LCOE Analysis of Tower Concentrating Solar Power Plants Using Diﬀerent Molten-Salts for Thermal Energy Storage in China

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Article LCOE Analysis of Tower Concentrating Solar Power Plants Using Diﬀerent Molten-Salts for Thermal Energy Storage in China

Xiaoru Zhuang, Xinhai Xu * , Wenrui Liu and Wenfu Xu

School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China; [email protected] (X.Z.); [email protected] (W.L.); [email protected] (W.X.) * Correspondence: [email protected]

Received: 11 March 2019; Accepted: 8 April 2019; Published: 11 April 2019

Abstract: In recent years, the Chinese government has vigorously promoted the development of concentrating solar power (CSP) technology. For the commercialization of CSP technology, economically competitive costs of electricity generation is one of the major obstacles. However, studies of electricity generation cost analysis for CSP systems in China, particularly for the tower systems, are quite limited. This paper conducts an economic analysis by applying a levelized cost of electricity (LCOE) model for 100 MW tower CSP plants in ﬁve locations in China with four diﬀerent molten-salts for thermal energy storage (TES). The results show that it is inappropriate to build a tower CSP plant nearby Shenzhen and Shanghai. The solar salt (NaNO3-KNO3, 60-40 wt.%) has lower LCOE than the other three new molten-salts. In order to calculate the time when the grid parity would be reached, four scenarios for CSP development roadmap proposed by International Energy Agency (IEA) were considered in this study. It was found that the LCOE of tower CSP would reach the grid parity in the years of 2038–2041 in the case of no future penalties for the CO2 emissions. This study can provide support information for the Chinese government to formulate incentive policies for the CSP industry.

Keywords: concentrating solar power; levelized cost of electricity (LCOE); molten-salt; tower system; thermal energy storage

1. Introduction Concentrating solar power (CSP) technology is one of the most promising renewable energy-based electricity generation technologies to deal with the increasing demand of power consumption and environmental sustainability. It can generate renewable electricity from direct sunlight and produce nearly no greenhouse gas emissions. In addition, compared to photovoltaic (PV) cells, the CSP systems can deliver dispatchable electricity regardless of the weather conditions by integrating with thermal energy storage (TES) units. In recent years, the Chinese government has vigorously promoted the development of CSP technology. The construction of CSP plants in China has increased at a great rate since starting in 2013 [1]. The installed capacity was about 29.3 MWe by the end of 2017, which was an increase of 3.53% over that in 2016. Meanwhile, the global installation capacity increase rate was 2.3% [2]. For the commercialization of CSP technology, the low cost of the electricity generation is one of the major obstacles. Among diﬀerent types of CSP technologies, the solar power tower technology can achieve higher operating temperature compared to the parabolic trough and linear Fresnel technologies. It can yield greater eﬃciency of thermal-to-electric conversion in the power block and result in lower cost for TES [1]. As a key factor to evaluate the economic feasibility of CSP systems, the electricity

Energies 2019, 12, 1394; doi:10.3390/en12071394 www.mdpi.com/journal/energies Energies 2019, 12, 1394 2 of 17 generation cost needs to be appropriately estimated [3,4]. Hernández-Moro and Martínez-Duart [5,6] established a mathematic model of the levelized cost of electricity (LCOE) for estimating the electricity generation cost of CSP plants. The future evolution (2010–2050) of the LCOEs based on the International Energy Agency (IEA) roadmaps for the cumulative installed capacity was predicted. It was found that the predicted LCOEs strongly depended, not only on the particular values of the cumulative installed capacity function in the targeted years, but also on the specific curved time-paths followed by the function. Parrado et al. [7] used the LCOE model to develop an economic analysis for a 50 MW CSP plant with five new storage materials in northern Chile between 2014 and 2050. The results showed those new molten-salts could reduce the storage costs in CSP plants, especially the molten-salt composed of Ca(NO3)2-NaNO3-KNO3 (48-7-45 wt.%). Additionally, a comparison for different locations in three countries (Chile, Spain and USA) was made which indicated that northern Chile had great potential to implement CSP plants. Hinkley et al. [8] summarized the costs of CSP plants deployed internationally and estimated the expected costs of trough and tower technologies in Australia. They found that there was significant potential to reduce the LCOE through operation at higher temperatures, which would improve the efficiency of a CSP plant. Dieckmann et al. [9] evaluated the future technical innovations and cost reduction potential for the parabolic trough and solar tower CSP technologies based on current research and development activities, ongoing commercial developments, and growth in the market scale. They calculated the LCOEs of those two technologies in 2015 and 2025 and found that the values matched very well to the published power purchase agreements (PPA) for the NOOR II and III power plants in Morocco when accounting for favorable financing conditions. The results also indicated that financing conditions were a major cost driver and offered potential for further cost reduction. Simsek et al. [10] analyzed the effects of incentives and financial conditions on the LCOE and government cost for both parabolic trough and solar tower CSP projects in Chile. The results showed that the debt fraction and discount rate indicated sensitivities on both the LCOE and government cost. In contrast, debt interest rate only showed significant sensitivity on the LCOE. However, the research of electricity generation cost analysis for CSP systems in China, particularly for the tower systems, are quite limited. As such, the electricity generation cost of CSP systems in China was reported to be approximately triple in comparison with the current feed-in tariff [6,11–13]. Zhao et al. [13] used a LCOE model to conduct a sensitivity analysis to examine the influences of investment over the construction period, annual operation and maintenance cost, annual electricity production, and the discount rate on the LCOE based upon a 50 MW parabolic trough CSP plant with 4 h TES in Erdos, Inner Mongolia. Furthermore, the impacts of incentive policies such as preferential loans, tax support, and zero land cost for CSP plants were also analyzed. They indicated that the 1 LCOE could be reduced to 1.173 RMB kWh− when those incentive policies functioned in parallel. Zhu et al. [14] studied and benchmarked the electricity cost of parabolic trough, tower, and dish CSP technologies in China by applying a LCOE model using plant-specific data in a national CSP database while cooperating with local CSP experts. The results showed that the LCOEs of 2014 for different 1 CSP plants were in a range of 1.2–2.7 RMB kWh− and tower CSP might be the most promising CSP technology in China. Li et al. [15] presented an integration analysis of geographic assessment, technical, and economic feasibility of the parabolic trough, tower, and dish Stirling CSP systems to investigate their development potential under Chinese conditions in the period of 2010–2050. The analysis demonstrated that China had sufficient potential for the utilization of parabolic trough and tower systems. Wu et al. [16] analyzed the effects of solar multiples and full load hours of heat storage on the generated electricity, net efficiency and LCOE of a Chinese demonstration trough CSP plant. The results showed that there existed the optimal solar multiples and full load hours of heat storage to reach the lowest LCOE and realize the highest economic benefit. Yang et al. [17] adopted the static payback period, net present value, net present value rate, and internal rate of return to calculate and discuss the cost-benefit of different CSP technologies based on the related data and design parameters of the first CSP demonstration projects in China. From the project perspective, a vast majority of CSP projects Energies 2019, 12, 1394 3 of 17

Energieshad a strong2019, 12, economic x FOR PEER feasibility REVIEW under the current technical level, policy, and resource endowment3 of 17 of China. The larger TES capacity usually could lead to a good cost-benefit. From the perspective of CSPtechnologies, technology the was cost-benefit better than of solarthat towerof parabolic CSP technology trough and was linear better Fresnel than thatCSP oftechnologies. parabolic trough And theand influence linear Fresnel of technology CSP technologies. type on Andthe cost-benefit the influence of of CSP technology was more type important on the cost-benefit than that of CSPTES capacity.was more Ren important et al. [18] than developed that of TES a cost-benefit capacity. Ren model, et al. which [18] developed covers the amultiple cost-benefit indices model, of the which total investmentcovers the multiple payback indices period, of net the present total investment value per paybackunit installed period, capacity, net present internal value rate per of unit return, installed and LCOE,capacity, to internal analyze rate the ofeconomic return, and benefit LCOE, of toCSP analyze industry the economicin China. benefitThey found of CSP that industry the generated in China. energy,They found DNI, that construction the generated costs, energy, and loans DNI, ratio construction had significant costs, andeffects loans on ratiothe economic had significant performance effects ofon CSP the economicindustry, while performance the influences of CSP of industry, operation-ma whileintenance the influences costs ofand operation-maintenance interest rate were relatively costs smallerand interest but still rate should were relatively not be ignored. smaller but still should not be ignored. Selecting the most appropriate sites is very criticalcritical forfor CSPCSP implementationimplementation inin China.China. Many geographic conditions need to be be considered considered such such as as solar solar resource, resource, water water resource, resource, energy energy demand, demand, and power grid accessibility [[15,19].15,19]. In In addition, addition, considering considering that that no no specific specific policies for the CSP industry havehave beenbeen enactedenacted yetyet in in China China [ 13[13],], more more data data about about the the CSP CSP electricity electricity cost cost isnecessary is necessary for forpolicy policy makers makers to make to make decisions. decisions. Thus, Thus, this paperthis pa conductedper conducted an economic an economic analysis analysis by applying by applying a LCOE a LCOEmodel model for 100 for MW 100 tower MW CSPtower plants CSP (shownplants (shown in Figure in1 Figure) in China 1) in with China four with di fffourerent different molten-salts molten- for saltsTES. Infor view TES. of In the view geographic of the conditions,geographic five conditions, locations five with locations various direct with normalvarious irradiance direct normal (DNI) irradiancewere selected. (DNI) The were calculations selected. of The both calculations the present of (2017) both valuethe present of LCOE (2017) and itsvalue future of LCOE evaluation and forits futurethe period evaluation (2018–2050) for the under period four (2018–2050) scenarios under proposed four by scenarios the IEA proposed (i.e. Blue Mapby the [20 IEA], Global (i.e. Blue Outlook Map [20],Advanced Global [21 Outlook], Global OutlookAdvanced Moderate [21], Global [21], and Outlook Roadmap Moderate [22]) were [21], presented. and Roadmap The time [22]) when were the presented.CSP grid parities The time would when be the attained CSP grid was parities estimated. would Moreover, be attained effects was of estimated. the land cost,Moreover, TES capacity, effects oflearning the land rate, cost, discount TES rate,capacity, lifetime, learning degradation rate, di rate,scount operation-maintenance rate, lifetime, degradation cost, and rate, insurance operation- cost maintenanceon the LCOE cost, were and also insurance investigated. cost on the LCOE were also investigated.

Figure 1. OperatingOperating principle principle of of a typical tower co concentratingncentrating solar power (CSP) plant [23]. [23]. 2. LCOE Model for CSP Systems 2. LCOE Model for CSP Systems The LCOE is one of the most frequently used characteristics to evaluate the electricity generation The LCOE is one of the most frequently used characteristics to evaluate the electricity generation cost of diﬀerent technologies, which calculates the cost of electricity during the lifetime and takes cost of different technologies, which calculates the cost of electricity during the lifetime and takes into into account the time value of money and the risks. It is equal to the sum of all the discounted costs account the time value of money and the risks. It is equal to the sum of all the discounted costs incurred during the lifetime of the project divided by the units of discounted energy produced over incurred during the lifetime of the project divided by the units of discounted energy produced over the entire lifetime [24] and can be expressed as follows: the entire lifetime [24] and can be expressed as follows: h i PN N −n n Ct +CLL+++ n= (()()V VIC +I)Ct1( +1 r+ r)− ttn1=1  LCOEtLCOE= = (1) Pt hN nnn − ni (1) N STF⋅⋅η( ()()11 − DR) ( + r+ ) Sn=1 TF η 1 DR 1 r − n=1 ·· − where all the symbols in the equation are summarized in Table 1. The numerator includes the capital cost of the system, land cost, operation and maintenance cost, and insurance cost. The denominator also shows the sum of the real electricity generated during the lifetime of the plant divided by the discount rate. The initial costs of the plant are considered as being paid up-front in this model,

Energies 2019, 12, 1394 4 of 17 where all the symbols in the equation are summarized in Table1. The numerator includes the capital cost of the system, land cost, operation and maintenance cost, and insurance cost. The denominator also shows the sum of the real electricity generated during the lifetime of the plant divided by the discount rate. The initial costs of the plant are considered as being paid up-front in this model, thereafter the capital and land costs do not have to be discounted. However, the operation and maintenance and insurance costs should be discounted as they are paid annually throughout the lifetime of the plant, which can be evaluated at a constant percentage of the total cost of the system. In addition, the annual electricity production can be directly calculated based on the available solar resource in cooperation with the tracking factor, performance factor, and degradation rate.

Table 1. Parameters and their values of the levelized cost of electricity (LCOE) model.

Symbol Description Value Units Levelized cost of electricity of a 1 LCOEt tower CSP system installed in the Calculated by Equation (1) RMB kWh− year t between 2017 and 2050 Capital cost of the system installed C Calculated by Equation (2) RMB W 1 t in the year t between 2017 and 2050 − 1 L Land cost In Table 3 RMB W− V Operation-maintenance cost 2 % I Insurance cost 0.5 % 2 1 S DNI Calculated by Equation (7) kWh m− year− TF Tracking factor 100 % 2 1 η Performance factor Calculated by Equation (8) m W− DR Degradation rate 0.2 % r Discount rate 10 % N Lifetime of the system 30 years

2.1. Capital Cost of the CSP System Several studies demonstrated that reduction in the capital costs of CSP plants can be characterized by the learning curves [25,26] and the cost of a system installed in a certain year, Ct, can be described as a function of the global cumulative installed capacity expressed as follows [6]:

log (1 LR) ! − Qt log 2 Ct = C0 (2) Q0 where Qt is the global cumulative installed capacity in a certain year (GW); Q0 is the cumulative installed capacity in the reference year (GW); LR is the learning rate (%); C0 is the capital cost of the 1 CSP system installed in the reference year (RMB W− ). In this paper, the year of 2017 was taken as the reference year and the capital cost of the LCOE present value was determined by C0 which was calculated by System Advisor Model (SAM) of National Renewable Energy Laboratory (NREL) [23].

2.2. Cumulative Installed Capacity The global cumulative installed capacity by the end of 2017 was reported to be 5.133 GW [2]. In order to evaluate the future cumulative installed capacity during the years of 2018–2050, four scenarios of CSP development roadmap proposed by IEA are used and listed in Table2. According to Hernández-Moro et al. [6] and Li et al. [15], the logistic function of an S-shaped curve can describe the objectives of cumulative installed capacity for Blue Map scenario and a 3-order polynomial equation can ﬁt the objectives for the other scenarios as follows:

 r(t 2010)  e −  1/0.82 1/M+er(t 2010)/M Qt =  − − (3)  k (t 2010)3 + k (t 2010)2 + k (t 2010) + 0.82 1 − 2 − 3 − Energies 2019, 12, 1394 5 of 17

Energies 2019, 12, x FOR PEER REVIEW 5 of 17 where t is the year and the parameters of r, M and ki (i = 1, 2, 3) are also summarized in Table2. ItAdvanced should be noticedscenario that is the valuesmost ambitious calculated because by Equation it considers (3) were all only useful used policies for the yearsto CSP of technology, 2018–2050 along the lines of the industry’s recommendations, from around the world. The objective of (shown in Figure2) when estimating the future evolution of LCOE, while Qt equaling to Q0 was used atcumulative the year of installed 2017 to calculatecapacity continues the present to valuegrow offast LCOE. and reaches It canbe 1524 seen GW that by the 2050. Global The OutlookBlue Map Advancedscenario has scenario the greatest is the most increase ambitious of the becausecumulative it considers installedall capacity useful policiesduring 2025 to CSP to 2040, technology, but the alongobjective the lines has ofseen the little industry’s growth recommendations, after 2045 and reache froms around 630 GW the by world. 2050 which The objective is less than of cumulative half of the installedGlobal Outlook capacity Advanced continues scenario. to grow fast and reaches 1524 GW by 2050. The Blue Map scenario has the greatest increase of the cumulative installed capacity during 2025 to 2040, but the objective has seen littleTable growth 2. Cumulative after 2045 installed and reaches capacity 630 objectives GW by 2050 of CSP which systems is less in thanInternational half of the Energy Global Agency Outlook Advanced(IEA) scenario. scenarios and the parameters of their fitting functions. Blue Global Outlook Global Outlook Scenario Roadmap Table 2. Cumulative installedMap capacity objectivesAdvanced of CSP systems in InternationalModerate Energy Agency (IEA) scenarios and the2010 parameters 0.82 of their ﬁtting functions. 0.82 0.82 0.82 Objectives 2020 20 Global 84 Outlook Global Outlook 69 148 Scenario Blue Map Roadmap (GW) 2030 250 Advanced 342 Moderate 231 337 2050 2010630 0.82 1524 0.82 0.82 831 0.82 1089 Objectives 2020 20 84 69 148 k1 = 0.0104; k1 = 0.0059; k1 = -0.0002; (GW) 2030R = 0.32; 250 342 231 337 k2 = - Parameters k2 = 0.6972; k2 = 0.4759; 2050M = 630 630 1524 831 1089 0.1016; k3 = 0.7565 k3 = 2.0815 k = 0.0059; k = 0.0002; k = 0.0104;k3 = 14.6982 R = 0.32; 1 1 − 1 Parameters k = 0.6972; k = 0.4759; k = 0.1016; M = 630 2 2 2 − k3 = 0.7565 k3 = 2.0815 k3 = 14.6982

Figure 2. Fitting curves about cumulative installed capacity of CSP systems for four IEA scenarios in theFigure years 2. of Fitting 2018–2050. curves about cumulative installed capacity of CSP systems for four IEA scenarios in the years of 2018–2050. 2.3. Learning Rate 2.3.The Learning range Rate of learning rate for solar thermal power tower technology is 5%–20% in the literatureThe[27 range–30]. of In learning this study, rate we for used solar a conservative thermal power estimation tower technology of 10% recommended is 5%–20% byin the IEA literature [20,22]. [27–30]. In this study, we used a conservative estimation of 10% recommended by IEA [20,22]. 2.4. Land Cost 2.4.Many Land Cost geographic conditions need to be considered while selecting a potential location for installing a tower CSP plant in China, including solar resource, water resource, energy demand, and power Many geographic conditions need to be considered while selecting a potential location for grid accessibility [15,19]. Water is a major concern in deciding on the cooling mode. Adopting the installing a tower CSP plant in China, including solar resource, water resource, energy demand, and dry cooling technology would increase the investment of 7%–9% and reduce the electricity output by power grid accessibility [15,19]. Water is a major concern in deciding on the cooling mode. Adopting 5% [31]. The energy demand is another crucial factor for site selection. The eastern region of China the dry cooling technology would increase the investment of 7%–9% and reduce the electricity output has the most population and the highest energy demand. However, the northwestern part has the by 5% [31]. The energy demand is another crucial factor for site selection. The eastern region of China highest DNI. has the most population and the highest energy demand. However, the northwestern part has the highest DNI.

Energies 2019, 12, 1394 6 of 17 Energies 2019, 12, x FOR PEER REVIEW 6 of 17

In this study, fivefive locationslocations withwith didifferentfferent DNIDNI values were selected to assess the potential for installing tower CSP plants in China, which are are Huizhou near Shenzhen, Shenzhen, Taicang Taicang near Shanghai, Yanqing inin Beijing,Beijing, LinxiLinxi inin InnerInner Mongolia,Mongolia, andand DelinghaDelingha inin QinghaiQinghai (as(as shownshown inin FigureFigure3 3).). FourFour ofof them areare locatedlocated in in eastern eastern China China where where the the electricity electricity demand demand is relatively is relatively high, high, near near cities cities with with grid transmission,grid transmission, and suandfficient sufficient water water resources resources in order into order analyze to analyze the economic the economic feasibility feasibility of building of towerbuilding CSP tower plants CSP in thisplants region, in this particularly region, particularly near the most near developed the most cities developed of China. cities Delingha of China. was 2 1 takenDelingha as a was reference taken site as wherea reference the annual site where DNI is the near annual the bottom DNI is threshold near the of bottom 2000 kWh threshold m− year of− 2000for installingkWh m-2 anyyear form-1 for of installing the CSP systemsany form to achieveof the aCSP reasonable systems economic to achieve performance a reasonable [32 ].economic It can be observedperformance from [32]. Figure It can3 that be Huizhou observed and from Taicang Figure have 3 that relatively Huizhou low and DNI Taicang among allhave the relatively five locations. low SinceDNI among DNI is all the the most five important locations. energy Since DNI input is data the most for CSP important systems energy in SAM, input the specificdata for DNI CSP forsystems those locationsin SAM, the are specific calculated DNI by for the those method locations based are on calc theulated air mass by whichthe method is introduced based on later. the air Then, mass the which land areasis introduced required later. to build Then, a tower the land CSP areas plant required of 100 MW to inbuild diff erenta tower locations CSP plant was of calculated 100 MW by in SAMdifferent and shownlocations in was Table calculated3. It can be by seen SAM that and the shown land price in Table and unit3. It landcan be costs seen of that Huizhou the land and price Taicang and were unit muchland costs higher of Huizhou than the otherand Taicang three locations. were much higher than the other three locations.

Table 3. Land costs for a tower CSP plant of 100 MW in didifferentﬀerent locations. 2 1 2 1 LocationLocation LandLand Area Area(m2 W (m-1) W Land− ) Price Land (RMB Price m-2 (RMB） Land m− )Cost Land(RMB Cost W-1) (RMB W− ) HuizhouHuizhou 0.0546 0.0546335 33518.3 18.3 TaicangTaicang 0.0554 0.0554252 25213.97 13.97 YanqingYanqing 0.0573 0.057370.8 70.84.05 4.05 Linxi 0.0566 84 4.75 Linxi 0.0566 84 4.75 Delingha 0.0513 25.5 1.31 Delingha 0.0513 25.5 1.31

2.5. Operation and Maintenance Cost 2.5. Operation and Maintenance Cost The operation and maintenance cost of CSP plants is lower than that of traditional power plants The operation and maintenance cost of CSP plants is lower than that of traditional power plants since fuel consumption is not necessary in CSP plants. This expense refers to the salary and welfare for since fuel consumption is not necessary in CSP plants. This expense refers to the salary and welfare the staﬀ, the costs of water used for cooling and cleaning, plant operation costs, and ﬁeld maintenance for the staff, the costs of water used for cooling and cleaning, plant operation costs, and field costs. The annual operation and maintenance cost was taken as 2% of the capital investment of the maintenance costs. The annual operation and maintenance cost was taken as 2% of the capital system, based on the reported values [6,33,34]. investment of the system, based on the reported values [6,33,34].

2.6. Insurance Cost Since the CSP plants have relatively high technological risks in contrast with the conventional power plants, an insurance policy should be considered. The insurance cost includes fixed assets Energies 2019, 12, 1394 7 of 17

2.6. Insurance Cost Energies 2019, 12, x FOR PEER REVIEW 7 of 17 Since the CSP plants have relatively high technological risks in contrast with the conventional powerinsurance plants, and an other insurance insurance policy which should can be assumed considered. as 0.5% The of insurance the capital cost investment includes ﬁxedof the assetssystem insurance[35]. and other insurance which can be assumed as 0.5% of the capital investment of the system [35].

2.7.2.7. Solar Solar Resource Resource TheThe DNI DNI is is strongly strongly a ﬀaffectedected by by the the composition composition of of atmosphere atmosphere and and the the weather weather corresponding corresponding toto a a speciﬁc specific location. location. The The DNI DNI on on the the earth’s earth’s surface surface between between sunrise sunrise and and sunset sunset can can be be calculated calculated accordingaccording to to the the following following experimentally experimentally determined determined equation equation [36 [36]:]:

=−h ()AM0.678AM 0.678 +i S =Shh1.353D 1.353(1 0.14 1 0.14h)0.7 0.7+ 0.14 0.14h (4)(4) D − wherewhereh ish is the the location location altitude altitude above above sea sea level level (m) (m) and andAM AMis is the the air air mass. mass.AM AMis is deﬁned defined as as [37 [37]:]: 1 AM = 1 AM = αα++ −1.6364 (5)(5) sin 0.50572( 6.07995 )1.6364 sin α + 0.50572(α + 6.07995◦)− where α is the solar elevation angle which can be calculated as a function of the solar declination whereangle,α δis, local the solar latitude, elevation ϕ, and angle solar which hour can angle, be calculated γ, [38]: as a function of the solar declination angle, δ, local latitude, φ, and solar hour angle, γ,[38]: αδ=+sin−1 () sin sinφ cosδ cosφγ cos (6) α = sin 1(sin δ sin φ + cos δ cos φ cos γ) (6) The DNIs of summer solstice in Huizhou,− Taicang, Yanqing, Linxi, and Delingha were calculated by theThe above DNIs mentioned of summer method solstice inand Huizhou, shown in Taicang, Figure Yanqing,4. It can be Linxi, seen and that Delingha the DNI wereat Linxi calculated is higher bythan the that above at mentionedthe other locations method except and shown for the in reference Figure4. Itsite can Delingha. be seen that the DNI at Linxi is higher than that at the other locations except for the reference site Delingha.

Figure 4. Direct normal irradiances (DNIs) of summer solstice in diﬀerent locations. Figure 4. Direct normal irradiances (DNIs) of summer solstice in different locations. Then, the annual DNI, S, as the integral of DNI along a year can be calculated as follows: Then, the annual DNI, S, as the integral of DNI along a year can be calculated as follows:

Z t2 t SSdt= 2 S = St DdtD (7)(7) 1 t1 where t1 and t2 indicate the beginning and end of the year, respectively. The annual DNI of Huizhou, where t and t indicate the beginning and end of the year, respectively. The annual DNI of Huizhou, Taicang,1 Yanqing,2 Linxi, and Delingha are shown in Table 4. It indicates that the annual DNI in Taicang, Yanqing, Linxi, and Delingha are shown in Table4. It indicates that the annual DNI in Delingha is much higher than other sites. The annual DNIs in Taicang and Huizhou are even lower Delingha is much-2 higher-1 than other sites. The annual DNIs in Taicang and Huizhou are even lower than 800 kWh m2 year 1. than 800 kWh m− year− . Table 4. Annual DNIs of different locations.

Location Huizhou Taicang Yanqing Linxi Delingha S (kWh m-2 year-1) 795.7 762.9 1189.9 1668.1 2080.5

2.8. Tracking Factor

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Table 4. Annual DNIs of diﬀerent locations.

Location Huizhou Taicang Yanqing Linxi Delingha 2 1 S (kWh m− year− ) 795.7 762.9 1189.9 1668.1 2080.5

2.8. Tracking Factor As the CSP systems can only use DNI, the tracking system is critical for CSP plants. For tower systems, azimuth-altitude dual axis solar tracking mechanisms are employed for the heliostat. Hence, the tracking accuracy to the sunlight is high and the tracking factor is one [33].

2.9. Performance Factor The performance factor can be calculated as follows:

1 Electricity produced per installed watt (kWh W− ) η = (8) 2 Solar resource utilized per year (kWh m− )

For CSP systems, the utilized solar resource approximates to the DNI. And the electricity production per installed watt can be obtained by dividing the annual electrical output to the power of the plant. The electrical output was estimated by SAM, which was not characterized by the solar ﬁeld but by the power of the turbines. In addition, since value of the performance factor is relevant to TES, the range of 0–10 h of TES capacity was evaluated.

2.10. Degradation Rate Because of degradation, the annual electrical output of CSP systems would be slightly diminished. The recommended value of degradation rate was 0.2% mainly owing to the degradation of turbines [7].

2.11. Discount Rate The discount rate is used to consider the time value of money and the risk of the investment. According to IEA [34], the discount rate is between 10% and 15% for CSP systems. A conservative value of 10% was adopted in the present paper.

2.12. Lifetime of the System For CSP systems, the common lifetime is in the range of 25–40 years [22,33,34,39,40]. In this study, a lifetime of 30 years was used as suggested by most similar studies.

3. Results and Discussion On the basis of the LCOE model described above, the economic analysis of 100 MW tower CSP plants in five locations in China with four different molten-salts for TES was estimated. Calculation results of both the LCOE present value in 2017 and its future evolution over the period of 2018–2050 under four scenarios proposed by the IEA were also presented. Moreover, the specific time when CSP grid parities can be attained was estimated and influences of the land cost, TES capacity, learning rate, discount rate, lifetime, degradation rate, operation-maintenance cost, and insurance cost on the LCOE were examined. As the reference, a 100 MW plant using solar salt (NaNO3-KNO3, 60-40 wt.%) with 6 h TES in Delingha was used in this study. Unless otherwise stated, the values for the input parameters of Equation (1) were used as specified in Table1. Table5 shows the initial costs and LCOE present values for di fferent locations under reference conditions. It can be seen that the initial costs of Huizhou and Taicang were much higher than that of Delingha which is mainly due to expensive land cost. The influence of land cost on the LCOE is illustrated in Figure5. The x axis shows the percentage of the land cost reduction. It was found that the land cost affected the LCOE significantly for Huizhou and Taicang as their land costs accounted Energies 2019, 12, 1394 9 of 17 for 38.8% and 30.8% of their total initial capital investment, respectively. Furthermore, the LCOE 1 in Huizhou and Taicang was still higher than 6 RMB kWh− even if the land cost dropped to zero. 2 1 It was mainly because the DNI in both locations is less than 800 kWh m− year− , which makes the capacity factor relatively low. Thus, it is inappropriate to install a tower CSP plant nearby Shenzhen and Shanghai regardless of the land cost. It can also be observed from Figure5 that the land cost had a Energies 2019, 12, x FOR PEER REVIEW 9 of 17 slight influence on the LCOE of CSP plants in Yanqing, Linxi, and Delingha. Yanqing is the selected locationthe eastern of the of firstChina. pilot Therefore, CSP plant it can in Beijing be concluded and the that land the cost major is quite factor low. resulting The LCOE in low of Linxi LCOE had for 1 thedifferent lowest locations value of 2.33is the RMB high kWh DNI,− whichbecause leads of itsto more highest produced DNI among energy, the higher four selected capacity locations factor, and at themore eastern efficient of China. TES. Therefore, it can be concluded that the major factor resulting in low LCOE for different locations is the high DNI, which leads to more produced energy, higher capacity factor, and more efficient TES.Table 5. Initial costs and LCOE present values for different locations in China.

Table 5. Initial costsSystem and cost LCOE Land present cost values LCOE for diﬀ2017erent locationsCapacity in factor China. Location (RMB W-1) (RMB W-1) (RMB kWh-1) (%) System Cost Land Cost LCOE Capacity Factor Location Huizhou 31.48 18.3 9.28 2017 7.6 (RMB W 1) (RMB W 1) (RMB kWh 1) (%) Taicang 31.45− 13.97 − 8.49 − 7.7 HuizhouYanqing 31.4831.38 4.05 18.3 3.5 9.28 15.1 7.6 Taicang 31.45 13.97 8.49 7.7 Linxi 31.24 4.75 2.33 23 Yanqing 31.38 4.05 3.5 15.1 LinxiDelingha 31.2430.6 1.31 4.75 1.45 2.33 33.3 23 Delingha 30.6 1.31 1.45 33.3

Figure 5. Effect of land cost on the LCOE for five different sites. Figure 5. Effect of land cost on the LCOE for five different sites. The TES unit is an important sub-system of CSP plants which guarantees dispatchable electricity supply.The Moreover, TES unit is high an important heat transfer sub-system fluid (HTF) of CSP operating plants which temperature guarantees in a CSP dispatchable plant is beneficial electricity becausesupply. itMoreover, can improve high the heat heat transfer to electricity fluid (HTF) conversion operating effi ciency.temperature The U.S. in a Department CSP plant is of beneficial Energy (DOE)because suggested it can improve that the the appropriate heat to electricity HTF candidates conversion must efficiency. be thermally The stable U.S. Department at temperatures of Energy up to 720(DOE)◦C[ 41suggested]. In the presentthat the study, appropriate four molten-salt HTF candidates eutectic must mixtures be thermally were selected stable as at the temperatures energy storage up mediato 720 to °C estimate [41]. In thethecorresponding present study, LCOE. four molten-s Except foralt solareutectic salt mixtures which was were selected selected as theas the reference, energy thestorage other media three saltsto estimate all have the the corresponding temperature ofLCOE. thermal Except stability for solar higher salt than which 715 was◦C andselected show as the the potentialreference, to the be usedother inthree commercial salts all have CSP plantsthe temper whichature has of been thermal reported stability in previous higher than studies 715 [ 42°C– 46and]. Theirshow thermophysical the potential to propertiesbe used in andcommercial TES costs CSP are plants listed which in Table has6[ been1,42 –reported47]. It can in beprevious seen that studies the chloride-salt[42–46]. Their mixture thermophysical (NaCl-KCl-ZnCl properties2, 8.1-31.3-60.6 and TES cost wt.%)s are has listed the lowestin Table TES 6 [1,42–47]. cost due toIt can the lowestbe seen saltthat price the chloride-salt and largest density. mixture And (NaCl-KCl-ZnCl the TES costs2, of 8.1-31.3-60.6 the fluoride-salt wt.%) mixture has the (LiF-NaF-KF,lowest TES cost 29.3-11.7-59 due to the wt.%)lowest and salt carbonate-salt price and largest mixture density. (Li2CO And3-Na the2 COTES3 -Kcosts2CO of3, the 32.1-33.4-34.5 fluoride-salt wt.%) mixture are much(LiF-NaF-KF, higher since 29.3- their11.7-59 salt wt.%) prices areand more carbonate-salt than three timesmixture of the(Li2 pricesCO3-Na of2CO solar3-K salt2CO and3, 32.1-33.4-34.5 chloride-salt mixture.wt.%) are much higher since their salt prices are more than three times of the prices of solar salt and chloride-salt mixture.

Table 6. Thermal properties and thermal energy storage (TES) costs of selected molten-salts.

NaNO3- Li2CO3-Na2CO3- NaCl-KCl- Molten-Salt LiF-NaF-KF KNO3 K2CO3 ZnCl2 29.3-11.7- Composition by wt.% 60-40 32.1-33.4-34.5 8.1-31.3-60.6 59.0 Melting point (°C) 220 454 400 229 Thermal stability (°C) 600 850 715 850

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Energies 2019, 12, x FOR PEER REVIEW 10 of 17 Table 6. Thermal properties and thermal energy storage (TES) costs of selected molten-salts. 1851.6- Density (kg m-3) 1708.4-1950.1 1959.7-2069.4 1946.2-2275.5 Molten-Salt NaNO3-KNO3 LiF-NaF-KF2116.5 Li2CO3-Na2CO3-K2CO3 NaCl-KCl-ZnCl2 HeatComposition capacity by (kJ wt.% kg-1 K-1) 60-401.48-1.55 29.3-11.7-59.01.28-1.82 32.1-33.4-34.5 1.61 8.1-31.3-60.6 0.9-0.92 MeltingViscosity point (mPa (◦C) s) 220 0.99-5.78 1.64-12.38 454 6.11-45.09 400 229 3.48-29.63 Thermal stability (◦C) 600 850 715 850 3 -1 - ThermalDensity conductivity (kg m− ) (W m K 1708.4-1950.1 1851.6-2116.5 1959.7-2069.4 1946.2-2275.5 1 1 0.33-0.40 0.05-0.27 0.45-0.49 0.30-0.38 Heat capacity (kJ1)kg − K− ) 1.48-1.55 1.28-1.82 1.61 0.9-0.92 Viscosity (mPa s)-1 0.99-5.78 1.64-12.38 6.11-45.09 3.48-29.63 Salt price (RMB kg1 )1 5.80 16.66 15.02 4.68 Thermal conductivity (W m− K− ) 0.33-0.40 0.05-0.27 0.45-0.49 0.30-0.38 1 t-1 TESSalt price cost (RMB (RMB kg −kWh) ) 5.80156.00 16.66316.79 15.02 490.15 4.68 111.69 1 TES cost (RMB kWht− ) 156.00 316.79 490.15 111.69 Figure 6 and Figure 7 indicate the effect of TES capacity on the performance factor and LCOE presentFigures value6 and using7 indicate different the esaltffect mixtures of TES capacityas the energy on the storage performance media factorunder andreference LCOE conditions. present valueIt can using be difoundfferent that salt the mixtures performance as the factor energy of storage solar salt media which under has referencea much lower conditions. temperature It can of bethermal found that stability the performance is higher than factor that of solarof the salt fluoride-salt which has amixture much lower and chloride-salt temperature ofmixture. thermal The stabilitychloride-salt is higher mixture than that with of the the lowest fluoride-salt performance mixture factor and has chloride-salt the highest mixture. LCOE when The chloride-saltthe CSP system mixturehas no with TES. the Since lowest a larger performance capacity factor TES hasunit the im highestproves LCOEthe performance when the CSPfactor system of CSP has systems no TES. as Sinceconfirmed a larger capacityby other TESstudies unit [48,49] improves then the the performance LCOE of all factor molten-salts of CSP systemsdecreases as as confirmed the capacity by other of TES studiesincreases. [48,49 However,] then the LCOEwhen the of all performance molten-salts factor decreases no longer as the improves capacity along of TES with increases. the increasing However, TES whencapacity, the performance then LCOE factorgoes up, no thereafter, longer improves as the cost along of withthe TES the rises. increasing Thus, TESthere capacity, is an optimal then LCOEcapacity goesof up,TES thereafter,resulting in as the the lowest cost of LCOE. the TES The rises. LCOE Thus, of the there chloride-salt is an optimal mixture capacity reduces of TES sharply resulting before in thethe lowestTES capacity LCOE. reaches The LCOE the optimal of the chloride-salt value because mixture of its reducescomparably sharply lowest before salt the price. TES As capacity such, the reachesincreasing the optimal trends value of the because LCOEsof of its the comparably fluoride-salt lowest and carbonate–salt salt price. As such, mixtures the increasing are more trendsremarkable of thewhen LCOEs the of TES the fluoride-saltcapacity is higher and carbonate–salt than the optimal mixtures value are due more to their remarkable high costs. when The the solar TES salt capacity has the is higherlowest thanLCOE the among optimal those value four due molten-s to theiralts high regardless costs. The of the solar TES salt capacity. has the lowest LCOE among those four molten-salts regardless of the TES capacity.

Figure 6. Inﬂuence of TES capacity on the performance factor for four diﬀerent molten-salts. Figure 6. Influence of TES capacity on the performance factor for four different molten-salts.

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Energies 2019, 12, x FOR PEER REVIEW 11 of 17 Energies 2019, 12, 1394 11 of 17

Figure 7. Influence of TES capacity on the LCOE for four different molten-salts. Figure 7. Influence of TES capacity on the LCOE for four different molten-salts. Figure 8 showsFigure 7.the Influence evolution of TES of capacitythe LCOE on thebetween LCOE for2017 four and different 2050 atmolten-salts. four different scenarios underFigure reference8 shows conditions. the evolution It can of be the deduced LCOE that between the future 2017 andcost 2050of tower at four CSP di electricityfferent scenarios decreases Figure 8 shows the evolution of the LCOE between 2017 and 2050 at four different scenarios underas the reference global cumulative conditions. installed It can be CSP deduced capacity that increases the future during cost ofthe tower period CSP of electricity2018-2050. decreases Moreover, under reference conditions. It can be deduced that the future cost of tower CSP electricity decreases asthe the future global LCOE cumulative in the Blue installed map CSPscenario capacity presents increases a slower during reduction the period than ofthe 2018-2050. other three Moreover, scenarios, as the global cumulative installed CSP capacity increases during the period of 2018-2050. Moreover, theparticularly future LCOE in inthe the initial Blue mapyears, scenario owing presentsto the lower a slower objectives reduction of thanthe CSP the other installation three scenarios, capacity. the future LCOE in the Blue map scenario presents a slower reduction than the other three scenarios, particularlyHowever, inthe the difference initial years, of the owing LCOE to theat differen lower objectivest scenarios of becomes the CSP installationquite small capacity. in 2050. However,The LCOE particularly in the initial years, owing to the lower objectives of the CSP installation capacity. theof ditowerfference CSP of systems the LCOE are at0.72, diff erent0.64, 0.70, scenarios and 0.67 becomes RMB quitekWh-1 small in 2050 in 2050.and represents The LCOE 49.88%, of tower 44.02%, CSP However, the difference of the LCOE at different scenarios becomes quite small in 2050. The LCOE systems47.94%, are and 0.72, 46.14% 0.64, 0.70,of its and present 0.67 RMBvalue kWh in 20171 in for 2050 Blue and map represents scenario, 49.88%, Global 44.02%, outlook 47.94%, advanced and of tower CSP systems are 0.72, 0.64, 0.70, and −0.67 RMB kWh-1 in 2050 and represents 49.88%, 44.02%, 46.14%scenario, of its Global present outlook value moderate in 2017 for scenario, Blue map and scenario, Roadmap Global scenario, outlook respectively. advanced scenario, Global 47.94%, and 46.14% of its present value in 2017 for Blue map scenario, Global outlook advanced outlook moderate scenario, and Roadmap scenario, respectively. scenario, Global outlook moderate scenario, and Roadmap scenario, respectively.

Figure 8. Future evolution of the LCOE for tower CSP systems and grid parities at four scenarios (solid Figure 8. Future evolution of the LCOE for tower CSP systems and grid parities at four scenarios line: tower CSP plants; dash line: coal-fired power plants). (solid line: tower CSP plants; dash line: coal-fired power plants). Figure 8. Future evolution of the LCOE for tower CSP systems and grid parities at four scenarios Figure8 also illustrates the time when the electricity cost of tower CSP plants will reach the grid (solidFigure line: 8 also tower illustrates CSP plants; the dash time line: when coal-fired the electric powerity plants). cost of tower CSP plants will reach the grid parity under reference conditions. Since more than three quarters of the electricity is generated by parity under reference conditions. Since more than three quarters of the electricity is generated by coal-firedFigure power 8 also plants illustrates in China the [time34], thewhen cost the evolution electricity of cost coal-fired of tower electricity CSP plants was will used reach as the the grid grid coal-fired power plants in China [34], the cost evolution of coal-fired electricity was used as the1 grid parityparity in under this study. reference The cost conditions. of electricity Since produced more than by coal-firedthree quarters power of plants the electricity is 0.41 RMB is generated kWh− [50 by] parity in this study. The cost of electricity produced by coal-fired power plants is 0.41 RMB kWh-1 [50] whichcoal-fired is only power considered plants thein China production [34], the costs cost and evolution would increase of coal-fired linearly electricity in the future was withused anas annualthe grid which is only considered the production costs and would increase linearly in the future with an conservativeparity in this growth study. The rate cost of 2.7% of electricity [15]. In addition, produced considering by coal-fired the power CO2 plantsemissions is 0.41 by RMB the coal-fired kWh-1 [50] annual conservative growth rate of 2.7% [15]. In addition, considering1 the CO2 emissions 1by the coal- power plants, the carbon emission prices of 162.5 RMB ton− CO2 [51] and 325 RMB ton− CO2 [20] which is only considered the production costs and would increase-1 linearly in the future-1 with an fired power plants, the carbon emission prices of 162.5 RMB ton CO2 [51] and 325 RMB ton1 CO2 [20] wereannual added conservative to estimate growth the grid rate paritiesof 2.7% [15]. with In an addition, emission considering factor of 0.9 the kg CO CO2 emissions2 kWh− by[34 the,52, coal-53]. were added to estimate the grid parities with an emission factor of 0.9 kg CO2 kWh-1 [34,52,53]. According to Figure8, the time when the grid parities of tower CSP-1 systems would be achieved-1 are fired power plants, the carbon emission prices of 162.5 RMB ton CO2 [51] and 325 RMB ton CO2 [20] were added to estimate the grid parities with an emission factor of 0.9 kg CO2 kWh-1 [34,52,53].

Energies 2019, 12, x FOR PEER REVIEW 12 of 17

According to Figure 8, the time when the grid parities of tower CSP systems would be achieved are Energies 2019, 12, 1394 12 of 17 summarized in Table 7. It can be observed that the LCOE of tower CSP would reach the grid parity in the years of 2038–2041 in the case of no future penalties for CO2 emissions. And the grid parity summarizedwould bring in forward Table7. about It can 7–8 be observedyears for the that carbon the LCOE emission of tower price CSP of 162.5 would RMB reach ton the-1 CO grid2 and parity about -1 in13–14 the years years of for 2038–2041 the carbon in emission the case ofprice no futureof 325 RMB penalties ton forCO CO2. 2 emissions. And the grid parity 1 would bring forward about 7–8 years for the carbon emission price of 162.5 RMB ton− CO2 and about 1 13–14 years for theTable carbon 7. Time emission when the price grid of parities 325 RMB of to tonwer− CSPCO systems2. would be achieved. Time When LCOE Equals to the Grid Parity TableScenario 7. Time when the grid parities of tower CSP systems would be achieved. 0 RMB ton-1 CO2 162.5 RMB ton-1 CO2 325 RMB ton-1 CO2 Blue map 2040Time When LCOE Equals 2032 to the Grid Parity 2028 Scenario 1 1 1 Global outlook advanced0 RMB ton− CO 20382 162.5 RMB 2031 ton − CO2 325 2025RMB ton− CO2 Blue map 2040 2032 2028 Global outlook moderate 2041 2033 2027 Global outlook advanced 2038 2031 2025 Global outlook moderateRoadmap 20412040 2033 2031 2024 2027 Roadmap 2040 2031 2024 In order to evaluate the influence of learning rate on the LCOE for tower CSP systems, the LCOE in theIn order year toof evaluate2050 with the the influence learning of rate learning ranging rate from on the 5% LCOE to 20% for towerunder CSPreference systems, conditions the LCOE are inestimated the year ofat four 2050 scenarios with the and learning shown rate in Figure ranging 9. fromIt illustrates 5% to 20%that the under LCOE reference reduces conditions as the learning are estimatedrate increases at four and scenarios the reduction and shown rates in Figureare 66.4%,9. It illustrates71.8%, 68.2%, that theand LCOE 69.9% reduces for Blue as map the learning scenario, rateGlobal increases outlook and theadvanced reduction scenario, rates are Global 66.4%, 71.8%,outlook 68.2%, moderate and 69.9% scenario, for Blue and map Roadmap scenario, scenario, Global outlookrespectively. advanced The scenario, result indicates Global outlook that this moderate parameter scenario, has a and significant Roadmap effect scenario, on the respectively. economic Thefeasibility result indicates of tower thatCSP thisplants. parameter has a significant effect on the economic feasibility of tower CSP plants.

Figure 9. Effect of learning rate on the LCOE for four scenarios. Figure 9. Effect of learning rate on the LCOE for four scenarios. Some studies in the literature [49,54,55] claimed that the discount rate is one of the most significant Some studies in the literature [49,54,55] claimed that the discount rate is one of the most variables affecting the LCOE of the CSP systems, which reflects the risk-adjusted opportunity of capital significant variables affecting the LCOE of the CSP systems, which reflects the risk-adjusted investment. Figure 10 shows that the present value of LCOE under reference conditions rises about 35% opportunity of capital investment. Figure 10 shows that the present value of LCOE under reference when the discount rate increases from 10% to 15%. Although the discount rate for the CSP technology conditions rises about 35% when the discount rate increases from 10% to 15%. Although the discount is relatively high for now, it is expected to decline in the future as CSP markets develop and the related rate for the CSP technology is relatively high for now, it is expected to decline in the future as CSP technologies become more mature. markets develop and the related technologies become more mature.

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Figure 10. Influence of discount rate on the LCOE. Figure 10. Inﬂuence of discount rate on the LCOE. In order to study the sensitivityFigure 10. Influence of the LCOE of discount with therate lifetimeon the LCOE. of tower CSP plants, the LCOE presentIn order value to in study different the sensitivitylifetime expectations of the LCOE under with reference the lifetime conditions of tower were CSP estimated plants, the and LCOE shown In order to study the sensitivity of the LCOE with the lifetime of tower CSP plants, the LCOE presentin Figure value 11. in It di canﬀerent be seen lifetime thatexpectations the LCOE is under less sensitive reference to conditions the lifetime. were As estimated the life time and increases shown present value in different lifetime expectations under reference conditions were estimated and shown infrom Figure 25 11to .40 It years, can be the seen LCOE that only the LCOE reduces is lessby 5.6%. sensitive to the lifetime. As the life time increases in Figure 11. It can be seen that the LCOE is less sensitive to the lifetime. As the life time increases from 25 to 40 years, the LCOE only reduces by 5.6%. from 25 to 40 years, the LCOE only reduces by 5.6%.

Figure 11. Effect of lifetime on the LCOE. Figure 11. Effect of lifetime on the LCOE. Figure 12 displays the impacts of degradation rate, operation-maintenance cost, and insurance cost Figure 12 displays the impactsFigure of 11. degradation Effect of lifetime rate, onoperation-maintenance the LCOE. cost, and insurance on the LCOE for tower CSP systems. The variables are changed within the range of 20% to 20% with cost on the LCOE for tower CSP systems. The variables are changed within the range− of -20% to 20% an intervalFigure of 12 5% displays around referencethe impacts conditions. of degradation It can berate, seen operation-maintenance that the effect of operation-maintenance cost, and insurance with an interval of 5% around reference conditions. It can be seen that the effect of operation- costcost is on greater the LCOE than thatfor tower of other CSP two systems. factors The and variab the influenceles are changed of the degradation within the range rate is of the -20% smallest. to 20% maintenance cost is greater than that of other two factors and the influence of the degradation rate is Onewith way an tointerval reduce of the 5% operation-maintenance around reference conditions. cost is to It improve can be theseen level that of the management effect of operation- and use the smallest. One way to reduce the operation-maintenance cost is to improve the level of highmaintenance quality CSP cost equipment. is greater than that of other two factors and the influence of the degradation rate is management and use high quality CSP equipment. the smallest. One way to reduce the operation-maintenance cost is to improve the level of management and use high quality CSP equipment.

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Figure 12. Sensitivity analysis of degradation rate, operation-maintenance cost, and insurance cost. Figure 12. Sensitivity analysis of degradation rate, operation-maintenance cost, and insurance cost. 4. Conclusions 4. Conclusions In order to provide support information for the Chinese government to formulate incentive policiesIn for order the CSP to provide industry, support we have information presented the for economic the Chinese analysis government based on to the formulate LCOE model incentive for 100policies MW tower for the CSP CSP plants industry, in the locationswe have ofpresented Huizhou the near economic Shenzhen, analysis Taicang based near Shanghai,on the LCOE Yanqing model infor Beijing, 100 MW Linxi tower in Inner CSP Mongolia, plants in andthe Delinghalocations inof QinghaiHuizhou in near China Shenzhen, with four Taicang molten-salt near eutecticShanghai, Yanqing in Beijing, Linxi in Inner Mongolia, and Delingha in Qinghai in China with four molten-salt mixtures of the chloride-salt mixture (NaCl-KCl-ZnCl2, 8.1-31.3-60.6 wt.%), fluoride-salt mixture eutectic mixtures of the chloride-salt mixture (NaCl-KCl-ZnCl2, 8.1-31.3-60.6 wt.%), fluoride-salt (LiF-NaF-KF, 29.3-11.7-59 wt.%), carbonate-salt mixture (Li2CO3-Na2CO3-K2CO3, 32.1-33.4-34.5 wt.%), mixture (LiF-NaF-KF, 29.3-11.7-59 wt.%), carbonate-salt mixture (Li2CO3-Na2CO3-K2CO3, 32.1-33.4- and solar salt (NaNO3-KNO3, 60-40 wt.%) as the energy storage media. Major conclusions were summarized34.5 wt.%), as and follows: solar salt (NaNO3-KNO3, 60-40 wt.%) as the energy storage media. Major conclusions were summarized as follows: (1)(1) It Itis is inappropriate inappropriate to to build build a towera tower CSP CSP plant plant nearby nearby Shenzhen Shenzhen and and Shanghai. Shanghai. Even Even if theif the land land costs drop to zero, the LCOEs would be still higher than 6 RMB kWh 1 mainly owing to the costs drop to zero, the LCOEs would be still higher than 6 RMB kWh− -1 mainly owing to the 2 1 relativelyrelatively low low DNI DNI of of less less than than 800 800 kWh kWh m− myear-2 year− .-1. The The impact impact of of DNI DNI on on the the LCOE LCOE is muchis much moremore significant significant than than the the other other influence influence factors. factors. (2)(2) There There is anis an optimal optimal capacity capacity of TESof TES resulting resulting in the in lowestthe lowest LCOE LCOE for a for certain a certain tower tower CSP plant,CSP plant, as theas performance the performance factor factor increases increases with thewith increasing the increasing TES capacity TES capacity and then and becomes then becomes stable. stable. The solarThe salt solar has salt the has lowest the lowest LCOE LCOE among among those those four molten-salts four molten-salts regardless regardless of the of TES the capacity. TES capacity. (3)(3) The The LCOE LCOE of of tower tower CSP CSP would would reach reach the the grid grid parity parity in in the the years years of of 2038–2041 2038–2041 in in the the case case of of no no futurefuture penalties penalties for thefor COthe2 emissionsCO2 emissions based onbased the fouron the scenarios four scenarios for CSP development for CSP development roadmap proposedroadmap by proposed IEA. As by such, IEA. the As grid such, parity the grid would parity be broughtwould be forward brought about forward 7–8 yearsabout for7-8 theyears 1 carbonfor the emission carbon priceemission of 162.5 price RMB of 162.5 ton− RMBCO2 andton-1 about CO2 and 13–14 about years 13–14 for the years carbon for emissionthe carbon 1 priceemission of 325 price RMB of ton 325− CORMB2. ton-1 CO2. (4)(4) The The LCOE LCOE of of the the tower tower CSP CSP systems systems is significantlyis significantly aff affectedected by by the the learning learning rate rate and and discount discount rate,rate, yet yet it isit is less less sensitive sensitive to to the the lifetime. lifetime. Moreover, Moreover, the the impact impact of of operation-maintenance operation-maintenance cost cost is is greatergreater than than that that of of insurance insurance cost cost and and degradation degradation rate. rate.

IncentiveIncentive policy policy is a is crucial a crucial force force for thefor rapidthe rapid development development of the of CSP the industryCSP industry and reducing and reducing the LCOEthe LCOE of CSP of plants. CSP Furtherplants. studiesFurther can studies be conducted can be conducted to evaluate to the evaluate impacts ofthe incentive impacts policiesof incentive for CSPpolicies projects for in CSP China, projects such asin investmentChina, such and as productioninvestment taxesand production credit, cash incentives,taxes credit, sales, cash or incentives, use tax reductionsales, or and use ditaxﬀerent reduction depreciation and different modes. depreciation modes. Author Contributions: Conceptualization, X.Z. and X.X.; methodology and investigation, X.Z.; resources and Author Contributions: Conceptualization, X.Z. and X.X.; methodology and investigation, X.Z.; resources and datadata curation, curation, W.L.; W.L.; writing—original writing—original draft draft preparation, preparation, X.Z.; X.Z.; writing—review writing—review and and editing, editing, X.X.; X.X.; supervision, supervision, W.X.;W.X.; project project administration, administration, X.X.; X.X.; funding funding acquisition, acquisition, X.Z. X.Z. and and X.X. X.X. Funding:Funding:This This research research was was funded funded by by National National Natural Natural Science Science Foundation Foundation of China,of China, grant grant number number 51706056 51706056 andand China China Postdoctoral Postdoctoral Science Science Foundation, Foundation, grant grant number number 2018M631927. 2018M631927.

Energies 2019, 12, 1394 15 of 17

Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

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