What Is the Effect of China’s Renewable Energy Market-Based Coupling Policy?—A System Dynamics Analysis Based on the Coupling of Electricity Market, Green Certificate Market and Carbon Market

Abstract

In the context of China’s electricity market reform, green certificate trading and carbon trading, as important policy tools to promote the development of renewable energy and energy conservation and emission reduction in the power industry, will inevitably be coupled with the electricity market. In order to study whether the coupled market can successfully achieve the goals of power supply structure adjustment and carbon emission reduction, this paper establishes a system dynamics (SD) model, analyzes the correlation and coordination mechanism among the green certificate market (TGC), carbon market (ET) and electricity market, including generation right trading, and simulates the changes of market price and power supply structure. The results show that (1) the power price under the coupling of three markets includes the TGC price and the ET price, so it is influenced by the ratio of renewable portfolio standards (RPS) and carbon reduction policy; (2) the combination of the TGC mechanism and the ET mechanism will be conducive to the optimization of long-term market power supply structure, so as to promote the realization of emission reduction targets; and (3) power generation rights trading, as a carbon reduction policy, will reduce the power generation of fossil energy in the short-term market, but in the long run, it will lead to the loss of momentum for the development of renewable energy. Therefore, regulators need to reasonably adjust different policies in order to give full play to the comprehensive regulatory role and help the energy and power industry and the low-carbon transformation of society.

1. Introduction

1.1. Background

With the increasing climate change and shortage of fossil fuels, expanding the production and consumption of renewable energy (RE) has become a universal goal worldwide. As the world’s largest energy consumer, China has pledged to peak its carbon emissions by 2030 and strive to become carbon neutral before 2060 [1]. The power generation industry, which accounts for the highest share of carbon emissions in the country, is of particular concern [2]. After years of exploration, China has formed a set of unique policy systems in the field of emission reduction in the power generation market. The State Electricity Regulatory Commission issued “Interim Measures for the Supervision of Generation Rights Trade” in 2008, which marked China’s new exploration in the field of energy conservation and emission reduction in the power generation market. At the same time, generation rights trade has become a unique electric energy trading variety in China. In 2009, the National Development and Reform Commission (NDRC) issued the “Notice of the National Development and Reform Commission on Improving the Policy on the Feed-in Tariff of Wind Power”. After many years, the feed-in-tariff (FIT) has successfully increased the power generation and installed capacity of China’s renewable energy [3,4]. Under the incentive of the above policies, China’s installed capacity of renewable energy has ranked first in the world [5], but the rapid growth of installed capacity has also brought about increased subsidy pressure and the problem of insufficient consumption of renewable energy [6,7]. In this context, China urgently needs to carry out a new round of power system reform. The NDRC, Ministry of Finance and National Energy Administration co-released the “Issue of Green Electricity Certificates and Voluntary Subscription Scheme (Trial)”. On 19 December 2017, China’s government launched the national emission trading system with the power industry as the breakthrough point of system construction, and China’s carbon market was officially launched in June 2021. On 3 March 2018, the government announced that renewable portfolio standards and a tradable green certificate had been implemented. On 15 May 2019, the NDRC and National Energy Administration jointly released the “Notice of Establishing and Improving the Guarantee Mechanism for Electricity Consumption from Renewable Energy Sources”(NDRC, 2019) [8], which marked the formal implementation of RPS [9,10,11,12]. As of 2024, these policies have been in place for five years, reflecting China’s ongoing commitment to renewable energy and its transition to a greener economy. According to policy directives released by six departments including the NDRC on 30 October 2024, China is vigorously implementing a renewable energy substitution initiative, which includes expanding the proportion of renewable energy in total energy consumption, with a target of over 30% by 2025 [13].

All of these are important policy tools introduced in the process of low-carbon development of the Chinese electric power industry. Due to the certain crossover and repeatability of different emission reduction policy coverage and policy effects, the implementation or adjustment of one policy tool often has a direct or indirect impact on the effect of other policies and the realization of goals. Then, in China’s actual national conditions and economic conditions, are the various policies promoted at the same time conducive to the development of renewable energy? How does our unique power generation rights trading policy cooperate with the new market policy? What impact will the profit-driven mentality of market participants have on the steady progress of renewable energy? Based on this, this paper establishes an SD model, comprehensively analyzes the coupling mechanism of the electricity market, carbon emission permit trading market and green certificate market within a very long time span and analyzes the utility of the renewable energy quota system, carbon emission permit trading system, green certificate trading system and power generation rights trading system. In addition, by constructing different scenarios, it clarifies the influence of different policies on China’s supply-side market and power structure, culminating in policy recommendations that are tailored to the nation’s specific conditions for energy conservation and emission reduction while also promoting the advancement of renewable energy sources. It also clarifies how different policies affect the supply-side market and the power structure of our country and finally gives policy suggestions for energy conservation and emission reduction in line with our national conditions and promoting the development of renewable energy.

1.2. Literature Review

At present, domestic and foreign scholars’ research on the power market, tradable green certificate market, carbon market and power generation rights trade mainly includes the following aspects.

Firstly, an essential area of study is the interaction between the power market and the tradable green certificate market. Zhao et al. found that the TGC system has inherent disadvantages, which will tilt capital and technology towards low-cost renewable energy types, while high-cost renewables, such as photovoltaic, will stagnate [14]. Ying et al. emphasized that the tradable green certificate market could increase profits for fossil power producers by affecting the demand side of TGC [15]. JI showed that the transfer cost behavior of traditional energy generators may raise the price of electricity, thus lowering the market price of green certificates and weakening the incentive effect on the growth of renewable energy power [16]. Wang et al. believed that under the policy of RPS, the electricity price shows a gradual downward trend, the transaction price of the green certificate is reduced by the transaction of the absorbing quantity certificate, and the transaction price of the green certificate will eventually become equal [17].

Secondly, the interplay between the carbon emission trading system and the renewable energy quota system has been another focal point. Feng et al. implied that the implementation of carbon emission rights and a renewable quota system has a synergistic effect. Both of them can play a positive role in the transformation of China’s power structure [3]. But Zhao et al. showed that a higher price for carbon rights will discourage the development of distributed renewable power [18,19,20,21]. However, Åberg et al. drew the opposite conclusion from the empirical analysis of the Nordic power market, suggesting significant regional differences that could influence the effectiveness of these systems. This opposite conclusion prompts a deeper examination of how regional differences in energy markets, policy frameworks and market structures can influence the effectiveness of carbon emission trading systems and renewable energy quota systems, highlighting the importance of considering local specificities when assessing the potential synergies and trade-offs between these two policy instruments [22]. Yi et al. discussed the impact of the implementation of the emissions trading scheme on the renewable energy sector, and they found that the implementation of the emissions trading scheme would increase the share of renewable energy generation and effectively improve the profitability of the power system [23]. Zhao et al. believed that there was a certain overlap between the RPS and ET systems. It was conducive to increasing the proportion of renewable energy in the power market, to optimize the power structure [4]. Bao et al. analyzed the policy effect of the interaction between the renewable energy quota system and carbon emission rights trading by establishing a two-level supply chain model and found that the introduction of carbon emission rights trading would lead to an increase in thermal power prices and a decrease in renewable energy power prices, which is conducive to the realization of the competition between the two at the same price level [24].

Thirdly, research on the impact of policy goal setting on market participants reveals several key insights. Shi pointed out that with the increase in the RPS mandatory ratio, the change in power energy structure would reduce the cost of emission reduction in the power industry, thus lowering the price of carbon emission permits [25]. However, it was not clear whether consumers’ expenditure would increase or decrease. Feng and Zhu et al. showed that higher quota requirements would suppress the price of carbon emission permits and an improper system design would result in system redundancy [3,26]. Zhang et al. showed that a higher RPS quota ratio would increase the share of renewable energy and reduce carbon emissions, thereby increasing social welfare [27]. However, Zhou et al. believe that in terms of China’s current real economy, when the quota is set in the interval (0,0.5), the social welfare under the RPS is always higher than that under the FIT [28]. Thombs and Jorgenson believed that when the carbon reduction policy goals setting are too strict, fossil energy generators will become resistant to RPS policies, leading to policy failure [29]. Bao et al. studied the policy effect when RPS and ET coexist in the power market by establishing an evolutionary game model and found that ET will reduce the profits of power generation enterprises, while thermal power generation companies are faced with double costs when introducing ET and RPS [30]. These findings underscore the complex dynamics between policy goals and market responses, highlighting the need for a nuanced understanding of how policy settings can influence market outcomes and overall welfare.

Finally, there are the studies related to power generation rights trading. As a product of the transition from the planning mechanism to the market mechanism in China’s power industry, power generation rights trade has distinctive Chinese characteristics. Li et al. first introduced the concept of generation rights trade [31]. Xi-fan et al. analyzed the price mechanism of generation rights trade in the power generation market [32]. Zhao et al. developed a generation rights trade model targeting regional maximum net benefits and analyzed the trade under the electricity market scenario. They believe that the TGC policy can improve the efficiency of power trading and thus increase the incentive of power generators to participate in power trading [4]. Hui et al. consider power generation rights trading as a multi-party game. They designed a profit allocation model on this basis and investigated how the generators can achieve the optimal profit through arithmetic analysis [33]. Zhao et al. investigated the origin and composition of the transaction price of power generation rights trading in Shanghai and found that power generation rights trading can improve the operating profit of renewable energy and reduce the power abandonment of renewable energy [34]. Wang et al. believed that an increase in carbon trading price will help promote the active participation of clean energy and conventional energy in power generation rights trading, reduce carbon emissions and improve economic benefits. Therefore, it is necessary to combine carbon trading with power generation rights trading and maintain a certain level of carbon trading price so as to mobilize power producers to actively participate in power generation rights trading [35].

Moreover, system dynamics is particularly adept at simulating system behavior and trends as control factors change due to its high-order, nonlinear nature [36]. More importantly, SD aims to reveal the causes of system evolution [37,38], and these models can facilitate policy simulation, allowing decision-makers to emulate and test different policy combinations, thus improving their decisions in terms of efficiency and outcomes. Currently, scholars mainly use this method to study the policy effects; for example, Zhang et al. analyzed the impact of FIT policy withdrawal on distributed photovoltaic generation (DPVG) using an SD model. They argue that the withdrawal of FIT will negatively affect the development of DPVG, while RPS will become an effective means for DPVG to achieve grid parity [39]. Ying and Xin-gang analyzed the impact of RPS on carbon emission policies, and they argued that the implementation of RPS weakens the demand for carbon credits, thus reducing the amount of power generation and the profits of manufacturers [40].

Although these studies all focus on the electricity, ET and TGC markets and their policies, most of these studies are limited to static efficiency analysis of the three markets, focusing mainly on the effects of the new policies in terms of benefits and trading volumes of market players after their implementation [30]. Fewer of these researches have studied the impact of electricity supply, power supply structure and carbon emissions from the power generation sector in the electricity market or have only studied the interaction between two policies, and it is not common to consider the impact of more than three emission reduction policies [41]. Few scholars have analyzed the complex behavior of market players influencing prices in other markets with the help of their own forces and the linkage of prices in the three markets from the perspective of the overall operation of the system. Therefore, it is hard to assess the impact of the complex relationships and chaotic characteristics after the introduction of the system and thus to understand the mechanism of electricity price formation in the new policy environment [42,43].

The main contributions and implications of this study are as follows:

• Based on the present situation of China’s electricity marketization reform, this paper constructs an SD model under the coupling effects of the electric power market, green certificate market and carbon market. This model reveals the internal mechanism of coupling the carbon market, green certificate market and electricity market and analyzes the price trends of the independent markets and the coupled market. At the same time, the model distinguishes the total quota and non-water renewable energy quota and accurately simulates the development trajectory of RES, hydropower and fossil energy under different mechanisms.
• Based on the Chinese Renewable Energy Policy (NDRC, 2019), this paper aims to analyze the impact of the transformation of RPS responsibility into e-commerce on China’s electricity market, TGC market and ET market, focusing on the impact of the carbon reduction policy and RPS ratio on the change in sales price and China’s power supply structure.
• As a unique mode of electricity trading, power generation rights trading is also a unique measure of energy conservation and emission reduction in China. This paper analyzes its relationship with the renewable energy quota system and carbon emission rights trading policy and makes clear the cooperation mechanism between different policies. Through SD model simulation, the influence of a power generation rights trading policy on the development momentum of different market players in the short- and long-term markets is clarified.

The rest of the paper is structured as follows: Section 2 clarifies the model principles and the interaction mechanism between the TGC market, ET market and electricity market, which sets the stage for the construction of the SD model. Section 3 carries out the construction of the SD model while setting up different scenario models. Section 4 discusses and analyzes the results of the model runs. Section 5 presents the relevant conclusions and policy recommendations. In summary, the overall logic of this paper is shown in Figure 1.

2. Theoretical Framework Analysis

2.1. Model Principle

System dynamics is a methodological approach specifically designed to analyze the behavior of complex systems [44]. It examines feedback loops and interdependencies among internal variables to understand how a system’s components interact over time [45]. SD is generally used to study and analyze complex system problems. It can deal with multivariable compound, multiple information feedback, and non-functional time-varying system problems; study the complex variable structure and feedback information in the system and carry out quantitative analysis of each function in the system [46]. One of the key concepts in system dynamics is the understanding of feedback loops, particularly R (reinforcing) and B (balancing) feedback loops. R loops, or reinforcing loops, occur when a system’s output reinforces the input, either increasing or decreasing it, leading to potential exponential growth or decay. Conversely, B loops, or balancing loops, work to counteract the input by adjusting the output to restore equilibrium. They operate through negative feedback to stabilize the system. While R loops can lead to significant shifts in system dynamics, B loops are crucial for maintaining stability and counterbalancing the effects of R loops. This interplay is essential for understanding the complex dynamics of the system. According to the different characteristics of the variables in the system, SD divides the variables in the system into state variables, flow, auxiliary variables and exogenous variables. The equations contained correspond to the variables, generally including state equation, flow equation and auxiliary equation. This method has some mature application experience in solving qualitative problems in benefit evaluation and visualization analysis of complex systems. In the field of power systems, the SD method is also applied in the analysis of incentive mechanisms of renewable energy and the evaluation of the effect of policy combination.

2.2. System Dynamics Characteristics of Coupled Markets and Policies

In essence, the coupling system of the TGC market, ET market and power market is a very complex dynamic system. Many variables such as RPS ratio, TGC price, TGC supply-and-demand situation and trader holdings are covered in green certificate trading. Carbon trading, on the other hand, includes factors such as the aggressiveness of the emission reduction policy (reflected in CO₂ emission reduction per unit of GDP), ET prices, ET supply-and-demand conditions and ET certificate holdings [47]. In the coupling system of three markets, each influencing factor interacts and feeds back to each other, forming a causal chain with multiple intermediate variables and a multilayer feedback structure. In the end, the multi-party game and interwoven interests in the power market will make the interaction mechanism and policy effect of the coupling market extremely complex [43].

The parallel implementation of the TGC system and ET system will have an important impact on many factors, such as supply and demand, price and cost in the power market [48]. An ET policy will increase the operating costs of fossil energy generators [3], and the increase in costs will affect the electricity market price [41]. The co-existence of TGC policies and ET policies will have redundant effects on the electricity market, so the incentive and compatibility effects between the policies need to be analyzed in markets where different policies co-exist [11].

Due to the spatial dispersion of market players, there is a time lag in information transmission and policy formulation, which leads to a time lag for the change of one factor to be reflected in the change of another factor, i.e., there is a time-lag effect in the coupling effect between variables in the system of three market interactions [49]. System dynamics can be used to establish first-order differential equations with time lags, construct quantitative time series models, objectively and realistically reflect the mechanism of action of the coupled system operation and clarify the causal relationship between the internal subsystems [50]. To sum up, the three-markets interaction system is a nonlinear, strongly coupled, multivariable, high-order dynamic feedback system with obvious system dynamics characteristics. Therefore, this paper will use SD to analyze the coupling effect of the three markets.

2.3. Three-Markets Interaction Mechanism

The participants, market relations and tradable certificate transmission process in the TGC market, ET market and power market are shown in Figure 2.

Green certificates will be traded on the TGC market with relevant market entities, including the government, RES power producers and grid companies. The government will stipulate the proportion of electricity purchased by power grid companies from RES according to the national energy and environment development strategy. Grid companies offset their quota requirements by purchasing RES electricity or a green certificate directly from the TGC market [51]. The main participants of the ET market are the government and fossil energy companies. The government allocates initial carbon allowances to the power sector by setting emission reduction targets and electricity demand. Fossil energy generators buy and sell ET certificates from the ET market based on the carbon demand of their units, and eventually all certificates are approved by the government for recycling [52]. The parallel implementation of the TGC system and ET system will reallocate the environmental cost among market entities. RES power generation companies have additional income from TGC in addition to electricity sales, enhancing market competitiveness. Fossil energy producers need to bear the cost of the carbon quota, and their competitiveness is reduced [17,53].

To sum up, the changes in every key factor related to the TGC market and the ET market, such as RPS, carbon emission quota, TGC price, ET price and the allocation method of initial carbon quota, will be transmitted to the electricity market and ultimately affect the equilibrium results of the power market [54].

Figure 3 is the causal loop diagram of the interaction of the TGC market, the ET market and the electricity market. The figure contains seven feedback loops, which are as follows: 1. TGC trading #1 negative feedback loop, 2. TGC trading #2 negative feedback loop, 3. RE generation negative feedback loop, 4. electricity trading negative feedback loop, 5. fossil energy generation negative feedback loop, 6. ET trading #1 negative feedback loop, 7. ET trading #2 negative feedback loop. The causal relationships within the feedback loops are shown in Appendix E.

3. Model Design

3.1. System Boundary and Assumptions

In constructing the SD model for the coupled role of the TGC market, ET market and electricity market under generation trading, carbon trading and the renewable energy quota system, the following assumptions are made in this paper.

Hypothesis 1. 

Assuming that there is a unified TGC market, ET market and electricity market nationwide, in the three markets, TGC trading, ET trading and power trading are separated from each other without binding trading.

Hypothesis 2. 

This paper mainly considers three major non-hydro power generation technologies, including wind power generation, photovoltaic power generation (PVG) and biomass power generation. To make the model more intuitive, non-hydro RES are studied as a whole, without considering the variability in the number of green certificates redeemed for wind, photovoltaic and biomass power technologies in the TGC market.

Hypothesis 3. 

The government certifies “renewable energy green certificates” for hydropower electricity and “non-hydropower green certificates” for non-hydropower renewables. Among them, renewable energy green certificates are only used for total quota assessment, while non-hydro green certificates are used for non-hydro quota assessment as well as total quota assessment. In order to reflect the difference between the total quota and the non-hydro renewable energy quota, after taking into account the generation costs of different power producers, this paper sets the parameters so that the certificate redemption amount is different for different types of RES, in which 1 MWh of electricity is redeemed for 1 TGC for non-hydro RES and 0.6 TGC for 1 MWh of hydropower.

Hypothesis 4. 

The market players responsible for the consumption of renewable electricity are the sales and distribution companies and large electricity consumers, and the electricity sales and distribution in China are undertaken by the integrated energy service companies under the power grid companies. So essentially, the grid is responsible for the consumption of renewable energy power. Considering the current stage of China’s electricity-trading market structure, this study only considers one RPS responsible subject, the grid company, which conducts TGC and electricity trading with RES on behalf of electricity-consuming enterprises and residents. In addition, no green certificates will be traded between RES generators, and the issuance, recovery and price of green certificates in the market will be regulated by the government.

Hypothesis 5. 

This paper only considers ET within the power industry and does not consider other industries. In the trading base period, the government will issue a certain proportion of free carbon emission quotas to the fossil energy companies, and the part of carbon emission exceeding the initial quota will need to buy ET certificates from the ET market. The price, issue and collection of ET certificates are also regulated by the government.

Hypothesis 6. 

The product to be traded in the electricity market is electricity, which will form a uniform feed-in tariff through a competitive electricity market. Power-trading entities include power grid companies and all of the above generators.

Hypothesis 7. 

This paper only studies the closed economic system of China, without considering international trade, economic crisis, inflation and other issues in market trading.

3.2. TGC Market Module

The module of the TGC market mainly studies the formation mechanism of the TGC price, including the TGC market, RE generation and generation rights trade module. Using a Vensim simulation tool [26], the system flow diagram of the TGC trading module is shown in Figure 4 and Figure 5. Figure 4 shows the stock flow diagram of the TGC trading market, and Figure 5 shows the stock flow diagram of the generation and generation rights trading part. The variables in the system and the correlations between the variables will be shown in Appendix A.

3.3. ET Market Module

The ET market model mainly studies the formation mechanism of the ET price. It mainly includes the ET market and fossil energy generation submodule, and its stock flow diagram is shown in Figure 6. The variables in the system and the relationship between the variables will be shown in Appendix B.

3.4. Electricity Market Module

The electricity market module mainly simulates the formation process of electricity price, which is mainly determined by the supply and demand of electricity. Its stock flow diagram is shown in Figure 7. The variables in the system and the relationship between the variables will be shown in Appendix C.

3.5. Three-Markets Interaction Module

In summary, the system stock flow diagram after the coupling of three markets is shown in Figure 8. The variables in the system and the relationship between the variables will be shown in Appendix D.

3.6. Data and Scenario Design

In this paper, simulations are conducted for the evolution of certificate prices, electricity prices and power supply structures using Vensim. The starting point of the simulation is set as 2011, and the assignment of all the initial variables of the factors in the SD model is based on the data in 2011. Months were then chosen as the time unit for the modeling run, and the simulation time was 240 months, with the research time period determined as 2011~2030. The data used in the simulation are from the China Energy Statistical Yearbook and the China Electricity Council, where the initial variable assignments are shown in

Table 1. Important parameters in the SD model.

Parameters	Unit	Value a
Initial TGC price	CNY/kW∙h	0.22
Initial ET price	CNY/t	50
Initial price of electricity	CNY/kW∙h	0.38
Initial demand of power	GW∙h	4.6 × 106
Initial rate of electricity demand growth	%	7
Power elasticity of demand for price	-	−0.1
Rate of RPS demand growth	%	13
Initial number of TGCs held by RES	-	390 × 108
Initial amount of ET held by sellers	t	2.231 × 108
Long-run marginal cost of RES	CNY/kW∙h	0.5
Long-run marginal cost of fossil energy sources	CNY/kW∙h	0.3
Long-run marginal cost of hydropower sources	CNY/kW∙h	0.4
Initial capacity of RES	GW∙h	48.64
Initial capacity of fossil energy sources	GW∙h	768.34
Initial capacity of hydropower sources	GW∙h	232.98
GDP growth rate	%	7
Network losses	%	10
Carbon emission value per unit GDP	t/million CNY	2.2

^a CEC, 2012. Statistics of China Power Industry 2012 https://english.cec.org.cn/#/menu/index.html?251 (accessed 13 November 2021). stats.gov.cn, 2012. China Statistical Yearbook 2012 https://www.stats.gov.cn/sj/ndsj/2012/indexch.htm (accessed 13 November 2021).

.

The parameters required for scenario simulation in this paper are set as follows. The RPS ratio planning target of 30% and carbon emission reduction rate per unit of GDP ($R_{carbon}$) of 0.75% are used as the baseline parameters. A rate of 0.75% is the CO₂ intensity reduction rate per unit of GDP in developing countries for 1990–2010 [55], and 30% is the recommended target in (NDRC, 2019). Scenarios T0, T1 and T2 in

Table 2. Scenario design in independent markets.

Scenario	T0	T1	T2	E0	E1	E2
RPS proportion	30%	35%	25%	-	-	-
Reduction rate of carbon emissions per unit GDP	-	-	-	0.75%	0.80%	0.70%

are parameter assumptions for the role of an independent TGC market, which do not need to consider the setting of a carbon reduction target; while scenarios E0, E1, and E3 are parameter assumptions for the role of an independent ET market, which similarly do not need to consider the difference in RPS ratio.

Table 3. Scenario design under three-markets coupling mechanism.

Scenario	S0	S1	S2	S3	S4
RPS proportion	30%	35%	25%	30%	30%
Reduction rate of carbon emissions per unit GDP	0.75%	0.75%	0.75%	0.80%	0.70%

shows the scenario parameter settings under the coupling effect of the three markets: S0 is the baseline scenario, S1 is the high RPS rate scenario, S2 is the low RPS rate scenario, S3 is the high carbon reduction target emission scenario, and S4 is the low scenario.

Table 4. Scenario design under different generation rights transaction ratios.

Scenario	R0	R1	R2	R3	R4	R5
Hydropower and fossil energy	0%	1%	2%	0%	0%	1%
RES and fossil energy	0%	0%	0%	1%	2%	1%

shows the parameter settings for different generation rights trading ratio scenarios under the three-markets coupling benchmark case, where R0 is no generation rights trading, R1 and R2 are water and thermal generation rights trading only, R3 and R4 are non-water renewable energy and thermal generation rights trading only, and R5 is generation rights trading of hydro, non-water renewable energy and thermal power at the same time.

4. Results and Discussion

4.1. Sensitivity Analysis

To ensure that the system dynamics model matches the behavior of the real system, this paper uses Monte Carlo sensitivity analysis with Vensim for validation of the model’s behavioral validity. Monte Carlo simulation explores the uncertainty and future possibility of selected outputs by performing a certain number of repeated simulations. Given uncertain parameters, different confidence intervals can reflect the uncertainty and future possibility of variables, as well as the validity of the model [56]. As a coupled SD model of the power generation market considering power generation rights trade, the focus of sensitivity analysis is to verify the price of the power market, TGC market and ET market.

As shown in Figure 9, the electricity sales price exhibits a complex interrelation with the spot market electricity price. This price is determined by a multitude of factors, with power supply being a key determinant. Power supply is influenced by the generation from RES, hydropower and fossil fuels. Real-time demand, which is a significant factor, is shaped by the overall electricity demand, the price elasticity of demand and the rate at which electricity demand grows. The analysis emphasizes the considerable influence that variations in these elements can exert on the electricity sales price, particularly the roles played by renewable energy generation and network losses.

As shown in Figure 10, the TGC price demonstrates a high degree of responsiveness to the market price of TGCs. This sensitivity is a result of the complex interaction between changes in TGC prices, sales estimates and purchase projections. The supply of TGCs is contingent upon the generation from RE and hydropower, whereas demand is influenced by the inventory of TGCs held by the grid and the existing TGC demand gap. The sensitivity analysis reveals the TGC price’s susceptibility to alterations in supply and demand, as well as the regulatory constraints imposed by the predefined upper and lower bounds of TGC pricing.

As shown in Figure 11, the ET price is subject to the dynamics of the ET market price. This market price is influenced by fluctuations in ET prices, sales estimates and purchase projections. The supply of ETs is governed by the holdings of sellers and is affected by the proportion of the power industry, the rate of GDP growth and the CO₂ emissions per unit of GDP. The demand for ETs is driven by the holdings of buyers and the carbon emissions associated with fossil energy sources. The analysis suggests that the ET price is highly sensitive to changes in the supply and demand of ETs, with the established price boundaries playing a pivotal role in the formation of market behavior.

Since the percentage of generation rights trading is not fixed, setting it between 0 and 50% is a more reasonable choice. Other benchmark parameters used in sensitivity analysis are shown in

Table 5. Baseline parameters for sensitivity analysis.

Parameter	Value	Range
Generation rights trade between hydropower and fossil power	0.001	[0, 0.5]
Generation rights trade between RES and fossil power	0.001	[0, 0.5]
CO2 emission reduction per unit GDP	−0.75	[−0.75, −0.95]

. All parameters are distributed randomly, and the number of simulations is set at 200. As shown in Figure 9, Figure 10 and Figure 11, it indicates that most of the test cases must fall within a 100% confidence interval in the sensitivity analysis. For example, 100% of the test cases of electricity price fall within a 100% confidence interval [57,58]. However, 5% of test cases in the sensitivity analysis for the ET price exceed the 100% confidence interval.

4.2. Authenticity Test

The system dynamics model is a simplified description of the real world and cannot include all elements in the system. Therefore, the simulation result of the model will have some error with respect to the real value, but for the SD model to be valid, the difference between simulation and reality must not be too large. In order to verify the validity of the model constructed in this paper, the veracity tests are all set up according to the baseline scenario, and the state variables that can reflect the system behavior, such as the installed capacity of non-water renewable units, the installed capacity of hydroelectric power generation, and the installed capacity of fossil energy generation, are compared with the true values in the study period. As shown in

Table 6. Authenticity test results.

Year	RES Capacity			Hydropower Capacity			Fossil Energy Capacity
	Simulation Value/ GW∙h	True Value a/ GW∙h	Error/ %	Simulation Value/ GW∙h	True Value a/ GW∙h	Error/ %	Simulation Value/GW∙h	True Value a/ GW∙h	Error/ %
2015	116.55	107.43	8.5	311.01	319.54	−2.7	1002.71	1005.54	−0.3
2016	213.1	223.78	−4.8	340.02	332.07	2.4	1011.1	1060.94	−4.7
2017	308.3	294.42	4.7	368.81	343.59	7.3	1020.73	1104.95	−7.6
2018	390.95	358.85	8.9	373.41	352.59	5.9	1048.29	1144.08	−8.4
2019	438.55	412.43	6.3	387.35	358.04	8.2	1095.1	1189.57	−7.9

^a stats.gov.cn, 2020. China Statistical Yearbook 2020 https://www.stats.gov.cn/sj/ndsj/2020/indexch.htm (accessed 13 November 2021).

, the test results do not differ from the true value by more than 10%, and the model is authentic to a good degree.

With the above analysis, the SD model developed in this paper has been shown to be consistent with the real system, so the next study will be based on the previous work.

4.3. Analysis of Price Trends in Independent and Coupled Markets

4.3.1. Green Certificate Price Change

Scenario simulation analysis is carried out on the SD model considering only the TGC market system, and the change trend chart of TGC prices is obtained under three scenarios, as shown in Figure 12: T0, T1 and T2. The main reasons for this situation are as follows.

Firstly, when only TGC market system conditions are considered, TGC prices are mainly affected by price ceiling, price floor and different RPS ratios. Before 2016, TGC prices were rising due to the mandatory requirements of RPS. Their rise will also lead to an increase in the profit margin of the market, which in turn will attract more investors to participate in renewable energy investments. However, according to the supply-and-demand relationship, when the quantity of TGCs sold in the market is greater than the demand, their price will start to fall. In both scenarios T0 and T1, the TGC price reaches the upper limit of the price stipulated by the government because the ratios of RPS in these two scenarios are higher than that in T2. A higher RPS ratio setting will amplify the scarcity of TGCs at the beginning of the trading due to increasing demand. Secondly, the inflection point is caused by the number of TGCs held by the grid going to zero. At the beginning of the TGC market, the grid companies, which are the main responsible parties for the RPS, will try to buy as many TGCs as possible before the TGC prices rise further in order to reduce the excess costs caused by their completion of the mandatory quota, thus avoiding the cost increase caused by buying after the price rises [59]. Finally, around 2026, with the continuous decline in TGC prices, it is no longer necessary for the power grid to purchase in advance to reduce costs. At this time, the power grid only needs to purchase the TGC quantity equal to the quota that needs to be completed in the current period. T1 does not show an inflection point, which is mainly caused by the short study time interval, and if the simulation time is further extended, the same inflection point occurs for T1. This phenomenon will lead to a gradual slowdown in the growth rate of RE’s installed capacity later in the market as the price of TGCs decreases and the incentive to install non-water renewables wanes [60].

4.3.2. Carbon Emission Certificate Price Change

Through simulation analysis of the SD model under an independent ET system, the changing trend in ET prices under E0, E1 and E2 can be obtained, as shown in Figure 13. Compared with the TGC price, the fluctuation trend of the ET price is more complex. According to the SD model (see Appendix B.1), the ET price is influenced by the upper and lower ET price limits and the ET market price, while the ET market price receives the effect of ET price changes. Since supply and demand affect market prices and market prices in turn affect supply and demand, the ET market is similarly influenced by multiple factors such as energy strategies, environmental policies and market maturity. Market players are dispersed in space, and there is a delay in policy making and information transmission in time. Therefore, the imbalance between supply and demand of carbon certificates does not immediately feed through to the market price. This is the reason why the ET price curve is not smooth. From 2011 to 2016, the ET price curve of different scenarios shows the same upward trend, and the ET price begins to fluctuate greatly in the middle of 2016. The long-term market ET price (220 to 240 months) line of different scenarios is shown in

Table 7. ET price (long-term market) in different scenarios.

Scenario	Independent Market			Combination Market
	E0	E1	E2	S0	S1	S2	S3	S4
ET price (CNY/t)	213	215	176	249	203	266	181	179

, and the main reasons are as follows.

When only the influence of the ET system is considered, the final stable price of ET increases with the increase in $R_{carbon}$. This is due to the fact that ET supply decreases when emission reduction policies become more aggressive, which in turn leads to higher ET prices. Similarly, as shown in S3 and S4, when the RPS ratio is constant, the ET price of scenarios with a higher carbon emission reduction rate (S3) will be higher than that of scenarios with a lower carbon emission reduction rate (S4). Due to the scenario setting, the difference in ET stabilization prices caused by different carbon reduction policies is not significant. The correlation between ET prices and policies will be more significant if more aggressive or negative carbon reduction measures are set up for simulation analysis.

Through comparative analysis of Figure 13 and Figure 14, the following conclusions can be drawn: Under the same carbon reduction policy, the ET price under the coupled market will reach a steady state earlier than that under the single ET system, and the fluctuation range of the price will be smaller. However, the ET price in scenarios S2 and S0 will be higher than that in scenario E0, and the stable ET price in scenario E0 is greater than that in scenario S1. To some extent, this reflects the coupling effect between different policies. Under the condition of a certain intensity of carbon reduction policies, more aggressive RPS policies must be implemented to restrain the development of fossil energy. When the RPS ratio is kept at a low level, the inhibition effect on fossil energy is not significant.

4.3.3. Sales Price Change

Figure 15 shows the changing trend in the sales electricity price under the condition of the coupled market, and the long-term market electricity price (220 to 240 months) line of different scenarios is shown in

Table 8. Electricity sales prices (long-term market) under different scenarios.

Scenario	S0	S1	S2	S3	S4
Electricity sales prices (CNY/kW∙h)	0.520	0.865	0.440	0.598	0.615

. It can be seen from the figure that the changing trend in the sale electricity price under the five different scenarios is roughly the same. Before 2016, it all rose rapidly. After 2016, the highest average level of electricity sales price is the S1 scenario, followed by S3, S4 and S0, and the lowest is S2. The reasons for this phenomenon are as follows.

First, the TGC market facilitated the construction of renewable energy generators in the early stage of ET and RPS policy implementation. However, due to the ET market, the profit margins of the fossil energy generators gradually decreased, which led to the decline in fossil energy power supply. Considering that fossil energy power generators account for the largest share of China’s power supply structure, this will lead to a smaller supply than demand in the electricity market, hence the increase in electricity prices. However, when the installed capacity of renewable energy further increases and the supply of power market exceeds the demand, the sales price begins to decline, and the equilibrium point of market supply and demand appears in 2016, which also exactly corresponds to the peak of the TGC price in Section 4.3.1. Secondly, from a longitudinal perspective, when $R_{carbon}$ is certain, the impact of different RPS ratios on the sales price of electricity is more intuitive. When the RPS ratio is higher, the electricity price is also higher due to the fact that the electricity price includes not only the price of electricity but also the TGC price and the ET price. Similarly, when the ET price is higher, the electricity price will also be higher. In summary, the electricity price under the three markets contains both the TGC price and the ET price and is therefore affected by both the RPS ratio and the carbon reduction policy: when the RPS ratio is larger and the carbon reduction policy is more aggressive, the sales price of electricity will be higher.

4.4. Power Structure Evolution Analysis

Figure 16 shows the growth trajectory of non-hydro renewable energy sources (RES) capacity over time under various scenarios (S0 to S4). It is evident that the implementation of both the TGC and ET systems drives a consistent annual increase in RES capacity. However, the development of RES in the mid to later stages of the study period diverges significantly due to different policy combinations.

Figure 17 shows the installed capacity of hydropower under the same set of scenarios. Similar to RES, there is a general upward trend in hydropower capacity. However, the influence of policy combinations on hydropower development is less pronounced compared to non-hydro RES. This can be attributed to China’s policy of ensuring basic market demand for hydropower, which is a stable factor in the country’s energy mix. By comparing different scenarios in Figure 16 and Figure 17, the following conclusions can be drawn.

A high RPS planning goal and a high carbon reduction policy are more conducive to the development of RES. Different $R_{carbon}$ have no obvious influence on the installed capacity of RES, while different RPS planning objectives have a greater influence on the development of RES. By comparing the changes in installed capacity of non-hydro RES and hydropower under the same scenario in Figure 16 and Figure 17, it can be seen that the influence of different policy combinations on the development of non-hydro RES is far greater than that on the development of hydropower. This is due to China’s policy to ensure the basic market demand for hydropower, while the development of non-hydropower RES is to be strongly promoted.

Figure 18 shows the trend in fossil energy installed capacity under different scenarios for the combined effect of TGC and ET mechanisms. By comparing scenarios S3, S4 and S0, it is found that when the RPS planning goal is set at the same time, the different carbon emission reduction goals have a relatively large impact on the installed fossil energy capacity. Aggressive carbon reduction policies (higher $R_{carbon}$) have a stronger inhibiting effect on the growth dynamics of installed thermal power capacity, slowing it down considerably, while moderate carbon reduction policies have the opposite effect.

Table 9. Power structures in different scenarios.

Time Interval	Scenario	S0	S1	S2	S3	S4
Short-term market (60 to 80 months)	Non-hydroelectric RES structure (%)	32.21	32.21	32.21	32.22	31.78
	Hydropower structure (%)	16.76	16.76	16.76	16.76	16.60
	Fossil energy source structure (%)	51.03	51.03	51.04	51.02	51.62
Mid-term market (120 to 140 months)	Non-hydroelectric RES structure (%)	49.96	50.23	49.26	49.51	47.47
	Hydropower structure (%)	15.11	15.03	15.15	14.98	14.90
	Fossil energy source structure (%)	34.93	34.74	35.5	35.50	37.64
Long-term market (220 to 240 months)	Non-hydroelectric RES structure (%)	59.41	61.74	58.83	60.26	55.82
	Hydropower structure (%)	14.03	13.73	14.24	14.22	13.74
	Fossil energy source structure (%)	25.56	20.53	26.93	25.52	30.44

also reflects the change in power mix under different scenarios. When the RPS ratio is constant (comparing S0, S3 and S4), the more aggressive carbon reduction policies will significantly reduce the installed share of fossil energy generators. When the carbon reduction policy is constant (comparing S0, S1 and S2), a higher RPS ratio will increase the installed share of RES. The change in installed capacity share of hydropower generators under different policy combinations is not obvious. In summary, it is foreseeable that a further higher RPS ratio along with a more stringent carbon reduction policy would boost RES development.

4.5. Generation Rights Trade Policy Analysis

China has implemented a generation rights trade policy for many years. This section simulates the effects of different generation rights trade ratios on different market prices in the case of generation market coupling. The results are shown in Figure 19, Figure 20 and Figure 21. The stable prices in different markets are shown in

Table 10. Stable market prices under generation rights trade.

Scenario	R0	R1	R2	R3	R4	R5
TGC price (CNY/kW∙h)	0.481	0.476	0.471	0.473	0.464	0.467
ET price (CNY/t)	284.35	186.96	139.07	182.70	171.70	152.66
Electricity sales prices (CNY/kW∙h)	0.527	0.604	0.670	0.561	0.609	0.648

.

As shown in Figure 19, the TGC price trends are consistent across scenarios, with significant differences emerging in the later stages of market operation. The highest TGC price is observed in scenario R0 and the lowest in R4. This indicates that the equilibrium price in the TGC market decreases with increased generation rights trading. The comparison between R0, R2 and R4 suggests that as the replacement of fossil energy by non-hydro renewable energy increases, the TGC price declines more significantly. The replacement of thermal power by hydropower has a lesser impact on TGC prices. This is attributed to a rise in non-hydro renewable generation and a corresponding increase in TGC supply, leading to higher market supply and lower prices. This analysis aligns with the findings that the TGC market’s equilibrium price is sensitive to the volume of generation rights traded and the types of energy sources involved in the replacement. As shown in Figure 20, ET prices are much higher in the R0 scenario than in the other scenarios. This is because when fossil energy generation is replaced by non-hydro renewable energy and hydropower, the demand of fossil energy producers for ET decreases, leading to a decline in the ET price. The impact of hydropower on ET prices is less pronounced compared to non-hydro renewables, which is consistent with the structural differences in power supply and the relative carbon intensity of these energy sources. Considering the difference in power supply structure between hydropower and RES, the impact of hydropower and fossil generation rights trade on the ET price is higher than that of the renewable energy and fossil energy transaction, which can be seen from scenarios R2 and R4. As shown in Figure 21, since the TGC price and the ET price are already included in the electricity sales price (see Equation (A55) in Appendix D), when the rate of generation rights trade increases, the TGC price decreases by less than the ET price, which leads to an increase in the sales price of electricity. This is the mathematical explanation for the increase in the sales price of electricity. In the actual system, the generation rights trade policy, as an emission reduction policy, leads to a reduction in the amount of electricity generated by fossil energy generators in the short-term market. Since fossil energy accounts for the largest share of the power structure, it leads to a decrease in the supply of electricity in the market, which leads to an increase in the electricity sales price. This is the economic explanation for the increase in electricity sales prices.

In summary, the power generation rights trade policy as a carbon reduction policy will lead to a decrease in the TGC price and the ET price at the same time but will lead to an increase in the electricity sales price. While this policy will increase renewable energy generation in the short-term market, it will lead to a decline in non-hydro renewable energy installations in the long-term market due to its dampening effect on TGC prices. Instead, the decrease in ET prices leads to an increase in profit margins for fossil energy generators, which in turn boosts their new installation. In other words, the generation rights trade policy will lead to a lack of development momentum for RES in the long-term market, i.e., profit margins, generation capacity, trading volume and newly installed capacity will not reach the expected levels.

5. Conclusions and Policy Implications

5.1. Conclusions

In this paper, we construct a coupled SD model of TGC market, ET market and electricity market and analyze the participating parties, organizational structure and the transmission process of certificates in the three markets. The model objectively simulates TGC prices, ET prices and sales tariffs in the power market under the combined effect of a TGC trading system and an ET trading system and analyzes the development trend of the power supply structure and the impact of generation rights trading policy on the coupled market. The following conclusions are drawn:

(1) When the RPS ratio is constant, the TGC price under the TGC system and the ET system combined will reach the upper limit earlier than the TGC price under the TGC system alone and remain at the high price level for a longer time. The impact of RPS on the TGC price under coupled market conditions will be more significant than that caused by a carbon reduction policy. In addition, at the beginning of the market operation, grid companies will overbuy TGCs to gain more profit. However, as TGC prices continue to fall in the later stages of the market, grid companies begin to sell off their hoarded TGCs, thus causing TGC prices to fall even faster and thus cutting the incentive for non-water renewables.

(2) Compared with the independent ET market, the interaction of the TGC system and the ET system in the coupled market will shorten the ET price adjustment cycle and make it converge to a reasonable stable value more quickly. However, under the coupled market conditions, if the RPS sets a low ratio of quotas, its disincentive effect with fossil energy will not be obvious.

(3) Under the joint action of the TGC and ET system, the electricity sales price includes not only the price determined by the power supply-and-demand relationship but also the cost of TGC and the income of ET. Therefore, the changes in the TGC price and the ET price will be fed back to the electricity sales price. The electricity sales price in the coupled market will first rise sharply at the beginning of the market operation and then turn to decline slowly when the critical point is reached. In the long run, as the RPS rate increases and the carbon reduction policy becomes more aggressive, the sales price will take more time to decrease to the initial level.

(4) ET policies have a weaker impact on non-hydro renewables, while RPS policies have a stronger impact on non-hydro renewables. This suggests that RES is more sensitive to RPS target setting, while fossil energy generators are more sensitive to emissions reduction policies. When implemented together, the TGC trading system and the ET trading system will inhibit thermal power development and promote non-hydro RES. It can also ensure the market demand for hydropower so that the share of thermal power will decline and RES installed capacity will be significantly increased, thus optimizing the power supply structure. Compared with other studies which concluded that ET and RPS policies would conflict [23] (Yi et al., 2019), this study argues that the synergy between the two regimes is better able to adjust China’s power supply structure and better promote the achievement of emission reduction targets.

(5) The power generation rights trading policy, as a carbon reduction policy and a unique power-trading practice in China, will lead to a decrease in the TGC price and the ET price, whereas the sales price of electricity will increase. The greater the trading ratio of non-hydro renewables power to thermal power, the more TGC prices fall, while trading of hydro-fossil generation rights has little effect on TGC prices. While the power generation rights trade reduces fossil energy generation in the short term, it causes a decline in installed capacity of non-hydro RES in the long-term market. At the same time, lower ET prices will lead to higher profit margins for fossil energy generators, which will boost their installations and thus affect the achievement of carbon reduction targets.

5.2. Policy Suggestion

Based on the above findings, this paper gives the following policy recommendations:

(1) In the early stages of market development, it is advisable to set a lower RPS target to encourage the growth of renewable energy without overwhelming the market. This approach will help in gradually increasing the RPS target as the market matures, fostering sustained expansion in renewable energy sectors. Concurrently, implementing a dynamic price regulation mechanism that monitors prices in the TGC market, ET market and electricity market in real time, with the establishment of reasonable price limits, will ensure market stability and prevent excessive price volatility. Scientific and reasonable RPS and carbon emission reduction targets can effectively promote the development of non-hydro renewable energy. The development of non-hydro renewables is more sensitive to the RPS targets, while that of fossil energy generators is more sensitive to carbon reduction policies. Regulators need to rationalize different policies in order to maintain a balance between promoting renewable energy and phasing out fossil energy.

(2) As the market reaches maturity, it is crucial to optimize RPS and carbon emission reduction policies based on market feedback and environmental objectives. This optimization will maintain a balance between promoting renewable energy and phasing out fossil energy. Additionally, adjusting incentive mechanisms, such as offering tax breaks and subsidies for non-hydro renewable energy, while imposing restrictions on fossil energy generation will further encourage the transition towards a more sustainable energy mix. For regulators, it is essential to keep an eye on prices in the TGC market, ET market and electricity market in real time. They should reasonably set the upper and lower price limits for different markets and ensure strong and effective regulation of the market so as to gradually guide the power supply structure in the long-term market in the direction of renewable energy.

(3) During periods of market volatility, timely government intervention is necessary to stabilize market sentiment and prevent excessive fluctuations. This can be achieved through the establishment of risk-management mechanisms, including price-hedging tools and market liquidity support, which will mitigate the uncertainty and risks faced by market participants. Such measures will help in maintaining market stability and ensuring the continuity of the energy supply. Dynamically adjusting the RPS ratios and carbon reduction targets according to the market operation can make the policies and markets work together more efficiently. For example, setting a low RPS ratio in the early stage of market operation and gradually increasing the ratio as the market proceeds while focusing on the trading volume and price in different markets can effectively avoid the problem of lack of momentum for renewable energy development in the later stage of the market.

(4) For long-term market development, it is essential to ensure policy synergy and avoid conflicts among different policies. For instance, while the generation rights trade policy may reduce fossil energy generation in the short term, it could affect the TGC price in the long term, potentially reducing the incentive for renewable energy development. Therefore, combining this policy with other measures such as RPS and carbon reduction policies is vital for achieving long-term energy transition goals. Enhancing market transparency by ensuring that all market participants have timely access to market information will also facilitate informed decision-making and contribute to the market’s overall efficiency.

(5) For non-hydro renewable energy, policies should focus on technological innovation and cost reduction. This can be achieved through measures such as R&D subsidies and tax incentives, which will enhance the competitiveness of non-hydro renewable energy sources. On the other hand, for fossil energy, policies should gradually increase the cost of carbon emissions through mechanisms like carbon taxes or carbon trading markets. This will make the cost of fossil energy generation rise, thereby encouraging its gradual exit from the market and promoting a cleaner energy landscape.

5.3. Limitations and Future Work

In this paper, we develop an SD model that includes the electricity market, TGC market and the ET market and analyzes the impact of different policies on the market price as well as the power supply structure. This SD model can provide support to the government in setting emission reduction targets. Although the model is a relatively realistic simulation of the real system, it is based on a number of assumptions. First, we set the upper and lower limits of market prices according to the historical data. In fact, the government can regulate the market by adjusting the upper and lower limits of market prices in real time. In subsequent studies, it could be modified to explore the impact of different price limits on generators and the grid. Second, this paper assumes that the number of TGCs redeemed for hydropower and non-hydro renewables is different as a way to reflect the difference between hydropower green certificates and non-hydro renewables green certificates and thus the difference between total quotas and non-hydro renewables quotas. This assumption still has some limitations and does not truly reflect the willingness of the grid to purchase different green certificates. Finally, this paper does not include large-scale electricity users as responsible parties of RPS and only considers the grid as a single party. Further research can build more detailed models that can help governments make better decisions.