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Near-zero carbon emission power (NZCEP) plants, consisting of gas-fired units; wind turbines; power-to-gas (P2G); and carbon capture, utilization and storage (CCUS) systems, have recently received a lot of attention due to their enormous benefits in reducing carbon emissions and increasing the consumption of renewable energy. However, a complex environment of interest and a combination of risks makes their development very slow. This paper establishes a risk analysis framework for NZCEP considering multi-stakeholder participation. Firstly, a synthetic risk factor system was constructed based on stakeholders' interests. Subsequently, interval type II trapezoidal fuzzy numbers were used and final weights were calculated from both subjective and objective aspects. Finally, we applied an acronym in Portuguese of the interactive and multi-criteria decision-making (TODIM) method to site selection to achieve a balance of interests of all stakeholders. In addition, a case study was conducted. The case result demonstrates that Zhengzhou in Henan Province is the best choice for a NZCEP power plant. A further finding is that government plays an important role in the development of NZCEP plants, with site selection results being the most sensitive to changes in the government's risk appetite. Moreover, human resources are an important factor in the siting of an NZCEP plant. [ABSTRACT FROM AUTHOR]

In recent years, the microgrid system (MGS) has become an important method for the consumption of renewable energy sources (RES). In the actual operation process, the uncertainties of RES add to the complexity of the MGS operation. Furthermore, the MGS is often operated by multiple subsystem operators. The benefit distribution among subsystem operators is an important factor affecting the overall stable operation of the MGS. In order to resist the interference of the above factors, a two-stage optimization method is proposed in this paper, which includes a bi-level robust optimization (BRO) model in the overall scheduling stage of the MGS and a nucleolus-based cooperative game (NCG) model in the internal cost allocation stage among the subsystem operators. The simulations demonstrated the following outcomes: (1) the P2G device can reduce the operating cost of the MGS by converting electricity into natural gas when the electricity price is low; (2) the two-stage optimization method can ensure the stable operation of the MGS by resisting the disturbance of uncertain wind power outputs in the overall scheduling stage and realizing a reasonable cost allocation among the subsystem operators in the internal cost allocation stage. [ABSTRACT FROM AUTHOR]

As the largest emitter of carbon dioxide all over the world, China requires a rapid breakthrough and large-scale commercial application of carbon capture, utilization and sequestration (CCUS) technology to achieve the 2060 carbon neutrality target. However, the process of CCUS technology commercialization in China is quite slow. Firstly, an obstacle system with 15 factors is established based on a literature review and expert consultation, namely on economic, technical, political, market, and social obstacles. Secondly, taking into account the uncertainty and randomness inherent in subjective judgment, Vague set is introduced for the first time to improve the DEMATEL-ANP (DANP) method in order to analyze comprehensive importance and causal relationship of obstacles. According to the study, in advancing CCUS's commercialization in China, economic obstacles are simply the tip of the iceberg, with the deeper reasons rooted in political obstacles. Specifically, seven critical obstacles are lack of standards and regulations, inadequate legal and regulatory framework, insufficient incentive policies, limited carbon dioxide conversion efficiency, high energy consumption, low rate of return on investments and low investment enthusiasm of enterprise. We conclude with a series of recommendations to address these obstacles, and these findings can be used as a guide for government regulation and business practice. [ABSTRACT FROM AUTHOR]

To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it's mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers. [ABSTRACT FROM AUTHOR]

In 2020, China proposed the goal of achieving carbon emission peaks by 2030 and carbon neutrality by 2060. For China, whose energy consumption structure has long been dominated by fossil energy, carbon trading and new energy are crucial for the realization of the emission target. By establishing a connectedness network model, this paper studies the static and dynamic spillovers between the Hubei carbon trading market, new energy stock market, crude oil market, coal market, and natural gas market in China, and draws the following conclusions: (1) the static spillover index of the carbon–energy–stock system is 3.57% and the dynamic spillover index fluctuates between 7.67% and 22.62%, indicating that the spillover effect of the system is low; (2) for the whole system, whether from a static or dynamic perspective, the carbon market always plays the role of net information receiver, while new energy is the net information transmitter; (3) the new energy stock market and the coal market always act as net information transmitters to the carbon market; and (4) the spillover effect of the system is asymmetric, wherein the system is more sensitive to negative information about price returns, and this asymmetry is much greater when the system is active. [ABSTRACT FROM AUTHOR]

Power grid investment behavior (PGIB) is a necessary prerequisite to ensure safe and reliable power supply. The supervision of PGIB is an important measure to reduce electricity price. It helps to release the dividends of the new power system reform and promote the sustainable development of the power industry. However, for a long time, China's regulation of PGIB was relatively weak, lacking clear supervision awareness and effective supervision methods. On the basis of clarifying the contents of PGIB supervision, this paper establishes an evaluation index system for the compliance of PGIB from the government supervision perspective, involving four dimensions and 14 secondary indicators. Furthermore, a hybrid evaluation model based on the combined decision-making trial and evaluation laboratory method (DEMATEL-ANP), analytical network process method, and vague set is proposed. The results reveal that the control over cost and efficiency of the PGIB in China is not enough. According to the results, this paper puts forward the indicators that close attention should be paid to and the work priorities in China's supervision of PGIB, which has certain practical value for the implementation of new power system reform and the sustainable development of Chinese power industry. [ABSTRACT FROM AUTHOR]

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020. [ABSTRACT FROM AUTHOR]

Energy transition is an important factor when dealing with climate change and energy crisis under resource constraints. The performance evaluation of it is significant for improving and promoting the process of energy transition. This paper explores the application of the support vector machine improved by the artificial bee colony algorithm (IABC-SVM) method in the energy transition performance evaluation process. It provides an intelligent evaluation tool for the evaluation of the regional energy transition performance. Firstly, the evaluation indicator system of energy transition is constructed from five dimensions: energy supply, demand, efficiency, institution, and environment. Then, the technique for order preference by a similar to ideal solution improved by a combination weighting (CW-TOPSIS) method and IABC-SVM are constructed. After that, according to the evaluation values of 30 provinces in China calculated by CW-TOPSIS, 10-fold cross validation is used to compare the errors of support vector machine (SVM), support vector machine optimized by the artificial bee colony algorithm (ABC-SVM), and IABC-SVM, which proves the effectiveness and accuracy of IABC-SVM in evaluating the performance of energy transition. Finally, the IABC-SVM is used to evaluate the energy transition performance of 30 provinces in 2016. Through a comparative analysis, the relevant suggestions of energy transition are put forward. [ABSTRACT FROM AUTHOR]

The main target of the energy revolution in the new period is coal, but the proportion of coal in primary energy consumption will gradually decrease. As coal is a major producer and consumer of energy, analyzing the trend of coal demand in the future is of great significance for formulating the policy of coal development planning and driving the revolution of energy sources in China. In order to predict coal demand scientifically and accurately, firstly, the index system of influencing factors of coal demand was constructed, and the grey relational analysis method was used to select key indicators as input variables of the model. Then, the kernel function of SVM (support vector machine) was optimized by taking advantage of the fast convergence speed of GSA (gravitational search algorithm), and the memory function and boundary mutation strategy of PSO (particle swarm optimization) were introduced to improve the gravitational search algorithm, and the improved GSA (IGSA)–SVM prediction model was obtained. After that, the effectiveness of IGSA–SVM in predicting coal demand was further proven through empirical and comparative analysis. Finally, IGSA–SVM was used to forecast China's coal demand in 2018–2025. According to the forecasting results, relevant suggestions about coal supply, consumption, and transformation are put forward, providing scientific basis for formulating an energy development strategy. [ABSTRACT FROM AUTHOR]

Choosing a distributed energy system (DES) is a multi-criteria decision-making problem. Decision-makers should not only consider the cost of the system, but also consider the energy efficiency and environmental protection of the system. In order to help decision-makers choose the best DES, this paper designs seven different DESs based on specific examples, using five criteria: investment cost, operation cost, primary energy consumption, primary energy utilization, and yearly CO2 emission. Additionally, three methods of super-efficiency Data Envelopment Analysis (DEA), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS) are used to evaluate the system priority and analyze the sensitivity under different decision-making scenarios. The results show that when decision-makers only consider cost factors, traditional systems are the best choice. However, renewable energy systems are the best choice when decision-makers consider energy efficiency and environmental protection rather than cost. Among them, the photovoltaic storage system is the best system in many decision-making scenarios, because of its comprehensive advantages in cost, energy efficiency, and environmental benefit. Simultaneously, the system's prioritization of different decision-making methods is different. In this paper, according to the Spearman correlation index test, the results achieved from TOPSIS and COPRAS are relevant and feasible. [ABSTRACT FROM AUTHOR]

Under the carbon trading mechanism, renewable energy projects can gain additional benefits through Chinese Certified Emission Reduction transactions. Due to the uncertainty of carbon trading system, carbon prices will fluctuate randomly, which will affect the investment timing of renewable energy projects. Thus, the value of the option will be generated. Therefore, renewable energy power generation project investment has the right of option. However, the traditional investment decision-making method can no longer meet the requirements of renewable energy investment in the current stage. In this paper, a real option model considering carbon price fluctuation is proposed as a tool for renewable energy investment. Considering optimal investment timing and carbon price, the model introduces a carbon price fluctuation as part of the optimization, studies the flexibility of enterprises’ delayed investment under the fluctuation of carbon price. A case study is carried out to verify the effectiveness of the proposed real option model by selecting a wind farm in North China. The model is expected to help investors to assess the volatility and risk of renewable energy projects more accurately, and help investors to make a complete plan for the project investment, thus promoting the efficient allocation of resources in the energy industry. [ABSTRACT FROM AUTHOR]