Optimal way to achieve renewable portfolio standard policy goals from the electricity generation, transmission, and trading perspectives in southern China

Energy Policy 139 (2020) 111319

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Optimal way to achieve renewable portfolio standard policy goals from the electricity generation, transmission, and trading perspectives in southern China Hongye Wang a, b, Bin Su b, *, Hailin Mu a, **, Nan Li a, Shusen Gui c, Ye Duan d, Bo Jiang a, Xue Kong a a

Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian, 116024, China Energy Studies Institute, National University of Singapore, 119620, Singapore c China Business Executives Academy at Dalian, Hongling Road 777, Dalian, 116086, China d College of Urban and Environmental Science, Liaoning Normal University, Dalian, 116029, China b

A R T I C L E I N F O

A B S T R A C T

Keywords: Optimal solution Electricity generation Electricity trading Renewable portfolio standard Feed in tariff Southern China

An important strategy to stimulate renewable energy consumption is adherence to renewable portfolio standard (RPS), which has cost advantages over feed-in tariff (FIT). China published its provincial RPS goals in 2018 and, since then, it has been pressurizing provinces to realise the goals. This study develops a mid-to long-term optimisation model for the area served by the China Southern Power Grid Corporation (CSPGC) based on powerplan constraints and RPS goals for 2016–2030. The results indicate an optimal method for the five provinces in the CSPGC area to achieve their RPS goals based on the power-plan constraints till 2030. For electricity gen­ eration, wind power development should precede solar power development; further, hydropower development is particularly significant for the region. To facilitate electricity transmission, the construction of transmission lines between Guangdong and Yunnan should be prioritised. In electricity trading, RPS policy implementation will cause Guangdong to buy more electricity from western provinces, aiding the completion of the West–East Electricity Transfer Project. Moreover, in the CSPGC area, RPS policy implementation will not significantly affect the total electricity supply cost due to the development of low-cost hydropower in the region.

1. Introduction In China, the conflict between electricity generation and environ­ mental protection efforts makes the implementation of policy incentives for renewable energy usage inevitable (Ang and Su, 2016; Gao et al., 2018; Wang et al., 2019a). Both feed-in tariff (FIT) and renewable portfolio standard (RPS) are popular and powerful incentive strategies to promote the development of renewable energy globally (Choi et al., 2018; Go et al., 2016; Zhang et al., 2018). The FIT policy has advantages over the RPS policy in terms of installed capacity, whereas the latter has cost advantages over the former (Sun and Nie, 2015). To increase its renewable energy generation capacity, China adopted a FIT policy in 2006 (Wang et al., 2018). Following 10 years of rapid expansion, the installed capacities of wind power, solar power, and hydropower in China attained 147.47, 76.31, and 332.07 GW in 2016 (CEPP, 2016).

However, the increase in capacity, high associated governmental costs, and curtailment of renewable energy generation associated with the FIT policy have caused China to implement an RPS policy that sets a mini­ mum for renewable energy consumption. China had planned to imple­ ment an RPS policy since 2011 (Wang et al., 2019a), and it adopted the policy in 2018 with the ratification of the ‘Renewable Electricity Quota and Assessment Methods’ document (National Energy Administration, 2018). Each province in China must satisfy the new RPS policy while relying on existing power sector plans, such as the 13th energy devel­ opment Five-Year Plan (FYP) published by the corresponding provincial government. Therefore, identifying the optimal method to realise the RPS goal in the middle to long term under the existing power-plan constraints is significant. The RPS policy was first applied in the United States (Ren, 2011) and has since then been implemented in numerous countries, including the United Kingdom, Belgium (Zhang et al., 2017c), Japan, South Korea,

* Corresponding author. ** Corresponding author. E-mail addresses: [email protected], [email protected] (B. Su), [email protected] (H. Mu). https://doi.org/10.1016/j.enpol.2020.111319 Received 7 August 2019; Received in revised form 2 January 2020; Accepted 29 January 2020 Available online 24 February 2020 0301-4215/© 2020 Elsevier Ltd. All rights reserved.

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Energy Policy 139 (2020) 111319

promote renewable energy trading as part of the West–East Electricity Transfer Project, serious renewable electricity curtailment problems continue to exist (Wang et al., 2019b). Therefore, solving the renewable energy curtailment problem and realising the RPS goal has important implications for not only the CSPGC area but also the entire country. Fig. 1 depicts the position as well as transmission lines of this region (Zhu et al., 2005). In 2018, China became the only country implementing an RPS policy to declare specific provincial RPS goals. Therefore, only limited research has been conducted on global RPS goal achievement. In China, only Wang et al. (2019a) have developed a cost-minimising model to examine RPS achievement in 29 regions in the country during 2018–2020. However, the model developed by that study cannot be used to solve the mid-to long-term goal-achievement problem in the CSPGC area, since not all the provinces were included in the research. In addition, that study focused on short-term RPS goal achievement during 2018–2020 without considering the mid-to long-term impacts of an RPS policy. Finally, Wang et al.’s model (Wang et al., 2019a) does not consider the existing power plans affecting the power sector, which are significant problems in China. Therefore, the primary contributions of this study are as follows: (1) it is the first study investigating the achievements of regional RPS goals in a country in the mid-to long-term period; (2) it establishes an opti­ misation model for multi-regional electricity generation and trading strategies based on power-plan constraints and RPS goals; (3) the model illustrates an optimal way to achieve RPS goals in the CSPGC area during 2016–2030 based on different investment plans released in the 13th FYP; and (4) the study proposes corresponding policies and proposals for the CSPGC and SGCC areas. This remainder of this paper is organised, as follows: Section 2 re­ views the mid-to long-term RPS achievement problem. Further, Section 3 explains the mid-to long-term RPS optimisation model for the CSPGC area. Section 4 demonstrates the optimisation results and their effect on the CSPGC area under two scenarios. Finally, Section 5 discusses the policy implications of the optimisation results and concludes the study.

Nomenclature Symbol Definition CTotal Total cost of the CSPGC area from 2016 to 2030 CPowerPlantCost;y Annual power generation cost of the CSPGC area during year y CPowerTransmissionCost;y Annual power transmission cost of the CSPGC area during year y CPowerEmissionCost;y Annual power emission cost of the CSPGC area during year y Loadi;y Load demand in region i during year y MAX GPT MIN m;n;y ; GPT m;n;y Minimum and maximum bounds of electricity

that could be transmitted through the transmission line from point m to point n during year y MAX GPGMIN Minimum and maximum bounds of generation i;g;y ; GPGi;g;y

technology g in region i during year y Unit generation cost in region i for generation technology g during year y UPTm;n;y Unit transmission cost for the transmission line from node m to node n during year y UPEi;g;y Unit emission cost in region i for generation technology g during year y LT Annual transmission loss ratio in the CSPGC area RPSi;y Annual RPS goal in region i during year y WHRPSi;y Annual RPS goal without hydropower in region i during year y GPowerGeneration;i;y Annual electricity generated by region i during year y GPowerBought;i;y Annual electricity bought by region i during year y GPowerSold ;i;y Annual electricity sold out by region i during year y UPPi;g;y

and China (Choi et al., 2015; Dong and Shimada, 2017; National Energy Administration, 2018). In 2018, China published its RPS policy goals, which are based on some energy laws and regulations (National Energy Administration, 2018). Unlike other countries, China has set provincial RPS goals and established a minimum percentage of renewable energy electricity generation as part of the total energy consumption per year. The RPS policy sets provincial goals both with and without hydropower (Wang et al., 2019a). Moreover, all the provincial governments, the power grid enterprises, the power sale companies, and power users have their own obligations to satisfy RPS goals (National Energy Adminis­ tration, 2018). Therefore, due to the reverse distribution of electricity resources and electricity demand in China (Lin and Wu, 2017; Wang et al., 2019b), well-organised provincial generation and electricity trading strategies are necessary to achieve provincial RPS goals. China is a country that utilises advanced power generation and transmission technologies (Goh et al., 2018), and the country’s elec­ tricity trading market is very active, particularly in the China Southern Power Grid Corporation (CSPGC) area. In China, electricity trading is conducted mainly by two companies, the State Grid Corporation of China (SGCC) and CSPGC (Zhang et al., 2017a). The CSPGC serves the provinces of Guangdong, Guizhou, Hainan, and Yunnan, as well as the Guangxi autonomous region (Wang et al., 2019b). For simplicity, this study refers to them as five provinces. Guangdong province, which is the most developed among the five aforementioned provinces, has the highest electricity demand (CEPP, 2017b) and faces the most severe environmental pressures, such as CO2 emissions (Wang et al., 2019b) and water shortages (Liu et al., 2011). On the other hand, Yunnan and Guizhou record high renewable energy generation potentials, particu­ larly for hydropower; however, they have low power demands due to their low levels of economic development (Feng et al., 2018; Zhang et al., 2011). Although the CSPGC has built many transmission lines to

2. Literature review Many researchers have studied power sector optimisation based on RPS policies in both developed countries, such as the United States (Bird et al., 2011; Ding and Somani, 2010; Novacheck and Johnson, 2015), Italy (Contaldi et al., 2007), South Korea, and Japan (Choi et al., 2015; Dong and Shimada, 2017; Park et al., 2016), and developing countries, such as Pakistan (Farooq et al., 2013) and China (Yi et al., 2017; Zhang et al., 2017c, 2017d; Zhao et al., 2017). The optimisation models used in these studies were mainly MARKAL/TIMES modelling (Contaldi et al.,

Fig. 1. Transmission lines in the CSPGC area in China. 2

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Energy Policy 139 (2020) 111319

2007; Farooq et al., 2013; Park et al., 2016), national optimisation models (Choi et al., 2015; Dong and Shimada, 2017), and regional optimisation models (Ding and Somani, 2010; Wang et al., 2019a). Some scholars applied optimisation models to study the influence of different RPS policies on the power sectors of various countries. For instance, Bird et al. proposed a regional energy deployment system model to benefit the U.S. power industry (Bird et al., 2011). Ding and Somani built a long-term investment planning model to examine the industry investment optimisation problem under the RPS policy con­ straints in the Midwest of U.S. (Ding and Somani, 2010). Novacheck and Johnson presented a unit commitment and economic dispatch model to evaluate an RPS policy’s impact on solar energy in the United States (Novacheck and Johnson, 2015). Further, Contaldi et al. developed a Markal-Macro-Italy method to research Italy’s renewable energy supply under electricity policy (Contaldi et al., 2007). Similarly, Choi et al. applied a bottom-up energy model to study an RPS policy’s impact on the development of the Korean power industry (Choi et al., 2015). Park et al. applied a TIMES model to study the optimum portfolio until 2050 under an RPS policy in South Korea (Park et al., 2016). Dong and Shi­ mada proposed a dominant firm-competitive fringe model to compare Japan’s RPS policy with the FIT policy (Dong and Shimada, 2017), whereas Farooq et al. built a bottom-up MARKAL model to analyse RPS policies’ impact on Pakistan (Farooq et al., 2013). These studies focused on the effects of implementing different RPS policies in various coun­ tries; however, they did not identify an optimal method to achieve RPS goals. Unlike in China, no RPS targets are imposed on sub-regions in the United States (Wiser et al., 2005), the European Union (Aune et al., 2012), Australia, South Korea, or India (Hua et al., 2016; Park et al., 2016; Shrimali and Tirumalachetty, 2013). Prior to the publication of provincial RPS goals, many researchers attempted to establish reason­ able regional RPS goals for China. For example, Zhang et al. proposed a dynamic game-theoretical equilibrium power market model to analyse the implementation of an RPS policy in six regions of China under different RPS policy scenarios (Zhang et al., 2017b) and further devel­ oped a multi-region power market model to analyse the impact of the RPS and FIT policies on 10 regions in China (Zhang et al., 2018). Similarly, Yi et al. presented an optimisation model to study China’s cross-regional power and coal optimisation issues (Yi et al., 2016). Based on this study, the researchers established a multiregional optimisation model for China’s power sector under the constraints of different RPS goals and found that renewable energy generation without hydropower use should be no more than 17% (Yi et al., 2017). Although many researchers promote the application of RPS goals in China, they often fail to specify an optimal method to achieve RPS goals based on the power-plan constraints of different provinces; however, the latest study by Wang et al. (2019a) is an exception. The researchers proposed a cost-minimising model to study RPS goal achievement in 29 regions in China during 2018–2020. However, they considered only three years of RPS goal achievement and did not consider all the prov­ inces in the CSPGC area. Their research also failed to account for power-plan constraints’ influence on RPS policy achievement. However, the period 2016–2030 is particularly significant for energy structure adjustment and renewable energy development in China. For the year of 2016 to 2020, 2016 is the starting year of the 13th FYP for energy in the CSPGC area, and 2020 is the ending year. So it is necessary to discuss the year of 2016 to 2020 in this paper. For the year of 2021 to 2030, the year of 2030 is the ending year of China’s national plan on implementation of the 2030 Agenda for Sustainable Development and China’s Intended Nationally Determined Contributions (INDC). What’s more, it is hard to predict the fast development of power industry after 2030, especially for the renewable energy generation technology. So the year of 2030 is a reasonable cut-off point for the ending of RPS policy in China for this research. Therefore, both the disadvantages necessitate the adoption of a new approach to ensure the mid-to long-term RPS goal achievement in the CSPGC area.

An analysis of extant research indicates that the studies had short­ comings in the following areas: (1) only limited research is available on provincial RPS goal achievement since China is the only country to impose clear provincial RPS goals; (2) since the study examines only short-term (2018–2020) RPS goal achievement in some regions of China (Wang et al., 2019a), the model cannot be applied to mid-to long-term RPS goal achievement in the CSPGC area; and (3) previous researchers did not consider power-plan constraints, particularly how power plans are constrained by the 13th FYP for energy in various provinces, and did not specify a detailed optimal method for RPS goal achievement across China. However, the investment plans released in the 13th FYP for en­ ergy will influence the results of this study. In recent years, the renew­ able energy in China has been fast developed. So to study the optimal way of RPS policy goals achievement from 2016 to 2030 in Southern China, it is important to know the current status and developing trend of renewable energy in this region. As the lifetime of renewable energy generation is between 20 to 70 years (Yi et al., 2016, 2017), the 13th FYP for energy from 2016 to 2020 will not only have an influence on the existed renewable energy capacity in 2016, but also affect the con­ struction of renewable energy capacity until 2020. Moreover, the 13th FYP for energy from 2016 to 2020 will also have an influence on the developing trend of renewable energy from 2021 to 2030. Therefore, this study establishes a time-series, cross-regional elec­ tricity trading model under power-plan constraints for the CSPGC area and provides an opportunity to achieve the RPS goals from electricity generation, transmission, and trading perspectives during 2016–2030. This study determines that cross-regional electricity trading can not only solve the renewable energy consumption problem but also help the CSPGC area develop renewable energy. 3. Methodology 3.1. Model structures and assumptions 3.1.1. Function definition This study’s model is based on the research by Wang et al. (2019b), m;n;i;j

which defines two types of functions, Geng;y

m;n;i;j

and Genig;y . In Geng;y

, i;j;

m; n 2 ½1; 5�, g 2 ½1; 7�, while y 2 ½1; 15�, m 6¼ n and i 6¼ j. Further, there m;n;i;j

are assumptions that Geng;y is the power sold to region j through the transmission line starting at node m and ending at node n that is generated using electricity generation technology g in region i during m;i;i;j

year y and is meaningful if m is directly connected to n, while Geng;y

and

j;n;i;j Geng;y

are meaningless. Similarly,

Genig;y

is the electricity produced

using electricity generation technology g in region i during year y. We m;n;i;j

consider Geng;y

and Genig;y , where g ¼ 1,2,3,4,5,6,7, indicate different

power generation methods, including wind power, solar power, hydro­ power, nuclear power, coal power, gas power, and oil power generation. This study is based on three assumptions: (1) for transmission lines, the transmission loss ratio is always 6.64% (CEPP, 2016); (2) all regions are only allowed to sell their generated electricity; and (3) the installed and transmission capacities should be completely utilised once planned. 3.1.2. Objective function This study aims to minimise the total cost, including the costs of power plant generation, power transmission, and power emission in the entire region, of the CSPGC area for 2016–2030: Min: ​ CTotal ¼

� 15 � X CPowerPlantCost;y þ CPowerTransmissionCost;y þ CPowerEmissionCost;y y¼1

ð1 þ nirÞy

1

(1) where

3

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Energy Policy 139 (2020) 111319 5 X 7 X

CPowerPlantCost;y ¼ i¼1

P P P m;n;i;j where 5i¼1 5j¼1 7g¼1 Geng;y refers to the forward transmission power, P5 P5 P7 n;m;i;j and represents the backward transmission i¼1 j¼1 g¼1 Geng;y

(2)

Genig;y ⋅UPPi;g;y

g¼1 5 X 5 X 5 X 5 X 7 X

CPowerTransmissionCost;y ¼ m¼1 n¼1 5 X 7 X

CPowerEmissionCost;y ¼ i¼1

i¼1

j¼1

Genm;n;i;j g;y ⋅UPTm;n;y

power.

(3)

(b) For any transmission line included in the lines connecting region i to region j during year y, the g kind of electricity flowing in this line should not surpass the trading electricity from i to j.

g¼1

(4)

Genig;y ⋅UPEi;g;y

g¼1

Genm;n;i;j g;y

The total cost is the sum of the power plant generation, electricity transmission, and generation emissions costs. Furthermore, in this study, nir reflects the time value of money, which is set as 10% (Yi et al., 2016).

(4) Power selling constraint: During year y, the g kind of electricity that can annually be sold by region i should be constrained by its annual generation. Accordingly, Genig;y

n¼1;n6¼i j¼1

(10)

Geni;n;i;j g;y � 0

(5) Node power balance constraint: Similar to the constraint in Kirchhoff’s law, for the electricity trading regions i and j, the sum of g kind of electricity flowing into one junction (node m) is equal to that flowing out of this junction for year y (Wang et al., 2019b).

For region i, the electricity supply during year y should not be less than the region’s annual electricity demand, that is,

8 7 X > > > GPowerGeneration;i;y ¼ Genig;y ⋅ð1 LTÞ > > > g¼1 > > > > > 5 5 X 7 < X X Genm;i;j;i GPowerBought;i;y ¼ g;y ⋅ð1 > > j¼1 g¼1 m¼1;m6 ¼ i > > > > 5 5 X 7 > X X > > > Geni;n;i;j > g;y : GPowerSold;i;y ¼

5 5 X X n¼1;n6¼i j¼1

(1) Electricity demand constraint:

GPowerSold;i;y � Loadi;y

(9)

Genm;j;i;j �0 g;y

m¼1

3.1.3. Operational constraints Regarding mid-to long-term interprovincial electricity trading opti­ misation under the RPS constraint in the CSPGC area, there are opera­ tional constraints on power plants and transmission lines in each region. Further, renewable energy consumption is constrained by the RPS goal with or without hydropower in each province. The constraints are as follows:

GPowerGeneration;i;y þ GPowerBought;i;y

5 X

(5)

5 X n¼1

Genm;n;i;j g;y

5 X

(11)

Genn;m;i;j ¼0 g;y

n¼1

(6) RPS with or without hydropower constraint: LTÞ

(6)

(a) According to the Chinese government’s ‘Renewable Electricity Quota and Assessment Methods’ (National Energy Administra­ tion, 2018), during year y, the consumption of renewable elec­ tricity in province i should meet the RPS goal.

g¼1

P5 P5 P7 i;n;i;j In Eq. (5) and Eq. (6), represents the n¼1;n6¼i j¼1 g¼1 Geng;y electricity sold to the other regions in the CSPGC area by region i during year y since any electricity sold by region i should be transmitted along the line starting at node i. P5 P5 P7 m;i;j;i Further, m¼1;m6¼i j¼1 g¼1 Geng;y ⋅ð1 LTÞ represents the elec­

GRenewablePowerGeneration;i;y þ GRenewablePowerBought;i;y

(12)

� RPSi;y ⋅Loadi;y 8 3 X > > > Genig;y ⋅ð1 LTÞ > GRenewablePowerGeneration;i;y ¼ > > g¼1 > > > > > 5 5 X 3 < X X GRenewablePowerBought;i;y ¼ Genm;i;j;i g;y ⋅ð1 > > m¼1;m6¼i j¼1 g¼1 > > > > 5 5 X 3 > X X > > > Geni;n;i;j > g;y : GRenewablePowerSold;i;y ¼

tricity bought from other regions in the CSPGC area by region i during year y, since any electricity bought by this region should be transmitted over the line ending at node i. Furthermore, transmission losses should be met by the region buying power. (2) Power generation constraint:

n¼1;n6¼i j¼1

In region i, for year y, and for electricity generation technology g, annual electricity generation has lower and upper bounds.

(3) Power transmission constraint:

� WHRPSi;y ⋅Loadi;y

(a) During year y, the sum of the transmission electricity flowing forward and backward between the nodes m and n should be between its lower and upper bounds, that is, 5 X 5 X 7 X i¼1

j¼1

g¼1

Genm;n;i;j þ g;y

5 X 5 X 7 X i¼1

j¼1

Genn;m;i;j � GPT MAX g;y m;n;y

(13)

g¼1

GWHRenewablePowerGeneration;i;y þ GWHRenewablePowerBought;i;y

GPT MIN m;n;y �

LTÞ

(b) According to ‘Renewable Electricity Quota and Assessment Methods’ (National Energy Administration, 2018), during year y, the consumption of renewable electricity without hydropower in province i should meet the RPS goal without hydropower.

(7)

MAX i GPGMIN i;g;y � Geng;y � GPGi;g;y

GRenewablePowerSold;i;y

(8)

g¼1

4

GWHRenewablePowerSold;i;y (14)

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Energy Policy 139 (2020) 111319

8 2 X > > > G ¼ Genig;y ⋅ð1 LTÞ WHRenewablePowerGeneration;i;y > > > > g¼1 > > > > 5 5 X 2 < X X Genm;i;j;i GWHRenewablePowerBought;i;y ¼ g;y ⋅ð1 > > m¼1;m6¼i j¼1 g¼1 > > > > 5 5 X 2 > X X > > > Geni;n;i;j > g;y : GWHRenewablePowerSold;i;y ¼ n¼1;n6¼i j¼1

Table 2 RPS goals for the years 2020 and 2030 in the CSPGC area.

LTÞ

Region

(15)

Guangdong Guangxi Hainan Guizhou Yunnan

g¼1

3.2. Scenarios and data source The study by Wang et al. (2019b) found that the interprovincial electricity trading mode is more beneficial than the current electricity market mode in the CSPGC area. In the interprovincial electricity trading mode, the five provinces in the CSPGC area are considered as a complete entity and optimised simultaneously. Hence, to study the ef­ fects of mid-to long-term RPS goals in the CSPGC area, the current study considers a minimum cost scenario (Scenario 1) under the interprovin­ cial electricity trading mode and power-plan constraints, including the 13th FYP without RPS goals. Further, this study specifies a minimum cost based on the RPS goal scenario (Scenario 2), in which the optimi­ sation objective is identical to that in Scenario 1 under the interpro­ vincial electricity trading mode and power-plan constraints, including the 13th FYP with RPS goals. The remaining parameters in Scenarios 1 and 2 are identical. The main sources of these data are depicted in Table 1, and the data processing method is based on the study by Wang et al., (2019b). Since the RPS policy goals declared by the government encompasses only the years from 2018 to 2020, we have to predict the remaining RPS goals until 2030. In this study, the remaining RPS goals are calculated according to the renewable capacity goals setted for different years from 2020 to 2030 (Yin, 2016) and the RPS goals setted for 2020 (National Energy Administration, 2018). We first set 2020 as the base year and then applied the liner interpolation method on the data of the RPS goal in 2020 and generation capacity in 2020 and 2030 to obtain the RPS goal from 2021 to 2030. Table 2 depicts the different RPS goals for various provinces in 2020 and 2030.

Yearly electricity demand of each province Upper and lower bounds of transmission lines RPS goals Generation cost and emission factors

Without hydropower (%)

With hydropower (%)

4.00 5.00 5.00 5.00 11.50

29.50 50.00 11.50 31.50 80.00

4.40 5.50 5.50 5.50 12.70

32.50 55.10 12.70 34.70 88.20

4.1. Mid-to long-term electricity generation changes in the CSPGC area To meet the RPS goal without hydropower pertaining to electricity generation in Scenario 2, it is necessary to ensure wind and solar energy generation in the CSPGC area. As indicated by the results of this study, wind energy will be developed before solar energy since the former is cheaper in the short term. However, the pace of solar energy develop­ ment is expected to increase since its cost is decreasing at a faster rate than the cost of wind energy. To meet the RPS goal with hydropower in Scenario 2, hydropower should be developed at a faster rate. Moreover, nuclear power will need to be developed in the short term. In addition, thermal power is an important generation technology in the CSPGC area, except in Yunnan, which is rich in hydropower resources. Compared with Scenario 1, the RPS policy pertaining to Scenario 2 will lower the electricity generated in Guangdong, thereby reducing the environmental pressures on this developed province. In this study, renewable power refers to wind power, solar power, and hydropower, and clean power includes both renewable and nuclear power. Fig. 2 depicts the electricity generation using different generation technologies in the five provinces for 2016–2030. Renewable power generation without hydropower (i.e. utilisation of wind and solar power alone) in the CSPGC area had no change from 2016 to 2017 due to the absence of any forced RPS goal during this period. From 2018 onwards, although the cost of wind and solar power remained high, their generation increased at a fast rate to meet the RPS goal, which reveals the impact and efficiency of the RPS policy. Among these two generation technologies, wind power will be developed earlier in the short term due to its lower cost compared to solar power. How­ ever, due to a faster decrease in solar energy generation cost compared to wind energy cost, the rate of development of solar energy will surpass that of wind energy in the CSPGC area in the middle to long term. Further, the provincial RPS goal without hydropower will increase the generation of wind and solar energy in the CSPGC area during the middle to long term, in general. In the CSPGC area, hydropower generation will increase only slightly in Guangdong and Hainan from 2016 to 2030 since hydropower re­ sources have already been exploited in these provinces. However, in

Table 1 Data sources. China Electric Power Yearbook 2016/2017, China Energy Outlook 2030, study by Wang et al. (CEPP, 2016, 2017a; Wang et al., 2019b ; Yin, 2016) China Electric Power Yearbook 2016/2017, China Energy Outlook 2030 (CEPP, 2016, 2017a; Yin, 2016) China Electric Power Yearbook 2016/2017, and Wang et al.’s research (CEPP, 2016, 2017a; Wang et al., 2019b) Renewable Electricity Quota and Assessment Method (National Energy Administration, 2018) Study by Wang et al. (Wang et al., 2019b)

With hydropower (%)

Compared with the data for 2015, the calculation results enable us to determine the optimal method to achieve the mid-to long-term (2016–2030) RPS target in the CSPGC area from the electricity gener­ ation, transmission, and trading perspectives.

(a) The annual provincial electricity generation by various genera­ tion technologies from 2016 to 2030; (b) The annual electricity trading in various transmission lines of the CSPGC from 2016 to 2030.

Upper and lower bounds of each province’s annual generation

Without hydropower (%)

(a) The electricity generated by renewable energy (with and without hydropower) and clean energy in the CSPGC area; (b) The electricity generated by non-renewable energy in the CSPGC area; (c) The electricity traded among the provinces in the CSPGC area; (d) Detailed capacity development of transmission lines in the CSPGC area.

Based on our calculations, this study directly provided two series of results:

Source

RPS goals for 2030

Using the direct results, we made the following calculations under various scenarios:

4. Results and discussion

Data

RPS goals for 2020

5

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Energy Policy 139 (2020) 111319

Fig. 2. Electricity generation changes in the CSPGC area.

Guangxi and Guizhou, it will increase quickly and reach its peak in 2026 to meet the electricity demand. Due to its potential to provide cheap and rich hydropower, Yunnan’s hydropower generation will increase until 2030 to meet electricity demands and RPS goals both locally and in other provinces, particularly Guangdong. As shown by Wang et al. (2019a), hydropower is the most important renewable energy resource in China if the country is to satisfy its RPS goals with the lowest cost. Our study focusing on southern China reveals that hydropower has the highest potential to satisfy electricity demands and achieve the RPS goal. Finally, nuclear energy is another important resource for the CSPGC area, particularly Guangdong. Nuclear power generation is both cheaper than wind and solar power generation and cleaner than thermal gen­ eration. Recently, Guangdong witnessed the rapid development of the China General Nuclear Power Group. Further, nuclear power generation in the province rapidly increased in 2017 and 2019, mainly due to the planned operation of the Taishan nuclear power plant in 2017 and of the rest four dynamos of the Yangjiang nuclear plant in 2019. From 2019 onwards, nuclear power generation will remain stable in Guangdong until 2030, although the province has the potential to generate even more power. The development of nuclear power in Guangdong is affected by not only the current power plan and mid-to long-term RPS policy but also the province’s thermal generation capacity, since some thermal power plants came online in 2016 when the RPS policy had not yet been implemented. Further, the total generation of Guangdong changes with time and under different scenarios. After 2018, the total generation under Scenario 2 was lower than that under Scenario 1 since the RPS goal forced Guangdong province to buy more electricity from other provinces due to insufficient local renewable energy generation. Similar to the studies by Yi et al. (2017) and Wang et al. (2019a), this study concludes that the RPS policy in the CSPGC area can promote the development of renewable energy and adjust the energy structure unlike currently implemented policies and plans. An RPS policy can increase renewable energy generation even when the cost of renewable energy is higher than that of non-renewable energy. To meet RPS goals, the five

provinces in the CSPGC area should reasonably exploit their renewable resources, solve the long-standing renewable energy curtailment prob­ lem, and promote electricity transmission and electricity trading. Therefore, this study will not only help accomplish the short-term goal published in the 13th energy development FYP and the mid-to long-term RPS goal of the CSPGC area but also help the area adjust its energy structure and develop its renewable energy resources. 4.2. Mid-to long-term changes in the CSPGC area’s transmission lines A well-organised electricity trading mechanism is vitally important in achieving the RPS goal because of the reverse distribution of elec­ tricity resources and electricity demand in China, particularly the CSPGC area. To guarantee electricity trading in the CSPGC area, the construction of transmission lines is an immediate requirement. Guangdong, which is already well developed, should buy a large amount of electricity from Yunnan, which has rich renewable energy resources but a low electricity demand. Therefore, the construction of trans­ mission lines between these two provinces is crucial to the renewable energy development in these provinces. Fig. 3 depicts changes of transmission line potential between Guangdong and Yunnan until 2030. Until the year 2030, the capacity of the transmission line between Guangdong and Yunnan will increase from its 2016 value. In Scenario 2, to meet Guangdong’s RPS target, the construction of a faster trans­ mission line should be started in 2022, that is, two years earlier than that specified in Scenario 1. The main reasons are as follows: in Scenario 2, although the RPS policy forces Guangdong to use more renewable en­ ergy electricity, the remaining, limited resources in the province will be extremely difficult to exploit due to associated costs after 2022; in other words, Guangdong will have to buy more electricity from Yunnan. However, in Scenario 1, without an RPS policy, there is no need for Guangdong to buy significant amounts of electricity from Yunnan until the 8.00-GW Baihetan hydropower plant comes online in 2024, when Yunnan will be able to produce much cheaper and cleaner renewable energy than ever before. From 2024 onwards, the operation of this 6

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Energy Policy 139 (2020) 111319

Fig. 3. Changes of transmission line potential between Yunnan and Guangdong.

significant hydropower plant will increase the electricity trade between Yunnan and Guangdong due to the plant’s cost-effective operations. The RPS policy is an additional incentive for the construction of the trans­ mission line to satisfy the demands of electricity trading. Furthermore, the potential of the transmission line between Guangdong and Yunnan in Scenario 2 will be 15.23 TWh more than that in Scenario 1 for year 2030. The transmission line, which is the most important carrier of elec­ tricity trading, should have sufficient capacity to meet electricity transmission demands. Although the CSPGC is a global leader in the field of power transmission, it continues to face a serious capacity waste problem. Further, Yi et al. (2016) indicated that the transmission lines ending in Guangdong in the CSPGC area should be improved immedi­ ately; however, their study did not clarify the annual optimisation of transmission line construction in the CSPGC area under the constraints of the RPS policy. Therefore, the current study provides detailed guid­ ance on the annual capacity that should be realised during 2016–2030 to help the CSPGC develop its power industry under RPS constraints.

4.3. Mid-to long-term changes in electricity trading in the CSPGC area To meet RPS goals, the five provinces in the CSPGC area must not only promote renewable energy generation in their own provinces but also establish an efficient electricity trading market. The RPS policy will promote electricity trading and the West–East Electricity Transfer Project from 2016 to 2030. Trading will be profitable for not only electricity importers, such as Guangdong, but also electricity exporters, such as Guizhou and Yunnan. Fig. 4 depicts in detail the electricity trading for the West–East Electricity Transfer Project under different scenarios. As shown in Fig. 4, Guangdong will have to import more electricity through the West–East Electricity Transfer Project every year except 2017 and 2019 in Scenario 2. As a province that lacks sufficient renewable energy resources, Guangdong will need to buy renewable energy electricity from other provinces in the CSPGC area, particularly Yunnan, to satisfy its RPS goal. Therefore, in the CSPGC area, the RPS policy is an effective policy to enhance the development of electricity trading.

Fig. 4. Electricity traded through the West–East Electricity Transfer Project. 7

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The reasons for the decrease in electricity trading in 2017 and 2019 are as follows: in 2017, the RPS policy was not implemented in the CSPGC area, that is, there was no need for Guangdong to buy much renewable energy from other provinces. Further, during this period, renewable energy projects and the Taishan nuclear power plant started contributing to Guangdong’s generation capacity. In 2019, the Yang­ jiang nuclear power plant started providing clean and cheap electricity to Guangdong. Moreover, due to the decrease in renewable energy generation costs, wind and solar power development in Guangdong province will eventually help it reduce the amount of renewable elec­ tricity that it needs to import.

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4.4. Mid-to long-term changes in electricity supply cost in the CSPGC area Currently, the cost of renewable energy generation is generally higher than that of fossil fuel generation. However, RPS policy imple­ mentation will not significantly affect the total electricity supply cost of the CSPGC area in the middle to long term. The total electricity supply costs in Scenarios 1 and 2 are 4.53 and 4.55 trillion Yuan, respectively. The development of renewable energy generation technologies will decrease the cost of wind power and solar power generation to the competitive level as fossil fuel power. Further, the policy will enhance the development of hydropower, which is cheaper than fossil fuel power. Therefore, the RPS policy can not only promote the expansion of the renewable energy industry but also be acceptable in terms of cost in the CSPGC area. In this manner, the RPS policy will trigger the development of renewable energy generation and electricity trading and help develop the West–East Electricity Transfer Project without significant increases in cost. The current study presents an optimal method to realise RPS policy goals through annual electricity generation, transmission, and trading in the CSPGC area. It offers a detailed energy guide for not only the five provinces and CSPGC but also the entire nation.

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5. Conclusion and policy implications This study establishes a mid-to long-term optimisation model based on power-plan constraints and RPS goal settings from 2016 to 2030 for the area served by the CSPGC and, thereby, provides an optimal method to achieve the RPS goal from the perspectives of electricity generation, transmission, and trading. The implementation of the mid-to long-term (2016–2030) RPS policy in the CSPGC area can solve the renewable energy curtailment problem by promoting renewable energy generation, transmission, and trading with an acceptable increase in total cost. In terms of electricity generation, it can force the five provinces to exploit their own renewable energy resources and adjust their energy structures accordingly. This policy promotes the utilisation of the wind and solar power resources and alleviates the long-standing problem of water curtailment in the southwest part of China. Moreover, the area’s nuclear energy generation will increase. In terms of electricity transmission and trading, the RPS policy implemented in the CSPGC area mandates the construction of transmission lines and trading of renewable electricity. To satisfy the demand for electricity and complete the West–East Elec­ tricity Transfer Project, the first step is to improve the transmission line between Guangdong and Yunnan. Therefore, the following detailed suggestions are proposed for the five provinces and CSPGC:

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(1) Guangdong Province: Due to the pressure exerted by the RPS policy, Guangdong should exploit its clean energy generation potential and gradually reduce its fossil fuel energy generation. In terms of electricity trading, Guangdong must actively promote the establishment of a renewable energy market in the CSPGC area, guarantee the market’s efficient operation, and participate in the formulation of the market’s operational standards to take complete advantage of the market since Guangdong is the biggest electricity importer in this region. Furthermore, due to the 8

instability of renewable energy generation, Guangdong should cooperate with provinces in southwest China, such as Yunnan and Guizhou, to ensure adequate power supply during dry seasons. This study suggests that due to the low cost of thermal power generation in southwest China, Guangdong can purchase more thermal power to not only ensure adequate power supply within the province but also mitigate environmental pressures. Guangxi and Hainan provinces: In these provinces, both the level of industrial development and power demands are low. In terms of electricity generation, Guangxi and Hainan should utilise their renewable energy potential to enhance their energy supply. Since the two provinces are subject to significant environmental pres­ sures, nuclear power, in addition to renewable energy, generation will be a reasonable choice. In terms of electricity transmission, strengthening their transmission connections with other prov­ inces in the CSPGC area to ensure their power supply and guar­ antee energy security is very important, as well. Guizhou Province: Currently, Guizhou relies more on thermal power generation than renewable energy generation since it has rich coal resources. Therefore, the province experiences rela­ tively high pressure on energy structure transformation. The midto long-term RPS policy can force Guizhou to use its own renewable energy potential and accelerate its energy structure transformation. Moreover, since Guizhou imports natural gas through pipelines (Wang et al., 2019b), the province can develop natural gas power generation to reduce SO2 and NOx and promote economic development. However, since Guizhou is an important renewable energy base in this region, it might have to consider trading electricity with other provinces in the CSPGC area. Yunnan Province: This province is the biggest power sales prov­ ince in the CSPGC area. Hence, its mid-to long-term RPS target significantly affects its energy development and economic development. This policy can encourage Yunnan to use its own renewable energy potential and convert more natural resources into clean electricity, which will help solve the long-term renewable energy consumption problem. However, due to the instability of renewable energy generation and increase in the proportion of generated renewable energy, Yunnan should identify an alternative source of renewable energy during the dry season to guarantee its power supply. This will enable Yunnan to utilise the province’s advantages in power resources and power generation costs. Finally, to increase its sale of electricity and enhance its economic development, Yunnan should cooperate with the company that constructs the transmission line to effi­ ciently utilise the technological advantage offered by trans­ mission lines. The CSPGC: Due to its monopoly in guaranteeing interprovincial electricity transmission in Southern China, the CSPGC plays a significant role in ensuring electricity transmission in the region. Therefore, to assist the five provinces in realising their respective RPS targets, the CSPGC should maintain the transmission network in this area, reduce its losses, and increase transmission capacity in the short term. In this manner, the company will promote electricity trading and generate significant profits. In addition, the CSPGC must break down interprovincial barriers to achieve unified electricity optimisation in the region and accel­ erate renewable electricity trading among the five provinces, In the middle to long term, since Guangdong will need to purchase much renewable energy from the southwestern provinces, the transmission lines between Guangdong and Yunnan are priori­ tised for construction. Moreover, due to the economic structures and environmental pressures experienced by Guangxi and Hainan, the CSPGC should help these two provinces strengthen their connection with the main grid of the CSPGC area to avoid power shortages. Finally, the CSPGC should actively help Yunnan and Guizhou sell more electricity. Since the two provinces are

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rich in energy resources, helping them sell electricity will not only promote regional development but also accelerate Guang­ dong’s energy structure transformation.

Contaldi, M., Gracceva, F., Tosato, G., 2007. Evaluation of green-certificates policies using the MARKAL-MACRO-Italy model. Energy Pol. 35, 797–808. Ding, J., Somani, A., 2010. A Long-Term Investment Planning Model for Mixed Energy Infrastructure Integrated with Renewable Energy, 2010 IEEE Green Technologies Conference, pp. 1–10. Dong, Y., Shimada, K., 2017. Evolution from the renewable portfolio standards to feed-in tariff for the deployment of renewable energy in Japan. Renew. Energy 107, 590–596. Farooq, M.K., Kumar, S., Shrestha, R.M., 2013. Energy, environmental and economic effects of renewable portfolio standards (RPS) in a developing country. Energy Pol. 62, 989–1001. Feng, Z., Niu, W., Cheng, C., 2018. Optimal allocation of hydropower and hybrid electricity injected from inter-regional transmission lines among multiple receivingend power grids in China. Energy 162, 444–452. Gao, C.X., Su, B., Sun, M., Zhang, X.L., Zhang, Z.H., 2018. Interprovincial transfer of embodied primary energy in China: a complex network approach. Appl. Energy 215, 792–807. Go, R.S., Munoz, F.D., Watson, J.-P., 2016. Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards. Appl. Energy 183, 902–913. Goh, T., Ang, B.W., Su, B., Wang, H., 2018. Drivers of stagnating global carbon intensity of electricty and the way forward. Energy Pol. 113, 149–156. Hua, Y., Oliphant, M., Hu, E.J., 2016. Development of renewable energy in Australia and China: a comparison of policies and status. Renew. Energy 85, 1044–1051. Lin, B., Wu, W., 2017. Cost of long distance electricity transmission in China. Energy Pol. 109, 132–140. Liu, C.-L., Zhang, Q., Singh, V.P., Cui, Y., 2011. Copula-based evaluations of drought variations in Guangdong, South China. Nat. Hazards 59, 1533–1546. National Energy Administration, 2018. Renewable Electricity Quota and Assessment Method [in Chinese]. Novacheck, J., Johnson, J.X., 2015. The environmental and cost implications of solar energy preferences in Renewable Portfolio Standards. Energy Pol. 86, 250–261. Park, S.Y., Yun, B.-Y., Yun, C.Y., Lee, D.H., Choi, D.G., 2016. An analysis of the optimum renewable energy portfolio using the bottom–up model: focusing on the electricity generation sector in South Korea. Renew. Sustain. Energy Rev. 53, 319–329. Ren, D., 2011. China’s renewable portfolio standards and implementing countermeasures [in Chinese]. Autom. Electr. Power Syst. 35, 25–28. Shrimali, G., Tirumalachetty, S., 2013. Renewable energy certificate markets in India—a review. Renew. Sustain. Energy Rev. 26, 702–716. Sun, P., Nie, P.-y., 2015. A comparative study of feed-in tariff and renewable portfolio standard policy in renewable energy industry. Renew. Energy 74, 255–262. Wang, B., Wei, Y.-M., Yuan, X.-C., 2018. Possible design with equity and responsibility in China’s renewable portfolio standards. Appl. Energy 232, 685–694. Wang, G., Zhang, Q., Li, Y., McLellan, B.C., 2019a. Efficient and equitable allocation of renewable portfolio standards targets among China’s provinces. Energy Pol. 125, 170–180. Wang, H., Su, B., Mu, H., Li, N., Jiang, B., Kong, X., 2019b. Optimization of electricity generation and interprovincial trading strategies in Southern China. Energy 174, 696–707. Wiser, R., Porter, K., Grace, R.J.M., Change, A.S.f.G., 2005. Evaluating Experience with Renewables Portfolio Standards in the United States, vol. 10, pp. 237–263. Yi, B., Xu, J., Fan, Y., 2016. Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: a multiregion bottom-up optimization model. Appl. Energy 184, 641–658. Yi, B., Xu, J., Fan, Y., 2017. Optimal pathway and impact of achieving the mid-long term renewable portfolio standard in China [in Chinese]. J. Syst. Eng. 32, 313–324. Yin, W., 2016. China Energy Outlook 2030. Economy & Management Publishing House, Beijing. Zhang, C., Zhong, L., Liang, S., Sanders, K.T., Wang, J., Xu, M., 2017a. Virtual scarce water embodied in inter-provincial electricity transmission in China. Appl. Energy 187, 438–448. Zhang, N., Lior, N., Jin, H., 2011. The energy situation and its sustainable development strategy in China. Energy 36, 3639–3649. Zhang, Q., Wang, G., Li, H., Li, Y., Chen, S., 2017b. Study on the implementation pathways and Key impacts of RPS target in China using a dynamic game-theoretical equilibrium power market model. Energy Procedia 105, 3844–3849. Zhang, Q., Wang, G., Li, Y., Li, H., McLellan, B., Chen, S., 2018. Substitution effect of renewable portfolio standards and renewable energy certificate trading for feed-in tariff. Appl. Energy 227, 426–435. Zhang, Y., Zhao, X., Ren, L., Liang, J., Liu, P., 2017c. The development of China’s biomass power industry under feed-in tariff and renewable portfolio standard: a system dynamics analysis. Energy 139, 947–961. Zhang, Y., Zhao, X., Ren, L., Zuo, Y., 2017d. The development of the renewable energy power industry under feed-in tariff and renewable portfolio standard: a case study of China’s wind power industry. J. Clean. Prod. 168, 1262–1276. Zhao, X., Zhang, Y., Ren, L., Zuo, Y., Zhigong, W., 2017. The policy effects of feed-in tariff and renewable portfolio standard: a case study of China’s waste incineration power industry. Waste Manag. 68, 711–723. Zhu, F., Zheng, Y., Guo, X., Wang, S., 2005. Environmental impacts and benefits of regional power grid interconnections for China. Energy Pol. 33, 1797–1805.

This study does not discuss the energy security problems caused by distance and assumes that the transmission and distribution losses are fixed. Further, due to the lack of available data, the study did not ac­ count for the seasonal factors affecting renewable energy generation and load demand. In addition, this study ignored other renewable energy types, including biomass and ocean energy, due to the aforementioned reason. Therefore, future studies should examine transmission losses, security problems caused by distance, seasonal factors affecting renewable energy generation, and other renewable energy sources. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Hongye Wang: Methodology, Software, Writing - original draft, Writing - review & editing. Bin Su: Conceptualization, Supervision, Project administration, Writing - review & editing. Hailin Mu: Project administration, Supervision, Funding acquisition. Nan Li: Data cura­ tion. Shusen Gui: Resources. Ye Duan: Validation. Bo Jiang: Formal analysis. Xue Kong: Visualization. Acknowledgements This study was supported by the China Scholarship Council [grant number 201706060076]; the National Natural Science Foundation of China [grant numbers 51976020, 71834003]; the Doctoral Start-up Funds of Liaoning Province [grant number 201601049]; and Research on Key Technologies and Demonstration of Intelligent Energy Manage­ ment System in Industrial Park (2017A050501060). Furthermore, this work is supported by the Supercomputing Centre of Dalian University of Technology. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.enpol.2020.111319. References Ang, B.W., Su, B., 2016. Carbon emission intensity in electricity production: a global analysis. Energy Pol. 94, 56–63. Aune, F.R., Dalen, H.M., Hagem, C., 2012. Implementing the EU renewable target through green certificate markets. Energy Econ. 34, 992–1000. Bird, L., Chapman, C., Logan, J., Sumner, J., Short, W., 2011. Evaluating renewable portfolio standards and carbon cap scenarios in the U.S. electric sector. Energy Pol. 39, 2573–2585. CEPP, 2016. China Electric Power Yearbook 2016 [in Chinese]. China Electric Power Press, Beijing. CEPP, 2017. China Electric Power Yearbook 2017 [in Chinese]. China Electric Power Press, Beijing. CEPP, 2017. China Energy Statistical Yearbook 2017 [in Chinese]. China Electric Power Press, Beijing. Choi, D.G., Park, S.Y., Hong, J.C., 2015. Quantitatively exploring the future of renewable portfolio standard in the Korean electricity sector via a bottom-up energy model. Renew. Sustain. Energy Rev. 50, 793–803. Choi, G., Huh, S.-Y., Heo, E., Lee, C.-Y., 2018. Prices versus quantities: comparing economic efficiency of feed-in tariff and renewable portfolio standard in promoting renewable electricity generation. Energy Pol. 113, 239–248.

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