1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi: 10.1016/j.egypro.2017.03.784
Energy Procedia 105 ( 2017 ) 3844 – 3849
ScienceDirect
The 8
thInternational Conference on Applied Energy – ICAE2016
Study on the Implementation Pathways and Key Impacts of
RPS Target in China using a Dynamic Game-Theoretical
Equilibrium Power Market Model
Qi Zhang
a*, Ge Wang
a, Hailong Li
b, Yan Li
a, Siyuan Chen
aa Academy of China Energy Strategy, China University of Petroleum-Beijing, 18 Fuxue Road, Changping, Beijing 102249, China b School of Business, Society and Engineering,Mälardalen University, Sweden
Abstract
China’s 2020 Renewable Portfolio Standards (RPS) target has been published by the government in early 2016. In the present study, in order to find its implementation pathways and estimate the key impacts of PRS target out to 2030, a multi-region power market model is proposed to investigate different RPS policy scenarios. Results show that RPS policy can promote the development of renewable energy efficiently, and Renewable Energy Certification (REC) trade can reduce the cost of electricity generated from renewable energy. However, a national wide free REC trade tends to result in a dilemma that renewable energy will be developed centralizedly in regions where the renewable resource is plenty. Therefore, detailed REC trade regulations need to be developed from more comprehensive viewpoints when adopting RPS policy.
© 2016 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICAE
Keyword: Renewable Portfolio Standards (RPS); Renewable Energy Credit; Game-Theoretical Analysis, Power Market Model
1. Introduction
In order to promote the development of renewable energy in China, ongoing efforts adopt a suite of policies. According to the publication of National Energy Administration in March 2016, renewable energy power will account for at least 9% of China’s power supply by 2020, and each province will meet their detailed quota standard. There have been some studies that have examined the effect of the RPS policy[1] using either empirical methods[2] or analytical models[3]. However, the effects of the upcoming RPS policy in China still need to be assessed as a new study since none of the countries or regions executing RPS today suffers such extremely uneven distributions in both energy consumption and renewable energy resource as China. Several works have modelled power market model with policy
* Corresponding author. Tel.: +86-10-89731752.
E-mail address: zhangqi@cup.edu.cn.
© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
modular[3, 4], nevertheless few have considered the detailed plant replacement and expansion, and thus failed to analyse the intertemporal effects, especially for multi-regions. Therefore, the purpose of this study is to find the implementation pathways and simulate the impacts of RPS and REC trading on renewable energy development and social welfare in China by using a proposed dynamic game-theoretical equilibrium power market model. Such game-theoretical model can consider all the market participants’ individual interest rather than assuming an social welfare maximization in traditional optimal models.
Three China’s power market scenarios from 2015 out to 2030 with complete RPS policy (RPS quota with renewable electricity credit (REC) trade), incompletion RPS policy (RPS quota without REC trade) and no RPS policy are investigated respectively by using the proposed model. The RPS implementation pathways and its impacts on multi-region and multi-period power generation expansion and operation, participants’ profit and environment were analysed and compared through the three case study.
Nomenclature Indices
Y years in the planning time horizon (e.g. 2015, 2020, 2025 and 2030) R regions (e.g. North, Northeast, East, Central, Northwest and South)
JN power plant types which is not counted in RPS (e.g. coal, gas, oil, nuclear and hydro) JR power plant types which is counted in RPS (e.g. PV and wind)
Parameters and Variables
INJN/JR initial capacity in each year (GW)
JN/JRFC capital investment cost of new power plants (billion RMB/GW) JN/JRVC variable cost including fuel cost and OM cost (billion RMB/GWh) DR discount rate
pow/recst power or REC sold by producers (GWh)
pow/recpf power purchase by traders or REC purchased by producers (GWh)
powpri equilibrium power price between producers and traders (billion RMB/GWh) recpri equilibrium REC price between producers (billion RMB/GWh)
newjn/jr new installed capacity of power plants (GW) jn/jrpp power output of power plants (GWh)
trast transmission capacity supply to traders by grid operators (GWh) trapf transmission capacity purchase from grid operators by traders (GWh) newtra new constructed transmission capacity (GW)
2. Methodology
In the proposed dynamic game-theoretical equilibrium power market model, China’s power sector is divided into six regional power grids[5]. In each region, there are one producer and one retailer, and there is one grid operator managing the interregional power grid. Power producers decide the expansion and operation path of both renewable and non-renewable power generators. Power retailers purchase power
from producers (transmission fee will be charged by grid operator) and then sell power to the local end-users at a regulated price. All of the producers, retailers and grid operator considered in the model are pursuing their own maximum discounted profit under different RPS policy scenarios.
The objective function of each producer is shown as equation (1), which depicts the revenue from selling electricity power to traders and selling REC to other producers and the cost of power generating, new capacity installation and purchasing REC. Especially, it is assumed that there is a 1% transaction fee on both supplier and consumer in REC trade.
ݎ݀ݑܿ݁ݎԢݏݎ݂݅ݐோൌ σǡோᇲ൫ݓݏݐǡோǡோᇲൈ ݓݎ݅ǡோǡோᇲ ݎ݁ܿݏݐǡோǡோᇲൈ ͲǤͻͻ ൈ ݎ݁ܿݎ݅ǡோǡோᇲെ
ݎ݂݁ܿǡோǡோᇲൈ ͳǤͲͳ ൈ ݎ݁ܿݎ݅ǡோᇲǡோ൯ ൈ ܦܴെ σ ൫ܬܸܰܥ ேൈ ݆݊ǡோǡே ൫ܬܴܸܥோെ ܨܫܶ൯ ൈ
݆ݎǡோǡோ൯ ൈ ܦܴെ σ ቀܬܰܨܥ ேൈ ݊݁ݓ݆݊ǡோǡே ܬܴܨܥோൈ ݊݁ݓ݆ݎǡோǡோቁ ൈ ܦܴ (1)
The producers are subjected to technical and economic constraints, which are depicted as Equations (2)-(4). Equations (2) and (3) give the technical constraints of power output of non-renewable and renewable power generators respectively, where LOL is the lower limit of power output for JN power plants and WEA depicts the weather conditions for JR power in terms of output power/rated power. Equation (4) indicates that each producer must either meet the RPS target by generating PV and wind power or trading REC with others.
൫ܫܰܬܰோǡǡே σᇲஸ݊݁ݓ݆݊ᇲǡோǡே൯ ൈ ܮܱேൈ ͺͲ ݆݊ǡோǡே ൫ܫܰܬܰோǡǡே σᇲஸ݊݁ݓ݆݊ᇲǡோǡே൯ ൈ
ͺͲ (2)
݆ݎǡோǡோ ൫ܫܰܬܴோǡǡோ σᇲஸ݊݁ݓ݆ݎᇲǡோǡோ൯ ൈ ܹܧܣǡோǡோൈ ͺͲ (3)
σ ൫ݎ݂݁ܿோᇲ ǡோǡோᇲെ ݎ݁ܿݏݐǡோǡோᇲ൯ σ ݆ݎோ ǡோǡோ ൫σே݆݊ǡோǡே σ ݆ݎோ ǡோǡோ൯ ൈ ܴܲܵǡோ (4)
The traders’ revenue is obtained by selling power to end-users and their cost contains purchasing power from retailer and transmission fee. To simplify the problem, the consumption volumes of end-users are fixed. The objective function of traders is shown as equation (5), where DEM is the power consumption in each region in each year and EPRI is the regulated electricity power price.
ݐݎܽ݀݁ݎᇱݏݎ݂݅ݐ ൌ σ ܦܧܯ
ǡோൈ ܧܴܲܫ ൈ ܦܴ
െ σǡோᇲ൫ݓ݂ǡோǡோᇲൈ ݓݎ݅ǡோᇲǡோ
ݐݎ݂ܽǡோᇲǡோൈ ݐݎܽݎ݅ǡோᇲǡோ൯ ൈ ܦܴ (5)
The traders should purchase enough electricity power and transmission capacity to meet end-users’ demand, which are depicted as equation (6) and equation (7), respectively, where TEF is the transmission efficiency of the power grid.
ܦܧܯǡோ σ ݓ݂ோᇲ ǡோǡோᇲൈ ܶܧܨோǡோᇲ (6)
ݓ݂ܻǡܴǡܴԢ ݐݎ݂ܻܽǡܴǡܴԢ (7)
The grid operator can supply transmission capacity and construct new grid capacity in order to maximize his profit, which is depicted as Equation (8), where TFC is the capital investment cost of new transmission grid and TVC is the variable cost of transmission grid. Moreover, the supplied transmission capacity to the traders should not exceed the physical grid capacity as shown in Equation (9), where TIC is the initial capacity of transmission grid.
݃ݎ݅݀݁ݎܽݐݎᇱݏݎ݂݅ݐ ൌ
σǡோǡோᇲݐݎܽݏݐǡோǡோᇲൈ ൫ݐݎܽݎ݅ǡோǡோᇲെ ܸܶܥோǡோᇲ൯ ൈ ܦܴെ σǡோǡோᇲ݊݁ݓݐݎܽǡோǡோᇲൈ ܶܨܥோǡோᇲൈ ܦܴ
(8)
ݐݎܽݏݐǡோᇲǡோ ൫ܶܫܥோǡோᇲ σᇲஸ݊݁ݓݐݎܽǡோǡோᇲ ݊݁ݓݐݎܽǡோᇲǡோ൯ ൈ ͺͲ (9)
The proposed game-theoretical model was solved as a complementarity problem by the General Algebraic Modeling System (GAMS) and PATH solver. It takes 60s to solve this problem on a computer with i7 2.5 GHZ CPU and 4G memory.
3. Data and assumptions
The predicted power demand of the end-user in each region and the RPS quota published by NEA are shown in Fig. 1 and Fig. 2, respectively. Practically, the RPS quota is assumed to increase 1 percent every year from 2015 out to 2030. The weather condition which determines the output of renewable electricity generators are shown in Fig 3.
In this paper, the annual discounted rate is assumed to be 5%, and the FIT for PV and wind power is assumed to be 0.4RMB/KWh and the regulated electricity price is assumed to be 0.6 RMB/KWh
Fig 1. Regional electricity demand Fig 2. Published RPS quota in China
Fig 3. Weather condition
4. Result
The realization pathways and key impact of different RPS policy on the development of renewable energy are measured from two aspects, the new installed renewable energy power capacity and the proportion of renewable electricity in generated electricity, as shown in Fig.4. There will no new PV capacity is installed since comparing to wind power, the cost of PV is much higher and its efficient is lower. In the scenario without RPS, there is no new renewable electricity capacity installed and no renewable electricity generated, because renewable electricity requires higher cost comparing with fossil or nuclear electricity. On the other hand, the incomplete RPS policy will lead to that each region shares parts of the new renewable electricity capacity, while in the complete RPS policy scenario, almost all new installed renewable electricity capacity and renewable electricity generation are deployed in North and Northeast China, where the renewable energy resources are sufficient.
(a) RPS Incompletion scenarios
b) Completion RPS policy scenario Fig 4. Development of renewable energy
4.2. Impacts on cost of developing renewable energy
The total cost is stipulated as the corresponding differences between all the market participants’ profits in no RPS scenario and incomplete /complete RPS policy scenarios from 2015 out to 2030. The impact of
different RPS policy on cost of developing renewable energy is shown in Table 1. And the annual prices of REC are listed in Table 2. The complete RPS policy can reduce the unit cost of renewable electricity comparing to incomplete RPS policy, because that the cost of purchasing REC is much less than that of producing renewable energy power in regions with less renewable energy resource, e.g. Central China and South China. The REC prices reflect the difference of the marginal costs of generating renewable electricity in different regions. In the US, the REC price is only about 0.003 RMB/KWh. Such huge gap between China’s and the US’s REC price mainly come from China’s uneven distributions of energy consumption and renewable energy resource.
Table 1. Cost of developing renewable energy in different RPS policy scenarios comparing with no RPS scenario (Billion RMB)
Incomplete RPS policy Complete RPS policy
Total cost (Billion RMB) 12037.22 9831.19
Increased renewable electricity capacity (GWe,GWp) 1032.32 801.74
Increased generated renewable electricity (TWh) 3025 3025
Unit cost of renewable electricity (RMB/KWh) 3.98 3.25
Table 2. Annual prices of REC in complete RPS policy scenario (RMB/KWh)
2020 2025 2030
REC Price (RMB/KWh) 0.71 0.62 1.40
5. Conclusion
In the present study, the pathways for the implementation of RPS and the impacts of the upcoming RPS policies on both the development of renewable energy power and corresponding cost are obtained by using a dynamic game-theoretical equilibrium power market model. The results show that implementing complete RPS policy will promote the development of renewable energy efficiently. While abandoning REC trade will make the unit cost of renewable electricity by 22.5%, resulting from renewable electricity generators work more in regions with less sufficient renewable energy resource. On the other hand, the REC trade will cause the “lock-in” effect, which means the renewable energy power will be locked in regions where the renewable resource is more sufficient, and thus detailed REC trade regulations need to be studied from more comprehensive viewpoints in the future work.
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