Transformation Of Energy Structure Research Articles

Forecasting and Early Warning of Wind‐Farm‐Tripping Event under Strong‐Wind and Cold‐Wave Conditions Based on Combined Nonparametric Estimation Model and K‐Value Method

Wind power, as a pivotal renewable energy source, is increasingly vital in the global energy structure transformation. However, extreme weather, such as strong winds and cold waves, can result in rapid fluctuations in wind power generation and even lead to large‐scale wind‐farm‐tripping events. Consequently, forecasting and early warning of these events under extreme weather conditions are crucial for ensuring the safe operation of the power system. In this article, a forecasting and early warning method for wind‐farm‐tripping events is proposed, which is based on a multi‐nonparametric estimation approach. In this article, the error distribution of forecasting wind speed and temperature in numerical weather prediction (NWP) is modeled, and interval forecasting for these parameters is conducted. When forecasting intervals of NWP reach the thresholds of wind‐turbine‐tripping protection strategy, it will lead to the wind‐farm‐power‐tripping events. Furthermore, in this article, a method for calculating the wind‐power‐tripping capacity based on the K‐value method is introduced. The effectiveness of the proposed method is verified through actual wind‐power‐tripping events in the wind farm. The methodology proposed in the article provides a solution of early warning and forecasting for strong–wind‐ and cold‐wave wind‐farm‐tripping events.

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

Which Provinces Will Be the Beneficiaries of Forestry Carbon Sink Trade? A Study on the Carbon Intensity–Carbon Sink Assessment Model in China

Carbon emissions pose a significant challenge to sustainable development, particularly for China, which is the world’s largest emerging economy and is under pressure to achieve carbon neutrality and reduce emissions amid escalating human activities. The variation in economic development levels and carbon sequestration capacities among its provinces poses a significant hurdle. However, previous research has not adequately examined this dual discrepancy from the perspective of spatial heterogeneity, resulting in a lack of differentiated management of forest carbon sinks across diverse regions. Therefore, to mitigate this discrepancy, this study presents an assessment methodology that analyzes over 100 types of natural and plantation forests using forest age and biomass expansion factors. This study presents a model that can significantly support the efforts of both China and the whole world to achieve carbon neutrality through the improved management of forest carbon sinks. This approach facilitates the assessment of carbon offsets required to meet reduction targets, the development of a provincial framework for carbon intensity and sequestration, and the exploration of their potential for trading markets. Analysis is conducted using MATLAB. Key achievements of this study include the following: (1) The collection of a comprehensive carbon stock dataset for 50 natural and 57 plantation forest types in 31 provinces from 2009 to 2018, highlighting the significant role of new forests in carbon sequestration. (2) The development of a provincial carbon status scoring system that categorizes provinces as carbon-negative, carbon-balancing, or carbon-positive based on local forest sink data and carbon credit demand. (3) The formulation of the carbon intensity–carbon sink assessment (CISA) model, which suggests that provinces with middle- to upper-middle-level economies may have a prolonged need for carbon sink credits during their peak carbon phase. Furthermore, the results show that carbon trading may benefit Guangxi and Yunnan, but may also bring opportunities and risks to Hunan and Hubei. To address regional imbalances, this study advocates tailored policies: carbon-negative and carbon-balancing provinces should enhance carbon sink management, while carbon-positive provinces must focus on energy structure transformation to achieve sustainable development goals.

Value Evaluation Model of Multi-Temporal Energy Storage for Flexibility Provision in Microgrids

With the advancement of distributed power generation technology and the deepening of the low-carbon transformation of energy structure, a high proportion of renewable energy has become an inevitable trend in future energy systems, especially for microgrids. However, the volatility and uncertainty associated with renewable energy pose significant challenges to the secure and stable operation of power systems, necessitating the exploration of the flexible regulation of resources. Energy storage, as a crucial flexible resource characterized by technological diversity and a variety of regulation capabilities, has been extensively studied and applied. Nonetheless, the high investment costs and limited returns of energy storage technology, coupled with the ambiguous utility in different scenarios under the current electricity market’s framework, complicate its broader application. To thoroughly analyze the utility of energy storage in facilitating flexible adjustments in microgrids, this study developed a composite weight-TODIM (an acronym in Portuguese for interactive and multi-criteria decision making) model for assessing the utility of energy storage that incorporates heterogeneity in the risk preferences. This model enabled a comparative analysis of the utility of energy storage technology across multiple scenarios, taking the risk preferences of decision-makers into account, thereby providing strategic insights for the application of multi-temporal energy storage in microgrids. The feasibility and effectiveness of the model were validated through a case study analysis.

Research on the spatial spillover effect of carbon trading market development on regional emission reduction

This paper introduces the current situation of carbon trading market in China and the effect of carbon emission reduction in each region. Theoretically, it expounds the influence mechanism and spatial spillover way of carbon trading market on regional carbon emission. Next, we use the data of 30 provinces in China from 2006 to 2019 to build a continuous spatial difference in difference model (SDM-DID) to empirically study the spatial spillover effect of carbon trading market on regional emission reduction. The results are as follows: First, the implementation of the pilot policy of carbon emission trading has significantly promoted the carbon emission reduction in the pilot areas. From the perspective of impact mechanism, the implementation of carbon trading pilot policy promotes carbon emission reduction in pilot areas by promoting clean transformation of energy consumption structure, improving technology absorption capacity and stimulating development of low-carbon technologies. From the perspective of spatial spillover effect, China’s regional carbon emissions have significant spatial spillover effect, and carbon emissions trading has spatial spillover effect. From the perspective of spatial spillover, the carbon emission trading market promotes the carbon emission reduction in neighboring areas by promoting the clean transformation of energy structure in neighboring areas, improving technology absorption capacity and promoting technological progress.

The influence effect of regional carbon emission reduction under the perspective of fiscal decentralization: Government intervention or market mechanism?

AbstractBased on the perspective of fiscal decentralization, the study focuses on 30 provinces in China and employs various econometric models including the threshold model, spatial econometric model, mediation model, and regulation model. The research findings indicate that fiscal decentralization has a double‐threshold effect on government intervention, market mechanisms, and regional carbon emission reduction. Both government intervention and market mechanisms have inhibiting effects on carbon emission, with significant coefficients of GOVI and MARM at the 1% level. The cooperation between government intervention and the market mechanism effectively limits carbon emissions. Government intervention facilitates regional carbon emission reduction through the construction of new infrastructures and energy structure transformation, yielding a significant intermediary effect. The market mechanism is positively regulated through green finance and technology innovation to promote regional carbon emission reduction. Moreover, government intervention enables the market to achieve carbon emission reduction more effectively, especially in areas with a higher degree of government intervention. Continuous improvement and upgrading of regional and national carbon markets are essential to attain the carbon peak and carbon neutrality goals. Furthermore, attracting more participants to these markets for emission control subjects is necessary to enhance the effectiveness of government–market coordination.

Rural household nonagricultural income and energy transition: Evidence from central China

Promoting China's energy transition is essential for achieving low-carbon development and achieving the nation's carbon neutrality target. Previous research has primarily focused on industrial production energy consumption transition paths, and rural household energy consumption has rarely been investigated. This study focuses on the impact of nonagricultural income on rural household energy consumption in the Huang-Huai-Hai region using questionnaire data, examining the contribution of rural households' nonagricultural income to the energy consumption transition (i.e., from dirty energy sources such as firewood to clean energy sources such as liquified petroleum gas (LPG)). Instrumental variable two-stage least squares (IV-2SLS) estimation and IV-tobit models are applied to solve potential endogeneity problems. The results demonstrate that nonagricultural income can promote rural households' energy structure transformation, increase spending on clean energy such as LPG and electricity, and decrease expenditure on dirty energy sources such as coal and firewood. Further examination reveals that nonagricultural income can enable rural households to afford modern necessities and transition to cleaner, more efficient energy. The study proposes promoting rural energy structure transformation to boost sustainable development and increase nonagricultural income. Strengthening skills training, encouraging more off-farm employment, and other approaches will diversify rural households' income.

Will artificial intelligence make energy cleaner? Evidence of nonlinearity

Energy plays a vital part in stimulating economic progress, and the shift towards a cleaner energy system is highly significant for ensuring the sustainable development of the economy. China's energy structure urgently needs to be transitioned. The fast advancement and implementation of artificial intelligence (AI) has provided a new and important tool for promoting the transition of energy structure. So, what is the relationship between the application of artificial intelligence and the transition of the energy structure? This research introduces artificial intelligence into the energy sector, focusing on the relationship between artificial intelligence and energy transition. Since nonlinear models are better able to study the complex effects and phase differences of artificial intelligence. Using China's provincial panel data spanning from 2006 to 2019, this study employs nonlinear modeling to explore the stage differences in the process of AI in facilitating energy structure transformation. This paper derives the following findings based on empirical research. First, there is a U-shaped relationship between artificial intelligence and the transition of energy structure. Specifically, before the inflection point, the initial application of artificial intelligence, artificial intelligence may adversely impact energy transition. When the inflection point is passed, AI will help facilitate the energy transition. Second, the U-shaped relationship between AI and energy transition is more pronounced in coastal and non-resource-based regions. Third, energy intensity, government investment in science and technology, and informatization will moderate the U-shaped relationship between artificial intelligence and energy transition, changing the steepness of the original U-shaped relationship and even reversing it. Hence, it is imperative to effectively utilize the technological benefits of artificial intelligence through the development patterns and distinctive features of different regions, thereby facilitating the smooth transition of the energy structure.

Research on vehicle speed prediction model based on traffic flow information fusion

Resource scarcity, global climate change and environmental pollution are increasingly constraining the development of the automotive industry. China proposes to reach the carbon peak by 2030; to reach the carbon neutral double carbon target by 2060 and gradually promote a green and low-carbon transition in energy development. The development of new energy vehicles is an important approach for China to realize its energy structure transformation in the automobile industry. HEV, as a transitional product of automobile energy transformation, has the advantages of both internal combustion engine vehicles and electric vehicles, which can improve the fuel efficiency and the emission problem of internal combustion engine vehicles and the range is longer compared to electric vehicles. One of the important aspects of HEV research is the design of whole vehicle energy management strategy based on the model predictions. Particularly, model-based predictive control is one of the mainstream energy management strategies nowadays, and its optimization effect is mainly subject to the model prediction accuracy. In this study, we constructed the ITS environment of a local roadway through simulation, compared the speed prediction effects of different speed prediction methods in different prediction time domains, and fused the historical information of vehicles (speed of the vehicle in front, distance, signal status, distance, and remaining time). It is found that N-BEATS is more effective in predicting vehicle speed in different prediction time domains, and the prediction accuracy of the speed prediction model is effectively improved after its fusion of multivariate information.

Financial openness and energy structure transformation

Energy structure transformation is the only way for China to achieve the “dual carbon” goal, and one of the difficulties faced by energy transformation is financing. In the context of China’s steadily promoting the high-level opening-up of financial industry, this paper uses the panel data of China’s provincial level from 2010 to 2019 to systematically examine the impact of financial opening-up on the transformation of energy structure. The results show that: 1) Financial openness has a significant positive impact on the energy structure transition; 2) In different stages of energy structure transformation, as the main driving force in the initial stage of energy structure transformation is the government’s policy support, with the continuous maturity of energy structure transformation, the impact of financial openness on energy structure transformation gradually increases; 3) With different levels of economic development, the driving effect of financial openness is also different. The lower the level of economic development is, the stronger the driving effect of financial openness on energy structure transformation is due to the lack of financing channels. This paper provides a theoretical basis for China’s energy structure transformation, and also provides rich policy implications for promoting China’s financial industry to open up at a high level.

Regional productivity in the transformation of China’s energy structure –estimation of the restricted cost function

ABSTRACT In this study, energy structure is introduced into the restricted cost function to study the influence of energy structure transformation (EST) on regional productivity. The analysis indicates that: (i) the average annual productivity of various regions in China has a positive growth, with an average annual growth rate of 6.97% and a downward growth trend from 12.15% to 4.13%. Technological progress has the greatest positive contribution to productivity growth, while energy structure and scale effect have slight negative effects. (ii) Further regional empirical analysis reveals a gradient distributions of eastern > central > western area for productivity growth. Although the effect of technological progress in western area is greater than that of eastern and central areas, low energy efficiency has dragged down its productivity growth. (iii) Factor input bias analysis demonstrates that technological progress tends to factor saving, while energy structure adjustment tends to a slight factor using with different preferences in regions. Inspired by the empirical results, strategies for optimizing policies and regional collaboration are proposed from a systematic perspective.

Synergizing policies for carbon reduction, energy transition and pollution control: Evidence from Chinese power generation industry

To mitigate climate change and achieve sustainable development, Chinese governments have introduced “1+N” “dual-carbon” policy system aimed at reducing CO2 emissions, transforming energy structures and controlling pollution. An effective policy mix and synergy is crucial for achieving the emission reduction targets and for ensuring the effectiveness of the policies, and it is therefore of great practical importance to provide a pathway for policy synergy based on assessing policy effectiveness. This paper selects three policies, namely the carbon Emission Trading System (ETS), Coal Control and Clean Utilization (CCCU), and Green Financial Reform and Innovation Pilot Zone (GFPZ), sets up ten scenarios, adopts difference-in-differences (DID) models to evaluate the impact, and give a pathway for policy synergy. The results show that ETS, CCCU, and GFPZ policies have significant positive effects on carbon emission reduction, energy structure transformation, and industrial environmental pollution control, respectively; CCCU has synergistic effects with ETS and GFPZ policies in controlling coal consumption and increasing the completion of industrial pollution control investments, respectively; while GFPZ policies have conflicting effects with CCCU and ETS in reducing carbon emissions and coal consumption, the GFPZ policy has conflicting effects with CCCU and ETS in reducing carbon emissions and controlling coal consumption. Finally, the study proposes a phased policy support system around carbon emission reduction in the power generation industry, energy structure transformation, and industrial environmental pollution control before 2030 and from 2030 to 2060.

Stable power supply system consisting of solar, wind and liquid carbon dioxide energy storage

Renewable energy is becoming the main subject of energy consumption with the accelerating transformation of energy structure. The renewable power supply systems sourced by wind and solar energies have attracted wide attention as they are of great significance to regions that are rich in renewable energy. In this study, the stable power system consisting of solar, wind and liquid carbon dioxide energy storage is proposed for the sake of meeting user electricity load. Thermodynamic and economic performance of the proposed systems with different application scenarios is analyzed and some interesting findings are summarized. The round trip efficiency and energy density of the liquid carbon dioxide energy storage system are 58.34 % and 23.41 kWh/m3, respectively. The start hour of dispatch can cause obvious influence on the energy storage capacity and there is an optimal dispatch start time to achieve the minimum energy storage capacity. The overall energy efficiency of energy storage-aided power system including solar and wind powers is much higher than that of the single sourced system. The energy efficiency of the solar-wind-LCES system is 94.61 % while it is only 80.31 % and 76.29 % for the wind-LCES and solar-LCES systems, respectively. The introduction of the liquid carbon dioxide energy storage into the renewable power supply system can greatly reduce the electricity purchasing investment.

Research on the air pollution reduction effect of winter clean heating policy

The international community is confronted with an urgent need to resolve the global climate crisis, which has emerged as a critical issue. China, in its capacity as a responsible major country, has proposed the “3060” goal and actively advocated for air pollution control. In northern China, a substantial quantity of coal is burned for heating in winter, leading to frequent air pollution, and seriously affecting people's health and quality of life. Clean heating is an important national livelihood strategy related to residents' heating and an essential manifestation of advancing ecological civilization construction. Based on panel data from 126 heating cities in the northern region from 2013 to 2020, this paper adopts the Difference-In-Difference (DID) model to investigate the effect and mechanism of the Winter Clean Heating Plan for Northern China on air pollution reduction. According to the research results, the policy has an abatement effect on northern region's air pollution emissions. The amount of air pollutant emissions has decreased by 17.3% since the policy's implementation. This policy has long-term effectiveness, which can become an effective measure for promoting air pollution reduction in the northern region. Secondly, economic factors, such as fiscal expenditure, and urban development factors, such as car ownership will have a driving effect on reducing air pollution, indicating that air pollution control is a systematic project that requires coordinated measures from all aspects. Thirdly, heterogeneity analysis indicates that the policy exerts a more substantial positive influence on air pollution control in Northwest China's cities and non-provincial capital cities. This indicates that cities should focus on implementing precise heating policies and not be “one size fits”. Finally, based on the theory of whole-process governance, this paper explores the indirect influence mechanism of the impact of the policy on air pollution reduction. By promoting energy structure transformation and promoting technological innovation, policy can indirectly improve the effectiveness of air pollution reduction. As an effective environmental regulation method, the policy can significantly reduce air pollution emissions in northern China, which is of great significance for the Chinese-style green modernization construction in this region.

Direct regeneration of cathode materials in spent lithium-ion batteries toward closed-loop recycling and sustainability

The transformation of energy structure is deemed as a paramount strategy in accomplishing the ambition of carbon neutrality. Lithium-ion batteries (LIBs), as one of the most exceptional energy storage and conversion devices available, play a pivotal role in this energy revolution. However, due to the extensive production and limited lifespan of LIBs, the retirements of LIBs are set to boom in the coming years. The proper and timely recycling of these batteries are of paramount importance, from both environmental and economic perspectives. Direct cathode regeneration technology is the most promising solution for the treatment of these waste cathodes, as it enables the healing of spent cathodes with minimal energy and chemical consumption, without compromising the original structure of the cathode. Herein, we analyzed the degraded mechanisms of spent LIBs and found that relithiation and defect reparation are crucial steps for the regeneration of spent cathode materials. Various direct regeneration methods previously reported have been summarized, and their respective advantages and limitations as well as technical challenges when implemented into practical industrial applications are discussed. In addition, possible solutions to these problems are proposed, which guide the future direction for the sustainable development of the battery recycling industry.

China can peak its energy-related CO2 emissions before 2030: Evidence from driving factors

China has pledged to achieve the CO2 emissions peak by 2030. This study aims to ascertain whether China can meet its commitment by predicting CO2 emissions. Based on industrial structure, energy structure, and energy intensity data forecasted by appropriate time series models, this study uses the Monte Carlo method to simulate the pathway of GDP growth rates and predict the trajectory of China's CO2 emissions from 2021 to 2035. The results reveal a 43.60% probability that China's CO2 emissions have peaked at 10.55 Gt in 2021. Furthermore, it is highly probable that China will achieve its CO2 emissions peak between 2023 and 2028. As the peak year shifts from 2023 to 2028, the mean value of CO2 emissions peak increases from 10.56 Gt to 10.71 Gt. Finally, the study employs the LMDI method to analyze the contributions of different driving factors to the CO2 emissions peak. The results indicate that energy structure transformation will be the primary factor driving the CO2 emissions peak, demonstrating the importance of transiting to a low-carbon energy system.

How does the transformation of the energy structure impact the coordinated development of economy and environment?

The energy structure transformation is of significant practical importance as it ensures energy security, accomplishes the "dual carbon" goal and realizes high-quality economic growth. In order to explore the impact exerted by energy structure transformation on the coordinated development of economic and environmental systems, this paper employs the coupled coordination degree model to measure the coupling degree of economic and environmental systems across 30 provinces in China from 2000 to 2021. Additionally, on the basis of theoretical analysis, it adopts the fixed effect model and the mediated effect model to provide a systematic explanation of the impact energy structure transformation exerts on the coordinated development of the economic and environmental systems, as well as the underlying mechanisms. The results reveal that China's structural transformation of energy consumption significantly promotes the coordinated development of the economic and environmental systems, a conclusion that remains robust even after consideration of the explanatory variable substitutions, municipality exclusion, and tail reduction. From the perspective of heterogeneity, regions with better economic development show a more obvious dividend effect of energy structural transformation on the coordinated development of the economy and the environmental system; energy structural transformation imposes a positive impact on the coordinated development of the economy and the environmental system only in regions characterized by high levels of human capital and urbanization. In terms of the mechanism of action, energy structural transformation can facilitate of industrial structure upgrading and then drive the coordinated development of economic and environmental systems. The study proposes several policy recommendations: transform the traditional fossil energy consumption mode, reduce the proportion of traditional fossil energy, introduce corresponding subsidy policies, encourage enterprises to invest in clean energy, boost investor confidence, and enhance employment quality and income level.

Can the digital economy promote urban green economic efficiency? Evidence from 273 cities in China

The development of the digital economy has changed human lifestyles and production and provided a new approach to achieving the United Nations Sustainable Development Goals (SDGs). As an important indicator reflecting both economic growth and ecological protection, the green economic efficiency (GEE) indicator is employed to express the level of sustainable development. By building an indicator system, introducing carbon dioxide emissions as an unexpected output and using a super-efficient slack-based measure (SBM) model, the the GEE of 273 cities in China are calculated. The development level of digital economy is calculated through principal component analysis (PCA). Unlike previous provincial-level studies, this study focuses on analyzing at the urban level. This study then uses a fixed effect model to analyze the effect of the digital economy on urban GEE. Based on various aspects, the transmission mechanisms of the digital economy on GEE have also been deeply analyzed. The results indicate that the digital economy has a positive effect on GEE. This conclusion is validated by endogeneity analysis and other robustness tests. The digital economy has a positive influence on urban GEE in Eastern, Central and Northeastern China. In large cities, medium-sized cities and cities with stricter governmental environmental regulations, the effect of the digital economy on GEE is more prominent. The mechanism analysis indicates that the digital economy mainly promotes urban GEE through the technological innovation effect, industrial structure optimization, energy structure transformation and market-oriented development. This study clarifies the role of the digital economy in advancing GEE and provides a reference to promote ecological development in developing countries.

The impacts of clean energy policies on air pollutants and CO2 emission reduction in Shaanxi, China

In response to environmental problems, China has implemented many policies to control its emission of air pollutants and CO2. As a significant source of pollutant emissions, energy use has received special attention. Shaanxi Province is a region of China with major thermal power generation, which consumes a large amount of fossil fuel and causes severe deterioration in ambient air quality. However, it is rich in natural resources that could support the development of solar energy, wind energy, and other renewable clean energies, making it a typical example of a region suitable for energy structure transformation. On the basis of environmental statistical data, field surveys, and data from the Shaanxi Provincial Bureau of Statistics, this study developed an inventory of anthropogenic source emissions of seven pollutants in 2017 in Shaanxi Province. In accordance with the established inventory, various scenarios were considered to analyze the impact of energy structure transformation on air pollutant emissions. In comparison with the Business-as-Usual scenario, the comprehensive clean energy use scenario reduced SO2, NOx, CO, PM10, PM2.5, VOCs, NH3 and CO2 emissions by 32.50%, 33.10%, 14.56%, 26.64%, 16.72%, 2.88%, 4.36% and 36.85%, respectively. Ambitious development of clean energy, such as wind and solar, could greatly reduce the emission of air pollutants, especially CO2, SO2 and NOx, that are derived mainly from fossil fuel combustion, showing that strict control of the energy structure could reduce pollutant emissions. This work is important for quantifying the impact of energy restructuring on regional air quality and providing pollution control policies for environmental managers.

Supply chain coordination mechanisms of coal power enterprises under renewable energy quota system: A perspective of game analysis

The renewable energy quota system has become an important policy tool for countries around the world to solve the differences in renewable energy resource endowments between regions and promote the transformation of energy structure. How to coordinate the supply chain of power generation enterprises from the micro level of enterprises has become a key issue to ensure the implementation of renewable energy quota system. By constructing a game analysis model, this research studied the coordination game problem between the supply chain of coal power enterprises and renewable energy enterprises, and compared and analyzed the differences in decision-making between centralized and decentralized situations. The results illustrate that in the case of centralized decision-making, although renewable energy enterprises and coal power enterprises can guarantee profits and carbon emission reduction effects. However, in the real situation of independent rational economic man, it is difficult to ensure the real realization of centralized decision-making. Further, the research proposed that in the case of decentralized decision-making, the negotiation mechanisms of revenue sharing, cost allocation, and revenue sharing + cost allocation were used to solve the supply chain coordination problem of renewable energy enterprises and coal power enterprises. The equilibrium solution of the game model under different negotiation mechanisms is obtained. Finally, the results of theoretical analysis were verified by practical examples and the corresponding sensitivity analysis is carried out. The conclusion has decision-making reference value for solving the coordination problem between coal power enterprises and renewable energy enterprises, improving the overall income of supply chain and carbon emission reduction, and effectively promoting the implementation of renewable energy quota system.