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Article

Assessment of Air-Pollution Control Policy’s Impact on China’s PV Power: A System Dynamics Analysis

School of Economics and Management, North China Electric Power University, Hui Long Guan, Chang Ping District, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Energies 2016, 9(5), 336; https://doi.org/10.3390/en9050336
Received: 3 February 2016 / Revised: 20 April 2016 / Accepted: 27 April 2016 / Published: 5 May 2016

Abstract

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Recently, China has brought out several air-pollution control policies, which indicate the prominent position that PV power hold in improving atmosphere environment. Under this policy environment, the development of China’s PV power will be greatly affected. Firstly, after analyzing the influencing path of air-pollution control policies on PV power, this paper built a system dynamics model, which can be used as a platform for predicting China’s PV power development in every policy scenario during 2015–2025. Secondly, different model parameters are put into the SD model to simulate three scenarios of air-pollution control policies. Comparisons between the simulated results of different policy scenarios measure the air-pollution control policy’s impact on China’s PV power in the aspect of generation, installed capacity, power curtailment and so on. This paper points out the long-term development pattern of China’s PV power under latest incentive policies, and provides reference for the policymakers to increase the effect and efficiency of air-pollution control policies.

1. Introduction

China’s increasingly serious air-pollution problem is caused by its unreasonable energy developing pattern and the lack of pollution-controlling consciousness [1,2]. In September 2013, the Chinese government has introduced a severe policy named The Action Plan for Air Pollution Control, which shows the beginning of powerful air-pollution management. To drive the implementation of this action plan, China’s energy industry introduced several supporting policies, which mention the necessity of adjusting energy structure. Among all the renewable energy generation technologies, photovoltaic (PV) power generation is regarded as the most perspective one because of its perfect match in time to the industry electrical load [3,4,5,6,7]. Then, China’s solar power industry issued Some Suggestions to Improving PV Industry Development to match the requirement of air-pollution controlling for PV power industry.
These air-pollution control policies fully indicate the prominent position that PV power hold in improving atmosphere environment and promoting energy substitution. The macrolevel policies will increase the relative demand of PV power generation by limiting coal consumption. In addition, microlevel policies will complete the electricity price and subsidy mechanism of PV power, give priority to the generation scheduling of PV power, and implement the full acquisition of PV power generation. This favor policy environment will bring China’s PV power an unprecedented developing opportunity [8,9]. However, on the other hand, the high targets for installed PV capacity set in these policies will bring PV power great challenges as well [10,11]. China’s PV power is growing rapidly now, with installed-capacity growing by 60% and generation growing by 200% year on year. However, in 2014, China’s installed PV capacity reached only 28,050 MW accounting for less than 1% of the total capacity. In addition, the newly-added capacity met only 76% of the national target. Thus, whether these air-pollution policies can give China’s PV power an expected boost remains to be studied further.
These air-pollution control policies contain many policy measures, which can impact every factors involved in developing system of PV power from several perspectives. It will be of great significance for assessing the impact of China’s air-pollution control polices to analyze the pathway and effect of each policy measure. Whether the modification of policy measures and targets are necessary can be determined by comparing the actual effects and expected effects. Further, analyzing the sensitivities of technical, economic and environmental indicators to specific policy measures is helpful to suggesting improvements for next policy making.
Many scholars have realized that air-pollution policies will have long-reaching effects on energy structure [12,13,14,15,16]. Other scholars have simulated the development of renewable energy resource including PV power under the influence of some energy-saving and renewable energy development policies in different countries [17,18,19,20,21,22]. However, none of these models considered China’s latest air-pollution policies, so they could not be applied to China’s current policy environment. The assessment of air-pollution control policy’s impact on PV power is a complex dynamic evolution process, because the policy has many acting points, and the policy effects will keep transmitting along the intricate structure of PV power development system, which consists of many elements in many fields. System dynamics (SD) method can not only model system’s real behavior but also explain the relationship between main variables within the system [17]. SD model has been used to assess the development of energy system [23,24,25] and national policies [26,27,28,29]. It enables us to stimulate generation increase, investment surge, capacity expansion, costs decline and composition change of China’s PV power under the influence of air-pollution control policies.
This paper first systematically studied the effect path of air-pollution policies on China’s PV power. On this base, a new platform for simulating the development of China’s PV power are offered using a SD model, which can predict its developing trends under different policy environments by adjusting the parameter settings of variables according to relevant policies measures. The differences between simulated results of three policy scenarios indicate the effectiveness of air-pollution control policies and their effect on China’s PV power. Additionally, this paper provides specific policy suggestions for guiding the development of PV power under the requirement of air-pollution control by analyzing the assessment results of these policies.

2. Air-Pollution Control Policy

2.1. Framework of Air-Pollution Control Policy

China’s air-pollution control policy is set by central decision-making body, guided by fundamental national policy, and implemented throughout the whole country including energy industry. If these macro fundamental policies really want to make impacts, they need relevant supporting policies to implement their measures on micro level. Thus, the ‘air-pollution control policy’ mentioned in this paper is not a single policy but a framework containing policies on national level, energy industrial level and PV industrial level (see Figure 1), which can influence the development of China’s PV industry. As essential supports for fundamental policy, Energy Industry’s Action Plan for Strengthening Air Pollution Control, Interim Regulations for Reducing and Replacing Coal Consumption and Several Recommendations to Promote Healthy Development of PV Industry are put into the framework of air-pollution policy together with The Action Plan for Air Pollution Control. Although these policies are not made only for controlling air-pollution, they are three of the twenty-two supporting policies of The Action Plan for Air Pollution Control, and all mentioned the important role that PV power plays in controlling air-pollution.

2.2. Analysis of Air-Pollution Policy Contents

In September 2013, China’s state council issued The Action Plan for the Control of Air Pollution. It pointed out several measures which can influence PV industry: (1) encourage electric power replacement; (2) restrict new coal-fired power generation project; (3) set non-fossil fuel energy share target in 2017 as 13%; (4) stimulate renewable energy generation; (5) limit high energy consumption industry; (6) promote green building with distributed rooftop PV installation; (7) improve price mechanism of renewable energy generation. These policies measures will undoubtedly increase the share of non-fossil fuel energy, and bring about uncertainty to electricity consumption.
In March 2014, the National Energy Administration (NEA) issued Energy Industry’s Action Plan for Strengthening Air Pollution Control, which formulated several measures to support The Action Plan for the Control of Air Pollution on energy industrial level: (1) set PV power as an important development direction for energy industry; (2) set targets for installed PV capacity and distributed PV installed capacity; (3) outline the ‘feed PV power to grid after self-consumption’ principle for the development of distributed PV power; (4) set the target of proportion of coal consumption in total energy consumption in 2017 as 65%; (5) set target for interregional power transmission; (6) improve subsidy mechanism of distributed renewable energy generation. These measures provided PV power with good opportunity and great challenge.
In December 2014, the National Development and Reform Commission (NDRC) issued other supporting policy Interim Regulations for Reducing and Replacing Coal Consumption. Its measures, which can influence PV power including: (1) set coal consumption reduction target; (2) close down backward production facilities; (3) accelerate power grids construction.
In July 2013, the state council issued Some Suggestions to Improving PV Industry Development to make sure PV power will make great contribution to air-pollution control. This policy is first proposed at the executive meeting of the state council on February 12 as a supporting policy of The Action Plan for the Control of Air Pollution, and formally announced a little earlier than The Action Plan. It (1) set benchmark prices for different PV resource areas; (2) set whole electricity subsidy (0.42 CNY/kWh before 2020) and purchase mechanism for distributed PV power; (3) free self-consumption electricity of distributed PV system from all taxes and fees levied along with electricity price; (4) accelerate technology advancement to increase utilization hours and reduce cost.
In general, air-pollution control policy on national level will indirectly affect PV industry by promoting electric power replacement and energy structure adjustment. In addition, air-pollution control policies on energy industrial level will indirectly stimulate PV industry by restricting coal consumption and setting it as an important development direction. These policies together with PV power stimulating policies constitute the framework of air-pollution control policy in this paper, to ensure PV power will give a boost to air-pollution control. The specific effects of policy measures on PV industry are shown in Figure 2.
Because of the uncertainties China’s PV power will face, it is of great significant to figure out how China’s PV power will be affected by these ‘air-pollution’ control policies. Considering its advantages on integrity and dynamics during the complex analysis, this paper plans to establish a SD model with different parameter settings, which can be determined by the policy analyses mentioned above, to simulate the development of China’s PV industry in different policy scenarios. Further, comparing the differences between simulation results in different policy scenarios can help us assess the effect of air-pollution control policy on China’s PV industry.

3. Methodology

China’s PV power development with and without air-pollution control policy can be predicted by the same SD model with different parameter settings. As a systems modeling and dynamic simulation methodology, SD can analyze dynamic complexities in socio-economic and bio-physical systems [30,31]. SD model can better simulate China’s PV power development than other types of quantitative modeling, because of its advantage of analyzing complex relationships among the variables involved in energy development system, and simulating the feedback effects of backward variables on forward variables. It also enables us to formulate relevant policies to control the dynamic behaviors of whole system, and understand the PV power system overall [32].

3.1. Causal Loop Diagram

Our SD model will involve main power generation and consumption sections, and the process of PV power investment, generation and transmission. As the first step of building SD model, a causal loop diagram of China’s PV power development under the influence of air-pollution control policies is made to analyze the relationship of key variables and mechanism of air-pollution control policies, as shown in Figure 3. Positive links, which means the forward elements stimulate the backward elements, are marked with plus sign. In addition, negative links, which means the forward elements restrain the backward elements, are marked with minus sign. Positive loops, which have an even number of negative links, are marked with plus sign. Negative loops, which have an odd number of negative links, are marked with minus sign.

3.2. Flow Diagram

After drawing the causal loop diagram, we built the following flow diagram of China’s PV power development under the influence of air-pollution control policies, as shown in Figure 4, using Vensim software. Flow diagram is a good tool for modeling the cause and effect relations between various variables of the SD model using directive arrows. This flow diagram contains three subsystems and over 70 variables including state variables (represent cumulative results, shown in boxes), rate variables (represent changing rate of state variables, shown with double triangles), and auxiliary variables (the rest relevant variables). Key variables and its initial values are provided in Table A1. There are over sixty controlling functions used to express the quantitative relationships among all the variables. Due to the limited length of the article, only the functional relationships with great significance and something unclear in the flow diagram are enumerated.

3.2.1. Power Generation and Consumption Subsystem

To stimulate the process of power generation and power consumption, we built the power generation and consumption subsystem, as shown in Figure 4. We assume that China’s total power generation equals total power consumption. Total power consumption consists of the primary industrial electricity consumption, the second industrial electricity consumption, the third industrial electricity consumption, and residential electricity consumption. To simplify model, we assume total power generation only consists of PV power generation, thermal power generation, hydropower generation, wind power generation and nuclear power generation.
As the core of our SD model, PV power generation (GP) is determined by total electricity consumption (TC), thermal power generation (GT), hydropower generation (GH), wind power generation (GW), nuclear power generation (GN) and transmission limit factor (TF), as in Equation (1). TF, which is the major restricted factor for PV generation, is set to a piecewise function of the gap between interregional power transmit demand (ITD) and actual value of transmission capacity (IT) as in Equation (2).
GP = ( TC × 0.995 GT GH GW GN ) × TF
TF = IF   THEN ELSE    ( ( ITD IT ) < 0 ,       1 ,       1 ( ITD IT ) / 10000 )
Further with the assumption that distributed PV power has the same annual utilization hours as PV power plant, total PV generation is distributed according to the proportion of distributed PV installed capacity (CPD) and PV plant installed capacity (CPP) in installed PV capacity. Thus, distributed PV power generation (GPD) is as in Equation (3) and PV plant power generation (GPP) is as in Equation (4).
GPD = GP × CPD / ( CPD + CPP )
CPP = GP × CPP / ( CPD + CPP )

3.2.2. Power Transmit Subsystem

To stimulate the restriction of interregional power transmission capacity on PV power generation, we built the power transmit subsystem, as shown in Figure 5. The transmission limit factor is determined by actual interregional power transmission capacity and transmission demand. Increasing rate of interregional transmission capacity is stimulated by PV power curtailment, which depends on the actual value and theoretical value of PV power generation.
As shown in Equation (5), the value of transmission capacity (IT) at time t depends on its value at time t-dt and newly-added interregional transmission capacity (ITN), which is the product of IT and its increasing rate (ITI). The increasing rate of interregional transmission capacity (ITI) is the product of fundamental increasing rate (ITIF) and stimulating factor of PV power curtailment (CPF), which is a piecewise function of PV power curtailment (PC). PC is set as an IF function of theoretical wind power generation (GPT) and actual generation (GP) as in Equation (6).
IT ( t ) = IT ( t dt ) + ( ITN ) dt = IT ( t dt ) + ( IT × ITI ) dt = IT ( t dt ) + ( IT × ITIF × CPF ) dt
CP = IF(GPT GP > 0 , ( GPT GP ) / GPT , 0 )

3.2.3. Installed PV Capacity Subsystem

To stimulate the development situation of China’s PV power, we built the installed PV capacity subsystem, as shown in Figure 6. At present, China has price subsidy for full generation of distributed PV power, which can be feed back to grid after self-consumption at coal-fired benchmark price. PV power plants can sale all electricity to grid at PV benchmark price. These electricity price and price subsidies determine the revenue of PV power industry, which will be partly used to make investment in new installed capacity. The investment is also influenced by the gap between installed PV capacity target and actual value. Therefore, the government can control installed PV capacity by adjusting capacity target. Newly-added installed PV power capacity is determined by its investment and construction cost per capacity. We assume that the utilization hours of distributed PV power and PV plant are equal. Thus, the composition of installed PV capacity decided the composition of PV power generation.
RPD = GPD × SPD + ( 1 PS ) × GPD × PD
RPP = GPP × PB
IPD = RPD × IPDP × PFD / ISP
IPP = RPP × IPPP × PFP / ISP
CPD ( t ) = CPD ( t dt ) + ( CPDN ) dt = CPD ( t dt ) + ( IPD × 100 / CPD ) dt
CPP ( t ) = CPP ( t d t ) + ( CPPN ) d t = CPP ( t d t ) + ( IPP × 100 / CPP ) d t
We can calculate the revenue of distributed PV power (RPD) as in Equation (7), and calculate the revenue of PV power plant (RPP) as Equation (8). Because distributed PV power has electricity price subsidy (PS) for the whole generation, and its excessive generation can be fed back to grid after self-consumption at desulfurating electricity price (PS). PV power plants can only get revenue (RPP) from selling all their generation (GPP) to grid at PV benchmark price (PB). Investment of distributed PV power (IPD) depends on RPD, discretionary investment proportion (IPDP), stimulating factor of policy (PFD) and proportion of investment subsidy (ISP) as in Equation (9). In addition, investment of PV power plant (IPP) is very similar as in Equation (10). Distributed PV installed capacity (CPD) at time t depends on CPD at time t-dt and newly-added capacity (CPDN), which determined by IPD and building costs per unit (CPD). The capacity of PV power plant (CPP) has very similar function as in Equation (12).

3.3. Model Validation

The validity of the model is tested using an authenticity test and a sensitivity test when the model design is finished. This paper used an authenticity test to justify the appropriateness of the proposed model by analyzing the error rate of eight variables between true value and simulation in 2012–2014. These variables cover all level variables and some key variables, which have overall situation and representativeness. Authenticity test results are provided in Table A2. It is worth mentioning that the true values of interregional transmission capacity are estimated by the development plan of China’s State Grid Corporation, which may underestimate the actual development situation. It may lead to an error rate of 7.80% in 2014. However, the errors of other variables are almost all controlled within −5%~5%, which completely meet the requirement of authenticity test. In addition, the simulation period has significance on behalf of those typical periods of China’s PV power development under the influence of ‘air-pollution control policy’. Thus, our SD model is valid, and can be used to predict China’s PV power development under different policy environments, if there are no structural changes in the future.
The sensitivity test is conducted to estimate the policy effects on PV power. PV power generation, installed PV capacity, PV power curtailment and the proportion of distributed PV installed capacity are chosen as studied variables, because they can basically reflect the development and utilization situation of PV power. In addition, ten parameters, which can simulate the effect of air-pollution control policies, are chosen as studied parameters. The sensitivities of four variables to ten parameters are shown in Table A3. The test results indicate that the proportion of coal consumption in total energy consumption, the increasing rate of interregional transmission capacity, interregional power transmit proportion and the proportion of the third industrial output value have a greater effect on PV power generation. The target of installed PV capacity, average benchmark price of PV power plant, and electricity price subsidy of distributed PV power have a greater effect on installed PV capacity. Average utilization hours, the target of distributed PV installed capacity, average benchmark price of PV power plant and increasing rate of interregional transmission capacity have a greater effect on PV power curtailment. The target of installed capacity, electricity price subsidy of distributed PV power and the increasing rate of interregional transmission capacity have a greater effect on the proportion of distributed PV installed capacity. Thus, these significant effect factors need to be further studied in policy scenario analysis.

3.4. Policy Scenarios

Air-pollution control policies can be divided into fundamental policies (The Action Plan for the Control of Air Pollution), which indirectly affect China’s PV industry; and supporting policies (Energy Industry’s Action Plan for Strengthening Air Pollution Control, Interim Regulations for Reducing and Replacing Coal Consumption, Some Suggestions to Improving PV Industry Development), which affect China’s PV industry more directly. The policy scenario with no ‘air-pollution control policy’ is set as Scenario 1, the scenario with only fundamental policy is set as Scenario 2, and the scenario with both fundamental and supporting policies is set as Scenario 3. The specific policy measures and policy objectives in three policy scenarios are shown in Table 1.
These policy measures in different scenarios are used to set model parameters, which can simulate the control of air-pollution control policies on system behavior, as provided in Table A4. In addition, the policy objectives are set as important evaluating indexes of China’s PV power development. Thus, the impact of air-pollution control policies can be assessed by comparing the predictions of evaluating indexes in different scenarios. To explain, most of the parameter settings vary every year. Our paper does not list the parameter settings of each year but the settings for time-invariant parameters and the settings for time-varying parameters in 2017 (some also in 2020) because of space cause. Year 2017 and 2020 are important years, because they are always highlighted in relevant policies. The parameter values of other years are evenly distributed on the trend-line determined by initial values and values in 2017 (some also in 2020).

4. Simulation Results and Analysis

First in this section, China’s PV power development during 2015–2025 in different policy scenarios are simulated by the SD model we built above using Vensim software. Later, air-pollution control policy’s effect on China’s PV power is analyzed by comparing the differences between simulated results.

4.1. Simulation Results in Different Policy Scenarios

4.1.1. Scenario 1: With no Policy Influence

In Scenario 1, China’s PV power industry will develop without any influence of air-pollution control policies. In this scenario, China will have excessive energy demand because of unreasonable industrial structure and high energy intensity. Clean power generation could not develop as expected due to the over-reliance on thermal power. In 2020, China’s PV power generation will be only 153.22 billion kWh accounting for 2.09% of total power generation. PV power curtailment stays at a high level during the whole simulation period. It will peak in 2025 at 25.60% because of the lack of transmission channel. At that time, China’s PV power generation will reach 220.19 billion kWh accounting for 2.59% of the total power generation. Installed PV capacity will have an accelerated growth during 2015–2020, and reach 109,855 MW in 2020. After that, the increasing rate will become steady, and installed PV capacity will reach 227,658 MW in 2025.
Without enough finance support, distributed PV power, which supposed to be the main developing trend of PV power internationally, will develop slowly in China due to high costs. In 2020 distributed PV installed capacity will reach 40,872 MW, which just meet the policy target, accounting for only 37.21% of total installed PV capacity. In addition, the generation of distributed PV power will be 96.21 billion kWh at that time. Although distributed PV power will develop steadily during the 13th five year plan period, its installed capacity will be only 88,836 MW, which lag behind the policy target (90,000 MW) in 2025. In addition, because of the relatively fast growth of PV power plant, the proportion of distributed PV power in PV power will generally remain at about 39% since 2020. The simulated results of all state variables and some important auxiliary variables in Scenario 1 are shown in Table A5.
In general, without air-pollution control policies, China can hardly get a reasonable energy structure, and PV power can hardly receive good development. Over-reliance on thermal power, high costs and outmoded transmission grid make China’s PV power (especially distributed PV power) could not meet the development targets, which is quite low.

4.1.2. Scenario 2: With Influence of Fundamental Policy

In Scenario 2, China’s PV power industry will develop with the influence of fundamental air-pollution control policy. Stimulated by this policy, China’s industrial structure will be more reasonable, and energy intensity will be lower as well. Further, this policy will reduce the proportion of coal consumption in total energy consumption to 65%. In consequence, thermal power generation will be about 10% less than that in Scenario 1. Non-fossil energy including PV power will get steady growth. In 2020 China’s PV power generation will be 260.89 billion kWh accounting for 3.72% of total power generation. After reaching its peak at 294.11 billion kWh in 2023, it will start to decline slowly because of the more rapid rise of nuclear and other clean power. In addition, without supporting policies, the proportion of PV power generation will start to drop from the peak in 2017 (4.06%), and will probably drop to the same level in Scenario 1.
Installed PV capacity will reach 131,229 MW in 2020, and reach 266,141 MW in 2025, which is not much higher than the simulations in Scenario 1. In order to relief the pressure of PV power curtailment, fundamental air-pollution control policy plans to enhance the construction of interregional transmission lines. However, without the supporting polices, which make plans for transmission lines to realize large-scale interregional electricity transmission, PV power curtailment will still stay at a high level in Scenario 2. At the end of our simulation, PV power curtailment will reach up to 29.28% with a rising trend.
In Scenario 2, the increase of generation will stimulate the investments of both distributed PV power and PV power plant. Although benchmark price and price subsidy are still not enough, the investment enthusiasm of PV power will be much higher than that in Scenario 1. In addition, the increasing rate of distributed PV installed capacity will be accelerated since 2015. In 2020, distributed PV installed capacity will be 67,262 MW, which exceeds the policy target (40,000 MW), accounting for 51.26% of total installed PV capacity. However, its increasing rate will slow down since 2021 as the end of electricity price subsidy of distributed PV power. Different from distributed PV power, the increasing rate of PV plant installed capacity will maintain at about 20%, because the benchmark price of PV power plant will remain stable. Thus, the proportion of distributed PV power will begin to drop since 2022. By 2025, distributed PV installed capacity will reach 121,293 MW, accounting for 45.57% of total installed PV capacity. At that time, distributed PV power generation will reach 128.31 billion kWh, and PV plant power generation will reach 147.85 billion kWh. The simulated results of all state variables and some important auxiliary variables in Scenario 2 are shown in Table A6.
In general, with fundamental air-pollution control policy, China can have a more reasonable energy structure. However, without relevant supporting policies, PV power will have lower competitiveness compared with other clean powers, which have obvious cost advantage. In this policy scenario, both distributed PV power and PV power plant will overly dependent on subsidies. Thus, the development of distributed PV power will slow down in 2021 as the end of electricity price subsidy. China’s PV power still needs long-term subsidy unless technical and power transmission problem can be overcome. However, the subsidy will undoubtedly bring huge financial pressure to Chinese government.

4.1.3. Scenario 3: With Influence of Fundamental and Supporting Policies

In Scenario 3, China’s PV power industry will develop with influence of both fundamental and supporting air-pollution control policies. In this scenario, China’s industrial structure and energy intensity are all control in reasonable range, just as Scenario 2. Stimulated by supporting air-pollution control policies, energy consumption and the proportion of coal consumption will be further reduced. With this background, the increasing rate of thermal power will be controlled within 1%. PV power generation will keep increasing at an annual rate of over 10% during 2015–2020 in Scenario 3. In 2020, China’s PV power generation will reach 301.00 billion kWh accounting for 4.30% of total power generation. After that, the increasing rate of PV generation will begin to decline, and the proportion of PV generation will maintain at about 4.5%. On the other hand, supporting air-pollution control policies will keep the increasing rate of interregional transmission capacity at about 10%. Thus, China’s PV power generation can gradually break the limitation of power transmission. According to the simulations, the generation will keep increasing at a low speed during 2021–2025, and reach 347.91 billion kWh accounting for 4.41% of total power generation in 2025. PV power curtailment will continue increasing during 2015–2020, because of the lag of electricity grid construction behind the increasing demand of interregional transmission. China’s PV power curtailment will reach its peak at 25.67% in 2020. As electricity grid becomes more mature, China’s PV power curtailment will start to drop since 2021. The curtailment will be only 8.21% in 2025, and it will probably be eliminated before 2030.
In Scenario 3, supporting policies increase the benchmark price of PV power plant and electricity price subsidy of distributed PV power, which makes installed PV capacity higher than that in Scenario 1 and 2. China’s Installed PV capacity will start to grow rapidly in 2015, and will reach 247,223 MW in 2020. However, along with the changes of policy environment, the increasing rate of installed PV capacity will fail suddenly and maintain at about 8% since 2021. At the end of our simulation, China’s installed PV capacity will reach 414,925 MW.
China’s distributed PV power will experience a revenue surge during 2016–2020, after that its revenue will return to pre-2015 levels and maintain at around 25 billion CNY as the stop of electricity price subsidy. Its investment has the same trend as revenue. Distributed PV installed capacity will experience accelerated growth during 2015–2020 because of investment surge. It will reach 143,343 MW accounting for 57.98% of total installed PV capacity in 2020. In this stage, distributed PV power will mostly benefit from subsidy not power generation, and become the mainstream of distributed PV. Compared with distributed PV power, PV power plant will develop more steadily. During 2015–2020, its generation will be restricted by insufficient transmission grids. In addition, its capacity will reach 103,880 MW just exceeding the policy target (90,000 MW) in 2020. Since 2021, as transmission grid become more mature, PV power plant will have greater development potential. Thus, the proportion of distributed PV installed capacity will begin to fall slightly. The simulated results of all state variables and some important auxiliary variables in Scenario 3 are shown in Table A7.
In general, with both fundamental and supporting air-pollution control policies, China’s PV generation will keep increasing during the whole simulation. Its proportion in total power generation will rise to 4.5% in 2021, and maintain at that level. PV power curtailment will first increase, then decrease, and probably be eliminated before 2030. In this scenario, quite a high subsidy will bring investment and capacity surge to distributed PV power, which makes it the mainstream of China’s PV power. In addition, developing distributed PV power is a good solution to deal with the problem of lagging transmission grids before 2021. In Scenario 3, the transmission problem will be significantly relieved since 2021. Therefore, that it is the proper time to end up the electricity price subsidy of distributed PV power and make the benchmark price of PV power plant on the same level as thermal power.

4.2. Analysis of Policy Effect

Based on the simulations, we analyze the impact of air-pollution control policy on China’s PV power by drawing curves of the same variable in three scenarios in one graph (see Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12, Figure 13, Figure 14, Figure 15 and Figure 16), and comparing the differences between them. We compared all variables presented in Table A5, Table A6 and Table A7, except for the investment of distributed PV power and PV plant, because they are not set as policy objectives, and can be reflected in installed capacity. The comparison results of PV power generation and its proportion, distributed PV power generation and PV plant power generation reflect the fulfillment of generation objectives mentioned in air-pollution control policy. The comparison results of PV power curtailment and interregional transmission capacity reflect the fulfillment of power transmission objectives. The comparison results of installed PV capacity, distributed PV installed capacity and PV plant installed capacity reflects the fulfillment of capacity objectives. In addition, the proportion of distributed PV capacity reflects the composition of PV power. In general, these ten variables cover almost all of the evaluate indexes of China’s PV power development.
The differences between the curves of Scenario 1 and 2 indicate the impact of fundamental air-pollution control policy (‘fundamental policy’ for short) on China’s PV industry. The differences between the curves of Scenario 1 and 3 indicate the impact of both fundamental and supporting air-pollution control policies (‘supporting policies’ for short). The differences between the curves of Scenario 2 and 3 indicate the impact of only supporting policies. We can tell from Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12, Figure 13, Figure 14, Figure 15 and Figure 16 that:
(1) Fundamental policy will obviously increase the generation of all clean energies including PV power by limiting emissions. However, the proportion of PV generation in Scenario 2 will eventually drop to nearly the same level in Scenario 1, because of the lack of special supports for PV power. Supporting policies can make PV power generation and its proportion remain stable. However these policies cannot make generation continue increasing, because wind power will still have more mature technology and more competitiveness unless new technological revolutions occur in PV power.
(2) Fundamental policy will make PV power curtailment more severe because of the lack of control measures. Supporting policies will firstly aggravate PV power curtailment during 2015–2020 because of the surge of installed PV capacity. While after that supporting policies can reduce the curtailment sharply and eventually eliminate it by accelerating the construction of transmission grid.
(3) Fundamental policy can promote the development of China’s PV power temporarily, but cannot maintain its growth trend. When China’s transmission grid stays immature, the supporting policies will keep PV power generation increasing by promoting distributed PV power, which does not need to be transmitted over distance. However, since 2021, transmission problem will no longer be an important factor to limit power generation of PV plant, and the saturation of architecture resources will become the restriction of distributed PV power. Thus, from that time PV power plant will be the new development priority, and the proportion of distributed PV power will start to drop slightly.
(4) Fundamental policy of China’s PV power only involves subsidy mechanism and capacity target, but no suitable development planning. Therefore, the development of PV power will run out of steam since 2021. Under the influence of supporting policies, the installed capacity of both distributed PV power and PV power plant will be significantly raised by high subsidy and benchmark price. As the end of subsidy, the generation and capacity increasing rate of distributed PV power will begin to stabilize since 2021. If the subsidy won’t end at this appropriate time, distributed PV power will face the problem of over-capacity. Since 2020, long-distance transmission and high costs won’t be the limiting factors for PV power plant. At that time, protective benchmark price of PV power plant cannot lead to increase in generation but only over-capacity.

5. Conclusions

Under the stimulations of air-pollution control policies, China’s PV power will develop better. It is of great significance to predict the developing trend of China’s PV power, and identify whether these policies can work as expected. Hence, our paper offers a new platform for simulating China’s PV power in different policy scenarios during 2015–2025. The simulations indicate that: (1) In the scenario of no air-pollution control policies, China’s PV power could not develop satisfactorily due to the lack of essential incentives. PV power generation will only account for less than 3% of total power generation in 2025; (2) In the scenario of only fundamental air-pollution control policies, thermal power generation will be restricted. Although PV power will enjoy rapid grow during 2015–2020, its developing room will be gradually crowded out by other clean powers since 2021; (3) In the scenario of fundamental and supporting policies, PV power has a clear development plan, which ensures its continued growth. Distributed PV power will be energetically supported when transmission network are still immature. Since 2021, big PV plant will be given priority over distributed PV power when distributed architecture resources become saturated. The proportion of PV power generation will maintain at about 4.5%. The differences between simulations of three policy scenarios indicate air-pollution control policy’s impact on China’s PV power. Our paper provides a SD model, which can simulate the comprehensive relationships among various factors, for predicting the development of China’s PV power. In addition, the results of sensitivity test and scenario analysis indicate the influence of single air-pollution control policy measure and a set of policies. Further, the assessments of policy efficiency provide reference for policy-making institutions.

Acknowledgments

Project supported by the National Natural Science Foundation of China (Grant No. 71471059).

Author Contributions

Xiaodan Guo designed this research and wrote this paper; Dongxiao Niu provided professional guidance; Bowen Xiao collected all the data and revised this paper. All authors contributed to writing this paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix

Table A1. Variable setting.
Table A1. Variable setting.
VariableUnitInitial ValueData Source
GDP108 CNY519,470China Statistical Yearbook (2012)
proportion of coal consumption in total energy consumption-0.676China Statistical Yearbook (2012)
energy consumption104 tce361,732China Statistical Yearbook (2012)
coal consumption rate of power generationg/kWh321China Statistical Yearbook (2012)
coal consumption proportion of power industry-0.49China Statistical Yearbook (2012)
wind power generation108 kWh1164survey data of Electricity Council
nuclear power generation108 kWh1055survey data of Electricity Council
hydropower generation108 kWh8641survey data of Electricity Council
interregional transmission capacity108 kWh7796survey data of Electricity Council
proportion of primary industry output-0.05China Statistical Yearbook (2012)
primary industrial electricity consumption intensitykWh/CNY0.02China Statistical Yearbook (2012)
proportion of second industry output-0.50China Statistical Yearbook (2012)
second industrial electricity consumption intensitykWh/CNY0.15China Statistical Yearbook (2012)
proportion of third industry output-0.45China Statistical Yearbook (2012)
third industrial electricity consumption intensitykWh/CNY0.02China Statistical Yearbook (2012)
population increasing rate-0.005China Statistical Yearbook (2012)
population108 people13.5China Statistical Yearbook (2012)
per capita residential electricity consumptionkWh/person443.83survey data of Electricity Council
PV module costs per capacityCNY/W7.74survey data of Electricity Council
construction and installation costs per capacityCNY/W2.12survey data of Electricity Council
average benchmark price of PV power plantCNY/kWh1.05survey data of Electricity Council
proportion of self-consumed PV electricity-0.80survey data of Electricity Council
desulfurating electricity priceCNY/kWh0.40survey data of Electricity Council
target of distributed PV installed capacityMW1950survey data of NEA
target of PV plant installed capacityMW4850survey data of NEA
subsidies of distributed PV power generationCNY/kWh0.35survey data of Electricity Council
proportion of self consumption-0.80survey data of Electricity Council
Table A2. Authenticity test results.
Table A2. Authenticity test results.
YearGDP (108 Yuan)Population (108)
True ValueSimulationErrorTrue ValueSimulationError
(%)(%)
2012519,470533,4842.70%13.513.54040.30%
2013568,845585,8342.99%13.567513.60720.29%
2014636,463638,1840.27%13.635313.5678−0.49%
YearTotal Electricity Generation (108 KWh)Interregional Transmission Capacity (108 KWh)
True ValueSimulationErrorTrue ValueSimulationError
(%)(%)
201249,86549,497.3−0.74%600060550.92%
201353,72053,294.7−0.79%62006381.972.94%
201454,63856,947.24.23%64006898.917.80%
YearPV Power Generation (108 KWh)Installed PV Capacity (MWe)
True ValueSimulationErrorTrue ValueSimulationError
(%)(%)
201238.240.21765.28%680068000.00%
20138790.38323.89%19,42019,7881.89%
2014250251.040.42%28,05026,721.4−4.74%
YearDistributed PV Installed Capacity (MWe)PV Plant Installed Capacity (MWe)
True ValueSimulationErrorTrue ValueSimulationError
(%)(%)
2012195019500.00%485048500.00%
201331003026−2.39%16,32016,7622.71%
201446704469−4.30%23,38022,252.4−4.82%
Table A3. Sensitivity test results.
Table A3. Sensitivity test results.
ParameterPV Power Generation
10%5%3%−3%−5%−10%
proportion of coal consumption in total energy consumption−56.5070%−31.5665%−16.9525%16.9516%19.9899%31.9621%
proportion of third industrial output value−3.9165%−5.4323%−6.7665%1.0978%10.5632%5.4613%
average utilization hours2.4975%4.2976%2.4204%0.1556%−1.3657%−4.0428%
increasing rate of interregional transmission capacity23.4556%20.1111%12.3479%−8.5482%−7.6413%−12.5345%
interregional power transmit proportion−15.6783%−5.3212%−4.7694%1.6759%7.1943%10.2224%
average benchmark price of PV power plant0.6320%0.3752%3.5119%−0.7400%−2.3142%−1.3181%
electricity price subsidy of distributed PV power0.3146%0.8239%3.3545%−1.8965%−0.4369%−0.4473%
target of distributed PV installed capacity1.0723%1.2645%0.3403%−0.7965%−0.8542%−0.9452%
target of PV plant installed capacity0.4924%1.0748%2.6349%0.9027%−1.7131%−2.0572%
PV module costs per capacity−0.0789%−0.5059%−0.8924%0.2377%1.3953%0.5583%
ParameterInstalled PV Capacity
10%5%3%−3%−5%−10%
proportion of coal consumption in total energy consumption−1.9429%−2.6110%−1.7509%0.7332%1.4256%1.1489%
proportion of third industrial output value−0.2456%−0.0562%−0.1439%0.4321%0.8554%0.6894%
average utilization hours1.2415%1.8771%0.3822%0.3812%−1.3812%−1.2749%
increasing rate of interregional transmission capacity3.6561%3.2007%3.9120%−0.9009%−1.7032%−0.6599%
interregional power transmit proportion1.9370%−0.1919%0.0942%2.5612%−1.5467%1.3276%
average benchmark price of PV power plant7.4613%4.6534%2.0081%1.2281%−3.1400%−2.0183%
electricity price subsidy of distributed PV power5.0719%3.7042%1.8215%−1.2913%−2.5717%−2.2938%
target of distributed PV installed capacity8.3553%5.7996%0.3745%−1.3208%−3.1112%−3.6953%
target of PV plant installed capacity5.7188%4.8869%2.9772%−1.0331%−5.2039%−5.6862%
PV module costs per capacity8.2651%7.1461%5.7966%3.5674%−0.8700%−7.6631%
ParameterPV Power Curtailment
10%5%3%−3%−5%−10%
proportion of coal consumption in total energy consumption22.5761%5.0570%13.7528%−18.2839%−12.4578%−33.2535%
proportion of third industrial output value2.5761%5.0570%3.7528%−4.2839%−4.7823%−3.2535%
average utilization hours40.2447%24.5764%17.9931%−4.9890%−11.3156%−28.7008%
increasing rate of interregional transmission capacity−10.4238%−0.5929%−11.1262%11.0953%2.0923%1.9621%
interregional power transmit proportion5.6155%3.5680%1.6237%0.3803%−5.1220%−4.9480%
average benchmark price of PV power plant20.5192%4.8071%11.3880%−3.6743%−8.9963%−11.4157%
electricity price subsidy of distributed PV power10.2132%10.5555%10.8774%−9.4164%−1.6983%−3.8737%
target of distributed PV installed capacity34.8166%16.1998%1.1035%−3.9547%−3.3207%−8.1859%
target of PV plant installed capacity15.9866%13.7694%8.5443%−4.4823%−6.6597%−17.8167%
PV module costs per capacity−2.5605%−6.4809%2.8937%1.1801%5.4245%4.8348%
ParameterProportion of Distributed PV Installed Capacity
10%5%3%−3%−5%−10%
proportion of coal consumption in total energy consumption−2.8318%−1.2204%−0.2072%−0.8877%1.0179%1.4538%
proportion of third industrial output value−0.8297%−1.2204%−1.2072%−0.8877%1.5485%1.4538%
average utilization hours0.8431%0.9931%0.2594%−0.5673%−0.1673%1.0807%
increasing rate of interregional transmission capacity5.6123%4.2340%4.9233%3.7384%2.2144%2.0417%
interregional power transmit proportion−0.4465%−0.9039%0.6207%0.0038%0.7912%0.4301%
average benchmark price of PV power plant−2.6509%−1.7261%0.1748%1.8040%2.2761%3.8606%
electricity price subsidy of distributed PV power6.9170%4.6754%2.7330%0.2575%−0.9905%−2.1951%
target of distributed PV installed capacity11.7028%9.9917%3.3423%−1.1340%−6.0873%−4.9864%
target of PV plant installed capacity−6.6163%−3.7157%−4.2703%3.5942%5.7294%6.6591%
PV module costs per capacity−2.3328%−0.6270%−0.1783%0.1187%−0.8746%0.3348%
Table A4. Parameter settings for different policy scenarios.
Table A4. Parameter settings for different policy scenarios.
ParameterScenario 1Scenario 2Scenario 3
Without Policy InfluenceOnly Fundamental PolicyFundamental and Supporting Policies
proportion of coal consumption in total energy consumption in 20170.66680.65000.6323
energy consumption in 2017 (104 tce)450,280425,707418,697
coal consumption rate of power generation in 2017 (g/kWh)306301300
proportion of second industrial output value0.42480.41420.4142
proportion of third industrial output value0.52520.53580.5358
nuclear power generation in 2017 (108 kWh)1606.341750.532800.00
wind power generation in 2017 (108 kWh)2440.332440.332614.97
hydropower generation in 2017 (108 kWh)11,688.9211,688.9212,175.86
average utilization hours in 2017 (h)129412941400
increasing rate of interregional transmission capacity0.090.090.11
interregional power transmit proportion0.140.150.15
average benchmark price of PV power plant in 2017/2020 (CNY/kWh)0.81/0.700.81/0.700.85/0.65
electricity price subsidy of distributed PV power before/after 2020 (CNY/kWh)0.35/0.000.35/0.000.42/0.00
target of distributed PV installed capacity in 2017/2020 (MW)15,000/40,00015,000/40,00035,000/60,000
target of PV plant installed capacity in 2017/2020 (MW)20,000/60,00020,000/60,00035,000/90,000
PV module costs per capacity in 2017 (CNY/W)554.5
Table A5. Simulated results of key variables in Scenario 1.
Table A5. Simulated results of key variables in Scenario 1.
YearPV Power Generation (108 kWh)Proportion of PV Power GenerationDistributed PV Power Generation (108 kWh)PV Plant Power Generation (108 kWh)Installed PV Capacity (MW)Distributed PV Installed Capacity (MW)PV Plant Installed Capacity (MW)Proportion of Distributed PV CapacityInvestment of Distributed PV Power (108 CNY)Investment of PV Plant (108 CNY)PV Power CurtailmentInterregional Transmission Capacity (108 kWh)
2015395.120.0066130.72264.4016,535547111,0650.3308536.83360.910.08097271
2016480.630.0077218.74261.8925,24611,49013,7560.4551226.61394.140.13467664
2017723.970.0111330.69393.2831,31114,30217,0090.4568691.371486.580.07518285
20181128.900.0166494.18634.7254,03023,65230,3780.4378521.201140.380.09168732
20191356.430.0191583.20773.2372,65931,24041,4190.4300620.532670.210.15149204
20201532.180.0209570.06962.12109,85540,87268,9820.3721611.861021.450.17969949
20211966.660.0259764.111202.55131,05450,91980,1350.3885203.251203.750.166310,755
20222041.560.0260748.881292.68148,37254,42593,9460.3668838.741235.160.190611,626
20232161.270.0268843.011318.26178,30269,547108,7550.3901487.821199.620.191912,568
20242213.420.0267860.561352.87202,40878,694123,7140.3888522.071169.550.218913,586
20252201.860.0259859.201342.66227,65888,836138,8220.3902537.291099.640.256014,687
Table A6. Simulated results of key variables in Scenario 2.
Table A6. Simulated results of key variables in Scenario 2.
YearPV Power Generation (108 kWh)Proportion of PV Power GenerationDistributed PV Power Generation (108 kWh)PV Plant Power Generation (108 kWh)Installed PV Capacity (MW)Distributed PV Installed Capacity (MW)PV Plant Installed Capacity (MW)Proportion of Distributed PV CapacityInvestment of Distributed PV Power (108 CNY)Investment of PV Plant (108 CNY)PV Power CurtailmentInterregional Transmission Capacity (108 kWh)
2015490.850.0083173.97316.8823,466831715,1490.3544178.61432.540.12957458
20161333.360.0220479.54853.8228,69410,32018,3740.3596993.611113.670.13078062
20171601.600.0253722.43879.1750,21722,65127,5650.4511755.181080.060.12458280
20182082.060.0317975.511106.5570,14332,86437,2790.46851028.841292.270.13618950
20192399.430.03521175.791223.6497,63347,84349,7900.49001251.041373.330.16289675
20202608.870.03721337.201271.67131,22967,26263,9660.51261435.261350.090.224110,459
20212929.570.04061569.501360.06169,53690,82878,7080.5357417.491361.420.191011,306
20222928.890.03961492.641436.25192,35998,03194,3280.5096417.941372.340.216712,222
20232941.050.03881435.081505.97216,347105,566110,7810.4879415.221370.430.221413,212
20242867.980.03701347.691520.30241,222113,352127,8700.4699408.801314.300.230114,282
20252716.620.03441238.091478.53266,141121,293144,8480.4557387.111210.910.292815,439
Table A7. Simulated results of key variables in Scenario 3.
Table A7. Simulated results of key variables in Scenario 3.
YEARPV Power Generation (108 kWh)Proportion of PV Power GenerationDistributed PV Power Generation (108 kWh)PV Plant Power Generation (108 kWh)Installed PV Capacity (MW)Distributed PV Installed Capacity (MW)PV Plant Installed Capacity (MW)Proportion of Distributed PV CapacityInvestment of Distributed PV Power (108 CNY)Investment of PV Plant (108 CNY)PV Power CurtailmentInterregional Transmission Capacity (108 kWh)
2015727.790.0123489.24238.5554,38336,55817,8250.6722582.20400.760.17997796
20161622.550.02681094.22528.3364,46243,47220,9900.67441312.332243.640.19628568
20172012.890.03181204.85808.05101,88460,98440,9000.59861456.263205.250.19669133
20182591.260.03941380.521210.74154,73482,43672,2980.53281681.471450.670.216510,037
20192882.780.04241599.551283.23197,186109,41287,7740.55491963.181401.280.217911,031
20203010.040.04301745.251264.78247,223143,343103,8800.57982158.301246.870.256712,123
20213357.200.04662033.861323.34302,356183,174119,1820.6058541.011184.170.166413,323
20223413.120.04622014.101399.02328,340193,755134,5850.5901563.951145.800.150414,642
20233478.600.04602008.731469.88355,652205,372150,2800.5775581.191136.950.124416,092
20243462.390.04481962.251500.14384,508217,913166,5950.5667595.211137.610.115417,685
20253479.050.04411939.461539.60414,925231,307183,6180.5575606.401167.530.082118,852

References

  1. Zhang, Y.; Song, J.; Hamori, S. Impact of subsidy policies on diffusion of photovoltaic power generation. Energy Policy 2011, 39, 1958–1964. [Google Scholar] [CrossRef]
  2. Liu, D.; Niu, D.X.; Wang, H.; Fan, L.L. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew. Energy 2014, 62, 592–597. [Google Scholar] [CrossRef]
  3. Claudio, M.; Tiago, S.; Fernandez-Jimenez, L.A.; Ramirez-Rosado, I.J.; Terreros-Olarte, M.S. Short-term power forecasting model for photovoltaic plants based on historical similarity. Energies 2013, 6, 4152–4169. [Google Scholar]
  4. Baltas, A.E.; Dervos, A.N. Special framework for the spatial planning & the sustainable development of renewable energy sources. Renew. Energy 2012, 48, 358–363. [Google Scholar]
  5. Nicolae, G.; George, C.L.; Mariacristina, R.; Dario, Z. Power quality assessment in small scale renewable energy sources supplying distribution systems. Energies 2013, 6, 634–645. [Google Scholar]
  6. Chen, Z.S.; Su, S.I. Photovoltaic supply chain coordination with strategic consumers in China. Renew. Energy 2014, 68, 236–244. [Google Scholar] [CrossRef]
  7. Jo, J.H.; Loomis, D.G.; Aldeman, M.R. Optimum penetration of utility-scale grid-connected solar photovoltaic system in Illinois. Renew. Energy 2013, 60, 20–26. [Google Scholar] [CrossRef]
  8. Nadia, A.; Daniel, M.K. Innovations in financing that drive cost parity for long-term electricity sustainability: An assessment of Italy, Europe’s fastest growing solar photovoltaic market. Energy Sustain. Dev. 2014, 19, 130–137. [Google Scholar]
  9. Ahmad, B.A.; Zeeshan, A.K. Recent progress in renewable energy—Remedy of energy crisis in Pakistan. Renew. Sustain. Energy Rev. 2014, 33, 236–253. [Google Scholar]
  10. Peggy, M.; Kenneth, B.K. Modelling tools to evaluate China’s future energy system—A review of the Chinese perspective. Energy 2014, 69, 132–143. [Google Scholar]
  11. Zhou, S.; Zhang, X. Nuclear energy development in China: A study of opportunities and challenges. Energy 2010, 35, 4282–4288. [Google Scholar] [CrossRef]
  12. Wang, Y.; Zhou, S.; Hou, H. Cost and CO2 reductions of solar photovoltaic power generation in China: Perspectives for 2020. Renew. Sustain. Energy Rev. 2014, 39, 370–380. [Google Scholar] [CrossRef]
  13. Guo, X.D.; Guo, X.P. China’s photovoltaic power development under policy incentives: A system dynamics analysis. Energy 2015, 93, 589–598. [Google Scholar] [CrossRef]
  14. Liu, L.; Zong, H.; Zhao, E.; Chen, C.; Wang, J. Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development. Appl. Energy 2014, 124, 199–212. [Google Scholar] [CrossRef]
  15. Zhao, X.; Ma, Q.; Yang, R. Factors influencing CO2 emissions in China’s power industry: Co-integration analysis. Energy Policy 2013, 57, 89–98. [Google Scholar] [CrossRef]
  16. Tan, Z.; Zhang, H.; Shi, Q.; Xu, J. Joint optimization model of generation side and user side based on energy-saving policy. Electr. Power Energy Syst. 2014, 57, 135–140. [Google Scholar] [CrossRef]
  17. SheikhiFini, A.; Moghaddam, M.P.; Sheikh-El-Eslami, M.K. A dynamic model for distributed energy resource expansion planning considering multi-resource support schemes. Electr. Power Energy Syst. 2014, 60, 357–366. [Google Scholar] [CrossRef]
  18. Zhu, H.; Huang, G. Dynamic stochastic fractional programming for sustainable management of electric power systems. Electr. Power Energy Syst. 2013, 53, 553–563. [Google Scholar] [CrossRef]
  19. Salman, A.; Razman bin, M.T. Using system dynamics to evaluate renewable electricity development in Malaysia. Renew. Electr. Dev. 2013, 43, 24–39. [Google Scholar]
  20. Li, L.; Sun, Z. Dynamic energy control for energy efficiency improvement of sustainable manufacturing systems using markov decision process. Cybern. Syst. 2013, 43, 1195–1205. [Google Scholar] [CrossRef]
  21. Santiago, M.; Luis, J.M.; Felipe, B. A system dynamics approach for the photovoltaic energy market in Spain. Energy Policy 2013, 60, 142–154. [Google Scholar]
  22. Garcia, E.; Mohanty, A.; Lin, W.; Cherry, S. Dynamic analysis of hybrid energy systems under flexible operation and variable renewable generation-Part II: Dynamic cost analysis. Energy 2013, 52, 17–26. [Google Scholar] [CrossRef]
  23. Guo, X.P.; Guo, X.D. Nuclear power development in China after the restart of new nuclear construction and approval: A system dynamics analysis. Renew. Sustain. Energy Rev. 2016, 57, 999–1007. [Google Scholar] [CrossRef]
  24. Frederick, A.A.; David, O.Y.; Alex, A.P. A systems dynamics approach to explore traffic congestion and air pollution link in the city of Accra, Ghana. Sustainability 2010, 2, 252–265. [Google Scholar]
  25. Jeon, C.; Shin, J. Long-term renewable energy technology valuation using system dynamics and Monte Carlo simulation: Photovoltaic technology case. Energy 2014, 66, 447–457. [Google Scholar] [CrossRef]
  26. Ali, K.; Mustafa, H. Exploring the options for carbon dioxide mitigation in Turkish electric power industry: System dynamics approach. Energy Policy 2013, 60, 675–686. [Google Scholar]
  27. Feng, Y.; Chen, S.; Zhang, L. System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China. Ecol. Model. 2013, 252, 44–52. [Google Scholar] [CrossRef]
  28. Li, F.; Dong, S.; Li, Z.; Li, Y.; Wan, Y. The improvement of CO2 emission reduction policies based on system dynamics method in traditional industrial region with large CO2 emission. Energy Policy 2012, 51, 683–695. [Google Scholar] [CrossRef]
  29. Nastaran, A.; Abbas, S. A system dynamics model for analyzing energy consumption and CO2 emission in Iranian cement industry under various production and export scenarios. Energy Policy 2013, 58, 75–89. [Google Scholar]
  30. Kamarzamana, N.A.; Tan, C.W. A comprehensive review of maximum power point tracking algorithms for photovoltaic systems. Renew. Sustain. Energy Rev. 2014, 37, 585–598. [Google Scholar] [CrossRef]
  31. Zheng, M.; Zhang, K.; Dong, J. Overall review of China’s wind power industry: Status quo, existing problems and perspective for future development. Renew. Sustain. Energy Rev. 2013, 24, 379–386. [Google Scholar]
  32. Yuan, X.H.; Ji, X.; Chen, H. Urban dynamics and multiple-objective programming: A case study of Beijing. Commun. Nonlinear Sci. Numer. Simul. 2008, 13, 1998–2017. [Google Scholar] [CrossRef]
Figure 1. Policy Framework.
Figure 1. Policy Framework.
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Figure 2. Air-pollution control measures and its effects on PV industry.
Figure 2. Air-pollution control measures and its effects on PV industry.
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Figure 3. Causal loop diagram.
Figure 3. Causal loop diagram.
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Figure 4. Flow diagram for power generation and consumption subsystem.
Figure 4. Flow diagram for power generation and consumption subsystem.
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Figure 5. Flow diagram for power transmit subsystem.
Figure 5. Flow diagram for power transmit subsystem.
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Figure 6. Flow diagram for installed PV capacity subsystem.
Figure 6. Flow diagram for installed PV capacity subsystem.
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Figure 7. PV power generation (108 kWh).
Figure 7. PV power generation (108 kWh).
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Figure 8. Proportion of PV power generation.
Figure 8. Proportion of PV power generation.
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Figure 9. PV power curtailment
Figure 9. PV power curtailment
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Figure 10. Interregional transmission capacity (108 kWh)
Figure 10. Interregional transmission capacity (108 kWh)
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Figure 11. Distributed PV power generation (108 kWh).
Figure 11. Distributed PV power generation (108 kWh).
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Figure 12. PV plant power generation (108 kWh).
Figure 12. PV plant power generation (108 kWh).
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Figure 13. Proportion of distributed PV capacity.
Figure 13. Proportion of distributed PV capacity.
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Figure 14. Installed PV capacity (MW).
Figure 14. Installed PV capacity (MW).
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Figure 15. Distributed PV installed capacity (MW).
Figure 15. Distributed PV installed capacity (MW).
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Figure 16. PV plant installed capacity (MW).
Figure 16. PV plant installed capacity (MW).
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Table 1. Policy measures and objectives in different scenarios.
Table 1. Policy measures and objectives in different scenarios.
ScenariosPolicy MeasuresPolicy Objectives
Scenario 1No air-pollution control policies.No policy objectives
Scenario 2fundamental policy:
(1) Restrict new coal-fired power generation project;
(2) Stimulate renewable energy generation;
(3) Improve price mechanism of renewable energy generation;
(4) Control energy consumption;
(5) Improve the efficiency of coal utilization;
(6) Adjust industrial structure.
fundamental policy:
(1) Non-fossil fuel energy share should reach 13% in 2017.
Scenario 3fundamental policy:
The same as above.
supporting policy:
(1) Restrict coal consumption;
(2) Outline the ‘feed PV power to grid after self-consumption’ principle for the development of distributed PV power;
(3) Set whole electricity subsidy and purchase mechanism for distributed PV power;
(4) Accelerate power grids construction;
(5) Increase benchmark prices for PV power generation;
(6) Accelerate technology advancement to increase utilization hours and reduce cost.
fundamental policy:
The same as above.
supporting policy:
(1) Installed PV capacity should reach 35,000 MW in 2017, and reach 100,000 MW in 2020;
(2) Distributed PV installed capacity should reach 15,000 MW in 2017; and reach 40,000 MW in 2020;
(3) Interregional power transmission to Jing-Jin-Ji region, Yangtse River Delta and Pearl River Delta should reach 68 million kWh.

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MDPI and ACS Style

Guo, X.; Niu, D.; Xiao, B. Assessment of Air-Pollution Control Policy’s Impact on China’s PV Power: A System Dynamics Analysis. Energies 2016, 9, 336. https://doi.org/10.3390/en9050336

AMA Style

Guo X, Niu D, Xiao B. Assessment of Air-Pollution Control Policy’s Impact on China’s PV Power: A System Dynamics Analysis. Energies. 2016; 9(5):336. https://doi.org/10.3390/en9050336

Chicago/Turabian Style

Guo, Xiaodan, Dongxiao Niu, and Bowen Xiao. 2016. "Assessment of Air-Pollution Control Policy’s Impact on China’s PV Power: A System Dynamics Analysis" Energies 9, no. 5: 336. https://doi.org/10.3390/en9050336

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