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Study of China's Optimal Concentrated Solar Power Development Path to 2050

1 Due to increasingly severe energy shortages and environmental pollution, renewable energy sources are becoming a larger part of the global energy mix, especially in the power industry (Zhang et al., 2017). Therefore, CSP is drawing more attention from experts and researchers. The relevant literature covers several aspects, including cost forecasting as well as diffusion models and integrated utilization of technologies.

2 Cost is a key constraint to compete with conventional energy sources. Wang (2010) pointed out that significant cost reduction is the crux of market acceptance. Compared with the mature parabolic trough CSP technology, there is potential for the linear Fresnel CSP technology to be cost effective (Sun et al., 2020) due to its simple structure and lesser need for reflectors. The learning curve model proposed by Wright (1936) is most commonly used to predict cost reductions. It has also been widely demonstrated that the cost of products decreases continuously based on economies of scale (Wright, 1936; Yelle, 1979; Wene, 2000), especially in renewable energy generation (Hernández-Moro and Martinez-Duart, 2013). Specifically, this cost reduction can be described as a certain percentage decrease in cost when the cumulative installed capacity is doubled, which is also known as “the learning by doing approach.” The approach assumes that changes in costs are generally attributable to experience, as verified in most of the literature on cost changes in renewable energy (Albrecht, 2007; Köberleet al., 2015; Elshurafa et al., 2018; Hong et al., 2020). Van der Zwaan andRabl (2003) explored the cost-cutting potential of PV technology in the first decade of the 21st Century through the learning curve model. The same model was used to analyze the cost of CSP plants in Africa and Europe by Viebahn et al.(2011), and the cost reduction curves were derived. Consistently with these papers, this study also adopted the learning curve model to predict the investment cost of CSP generation in China.

3 Technology diffusion is a subsequent sub-process of the innovation process (Wu et al., 1997), which combines the complex technology with the economy and the market (Cao and Chai,2013). Renewable energy, as a new form of energy, has a development trend that resembles that for new technology (Lu et al., 2015), with both following the S-shaped growth pattern (Sheng, 2002). Researchers have conducted many studies on renewable energy development based on technology diffusion models. Xie and Fan (2017) built a logistic model to determine the technology maturity of CSP and revealed that the CSP technology was still in the early stage of development. It is expected that global CSP technology will become highly mature and will enter the stage of large-scale commercial application around 2032. Grafström and Lindman (2017) provided a technical development model for economic analysis of the European wind power sector and suggested natural gas prices and feed-in tariffs as crucial factors in the spread of wind power. Other studies have analyzed renewable sources using technology diffusion models (Raoand Kishore, 2010; Popp et al., 2011; Pfeiffer and Mulder, 2013; Kumar and Agarwala, 2016; Fadly and Fontes, 2019). The Bass model plays an important role in the diffusion modeling. Since it was proposed in 1969, it has become the main research tool of market diffusion theory (Yang, 2006). Rao and Kishore(2009) used the Bass model to study the growth patterns of wind power technology in several Indian states; the model which provided a good foundation for the study of capital-intensive equipment such as wind power generators. Radomes and Arango (2015) analyzed the diffusion of photovoltaic systems in Colombia using an extended Bass model in which the adoption rate was a function of promotional activities and social interactions. It can be concluded that technology diffusion modeling is generally mature in renewable energy research based on the above papers, but there is little research about CSP. Yang (2006) pointed out that the fitting result of the Bass model is better than that of the logistic model. Therefore, this study used the Bass model to explore the diffusion path of CSP in China.

4 Due to the strict data requirements of the Bass model, some studies have focused on parameter estimation methods. Generally speaking, the ordinary least-squares method(Satoh, 2001), the non-linear least-squares method (Wang et al., 2017), the maximum likelihood estimation method (Razo, 2017), the Kalman filtering method(Chow, 2004), and the grey theory method (Wang, 2013) can be used to estimate parameters when data are sufficient, whereas judgment and analogical methods can be used otherwise. However, when using judgment or analogical methods, much external information is required, leading to subjectivity in the estimated parameters. Because CSP development in China started late, this study had insufficient data. Due to the heavy reliance of the accuracy of Bass model prediction on the number of data points (Mahajan et al., 1990), more than 14 data points are usually required to produce reliable statistical results(Zhang, 2006). Yang (2006) indicated that the accuracy of estimating Bass model parameters using a genetic algorithm is higher, which is of great significance for forecasting product diffusion in the growth period. Genetic algorithms(GA), proposed by Holand (1975), are global search methods based on natural selection and genetic variation. Sohn et al. (2009) developed a dynamic pricing model using the Bass model with GA for Korean mobile phone manufactures, and this method was also applied to the optimal electric vehicle charging location problem by Akbari et al. (2018). Similar papers include (Venkatesan and Kumar,2002; Wang and Chang, 2009; Kong and Bi, 2014). Therefore, the application of GA to estimate the parameters of the Bass model appears to be relatively mature. Hence, GA was used for parameter estimation in this study.

5 The studies discussed above considered the impacts of cost and technology diffusion on renewable energy development. Other uncertain factors may also influence progress. Therefore, researchers have been investigating the comprehensive development of renewable energy (Xu et al., 2020). For instance, Lund (2007), in a case study in Denmark, discussed the problems and prospects of switching an existing energy systems completely into a renewable energy (wind, solar, wave, and biomass) system. Incentive policies for renewable energy power generation in China were explored by Zhao et al. (2016), including R&D incentives, fiscal and tax incentives, grid-connection and tariff incentives, and market development incentives. The results showed that these policies indeed substantially promoted renewable energy power generation development. Dynamic programming, as an optimization method for multi-stage decision problems, is more suitable for studying path-dependent problems under the influence of uncertain factors. Dynamic programming has also been widely applied to renewable energy studies (Boaro et al., 2012; Marano et al., 2012; Feng et al.,2018; Jafari and Malekjamshidi, 2020). Ding et al. (2020) researched the sensitivity of cost and price elasticity and policy performance to renewable energy technology diffusion by constructing a dynamic programming model. Lu etal. (2015) and Xu et al. (2020) also took these two models as constraints to explore the optimal path of China's wind power and PV power development, respectively. They both took resource potential, economic development, incentive policies, emission regulatory schemes, and grid absorptive capacity into account. However, few researchers have studied the CSP development path by applying dynamic programming methods.

6 The studies just described have analyzed the progress of renewable energy from various perspectives, but there have been few studies on CSP, especially on its development path. Therefore, this study draws on the more mature theories and methods to build adynamic programming model with the goal of cost minimization. Based on the 2050 development target, the optimal development path of CSP in China was studied under the constraints of a learning curve model, a technology diffusion model, economic development, policy incentives, emission regulation schemes, and grid absorptive capacity. (1558 words)

Notes:

1. Authors: XinZhang, Xiaojia Dong and Xinyu Li (2021). Study of China's Optimal ConcentratedSolar Power Development Path to 2050. Front. Energy Res., 16 November 2021

Sec. SustainableEnergy Systems. Volume 9-2021 https://doi.org/10.3389/fenrg.2021.724021

2. Keywords:concentrated solar power, development path, learning curve, innovationdiffusion model, genetic algorithm, dynamic programming

3. Abstract: As animportant form of clean energy generation that provides continuous and stable power generation and is grid-friendly, concentrated solar power (CSP) has been developing rapidly in recent years. It is expected that CSP, together with wind and solar photovoltaic, will constitute a stable, high percentage of renewable energy generation system that will be price-competitive with conventional energy sources. In this study, a dynamic programming approach based on minimum cost was used to explore the optimal development path of CSP generation inChina by 2050. A learning curve model and a technology diffusion model were used as constraints. The impact of factors such as Gross Domestic Product (GDP)growth, incentive policies, technological advances, grid absorptive capacity, and emission regulation schemes on the development of CSP generation was discussed in the context of sensitivity analysis and scenario comparison. This study has reached the following conclusions: 1) the government cannot achieve the target for cumulative installed capacity in 2050. Considering the interaction of relevant factors, the target would be hard to achieve even underfavorable conditions; 2) as a key factor affecting the development of CSP, the incentive policy is closely related to construction cost. It is noteworthy that although the target can be achieved with a higher investment ratio, the CSP industry has failed to create a good ecological environment in the early stage of development; 3) GDP growth and learning rate are important factors influencing the development path in later stages; and 4) although they operateas potential factors affecting construction costs, grid absorptive capacity and carbon permit prices have limited impact on the development of CSP generation.

Task4: Complete the following summarywith the key information from the text.

The text discussed various aspects related to the development and analysis of renewable energy, specifically focusing on the Bass model and its _____(1) _____(2) methods. It highlighted the need for reliable _____(3) _____(4) and the use of techniques such as least-squares, maximum likelihood estimation, and _____(5) _____(6). The impact of cost andtechnology _____(7) on renewable energy development was examined, along with incentive policies in China. _____(8) programming was identified as a suitable methodfor studying path-dependent problems in _____(9) environments. The importance of previous studies in understanding _____(10) _____(11) and PV power development was emphasized. However, there was a literature _____(12) regarding concentrated solar power (CSP) development. To address this gap, the study proposed applying mature theories and methods, like _____(13) _____(14), forcost minimization in CSP development. Overall, the text provided insights into parameter estimation, policy impact, and _____(15) _____(16), offering acomprehensive overview of renewable energy research.

Task6: Choose the appropriate word tofill in the blanks from the words given below. Change the form where necessary.

1. Solar and wind power are types of____________________ energy that provide sustainable alternatives totraditional thermal power generation.

2. Energy ____________________ areessential for predicting the integration of solar and wind power into the grid,alongside more established sources like hydro and nuclear power.

3. The ____________________ of solar panelsand wind turbines has been rapid, as these technologies become moreeconomically viable and widely adopted for electricity generation.

4. Engineers ____________________ thepotential energy output of a hydroelectric dam based on water flow rates andturbine capacities.

5. The ____________________ advancements inwind turbine technology have led to increased efficiency and reduced costs forwind power generation.

6. ____________________ factors, such asthe cost of fuel and maintenance, influence the viability of thermal powerplants compared to renewable sources.

7. ____________________ in nuclear powerplant design aim to enhance safety and efficiency, addressing public concernsand improving the technology's competitiveness.

8. Modern Combined Cycle Gas Turbines(CCGT) in thermal power plants are more ____________________ in converting fuelinto electricity than older technologies.

9. Energy planners seek the____________________ mix of solar, wind, hydro, and nuclear power to diversifythe energy supply and ensure reliability.

10. The electricity market is____________________, with the rise of renewable energy sources like solar andwind challenging the traditional reliance on thermal and nuclear power.

Task7: Please translate the followingexpressions according to the text.

Task8: Please read and translatefollowing sentences into English or Chinese by using expressions above.

1. Concentrated solar power systems usemirrors to focus sunlight onto a small area, generating intense heat that canbe converted into electricity.

2. In the renewable energy sector, accuratecost forecasting is essential for planning future projects and securing thenecessary financial investments.

3. Diffusion models are used to predict thespread of new technologies, such as the adoption of electric vehicles or theintegration of solar panels in the power industry.

4. The concept of integrated utilization inenergy systems involves combining different forms of energy to optimizeefficiency and reduce environmental impact.

5. The growth of renewable energygeneration is a key factor in reducing greenhouse gas emissions and combatingclimate change.

6. 评估一种新的电力生成方法的技术成熟度，如海上风力涡轮机，对于其大规模实施至关重要。

7. 参数估计在电厂设计中至关重要，因为它有助于确定最大效率下的最佳大小和配置。

8. 一个国家风力发电的累积安装容量反映了其对可再生能源的承诺及其在能源独立方面的进步。

9. 电力行业的创新，如智能电网和能源存储，正在改变电力的生成、分配和消费方式。

10. 政府通常使用上网电价补贴来鼓励对可再生能源的投资，通过保证这些来源产生的电力有一个固定价格。