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In the face of the dual crisis of energy shortages and global warming, the vigorous development of renewable energy represented by wind-solar energy is a significant approach towards achieving energy transition, carbon peaking, and carbon neutrality goals. Targeting the park-level integrated energy system (PIES) with high penetration of wind-solar energy, we propose a day-ahead dispatching strategy that takes into account the flexible supply and the reward-punishment ladder-type carbon trading mechanism (RPLTCTM). Firstly, RPLTCTM and carbon capture equipment (CCE) are considered in the dispatching model, and the mechanism of coordinated operation of CCE and RPLTCTM is explored to further improve the system’s ability to restrain carbon emissions. Secondly, power-based flexibility indicators (PFIs) are adopted to quantitatively evaluate the flexibility supply, and based on the load demand response characteristics, the dispatchable resources on the load side are guided to improve the system’s operation flexibility. On this basis, a multi-objective optimal dispatching model that takes into account the carbon emission cost, energy cost, and flexibility supply are constructed, and the original problem is transformed into a mixed-integer single-objective linear problem through mathematical equivalence and flexibility cost. Finally, simulation examples validate that the economy, flexibility, and low-carbon level of the dispatching plan can be synergistically improved by the proposed strategy.

PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

Continuous incorporation of rice straw has caused significant CH4 emissions from the paddy field production system in East China. Anaerobic digestion (AD) of the rice straw has been considered as a promising approach that could not only mitigate the land-based CH4 emissions, but also generate low-carbon electricity and high-quality organic fertilizer. However, this approach, in many circumstances, is unable to be cost-competitive with other straw treatment processes or power sources. To understand the potential incentives that recently launched carbon trading schemes, the China Carbon Emission Trade Exchange (CCETE) and Chinese Certified Emission Reduction (CCER), could bring to the rice straw utilization value chain, we conducted a cradle-to-factory gate life cycle assessment and economic analysis of a small-scale AD system with rice straw as the main feedstock in East China. The results indicate that, depending on the choice of allocation method, the climate change impact of the bioenergy generated through the studied small-scale AD system is 0.21 to 0.28 kg CO2eq./kWh, and the digester fertilizer produced is 6.88 to 22.09 kg CO2eq./kg N. The economic analysis validates the financial sustainability of such small-scale AD projects with rice straw feedstock under carbon trading mechanisms. The climate mitigation potential could be achieved at the marginal reduction cost of 13.98 to −53.02 USD/t CO2eq. in different carbon price scenarios.

Renewable power generation is unpredictable due to its intermittency, making grid-connected microgrids difficult to operate, control, and manage. Currently used prediction models for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with the carbon trading mechanism are inefficient as they cannot account for all eventualities and are not well studied. Therefore, a two-stage robust optimisation model based on Bidirectional Temporal Convolutional Networks (BiTCN) and Transformer prediction for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with a carbon trading mechanism is proposed to solve this problem. First, BiTCN extracts implicit wind speed and wind power output sequences from historical data and feeds it into the Transformer model for point prediction using the attention mechanism. Ablation computation modelling is then performed. The proposed prediction model’s Mean Absolute Error (MAE) is found to be 1.3512, and its R2 is 0.9683, proving its efficacy and reliability. Second, the proposed model is used to perform interval prediction in two typical scenarios: high wind power and low wind power. After constructing the robust optimisation model uncertainty set based on the prediction results, simulation experiments are performed on the proposed optimisation model. The simulation results suggest that the proposed optimisation model enhances renewable energy use, emissions reductions, microgrid operating costs, and system reliability. The study also reveals that the total system cost and carbon emission cost in the low wind scenario are 283% (2.83 times) and 314% (3.14 times) higher than in the high wind scenario; hence, a significant percentage of renewable energy is needed for microgrid stability.

The implementation of demand response (DR) could contribute to significant economic benefits meanwhile simultaneously enhancing the security of the concerned power system. A well-designed carbon emission trading mechanism provides an efficient way to achieve emission reduction targets. Given this background, a virtual power plant (VPP) including demand response resources, gas turbines, wind power and photovoltaics with participation in carbon emission trading is examined in this work, and an optimal dispatching model of the VPP presented. First, the carbon emission trading mechanism is briefly described, and the framework of optimal dispatching in the VPP discussed. Then, probabilistic models are utilized to address the uncertainties in the predicted generation outputs of wind power and photovoltaics. Demand side management (DSM) is next implemented by modeling flexible loads such as the chilled water thermal storage air conditioning systems (CSACSs) and electric vehicles (EVs). On this basis, a mixed integer linear programming (MILP) model for the optimal dispatching problem in the VPP is established, with an objective of maximizing the total profit of the VPP considering the costs of power generation and carbon emission trading as well as charging/discharging of EVs. Finally, the developed dispatching model is solved by the commercial CPLEX solver based on the YALMIP/MATLAB (version 8.4) toolbox, and sample examples are served for demonstrating the essential features of the proposed method.

Due to geographical constraints, island regions at edge distribution networks generally face challenges of resource shortages and high carbon emissions. To enhance resource utilization efficiency, this paper proposes a multi-energy utilization module (MEUM) for distributed-level island integrated energy systems (IES). The module efficiently recovers and utilizes secondary resources generated during system operation, thereby providing additional economic benefits for the system. Furthermore, to incentivize system units to participate in carbon emission reduction, the incentive-penalty stepped carbon trading mechanism (IPSCTM) is introduced in the system operation stage, which enhances the willingness of units to engage in carbon trading and reduces carbon emissions. Meanwhile, the scheduling problem of island IES that simultaneously considers efficient resource utilization and carbon emission reduction involves numerous interrelated variables, where traditional optimization methods rely on accurate models or predictive information. Therefore, to avoid modeling and prediction, this paper proposes a model-free deep reinforcement learning (DRL) approach to deal with the island IES scheduling problem. To validate the effectiveness of the proposed island IES model and solution approach, simulations are conducted based on operational datas from a representative island in northern China. The simulation results demonstrate that the proposed model can significantly reduce both the total operational cost and carbon emissions. Moreover, the proposed solution approach outperforms other methods in terms of optimization effectiveness and computational time.

PurposeThe purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).Design/methodology/approachAdopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.FindingsFirst, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.Originality/valueBy analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

To realize a carbon-efficient and economically optimized dispatch of the integrated energy system (IES), this paper introduces a highly efficient dispatch strategy that integrates demand response within a tiered carbon trading mechanism. Firstly, an efficient dispatch model making use of CHP and P2G technologies is developed to strengthen the flexibility of the IES. Secondly, an improved demand response model based on the price elasticity matrix and the capacity for the substitution of energy supply modes is constructed, taking into account three different kinds of loads: heat, gas, and electricity. Subsequently, the implementation of a reward and penalty-based tiered carbon trading mechanism regulates the system’s carbon trading costs and emissions. Ultimately, the goal of the objective function is to minimize the overall costs, encompassing energy purchase, operation and maintenance, carbon trading, and compensation. The original problem is reformulated into a mixed-integer linear programming problem, which is solved using CPLEX. The simulation results from four example scenarios demonstrate that, compared with the conventional carbon trading approach, the aggregate system costs are reduced by 2.44% and carbon emissions are reduced by 3.93% when incorporating the tiered carbon trading mechanism. Subsequent to the adoption of demand response, there is a 2.47% decrease in the total system cost. The proposed scheduling strategy is validated as valuable to ensure the low-carbon and economically efficient functioning of the integrated energy system.

This paper explores the concept of \"carbon peak and carbon neutrality\" and its impact on Yunnan Province, a key region in China's southwest forest area. It uses data from the National Forest Resources Inventory, biomass conversion factor method, and carbon dioxide emission measurement algorithm to estimate forest carbon sequestration and increment during nine National Forest Resources Inventory periods. The paper also compares carbon emissions to carbon absorption, revealing a gap between emissions and absorption. The paper proposes a carbon neutralization path from three levels: forest carbon sequestration supply, demand, and market. It suggests that Yunnan Province should combine the \"carbon emission reduction and carbon neutralization\" plan with the provincial circumstances, afforestation, and reforestation, reducing deforestation and forest degradation, increasing forest carbon sequestration supply, and building a database for supply information. It also accounts for carbon dioxide emissions from polluting enterprises and encourages industries to engage in forest carbon sequestration to promote Yunnan's energy structure transformation and industrial structure upgrading. The government should establish a cooperation mechanism with farmers, forestry departments, financial institutions, and carbon trading platforms to achieve carbon sequestration benefits. The study aims to advance Yunnan's efforts to reduce CDE and become emissions-free from a variety of perspectives and fields. The novelty of the study covers several factors such as forest types, carbon storage capacity, and forest management practices. Based on available information and remote sensing techniques, and examines the potential of present forest capture of carbon in the province of Yunnan.

Carbon trading mechanism is an effective means to control greenhouse gas emissions. This paper focuses on the low‐carbon economic operation of the integrated energy system under carbon trading mechanism in China. The integrated energy system includes the energy storage, ground source heat pump, and other equipment. The objective of this paper was to minimize the annual total cost of the system considering the carbon trading cost and study the operation modes under different carbon trading prices by commercial optimization software. The simulation results show the operation modes in summer are changed obviously with the increase of the carbon trading prices, while the operation modes in winter basically are not changed with the fluctuation of the carbon trading prices; under the carbon trading mechanism, the integrated energy system can not only reduce the carbon emissions, but also reduce the annual total cost through carbon trading, which shows its advantages and good development prospect. In addition, the setting of the carbon emission quota has a direct impact on the economy of the enterprises. Furthermore, the ground source heat pump and the electric refrigeration units can stabilize the fluctuation of the equipment output caused by the change of natural gas prices in this integrated energy system. This paper focuses on the low‐carbon economic operation of integrated energy system under carbon trading mechanism in China. The integrated energy system includes the energy storage, ground source heat pump, and other equipment. The objective of this paper was to minimize the annual total cost of the system considering the carbon trading cost and to study the operation model under different carbon trading prices by means of commercial optimization software.