100% decarbonization of the case study. River hydro being the predominant renewable energy source in this region is constructed considering three levels of hydro (dry, normal, and wet) years. The 2017 energy demand and supply data are first used to develop/validate the model on the EnergyPLAN simulation platform, then the 2030 proposed pathway to peak carbon emission by the government is analyzed. Three innovative pathways to net-zero emission attainment by 2050 are proposed in this study considering the additional use of biomass (BIO model), pumped hydro storage (EES model), and the import of clean electricity from neighboring provinces (IMP model). Based on the analyses in this study, the proposed pathway by the government cannot achieve net-zero emission by 2050, however, the three optimized strategies/models presented in this study show a clearer and faster path to decarbonization. Carbon emission will reduce by 13.26 %, 14.77%, and 15.3 % between 2030 and 2050 reference models for the dry, normal, and wet year scenarios respectively. From the three optimization models, the total cost of the import model was the lowest under different scenarios. Therefore, the economic feasibility of this approach proves the superiority of the import model in terms of economic benefits. [ABSTRACT FROM AUTHOR]

• A bi-level model is proposed to obtain optimal capacity and operation simultaneously. • SA-CHP subsystem linear model considering heat and power characteristics is built. • The optimization model is applied to optimize an MCC system as a study case. • The effects of capacity parameters on the performances of MCC system are revealed. Combining renewable energy with fossil fuel-based energy systems is a promising way to develop renewable energy power generation and decrease CO 2 emissions with lower capital investment. In this paper, a multi-source complementary cogeneration (MCC) system is investigated in which the wind farm and solar aided combined heat and power (SA-CHP) system are integrated at the power grid level. A bi-level capacity-operation collaborative optimization model for this MCC system has been established to simultaneously optimize main components capacities and system annual load dispatch. In the proposed model, the upper level is a multi-objective optimization searching for the optimal trade-off between economy and CO 2 emissions. At the same time, the lower level aims to obtain the optimal load dispatch of the MCC system for the whole year to maximize the annual operating income. The linear model of the SA-CHP system is built to simplify the lower-level problem–solution process. The bi-level optimization model is handled with a nested approach that combines non-dominated Sorting Genetic algorithm-II and linear programming. The proposed bi-level model is applied in a study case to simultaneously optimize thermal energy storage capacity, solar field size, wind farm capacity, as well as annual power and heat load dispatch of an MCC system located in Zhangbei, China. Besides, the influences of capacity parameters on the performances of the MCC system are analyzed. [ABSTRACT FROM AUTHOR]