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

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Air-Pollution Control Policy

#### 2.1. Framework of Air-Pollution Control Policy

#### 2.2. Analysis of Air-Pollution Policy Contents

## 3. Methodology

#### 3.1. Causal Loop Diagram

#### 3.2. Flow Diagram

#### 3.2.1. Power Generation and Consumption Subsystem

#### 3.2.2. Power Transmit Subsystem

#### 3.2.3. Installed PV Capacity Subsystem

#### 3.3. Model Validation

#### 3.4. Policy Scenarios

## 4. Simulation Results and Analysis

#### 4.1. Simulation Results in Different Policy Scenarios

#### 4.1.1. Scenario 1: With no Policy Influence

#### 4.1.2. Scenario 2: With Influence of Fundamental Policy

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

#### 4.2. Analysis of Policy Effect

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix

Variable | Unit | Initial Value | Data Source |
---|---|---|---|

GDP | 10^{8} CNY | 519,470 | China Statistical Yearbook (2012) |

proportion of coal consumption in total energy consumption | - | 0.676 | China Statistical Yearbook (2012) |

energy consumption | 10^{4} tce | 361,732 | China Statistical Yearbook (2012) |

coal consumption rate of power generation | g/kWh | 321 | China Statistical Yearbook (2012) |

coal consumption proportion of power industry | - | 0.49 | China Statistical Yearbook (2012) |

wind power generation | 10^{8} kWh | 1164 | survey data of Electricity Council |

nuclear power generation | 10^{8} kWh | 1055 | survey data of Electricity Council |

hydropower generation | 10^{8} kWh | 8641 | survey data of Electricity Council |

interregional transmission capacity | 10^{8} kWh | 7796 | survey data of Electricity Council |

proportion of primary industry output | - | 0.05 | China Statistical Yearbook (2012) |

primary industrial electricity consumption intensity | kWh/CNY | 0.02 | China Statistical Yearbook (2012) |

proportion of second industry output | - | 0.50 | China Statistical Yearbook (2012) |

second industrial electricity consumption intensity | kWh/CNY | 0.15 | China Statistical Yearbook (2012) |

proportion of third industry output | - | 0.45 | China Statistical Yearbook (2012) |

third industrial electricity consumption intensity | kWh/CNY | 0.02 | China Statistical Yearbook (2012) |

population increasing rate | - | 0.005 | China Statistical Yearbook (2012) |

population | 10^{8} people | 13.5 | China Statistical Yearbook (2012) |

per capita residential electricity consumption | kWh/person | 443.83 | survey data of Electricity Council |

PV module costs per capacity | CNY/W | 7.74 | survey data of Electricity Council |

construction and installation costs per capacity | CNY/W | 2.12 | survey data of Electricity Council |

average benchmark price of PV power plant | CNY/kWh | 1.05 | survey data of Electricity Council |

proportion of self-consumed PV electricity | - | 0.80 | survey data of Electricity Council |

desulfurating electricity price | CNY/kWh | 0.40 | survey data of Electricity Council |

target of distributed PV installed capacity | MW | 1950 | survey data of NEA |

target of PV plant installed capacity | MW | 4850 | survey data of NEA |

subsidies of distributed PV power generation | CNY/kWh | 0.35 | survey data of Electricity Council |

proportion of self consumption | - | 0.80 | survey data of Electricity Council |

Year | GDP (10^{8} Yuan) | Population (10^{8}) | ||||

True Value | Simulation | Error | True Value | Simulation | Error | |

(%) | (%) | |||||

2012 | 519,470 | 533,484 | 2.70% | 13.5 | 13.5404 | 0.30% |

2013 | 568,845 | 585,834 | 2.99% | 13.5675 | 13.6072 | 0.29% |

2014 | 636,463 | 638,184 | 0.27% | 13.6353 | 13.5678 | −0.49% |

Year | Total Electricity Generation (10^{8} KWh) | Interregional Transmission Capacity (10^{8} KWh) | ||||

True Value | Simulation | Error | True Value | Simulation | Error | |

(%) | (%) | |||||

2012 | 49,865 | 49,497.3 | −0.74% | 6000 | 6055 | 0.92% |

2013 | 53,720 | 53,294.7 | −0.79% | 6200 | 6381.97 | 2.94% |

2014 | 54,638 | 56,947.2 | 4.23% | 6400 | 6898.91 | 7.80% |

Year | PV Power Generation (10^{8} KWh) | Installed PV Capacity (MWe) | ||||

True Value | Simulation | Error | True Value | Simulation | Error | |

(%) | (%) | |||||

2012 | 38.2 | 40.2176 | 5.28% | 6800 | 6800 | 0.00% |

2013 | 87 | 90.3832 | 3.89% | 19,420 | 19,788 | 1.89% |

2014 | 250 | 251.04 | 0.42% | 28,050 | 26,721.4 | −4.74% |

Year | Distributed PV Installed Capacity (MWe) | PV Plant Installed Capacity (MWe) | ||||

True Value | Simulation | Error | True Value | Simulation | Error | |

(%) | (%) | |||||

2012 | 1950 | 1950 | 0.00% | 4850 | 4850 | 0.00% |

2013 | 3100 | 3026 | −2.39% | 16,320 | 16,762 | 2.71% |

2014 | 4670 | 4469 | −4.30% | 23,380 | 22,252.4 | −4.82% |

Parameter | PV 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 hours | 2.4975% | 4.2976% | 2.4204% | 0.1556% | −1.3657% | −4.0428% |

increasing rate of interregional transmission capacity | 23.4556% | 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 plant | 0.6320% | 0.3752% | 3.5119% | −0.7400% | −2.3142% | −1.3181% |

electricity price subsidy of distributed PV power | 0.3146% | 0.8239% | 3.3545% | −1.8965% | −0.4369% | −0.4473% |

target of distributed PV installed capacity | 1.0723% | 1.2645% | 0.3403% | −0.7965% | −0.8542% | −0.9452% |

target of PV plant installed capacity | 0.4924% | 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% |

Parameter | Installed 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 hours | 1.2415% | 1.8771% | 0.3822% | 0.3812% | −1.3812% | −1.2749% |

increasing rate of interregional transmission capacity | 3.6561% | 3.2007% | 3.9120% | −0.9009% | −1.7032% | −0.6599% |

interregional power transmit proportion | 1.9370% | −0.1919% | 0.0942% | 2.5612% | −1.5467% | 1.3276% |

average benchmark price of PV power plant | 7.4613% | 4.6534% | 2.0081% | 1.2281% | −3.1400% | −2.0183% |

electricity price subsidy of distributed PV power | 5.0719% | 3.7042% | 1.8215% | −1.2913% | −2.5717% | −2.2938% |

target of distributed PV installed capacity | 8.3553% | 5.7996% | 0.3745% | −1.3208% | −3.1112% | −3.6953% |

target of PV plant installed capacity | 5.7188% | 4.8869% | 2.9772% | −1.0331% | −5.2039% | −5.6862% |

PV module costs per capacity | 8.2651% | 7.1461% | 5.7966% | 3.5674% | −0.8700% | −7.6631% |

Parameter | PV Power Curtailment | |||||

10% | 5% | 3% | −3% | −5% | −10% | |

proportion of coal consumption in total energy consumption | 22.5761% | 5.0570% | 13.7528% | −18.2839% | −12.4578% | −33.2535% |

proportion of third industrial output value | 2.5761% | 5.0570% | 3.7528% | −4.2839% | −4.7823% | −3.2535% |

average utilization hours | 40.2447% | 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 proportion | 5.6155% | 3.5680% | 1.6237% | 0.3803% | −5.1220% | −4.9480% |

average benchmark price of PV power plant | 20.5192% | 4.8071% | 11.3880% | −3.6743% | −8.9963% | −11.4157% |

electricity price subsidy of distributed PV power | 10.2132% | 10.5555% | 10.8774% | −9.4164% | −1.6983% | −3.8737% |

target of distributed PV installed capacity | 34.8166% | 16.1998% | 1.1035% | −3.9547% | −3.3207% | −8.1859% |

target of PV plant installed capacity | 15.9866% | 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% |

Parameter | Proportion 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 hours | 0.8431% | 0.9931% | 0.2594% | −0.5673% | −0.1673% | 1.0807% |

increasing rate of interregional transmission capacity | 5.6123% | 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 power | 6.9170% | 4.6754% | 2.7330% | 0.2575% | −0.9905% | −2.1951% |

target of distributed PV installed capacity | 11.7028% | 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% |

Parameter | Scenario 1 | Scenario 2 | Scenario 3 |
---|---|---|---|

Without Policy Influence | Only Fundamental Policy | Fundamental and Supporting Policies | |

proportion of coal consumption in total energy consumption in 2017 | 0.6668 | 0.6500 | 0.6323 |

energy consumption in 2017 (10^{4} tce) | 450,280 | 425,707 | 418,697 |

coal consumption rate of power generation in 2017 (g/kWh) | 306 | 301 | 300 |

proportion of second industrial output value | 0.4248 | 0.4142 | 0.4142 |

proportion of third industrial output value | 0.5252 | 0.5358 | 0.5358 |

nuclear power generation in 2017 (10^{8} kWh) | 1606.34 | 1750.53 | 2800.00 |

wind power generation in 2017 (10^{8} kWh) | 2440.33 | 2440.33 | 2614.97 |

hydropower generation in 2017 (10^{8} kWh) | 11,688.92 | 11,688.92 | 12,175.86 |

average utilization hours in 2017 (h) | 1294 | 1294 | 1400 |

increasing rate of interregional transmission capacity | 0.09 | 0.09 | 0.11 |

interregional power transmit proportion | 0.14 | 0.15 | 0.15 |

average benchmark price of PV power plant in 2017/2020 (CNY/kWh) | 0.81/0.70 | 0.81/0.70 | 0.85/0.65 |

electricity price subsidy of distributed PV power before/after 2020 (CNY/kWh) | 0.35/0.00 | 0.35/0.00 | 0.42/0.00 |

target of distributed PV installed capacity in 2017/2020 (MW) | 15,000/40,000 | 15,000/40,000 | 35,000/60,000 |

target of PV plant installed capacity in 2017/2020 (MW) | 20,000/60,000 | 20,000/60,000 | 35,000/90,000 |

PV module costs per capacity in 2017 (CNY/W) | 5 | 5 | 4.5 |

Year | PV Power Generation (10^{8} kWh) | Proportion of PV Power Generation | Distributed PV Power Generation (10^{8} kWh) | PV Plant Power Generation (10^{8} kWh) | Installed PV Capacity (MW) | Distributed PV Installed Capacity (MW) | PV Plant Installed Capacity (MW) | Proportion of Distributed PV Capacity | Investment of Distributed PV Power (10^{8} CNY) | Investment of PV Plant (10^{8} CNY) | PV Power Curtailment | Interregional Transmission Capacity (10^{8} kWh) |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2015 | 395.12 | 0.0066 | 130.72 | 264.40 | 16,535 | 5471 | 11,065 | 0.3308 | 536.83 | 360.91 | 0.0809 | 7271 |

2016 | 480.63 | 0.0077 | 218.74 | 261.89 | 25,246 | 11,490 | 13,756 | 0.4551 | 226.61 | 394.14 | 0.1346 | 7664 |

2017 | 723.97 | 0.0111 | 330.69 | 393.28 | 31,311 | 14,302 | 17,009 | 0.4568 | 691.37 | 1486.58 | 0.0751 | 8285 |

2018 | 1128.90 | 0.0166 | 494.18 | 634.72 | 54,030 | 23,652 | 30,378 | 0.4378 | 521.20 | 1140.38 | 0.0916 | 8732 |

2019 | 1356.43 | 0.0191 | 583.20 | 773.23 | 72,659 | 31,240 | 41,419 | 0.4300 | 620.53 | 2670.21 | 0.1514 | 9204 |

2020 | 1532.18 | 0.0209 | 570.06 | 962.12 | 109,855 | 40,872 | 68,982 | 0.3721 | 611.86 | 1021.45 | 0.1796 | 9949 |

2021 | 1966.66 | 0.0259 | 764.11 | 1202.55 | 131,054 | 50,919 | 80,135 | 0.3885 | 203.25 | 1203.75 | 0.1663 | 10,755 |

2022 | 2041.56 | 0.0260 | 748.88 | 1292.68 | 148,372 | 54,425 | 93,946 | 0.3668 | 838.74 | 1235.16 | 0.1906 | 11,626 |

2023 | 2161.27 | 0.0268 | 843.01 | 1318.26 | 178,302 | 69,547 | 108,755 | 0.3901 | 487.82 | 1199.62 | 0.1919 | 12,568 |

2024 | 2213.42 | 0.0267 | 860.56 | 1352.87 | 202,408 | 78,694 | 123,714 | 0.3888 | 522.07 | 1169.55 | 0.2189 | 13,586 |

2025 | 2201.86 | 0.0259 | 859.20 | 1342.66 | 227,658 | 88,836 | 138,822 | 0.3902 | 537.29 | 1099.64 | 0.2560 | 14,687 |

Year | PV Power Generation (10^{8} kWh) | Proportion of PV Power Generation | Distributed PV Power Generation (10^{8} kWh) | PV Plant Power Generation (10^{8} kWh) | Installed PV Capacity (MW) | Distributed PV Installed Capacity (MW) | PV Plant Installed Capacity (MW) | Proportion of Distributed PV Capacity | Investment of Distributed PV Power (10^{8} CNY) | Investment of PV Plant (10^{8} CNY) | PV Power Curtailment | Interregional Transmission Capacity (10^{8} kWh) |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2015 | 490.85 | 0.0083 | 173.97 | 316.88 | 23,466 | 8317 | 15,149 | 0.3544 | 178.61 | 432.54 | 0.1295 | 7458 |

2016 | 1333.36 | 0.0220 | 479.54 | 853.82 | 28,694 | 10,320 | 18,374 | 0.3596 | 993.61 | 1113.67 | 0.1307 | 8062 |

2017 | 1601.60 | 0.0253 | 722.43 | 879.17 | 50,217 | 22,651 | 27,565 | 0.4511 | 755.18 | 1080.06 | 0.1245 | 8280 |

2018 | 2082.06 | 0.0317 | 975.51 | 1106.55 | 70,143 | 32,864 | 37,279 | 0.4685 | 1028.84 | 1292.27 | 0.1361 | 8950 |

2019 | 2399.43 | 0.0352 | 1175.79 | 1223.64 | 97,633 | 47,843 | 49,790 | 0.4900 | 1251.04 | 1373.33 | 0.1628 | 9675 |

2020 | 2608.87 | 0.0372 | 1337.20 | 1271.67 | 131,229 | 67,262 | 63,966 | 0.5126 | 1435.26 | 1350.09 | 0.2241 | 10,459 |

2021 | 2929.57 | 0.0406 | 1569.50 | 1360.06 | 169,536 | 90,828 | 78,708 | 0.5357 | 417.49 | 1361.42 | 0.1910 | 11,306 |

2022 | 2928.89 | 0.0396 | 1492.64 | 1436.25 | 192,359 | 98,031 | 94,328 | 0.5096 | 417.94 | 1372.34 | 0.2167 | 12,222 |

2023 | 2941.05 | 0.0388 | 1435.08 | 1505.97 | 216,347 | 105,566 | 110,781 | 0.4879 | 415.22 | 1370.43 | 0.2214 | 13,212 |

2024 | 2867.98 | 0.0370 | 1347.69 | 1520.30 | 241,222 | 113,352 | 127,870 | 0.4699 | 408.80 | 1314.30 | 0.2301 | 14,282 |

2025 | 2716.62 | 0.0344 | 1238.09 | 1478.53 | 266,141 | 121,293 | 144,848 | 0.4557 | 387.11 | 1210.91 | 0.2928 | 15,439 |

YEAR | PV Power Generation (10^{8} kWh) | Proportion of PV Power Generation | Distributed PV Power Generation (10^{8} kWh) | PV Plant Power Generation (10^{8} kWh) | Installed PV Capacity (MW) | Distributed PV Installed Capacity (MW) | PV Plant Installed Capacity (MW) | Proportion of Distributed PV Capacity | Investment of Distributed PV Power (10^{8} CNY) | Investment of PV Plant (10^{8} CNY) | PV Power Curtailment | Interregional Transmission Capacity (10^{8} kWh) |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2015 | 727.79 | 0.0123 | 489.24 | 238.55 | 54,383 | 36,558 | 17,825 | 0.6722 | 582.20 | 400.76 | 0.1799 | 7796 |

2016 | 1622.55 | 0.0268 | 1094.22 | 528.33 | 64,462 | 43,472 | 20,990 | 0.6744 | 1312.33 | 2243.64 | 0.1962 | 8568 |

2017 | 2012.89 | 0.0318 | 1204.85 | 808.05 | 101,884 | 60,984 | 40,900 | 0.5986 | 1456.26 | 3205.25 | 0.1966 | 9133 |

2018 | 2591.26 | 0.0394 | 1380.52 | 1210.74 | 154,734 | 82,436 | 72,298 | 0.5328 | 1681.47 | 1450.67 | 0.2165 | 10,037 |

2019 | 2882.78 | 0.0424 | 1599.55 | 1283.23 | 197,186 | 109,412 | 87,774 | 0.5549 | 1963.18 | 1401.28 | 0.2179 | 11,031 |

2020 | 3010.04 | 0.0430 | 1745.25 | 1264.78 | 247,223 | 143,343 | 103,880 | 0.5798 | 2158.30 | 1246.87 | 0.2567 | 12,123 |

2021 | 3357.20 | 0.0466 | 2033.86 | 1323.34 | 302,356 | 183,174 | 119,182 | 0.6058 | 541.01 | 1184.17 | 0.1664 | 13,323 |

2022 | 3413.12 | 0.0462 | 2014.10 | 1399.02 | 328,340 | 193,755 | 134,585 | 0.5901 | 563.95 | 1145.80 | 0.1504 | 14,642 |

2023 | 3478.60 | 0.0460 | 2008.73 | 1469.88 | 355,652 | 205,372 | 150,280 | 0.5775 | 581.19 | 1136.95 | 0.1244 | 16,092 |

2024 | 3462.39 | 0.0448 | 1962.25 | 1500.14 | 384,508 | 217,913 | 166,595 | 0.5667 | 595.21 | 1137.61 | 0.1154 | 17,685 |

2025 | 3479.05 | 0.0441 | 1939.46 | 1539.60 | 414,925 | 231,307 | 183,618 | 0.5575 | 606.40 | 1167.53 | 0.0821 | 18,852 |

## References

- 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] - 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] - 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] - 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] - 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] - Chen, Z.S.; Su, S.I. Photovoltaic supply chain coordination with strategic consumers in China. Renew. Energy
**2014**, 68, 236–244. [Google Scholar] [CrossRef] - 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] - 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] - 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] - 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] - Zhou, S.; Zhang, X. Nuclear energy development in China: A study of opportunities and challenges. Energy
**2010**, 35, 4282–4288. [Google Scholar] [CrossRef] - Wang, Y.; Zhou, S.; Hou, H. Cost and CO
_{2}reductions of solar photovoltaic power generation in China: Perspectives for 2020. Renew. Sustain. Energy Rev.**2014**, 39, 370–380. [Google Scholar] [CrossRef] - 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] - 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] - Zhao, X.; Ma, Q.; Yang, R. Factors influencing CO
_{2}emissions in China’s power industry: Co-integration analysis. Energy Policy**2013**, 57, 89–98. [Google Scholar] [CrossRef] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - Feng, Y.; Chen, S.; Zhang, L. System dynamics modeling for urban energy consumption and CO
_{2}emissions: A case study of Beijing, China. Ecol. Model.**2013**, 252, 44–52. [Google Scholar] [CrossRef] - Li, F.; Dong, S.; Li, Z.; Li, Y.; Wan, Y. The improvement of CO
_{2}emission reduction policies based on system dynamics method in traditional industrial region with large CO_{2}emission. Energy Policy**2012**, 51, 683–695. [Google Scholar] [CrossRef] - Nastaran, A.; Abbas, S. A system dynamics model for analyzing energy consumption and CO
_{2}emission in Iranian cement industry under various production and export scenarios. Energy Policy**2013**, 58, 75–89. [Google Scholar] - 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] - 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] - 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]

Scenarios | Policy Measures | Policy Objectives |
---|---|---|

Scenario 1 | No air-pollution control policies. | No policy objectives |

Scenario 2 | fundamental 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 3 | fundamental 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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## Share and Cite

**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