Tradable Green Certificates Research Articles

Optimization strategies for green power and certificate trading in China considering seasonality: An evolutionary game-based system dynamics

China has adopted renewable portfolio standards and tradable green certificates (including hydropower) to increase the proportion of green power used and reduce carbon emissions. However, hydropower generation is affected by seasonal factors, making it difficult to increase green power consumption in water-scarce areas. Trading electricity and green certificates in regions with different resource endowments can effectively solve this problem. Therefore, this study uses evolutionary game theory to explore the impact of the change in the policy of issuing green certificates for hydropower on the strategic choices of the trading parties. Based on the evolutionary game model, a system dynamics model is established to explore the optimization strategies of green power and certificate trading under the influence of seasonal factors. The results of the case study show that (1) the price of green certificates varies with seasonal changes. (2) Sellers with abundant hydropower resources should increase the amount of hydropower connected to the grid during the water-abundant period to help deficit areas achieve their consumption targets (3) Expanding the scope of green certificates can gradually eliminate government subsidies and incentives and reduce the government's financial pressure.

Renewable energy technology diffusion model: Evaluation of financial incentives in the electricity market of ecuador

This paper develops a simulation model using the System Dynamics paradigm to evaluate the effect of applying financial incentives on the diffusion of non-conventional renewable energy technologies in the Ecuadorian electricity market. The barriers to the diffusion of renewable energies are studied, the diffusion models are characterised, and the model is built with market variables and financial indicators integrated into its base structure. The Ecuadorian electricity market is described as a case study focusing on the incentive scheme. Based on investment, generation, and indirect incentives, this scheme plays a crucial role in promoting renewable energy technologies. Different long-term diffusion scenarios are simulated and compared with each other and with the baseline scenario to obtain diffusion rates and financial information for five renewable generation technologies: non-conventional hydro, wind, solar photovoltaic, biomass, and geothermal. The results of the simulations show that the combination of incentives leads to high diffusion rates, reassuring the audience about the potential impact of the proposed model. However, the feed-in tariff and tradable green certificate incentives are the most effective in promoting the use of renewable energy. For the Ecuadorian electricity market, the results show that wind and biomass technologies would be the most profitable, as opposed to geothermal energy, whose diffusion will not be feasible within the simulated period.

Navigating the energy transition with the Carbon-Energy-Green-Electricity scheme: An industrial chain-based approach for China's carbon neutrality

In exploring the intricate dynamics of China's energy transition towards a national certificate trading market, this study harnesses a two-stage game model to unravel the nuanced decision-making within the intertwined carbon emissions trading, energy-consumption permit trading, and electricity markets. A novel industrial chain-based policy framework, i.e., the Carbon-Energy-Green-Electricity (CEGE) scheme, is introduced to address the complexity inherent in aligning these trading markets towards carbon neutrality goals. Findings reveal that the current isolated energy transition policies lead to increased electricity prices and imposes redundant regulatory burdens on power companies. By advocating for the CEGE scheme, the study presents a strategic approach to alleviate these challenges, highlighting its potential to lower electricity costs and foster more efficient carbon reduction efforts. Furthermore, it underscores the importance of integrating energy transition policies, including CET, ECPTS, the renewable portfolio standards (RPS), and the tradable green certificate program (TGC), to enhance renewable energy adoption, offering a path to relieve financial pressures on households while supporting broader environmental and economic welfare improvements.

Optimal scheduling of a multi-energy complementary system simultaneously considering the trading of carbon emission and green certificate

This paper uses a multi-energy complementary system composed of thermal, wind, photovoltaic power generation, and electric energy storage units to participate in four market mechanisms and construct the optimal scheduling model of these trading mechanisms. These four mechanisms are carbon emission trading, green certificate trading, these two trading that are not joint, and joint. Under the latter two mechanisms, the excess carbon emissions of the system can be reduced to 9258t, and the utilization rate of renewable energy can reach 100 %. However, the operating costs under the fourth mechanism are 9695.80 $ lower than the third one because 5947 pieces of green certificates are converted into carbon emission rights. When the carbon price rises, the cost of the fourth mechanism increases slower than that of the third mechanism. The conversion volume of green certificates increases from 5947 to 10947, and the cost difference between the two increases to 67558.33 $. When green certificate prices rise, the cost under the fourth trading mechanism decreases faster. As that cost steadily declines, the carbon emission rights are converted into 907 green certificates, and the cost difference between this and the third mechanism increases to 4695.52 $.

Which decarbonization policy mixes are better for China's power sector? A simulation balancing aggregate abatement effects and economic impacts

The decarbonization policies are currently being planned or implemented worldwide, particularly for emerging economies such as China. When the government designs a preferred decarbonization policy mix, both environmental and economic impacts should be considered. This paper focuses on decarbonization policy mixes in China's power sector, including renewable electricity subsidies (ES), tradable green certificates (TGC), and two carbon pricing instruments. An integrated model combining a partial equilibrium model and an input-output framework was constructed to investigate their abatement effects and economic impacts. The results show that the combination of multiple decarbonization policies can achieve greater emission reduction or lower economic losses compared to implementing them separately. The mix of carbon pricing and TGC shows the least economic losses; the mix of carbon pricing, ES, and TGC achieves maximum abatement effect. A narrow focus on carbon emission reduction would hinder the economic development. The combination of multiple decarbonization policies can trigger more substantial variations in the supply chain of the power sector than their individual implementation. Those policy mixes including carbon tax may achieve lower carbon emissions and economic losses compared to those including a carbon emissions trading market. Therefore, policymakers should conduct a comprehensive simulation of the multiple impacts of policy mixes before implementing them, encompassing both direct impacts on the power sector and indirect impacts on its upstream sectors.

Do Tradable Green Certificates Promote Regional Carbon Emissions Reduction for Sustainable Development? Evidence from China

The tradable green certificate (TGC) scheme is an important approach for mitigating carbon emissions within the context of a renewable energy development strategy and regional sustainable development. However, studies investigating the role of TGCs in encouraging carbon emissions reduction in China are limited and inconclusive due to ignoring the interference of other renewable energy policies and little distinguishing the impact of different green certificates. Using Chinese provincial data from 2013 to 2023, this study employs a difference-in-differences strategy to estimate the effect of the TGC policy on regional carbon emissions. The results reveal that the TGC policy significantly reduces provincial carbon emissions, and this reduction is predominantly contributed by certificate-electricity integration green certificates rather than certificate-electricity separation certificates. A 1% increase in the provincial trade volume of certificate-electricity integration green certificates can reduce total provincial carbon emissions by 0.8–1.3%. These findings hold across a series of rigorous robustness tests. This study also explains the different effects between certificate-electricity integration and certificate-electricity separation green certificates by the concept of additionality. To effectively reduce carbon emissions in the future, the TGC system must meet the requirement of additionality. These insights can provide reference for the improvement of TGC policy to better achieve the carbon reduction objective and sustainable development.

An inter-provincial coordinate model under Renewable Portfolio Standards policy based on tradable green certificate options trading

The current territorial management model under Renewable Portfolio Standards in China cannot fully develop renewable energy due to the mismatch between high energy-consumption regions and renewable energy-rich regions. Therefore, we proposed an inter-provincial coordinate model based on tradable green certificate options trading (CMTOT), which includes four components: (1) A dual-objective optimization model is established to minimize the RPS target realization cost and maximize the pollutant emission reduction based on Blacke-Scholes option pricing model. (2) The identification of buyer and seller provinces of tradable green certificate (TGC) options based on the above model. (3) An optimization model in buyers' or sellers' cooperation alliance is established. (4) The cost allocation models by Shapley value and Nucleolus are established to ensure the fairness in different scenarios. A case study of Tianjin, Shandong, Shanxi and Inner Mongolia shows that the CMTOT can effectively reduce the RPS target realization cost by 16.79 % and increase the pollutant emission reduction by 4.32 %. A sensitivity analysis confirms the robustness of the CMTOT. The results show that the CMTOT can both fully develop renewable energy and effectively reduce pollutant emission. Therefore, we proposed some policy recommendations to support the implementation of the CMTOT.

A Non-Transferable Trade Scheme of Green Power Based on Blockchain

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user’s identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users’ privacy.

Optimal Bidding Strategies for Wind-Thermal Power Generation Rights Trading: A Game-Theoretic Approach Integrating Carbon Trading and Green Certificate Trading

In response to the challenges of low wind power consumption and high pollution emissions from thermal power, the implementation of wind-thermal power generation rights trading is a proactive attempt to reduce wind power curtailment and promote its consumption. This study first regards the alternating bidding process between the two parties as a dynamic game, using the Rubinstein bargaining game model to determine the incremental profit allocation and optimal bidding for both parties in power generation rights trading. Secondly, an energy conservation and emission reduction model is constructed to analyze the benefits from the perspectives of standard coal consumption saving and the carbon emission reduction caused by power generation rights trading. Finally, a combined trading revenue model is established to analyze the final profit of both parties involved in the trading. The results show that the combined trading of wind-thermal power generation rights, incorporating carbon trading and green certificate trading, can effectively promote coal consumption savings in thermal power units and reduce the carbon emissions of the power industry. Moreover, it significantly increases the final profit for both parties, stimulating the enthusiasm of generators for participating in power generation rights trading, and ultimately promoting wind power consumption.

The optimization and dispatch of regional multi-energy complementary power system in green certificate Trading - Carbon emission trading

Abstract This proposal of the “double carbon” goal underscores the pressing need to reduce carbon emissions. To address this issue, the utilization of an optimal scheduling model for an integrated multi-energy complementary system, comprising wind, solar storage, and thermal power generation, is proposed. Two renewable energy generating units, wind and photovoltaic power, cooperate with thermal power units to generate electricity to meet urban base load requirements. Energy storage units compensate for the instability of power generation from renewable energy units. To significantly decrease system carbon emissions, a green certificate trading-carbon emission trading system is introduced. The carbon emission reduction facilitated by the green certificate can offset a portion of the carbon emissions, and the quantities of tradable green certificates and carbon emissions in their respective markets are adjusted accordingly. This model aims to maximize the system’s comprehensive operating earnings while considering the low-carbon economy aspect. The proposed model undergoes simulation analysis, and the results demonstrate its reasonableness and efficacy in terms of carbon reduction and economic viability.

Cross-provincial collaborative transaction that considers both the green electricity and the green certificate markets under a renewable portfolio standard policy

In the current implementation of the Renewable Portfolio Standard (RPS), various quota entities are facing challenges, including high costs and limited flexibility within a non-collaborative, single green certificate market mechanism. To address these issues, we developed a cross-provincial collaborative transaction model that considers both the green electricity and the green certificate markets. It consists of five sub-models: a green electricity production net revenue maximization sub-model, a green electricity consumption net benefit maximization sub-model, a green electricity and green certificate market categorization sub-model, a cooperative game sub-model for green electricity and green certificate buyers and sellers, and a cooperative benefits allocation sub-model. The roles of each province (i.e., green electricity and certificate buyers and sellers), along with the optimal green electricity generation, green certificate trading volume, and cost of achieving RPS objectives for each province, can be determined by utilizing the first four sub-models. Ultimately, the cooperative benefit allocation sub-model promotes cooperation among the provinces. To validate the model's effectiveness, we chose five representative provincial-level regions of China for empirical analysis. We found that the cooperative mode decreased the cost by 238.554 × 108 CNY (15.9%) compared with each province independently achieving RPS targets in a non-cooperative mode. Moreover, cooperation increased green electricity generation by 447.94 × 108 kWh (13.3%). The cooperative mode will therefore encourage provincial governments to formulate cooperative strategies for achieving RPS objectives, ultimately reducing implementation costs and increasing flexibility and effectiveness.

Analysis on the synergy between markets of electricity, carbon, and tradable green certificates in China

China has integrated tradable green certificates and carbon markets with its electricity sector to promote renewable energy and reduce carbon emissions. However, the impact mechanism of decision-making behaviors of involved entities in these integrated markets on the market equilibrium is still unclear, hence limiting the further development of renewable energy industries and carbon reduction progress to some extent. Therefore, conducting in-depth research to inform policy makers about the impacts of entities' decision-making behaviors on market equilibrium within the context of integrated multiple markets is crucial. This study aims to analyze the characteristics of entities' decision-making behaviors and equilibrium states within the collaborative dynamics of carbon, electricity, and green certificate markets. Specifically, an optimal decision-making model for entities given the multi-market coupling was developed by employing mathematical programming methods to analyze market equilibriums across various market combinations and policy parameters. The conclusions underscore equilibrium price shifts and decision-making characteristics among entities across multiple markets. The result shows that the carbon market could increase power prices and decrease green certificate prices. To ensure sustainable energy development, policymakers should consider the decision-making behavior of entities under multi-market coupling, which can promote the low-carbon transformation of the Chinese energy industry.

Research on multi-time scale integrated energy scheduling optimization considering carbon constraints

To address the challenge of grasping carbon emission costs in different dispatching cycles under multiple time scales of the Integrated Energy System (IES), a carbon-constrained multi-time scale optimization method for integrated energy systems is proposed. First, a novel mechanism was proposed, Carbon Constraint Mechanism, which includes carbon tax, green certificate trading, and tiered carbon trading mechanism. Second, the carbon constraint mechanism was introduced into multi time scale scheduling optimization, and an IES multi time scale low-carbon optimization scheduling model was established. Third, the economic cost, carbon emissions, and primary energy consumption were considered as objective functions in multi-objective optimal scheduling. Moreover, utilized day ahead scheduling results and real-time load forecasting to dynamically adjust the output power of IES equipment in combination with power change penalties and carbon constraints. The simulation results showed that, compared to dispatch optimization strategies that solely prioritized economy, this study reduced carbon emissions by 28.38 % across multiple time scales while enhancing the economic performance of IES by considering carbon constraints. The multi-time scale dispatch optimization, incorporating carbon constraint mechanisms, improved the system's stability and low-carbon economy, proving the feasibility and effectiveness of the proposed optimal dispatch strategy.

Research on the cost allocation method of deep sea wind power considering carbon trading and green certificate trading

Climate change imposes tighter limits on carbon emissions, which require the development of more green electricity. Deep sea wind power has the advantages of high wind energy density, high power generation utilization hours, no land occupation, and near to power load centers for easy on-site consumption, which lead to a broad market prospect. In the foreseeable future, deep sea wind power will usher in large-scale construction and development. The initial investment and construction cost of deep sea wind power is high, subsidies are gradually decreasing, and there is a lack of full life cycle economic analysis combined with various market policies, resulting in a lack of guidance for its investment and construction. In order to promote the development of deep sea wind power, relevant cost recovery mechanisms must be found. This paper proposes a system cost allocation method based on the comparative difference method and load similarity with consideration of the transformation and operation costs of thermal power, demand-side response, and energy storage; a cost allocation method based on carbon trading and green certificate trading income is proposed; and a time sequence optimization operation simulation model is established to maximize the deep sea wind power income. A case study is proposed for a 300 MW deep sea wind farm, the results show that the cost allocation method and cost allocation model proposed in this paper have a significant effect on wind power cost recovery, and the effectiveness of the proposed method and model is verified.

Benchmarking and contribution analysis of carbon emission reduction for renewable power systems considering multi-factor coupling

The renewable power system serves as a vital path towards achieving carbon peaking and neutrality goals. Accurately quantifying the carbon reduction emissions of renewable power systems and evaluating the contribution of various influencing factors to carbon reduction is quite important, which is related to the formulation of energy policies and regional power planning by the power grid or relevant departments. However, in the current research, there are few studies related to the quantification of carbon emission reduction, and in terms of optimal scheduling, the existing studies often only consider a single factor, ignoring the coupling effect between multiple factors, which makes the contribution of each optimization strategy to carbon emission reduction unclear. Therefore, based on the current power system scheduling mechanism and dual-carbon background, this paper optimizes the carbon emission reduction benchmark factor and its corresponding weight, proposes a new carbon emission reduction benchmark of renewable power system, and verifies the accuracy of this benchmark based on the classical scheduling model. On this basis, the mathematical models of thermal power deep peak shaving (DPS), electricity export (ELE) and green certificate trading (GCT) are established. Each factor is introduced into the classical scheduling model to simulate and verify the promoting effect of each influencing factor on carbon emission reduction. Finally, the above different influencing factors were constructed in different coupling scenarios to calculate the carbon emission reduction under each scenario, and the contribution degree of each influencing factor to carbon emission reduction was analyzed sharply according to the value of cooperation game instead of the traditional calculation method. The results show that the proposed benchmark can reduce the error by 2.6%–15.5 %. In the carbon emission reduction contribution simulation, the three factors have coupling effects, and deep peak shaving and electricity export are more sensitive to carbon emission reduction contribution. This study provides valuable insights for the development of carbon reduction strategies for renewable power systems.

Grappling with the trade-offs of carbon emission trading and green certificate: Achieving carbon neutrality in China

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms’ optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings.

Optimal production decision of hybrid power generation enterprises in multi-quota policy coupled markets

At present, China is adopting both Carbon Emission Trading (CET) and Tradable Green Certificate (TGC) markets to optimize the resource allocation of its power industry. To mitigate the impacts of the above dual markets, more and more power generation enterprises are adopting hybrid production mode, generating electricity from thermal and new energy units simultaneously. In these complex production and market conditions, enterprises are difficult to arrange their generation activities. This research describes the production decision process of hybrid power generation enterprises with different market positions, obtains their optimal yield, and then offers market equilibrium results. In addition, the consequences of enterprise collusion behaviors and government interventions are also measured. Numerical experiment results confirm the correctness of the production decision model and reveal the following facts: 1) CET and TGC markets effectively control the carbon emission level and promote the consumption of new energy, but magnify the failure of electricity market to a certain extent. 2) CET and TGC markets promote each other in function. The existence of one market can help the other to achieve the anticipated goal. 3) Enterprises first offset the impact of market condition changes by adjusting the share of new energy generation, and then digest the residual influence by adjusting the total power generation. 4) Enterprise collusion behaviors aggravate the welfare loss of electricity consumer and then lower the expected effects of CET and TGC markets.

Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China’s green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.

Multi-timescale optimization of integrated energy system with diversified utilization of hydrogen energy under the coupling of green certificate and carbon trading

Promoting renewable energy and developing low-carbon integrated energy systems are noteworthy in the energy sector. However, in existing works on the integrated energy system, the coupling of green certificate and carbon trading mechanism under diversified utilization of hydrogen energy has not been fully considered to provide an incentive effect for uncertain renewable energy resources. Based on these considerations, a multi-timescale optimization of an integrated energy system with diversified utilization of hydrogen energy is studied under the coupling of green certificate and carbon trading mechanism. First, the coupled interaction mechanism of green certificate trading-stepped carbon trading is proposed by linking stepped carbon trading to green certificate trading through green certificates. Furthermore, the establishment of the diversified utilization unit of hydrogen energy addresses carbon emissions from the perspective of energy application and offers fresh concepts for a low-carbon economy. Then, an optimized scheduling model for multiple time scales of day-ahead/intraday/real-time is developed to reduce the impact of renewable energy prediction errors on system operation. Results acquired from case studies demonstrate the effectiveness in terms of achieving economic and low carbon. The accommodation level of renewable energy with a coupling mechanism improves by 3.08% compared to that without considering coupling. In addition, the diversified utilization of the hydrogen energy model enables the reduction in total cost and carbon emission by 5.14% and 2.4927 tonnes in comparison to the traditional power-to-gas mode.

Exploring the efficacy of renewable energy support policies in uncertain environments: A real options analysis

This paper explores the impact of various renewable energy support policies on investor behavior and environmental value. Specifically, the study investigates the effects of fixed feed-in tariff (FIT) and tradable green certificates (TGC) on investor’s flexibility and environmental value under conditions of uncertainty. Real options theory and envelope theory are employed to analyze these uncertainties, with an empirical analysis of China’s electricity market serving as the basis for the study. The results of the analysis demonstrate that the use of TGC can lead to higher environmental value for utility-scale photovoltaic (PV) projects in China. For onshore wind, substituting FIT with TGC may be more appropriate. However, for offshore wind projects, FIT may be more suitable due to their increased vulnerability to uncertainty. Additionally, the study finds that greater support is required for projects deployed under FIT policies.