Comparison of Utility-scale Solar Power Generation Technologies in Yunnan Province, China

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Comparison of Utility-scale Solar Power Generation Technologies in Yunnan

Province, China

Ignacio Cortese

Master of Science Thesis

KTH School of Industrial Engineering and Management Energy Technology EGI_2017-0078 MCS EKV1203

SE-100 44 STOCKHOLM

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Examensarbete EGI_2017-0078 MCS EKV1203

Jämförelse av storskalig energi genererad av solkraft i Yunnan-provinsen, Kina

Ignacio Cortese

Godkänt

2017-mån-dag

Examinator

Björn Laumert

Handledare

Rafael Guédez

Uppdragsgivare

Zhejiang University

Kontaktperson

Yu Zitao Wang Ziyuan

Sammanfattning

Arbetet ger en jämförelse mellan två tekniker för solenergi, nämligen fotovoltaisk (photovoltaic, PV) och koncentrerad solenergi (concentrated solar power, CSP) för olika praktiska fall i den kinesiska provinsen Yunnan. Jämförelsen inkluderar kostnaden för variation i kraftproduktion inom projektekonomin för att göra en rättvis jämförelse mellan alternativen. I sin användning, hybridiseras CSP med fossila bränslen (CSP) medan PV kombineras med ett batteri (PV + B), med en förbränningsmotorgeneratorsats (PV + E) eller med en överföringsledning till en robust nätförbindelse där transportkapaciteten kan antas obegränsad (PV + T).

Inledningsvis, definierades åtta potentiella fall baserat på närhet till en robust nätinfrastruktur, tillgång till naturgas, samt bostads- eller industriella behovsprofiler.

Platser hittades i Yunnanprovinsen för sju av de åtta potentiella fallen. Typiska meteorologiska väderprofiler för ett år erhölls för dessa platser. Profiler för industriella- och bostadsbehov byggdes för jämförelsen. Den efterfrågade toppkraften antogs vara 10 MW och nettovikten motsvarar de typiska profilerna.

Därefter valdes huvudkomponenterna för varje typ av kraftverk. Tekno-ekonomiska parametrar och skalningsvariabler beräknades baserat på litteraturforskning och beräkningar. Dessa variabler omfattade, bland annat, förluster och effektivitet, kapitalkostnader per kvadratmeter och utsläpp per installerad megawatt.

Slutligen simulerades olika CSP- och PV-solfältstorlekar i System Advisor Model (SAM) på varje plats och en Excel-baserad modell byggdes för att leverera de ekonomiska, utsläpps- och energiprestanda för varje tekniskt alternativ baserat på interpolering av SAM:s output. SAM-modellen matades med väderfilerna och solfältparametrarna medan den Excel-baserade modellen inkorporerade väderinformationen, SAM:s

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output, behovsprofiler och techno-ekonomiska parametrar som inte hörde till solfältet.

Läsarna måste vara medvetna att resultaten är endast tillämpliga på de valda platserna, i Yunnanprovinsen och nätkontexten och att antaganden om import och otillräcklig efterfrågan kan överskatta prestandan för PV-teknik.

De viktigaste slutsatserna är att (i) om stor transportkapacitet finns i närheten, är det ekonomiska resultatet för PV och fossila bränslen likartade, även om billig naturgas är tillgänglig; (ii) PV i kombination med en förbränningsmotor är ekonomisk överlägsen över en bränslekostnad på 8,09 - 14,35 $ / GJ; (iii) ett solfält är nödvändigt för att ersätta diesel som ett klokt finansiellt val på de flesta platser; (iv) en minimivärdering av ~ 300 $ / MWh är nödvändig för att motivera batterilagring, och i så fall kan överdimensioneringen av PV-fältet ersätta batterier för att leverera samma ekonomi; (v) PV + T med ett fält av 9 till 11 MWdc är det bästa alternativet i nästan alla fall med bränslekostnad över 3,67 $ / GJ, om avståndet är mindre än ~ 80 (8,09 $ / GJ), ~ 210 (14,35 $ / GJ) och ~ 360 kilometer (22,86 $ / GJ), beroende på bränslepriset; (vi) PV + E överträffar konsekvent CSP. Dess idealiska PV-fältstorlek ligger mellan 16-25 MWac beroende på bränslekostnaden; (vii) PV + T och PV + B visar låga utsläpp och hög energiåtervinning på Energi Return On Energy Invested (EROEI) medan CSP och PV + E visar höga utsläpp och sällan återvinner den energi som investeras i dem; (viii) trots PV + B aldrig är bland de sundaste ekonomiska alternativen, fungerar det alltid bättre än CSP i utsläpp och EROEI och det är mer ekonomiskt i solaktier över 70%; (ix) PV + B kan leverera upp till 90% av efterfrågan till en rimlig kostnad.

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Master of Science Thesis EGI_2017-0078-MCS EKV1203

Comparison of Utility-scale Solar Power Generation Technologies in Yunnan Province,

China

Ignacio Cortese

Approved

2017-month-day

Examiner

Björn Laumert

Supervisor

Rafael Guédez

Commissioner

Zhejiang University

Contact person

Yu Zitao Wang Ziyuan

Abstract

The present work delivers a comparison between two solar power technologies, namely photovoltaic (PV) and concentrated solar power (CSP), for various practical cases in the Chinese province of Yunnan. The comparison includes the cost of power generation variability in the project economics to make a fair comparison between the alternatives. In their use, CSP is hybridized with fossil fuels (CSP) whereas PV is combined with a battery pack (PV+B), with an internal combustion engine generator set (PV+E) or with a transmission line to a robust grid connection where transport capacity may be assumed unlimited (PV+T).

Initially, eight potential cases were defined based on their proximity to a robust grid infrastructure, availability of natural gas, and residential or industrial electrical demand profiles. Locations were found in Yunnan province for seven out of the eight potential cases. Typical meteorological year weather files were obtained for these locations.

Industrial and residential demand profiles were built for the comparison. The peak power demand to be satisfied was assumed 10 MW and the net load equal to the typical profiles.

Thereafter, the main components of each type of power plant were selected. Based on literature search and calculations, techno-economic parameters and scaling variables were calculated. These included, among others, losses and efficiencies, capital costs per square meter and emissions per installed megawatt.

Finally, various CSP and PV solar field sizes were simulated in System Advisor Model (SAM) at each location and an Excel-based model was built to deliver the economic, emissions and energy performances of each technological alternative based on the

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interpolation of SAM’s output. SAM model was fed with the weather files and the solar field parameters, while the Excel-based developed model incorporated some of the weather information, SAM’s output, demand profiles and techno-economic parameters which did not belong to the solar field.

Readers must be aware that the results are only applicable to the selected sites, in the Yunnan province and grid context and that assumptions on imports and unsupplied demand might overestimate the performance of PV technologies.

The main conclusions are that: (i) if large transport capacity is available in the vicinity, PV and fossil fuel alternatives economic performances are similar, even if low-cost natural gas is available; (ii) PV in combination with an internal combustion engine makes economic sense above a fuel cost of 8.09 - 14.35 $/GJ; (iii) a solar thermal field is required to replace diesel to be a wise financial election in most locations; (iv) a minimum valuation of ~300 $/MWh is needed to justify battery storage and, in this case, the over dimensioning of the PV field can replace batteries to deliver the same economics; (v) PV+T with a 9 to 11 MWdc field is the best alternative in almost all cases with fuel cost above 3.67 $/GJ, if the distance less than ~80 (8.09 $/GJ), ~210 (14.35 $/GJ) and ~360 kilometers (22.86 $/GJ), depending on the fuel price; (vi) PV+E consistently outperforms CSP, and its ideal PV field size is between 16-25 MWac, depending on the fuel cost; (vii) PV+T and PV+B show low emissions and high Energy Return On Energy Invested (EROEI) while CSP and PV+E show high emissions and rarely recover the energy invested in them; (viii) despite PV+B is never among the soundest economic alternatives, it always performs better than CSP in emissions and EROEI, and it is more economic in solar shares above ~70%; (ix) PV+B can supply up to 90% of the demand at a reasonable cost.

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分类号： TK16

密级： Public

硕士学位论文

中文中文

中文中文论文文文文题目目目目:::: 英文英文英文

英文论文文文文题目目目目 ::::

申申申

申请人姓名人姓名人姓名人姓名指

指指指导教教教教合作合作合作合作导师 专业名称名称名称名称研究方向研究方向研究方向研究方向所在学院：

所在学院：所在学院：

所在学院：

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单位代码： 10335

学号： 21627127

硕士学位论文

中国云南省大型太中国云南省大型太中国云南省大型太

中国云南省大型太阳阳阳能阳能能能发电技技技技术比比比比

Comparison of utility-scale solar

generation technologies in Yunnan province, China

人姓名人姓名人姓名

人姓名：：：Ignacio Cortese ：教

教教

教师：：：俞自涛：自涛自涛自涛教授教授教授教授////博博博博导 导师：：：：钟崴崴崴崴名称

名称

名称名称：：：能源：能源能源能源环境工程境工程境工程境工程研究方向

研究方向研究方向

研究方向：：：：太太太阳太阳阳能利用阳能利用能利用能利用所在学院：

所在学院：所在学院：

所在学院：能源工程学院能源工程学院能源工程学院能源工程学院

文提交日文提交日文提交日

文提交日期期期期 2017 年年年年 8 月月月月

10335 21627127

比比比比较

solar power Yunnan

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硕士学位论文

Master’s Degree

Thesis Title: Comparison of utility generation technologies

姓名姓名姓名姓名学号学号学号学号学科学科学科

学科、、、、专业专业专业专业指导教师指导教师指导教师指导教师所在学院所在学院所在学院所在学院提交日期提交日期提交日期提交日期

硕士学位论文

Degree Thesis Report

Comparison of utility-scale solar power generation technologies in Yunnan province, China

Ignacio Cortese 21627127 能源环境工程俞自涛教授/博导

能源工程学院 2017 年 8 月

Report

scale solar power

Yunnan province, China

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中国云南省大型太中国云南省大型太中国云南省大型太

中国云南省大型太阳阳阳阳能能能能发电技技技技术比比比比较

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Comparison of utility-scale solar power generation technologies in Yunnan province, China

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浙江大学研究生学位论文独创性声明

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Zhejiang University Master Thesis

Acknowledgments

I wish to thank, first and foremost, my family and friends who have supported me during this process and Sweden and The People’s Republic of China that have warmly host me and been my home during the last two years.

I owe my deepest gratitude to Zhejiang University and KTH Royal Institute of Technology which have been outstanding academia which have provided me with, besides great friendships, a world-class education on sustainable energy engineering.

It has been an honor to receive lectures from Professors such as Joachim Claesson, Miroslav Petrov, Vera Nemanova, Andrew Martin, Rafael Guedez, Xuecheng Wu or Hua Zhu, among others. I would not like to forget to Dalarna University and Prof.

Tomas Persson.

I am deeply thankful to these universities and all their staff. I am particularly indebted to my Dr. Ziyuan Wang, Zofia Laine, Tuija Venermo and Katie Zmijewski for their continuous assistance in the KTH-ZJU dual degree arrangements.

I cannot find words to express my gratitude to Professors Yu Zitao, Wei Zhong and Rafael Guedez without whom this thesis would not have been possible. Their guidance and suggestions have greatly influenced and improved the outcome.

It is with immense gratitude that I acknowledge the Swedish education policies towards foreigner students and the economic support and help of KTH Royal Institute of Technology, Zhejiang University and Zhejiang Province.

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ii

Abstract

The present work delivers a comparison between two solar power technologies, namely photovoltaic (PV) and concentrated solar power (CSP), for various practical cases in the Chinese province of Yunnan. The comparison includes the cost of power generation variability in the project economics to make a fair comparison between the alternatives. In their use, CSP is hybridized with fossil fuels (CSP) whereas PV is combined with a battery pack (PV+B), with an internal combustion engine generator set (PV+E) or with a transmission line to a robust grid connection where transport capacity may be assumed unlimited (PV+T).

Initially, eight potential cases were defined based on their proximity to a robust grid infrastructure, availability of natural gas, and residential or industrial electrical demand profiles. Locations were found in Yunnan province for seven out of the eight potential cases. Typical meteorological year weather files were obtained for these locations. Industrial and residential demand profiles were built for the comparison.

The peak power demand to be satisfied was assumed 10 MW and the net load equal to the typical profiles.

Thereafter, the main components of each type of power plant were selected.

Based on literature search and calculations, techno-economic parameters and scaling variables were calculated. These included, among others, losses and efficiencies, capital costs per square meter and emissions per installed megawatt.

Finally, various CSP and PV solar field sizes were simulated in System Advisor Model (SAM) at each location and an Excel-based model was built to deliver the economic, emissions and energy performances of each technological alternative based on the interpolation of SAM’s output. SAM model was fed with the weather files and the solar field parameters, while the Excel-based developed model incorporated some of the weather information, SAM’s output, demand profiles and techno-economic parameters which did not belong to the solar field.

Readers must be aware that the results are only applicable to the selected sites, in the Yunnan province and grid context and that assumptions on imports and unsupplied demand might overestimate the performance of PV technologies.

The main conclusions are that: (i) if large transport capacity is available in the vicinity, PV and fossil fuel alternatives economic performances are similar, even if low-cost natural gas is available; (ii) PV in combination with an internal combustion engine makes economic sense above a fuel cost of 8.09 - 14.35 $/GJ; (iii) a solar thermal field is required to replace diesel to be a wise financial election in most

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locations; (iv) a minimum valuation of ~300 $/MWh is needed to justify battery storage and, in this case, the over dimensioning of the PV field can replace batteries to deliver the same economics; (v) PV+T with a 9 to 11 MWdc field is the best alternative in almost all cases with fuel cost above 3.67 $/GJ, if the distance less than ~80 (8.09

$/GJ), ~210 (14.35 $/GJ) and ~360 kilometers (22.86 $/GJ), depending on the fuel price; (vi) PV+E consistently outperforms CSP, and its ideal PV field size is between 16-25 MWac, depending on the fuel cost; (vii) PV+T and PV+B show low emissions and high Energy Return On Energy Invested (EROEI) while CSP and PV+E show high emissions and rarely recover the energy invested in them; (viii) despite PV+B is never among the soundest economic alternatives, it always performs better than CSP in emissions and EROEI, and it is more economic in solar shares above ~70%; (ix) PV+B can supply up to 90% of the demand at a reasonable cost.

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iv

摘要摘要摘要摘要

本次研究工作提供了光伏和 CSP 太阳能技术在中国云南省的各种实际应用情况的比较。建议的替代方案将太阳能发电的波动性包含在项目经济性中以使得不同方案间的比较更加公平。CSP 与化石燃料（CSP）相结合而光伏发电与内燃机发电机组相结合

（PV + E），以电池组（PV + B）或传输线作为强电网连接（PV + T）。

首先，基于强电网基础设施的连接、天然气的可用性、住宅及工业电力需求概况，

定义了八个可能的情况。在云南省发现了满足八个情况中的七个的地点。随后获得这些位置的天气参数文件。建立了典型的工业和住宅需求概况描述以用于比较。假定需满足的峰值功率为10MW，净负荷等于计算值。

随后，对各电厂的主要部件进行了选型。基于文献研究、技术经济参数和缩放变量计算，例如，损失和效率、每平方米的资金成本、每兆瓦安装的排放量等。

最后，针对不同每个地点的 CSP 和光伏太阳能领域的规模大小利用系统顾问模型

（SAM）进行了模拟，并建立了一个基于Excel的模型以提供基于SAM模型输出的每个技术选择的经济、排放和能源表现。SAM采用天气文件和太阳能面积参数，而Excel 为基础的模型开发了一些不属于太阳能领域的气象文件信息、SAM 的输出、需求概况和技术经济参数。

主要结论是：（1）如果附近有大量可用的容量, PV 和化石燃料替代品的经济性能是错误的, 即使有低成本天然气；（2）光伏领域结合内燃机的经济性高于8.09 - 14.35 美元/ GJ；（3）在大多数地点，太阳能热场更换柴油机是最明智的经济性选择；（4）最低估价 300 美元/兆瓦时需要电池储能，在这种情况下，大尺寸光伏可以替代电池以达到相同的经济性；（5）PV + T在9到11 MWdc的面积在几乎所有的情况下都是最好的选择，燃料成本超过3.67 $/GJ，如果根据燃料价格距离小于~ 80（8.09$/GJ），~ 210

（14.35$/GJ）和~ 360公里（22.86$/GJ）；（6）在液体燃料环境，PV + E一贯优于

CSP，在16-21 MWac或21-25 MWac之间，理想光伏面积的大小取决于燃料成本；（7）

PV + T和PV + B表现出低排放和高能量回报率，而CSP和PV+ E表现出高排放且能

量回收少，即使采用大面积太阳能；（8）尽管PV+ B是从来不是最优的经济选择，但它在排放和回报率方面总是优于 CSP，且在太阳能份额在~ 70%以上时更经济；（9）

PV + B可以以合理的成本供应高达90%的需求。

读者需要注意到进口、细致需求假设对计算结果有很大的影响，因此本文结论只适用于云南电网的背景。

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Contents

Acknowledgments ... i

Abstract ... ii

摘要 ... iv

Contents ... v

Lists of Figures and Tables ... vi

List of Abbreviations and Symbols ... xi

Introduction ... 1

I. Energy in China: the need for renewables ... 1

II. Renewable electricity generation and the cost of integration ... 2

III. Variable and non-variable solar technologies ... 5

IV. The value of solar technologies comparison in Yunnan, China ... 7

Research work ... 10

1. Scope, assumptions and general description of the work ... 10

2. Description of cases and selection of the locations ... 12

3. Weather information ... 19

4. Demand profiles ... 21

5. Power plants design ... 23

6. Model description ... 36

7. Results and discussion ... 38

Conclusions ... 58

References ... 61

Annexes ... 70

A. Detail of locations selection ... 70

B. Detail of weather files definition for the selected sites ... 75

C. Detail of demand profiles definition ... 84

D. Detail on CSP design and parameters estimation ... 88

E. Photovoltaic design and parameters estimation ... 98

F. Lithium batteries selection and parameters estimation ... 104

G. Transmission line selection and parameters ... 111

H. Generator set and complimentary fuels selection and parameters ... 114

I. Case by case results ... 119

Achievements... 125

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vi

Lists of Figures and Tables

Figure 2-1: Cases and codification ... 13

Figure 2-2: Map of Yunnan solar potential... 14

Figure 2-3: Map of Yunnan province including main power plants ... 15

Figure 2-4: Map of the main power grid in Yunnan province ... 16

Figure 2-5: Map of the natural gas pipelines in Yunnan province ... 16

Figure 2-6: Map of main economic activities centers in Yunnan province ... 17

Figure 2-7: Map of Yunnan province solar irradiation and selected locations ... 18

Figure 2-8: Map of Yunnan province main electric system and selected locations ... 18

Figure 2-9: Map of Yunnan province natural gas pipelines and selected locations ... 19

Figure 2-10: Map of Yunnan province economic activities and selected locations ... 19

Figure 3-1: Map of available TMY weather files in Yunnan province and nearby ... 20

Figure 3-2: Map of TMY weather files and locations with analogue identification... 21

Figure 4-1: Map of Chinese weather types ... 22

Figure 4-2: Industrial demand profile used in the model ... 23

Figure 4-3: Residential demand profile used in the model ... 23

Figure 5-1: Engine-Generator set efficiency as function of the load ... 34

Figure 6-1: Equivalent Annuity Calculation ... 37

Figure 6-2: Schematic diagram of the model ... 38

Figure 7-1: Technologies ranking in each case in the low end ... 39

Figure 7-2: Best technology for each case in the high end ... 39

Figure 7-3: Case F electricity cost, emissions and EROEI comparison of the lowest cost solution of each technology in the high and low ends ... 40

Figure 7-4: Histogram of hourly demand ... 41

Figure 7-5: Case F electricity cost and emissions comparison of all technologies and sizes in the higher and lower ends ... 42

Figure 7-6: Case F electricity cost and EROEI comparison of all technologies and sizes in the higher and lower ends ... 43

Figure 7-7: Case E electricity cost of CSP as function of field and storage sizes for lower and higher of fuel price ... 45

Figure 7-8: Case G electricity cost of CSP as function of field and storage sizes for lower and higher of fuel price ... 45

Figure 7-9: EROEI and emissions of CSP plants in Case A ... 46

Figure 7-10: Curtailment and fuel consumption of CSP plants in Case A ... 46

Figure 7-11: Example of hourly solar flux in the receiver in Ruili ... 46

Figure 7-12: Case H electricity cost of PV+B as function of field and storage sizes for lower and higher ends of unsatisfied demand cost ... 48

Figure 7-13: Unsatisfied demand of the lowest cost PV+B plants in the low end ... 49

Figure 7-14: Electricity and curtailment of PV+B alternatives, case H high end ... 49

Figure 7-15: Electricity cost versus unsatisfied demand for PV+B plants, Case A high end .. 49

Figure 7-16: Lowest and 90% demand supply cost comparison of PV+B plants, Case A high end ... 50

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Figure 7-17: Case C electricity cost of PV+T as function of field size for lower and higher ends of

trading cost ... 51

Figure 7-18: Case A electricity cost of PV+E as function of field size for lower and higher ends of fuel price ... 53

Figure 7-19: Case C electricity cost of PV+E as function of field size for lower and higher ends of fuel price ... 54

Figure 7-20: Solar shares comparison between CSP and PV+E in the cases of liquid fuels . 54 Figure 7-21: Electricity cost versus solar share of CSP, PV+B and PV+E, case A high end .. 54

Figure 7-22: Integrated cost evolution, case B ... 57

Figure 7-23: Integrated cost evolution, case H ... 57

Figure A-1: Direct normal irradiation in Yunnan province map #1 ... 70

Figure A-2: Global horizontal irradiation in Yunnan province map #2 ... 71

Figure A-3: Global horizontal irradiation in Yunnan province SolarGIS ... 71

Figure A-4: Direct normal irradiation in Yunnan province Solar GIS ... 71

Figure A-5: Global horizontal irradiation in Yunnan province map #3 ... 72

Figure A-6: Direct normal irradiation in Yunnan province map #3 ... 72

Figure A-7: Power grid in Yunnan province map #1 ... 72

Figure A-8: China Southern Grid map #1 ... 73

Figure A-9: China Southern Grid map #2 ... 73

Figure A-10: China Southern grid map #3 ... 73

Figure A-11: Natural gas pipelines in China map #1 ... 74

Figure A-12: Natural gas pipelines in China map #2 ... 74

Figure A-13: Main pipelines in southwest China map ... 75

Figure A-14: Pipelines in China map #3 ... 75

Figure B-1: Global, direct and beam radiation comparison between Ruili and potential analogues ... 77

Figure B-2: Radiation, sunshine duration, temperature and rains between Ruili and Tengchong weather station ... 78

Figure B-3: Global, direct and beam radiation comparison between Gengma Dai Wa and potential analogues ... 79

Figure B-4: Radiation, sunshine duration, temperature and rains between Gengma Dai Wa and Lincang weather station ... 80

Figure B-5: Global, direct and beam radiation comparison between Ninglang Yi and potential analogues ... 81

Figure B-6: Radiation, sunshine duration, temperature and rains between Ninglang Yi and Lijiang weather station ... 82

Figure B-7: Global, direct and beam radiation comparison between Dongchuan and potential analogues ... 83

Figure B-8: Radiation, sunshine duration, temperature and rains between Dongchuan and Kunming weather station ... 83 Figure C-1: Typical summer and winter day power demand profiles in Zhejiang Power Grid 84 Figure C-2: Typical summer and winter day power demand profiles in Shanghai Power Grid 84

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Figure C-3: Typical day power demand profile in Guanxi Power Grid ... 84

Figure C-4: Typical day power demand profile in Guangdong Power Grid ... 85

Figure C-5: Typical power demand profiles by day in Billing substation ... 85

Figure C-6: Weather map #2 of China ... 86

Figure C-7: Temperature average and amplitude comparison between power grids and locations ... 86

Figure D-1: Correlation between energy flux in the receiver and solar field size ... 89

Figure D-2: Correlation between reflective surface and solar field size ... 89

Figure D-3: Electrical efficiency of generators ... 90

Figure D-4: Other efficiencies and overall CSP power plant efficiency as function of ambient temperature and load ... 90

Figure D-5: Efficiencies comparison between the power cycle with and without reheat ... 91

Figure D-6: Cost of the tower and receiver as function of solar field size ... 92

Figure E-1: I-V curve and main technical details of the selected module ... 98

Figure E-2: Efficiency curve and main technical details of the selected inverter ... 98

Figure E-3: Correlation between AC power output and solar field capacity ... 99

Figure E-4: Historic evolution of PV manufacturing energy intensity ... 102

Figure F-1: Expected life cycle of LiFePO4 battery ... 104

Figure F-2: Literature reported cost versus capacity of Li batteries ... 105

Figure F-3: Comparison of emissions and energy values of Li batteries from two studies ... 109

Table 2-1: Cases and locations in Yunnan province ... 17

Table 3-1: Locations and best analogue weather file ... 20

Table 4-1: Classification of available weather profiles by weather and consumer profile ... 21

Table 5-1: Summary of the CSP solar field parameters ... 25

Table 5-2: Number of heliostats, reflective surface, area, tower height and receiver dimensions for each solar field size ... 26

Table 5-3: Summary of the CSP thermal storage parameters ... 26

Table 5-4: Summary of the CSP power cycle parameters ... 28

Table 5-5: Receiver to Net Electrical efficiency of the CSP plant as function of the ambient temperature and the load ... 28

Table 5-6: Summary of the PV power plant parameters ... 30

Table 5-7: Comparison of Li batteries technologies ... 31

Table 5-8: Summary of the energy storage battery pack parameters ... 32

Table 5-9: Summary of the engine battery pack parameters ... 32

Table 5-10: Summary of the transmission line parameters ... 33

Table 5-11: Summary of the piston engine - generator set parameters ... 34

Table 5-12: Summary of the fuel prices and other parameters ... 36

Table 7-1:Low and high ends ... 39

Table 7-2: The lowest cost of electricity for each technology by case for low and high ends . 40 Table 7-3: Statistical parameters of hourly demand profiles ... 41

Table 7-4: Lowest electricity cost and CSP capex variation to achieve it ... 47

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Table 7-5: Equilibrium distance in kilometers for the lowest cost PV+T plant size by case .... 51

Table 7-6: Parameters of imported electricity from hydro ... 52

Table 7-7: Sensitivity example to integration with hydro, Case F ... 52

Table 7-8: Current or recent cost of emissions (Wikipedia, 2017) ... 55

Table 7-9: Integrated cost indicator for each case ... 56

Table B-1: Description of weather file sources for PVSyst ... 76

Table B-2: Description of weather file sources for SAM ... 76

Table C-1: Holidays in China considered in the model ... 87

Table C-2: Number of days and hours per month considered in the model ... 87

Table D-1: Tower height, receiver dimensions and heliostat count for each location and solar field size ... 88

Table D-2: Electrical efficiency of the generator considered in the model ... 89

Table D-3: Turbine inlet and outlet conditions ... 90

Table D-4: CSP power plant cost per MW for various solar field and storage sizes ... 92

Table D-5: Economic parameters and scaling factors for CSP ... 93

Table D-6: Literature reported values of energy and emissions and metrics calculations for CSP #1 ... 94

Table D-9: Literature reported values of emissions and metrics calculations for CSP #4 ... 97

Table D-11: Energy and emissions parameters and scaling factors for CSP ... 98

Table E-1: Modules DC power, number of inverters, modules per rack and lines for each solar PV field size ... 99

Table E-2: PV power plant cost break down by Watt of DC capacity ... 99

Table E-3: Economic parameters and scaling factors for the PV power plant ... 100

Table E-4: Literature reported values of energy and metrics calculations for PV #1 ... 101

Table E-5: Literature reported values of energy and emissions and metrics calculations for PV #2 ... 101

Table E-6: Literature reported values of energy and emissions for PV #3 ... 102

Table E-7: Energy use and emissions of the module manufacturing process of leading companies ... 103

Table E-8: Energy and emissions parameters and scaling factors for PV ... 103

Table F-1: Economic parameters and scaling factors for Li battery packs ... 105

Table F-2: Literature reported values of energy and emissions and metrics calculation for Li batteries #1 ... 106

Table F-3: Literature reported values of energy and emissions and metrics calculation for Li batteries #2 ... 108

Table F-4: Literature reported values of energy for batteries #3 ... 109

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x

Table F-5: Literature reported values of energy and metric calculation for Li batteries #4 .... 110

Table F-6: Literature reported values of emissions and metric calculation for Li batteries #5110 Table F-7: Literature reported values of emissions and metric calculation for Li batteries #6 111 Table F-8: Energy and emissions parameters and scaling factors for Li battery packs ... 111

Table G-1: Economic parameters and scaling factors for transmission line... 113

Table G-2: Literature reported values of energy and emissions and metric calculation for transmission line #1 ... 113

Table G-3: Literature reported values of emissions and metric calculation for transmission line #2 ... 114

Table G-4: Energy and emissions parameters and scaling factors for transmission line ... 114

Table H-1: Economic parameters and scaling factors for the piston engine-generator set.... 115

Table H-2: Fuels description and price ... 115

Table H-3: Literature reported values of energy and metric calculation for piston engine #1 116 Table H-4: Literature reported values of energy and metric calculation for piston engine #3 116 Table H-5: Literature reported values of energy and emissions and metrics calculation for piston engine #3 ... 117

Table H-6: Literature reported values of energy and emissions and metrics calculation for piston engine #4 ... 117

Table H-7: Literature reported values of emissions and metric calculation for piston engine #5 ... 118

Table H-8: Energy and emissions parameters and scaling factors for the piston engine ... 118

Table H-9: Energy and emissions literature reported values and used parameters for the complimentary fuels ... 118

Table I-1: CSP Results Case by Case ... 119

Table I-2: PV+B Results Case by Case ... 121

Table I-3: PV+T Results Case by Case ... 123

Table I-4: PV+E Results Case by Case ... 124

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List of Abbreviations and Symbols

$: United States dollars

€: Euros

AC or ac: alternating current

ºC or C: Celsius degrees, temperature CapEx: Capital expenditures or Capital cost China: People’s Republic of China

CO2: carbon dioxide

CO2eq: equivalents to carbon dioxide, emissions

CSP: Concentrated Thermal Solar Power DC or dc: Direct current

e: Units of electricity, electric

EROEI: Energy return on energy invested EU: European Union

G: Giga, thousands of million (10E9) units GHGs: Green House Gases

h: Hour/s

HRPS: High Renewables Penetration Scenario

HTF: Heat transfer fluid

IG: Inverter-based electrical generation IRR: Internal Rate of Return

J: Joule, energy

k: Kilo, thousand (10E3) units ºK or K: Kelvin degrees, temperature LCOE: Levelized Cost of Electricity LCOF: Levelized Cost of Flexibility Li: Lithium

LiFePO4: Lithium Iron Phosphate m: meters

m2: square meters

MM or M: Mega, million (10E6) units NPV: Net Present Value

NREL: National Renewable Energy Laboratory of the United States

NVG: Non-variable electrical generation OpEx: Operating expenditures or Operating cost

PM: Particulate matter

PV: Photovoltaic power generation

SAM: System Advisor Model simulation program from NREL

T: Tera, millions of million (10E12) units TCE: Metric Tons of Coal Equivalent, energy th: Units of heat, thermal

TMY: Typical meteorological year tn: Metric tons

US: United States of America V: Volt

VA: Volt amperes

VG: Variable electrical generation W: Watts, power

WP: Wind power generation Y: Chinese Yuan

yr: Year/s

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Zhejiang University Master Thesis Introduction

1

Introduction

I. Energy in China: the need for renewables

China is a rapidly developing country whose leadership aims to bring economic welfare and high life quality levels to all its citizens. Human development and welfare are positively logarithmically correlated with energy consumption (D. M. Martinez, 2008). Despite the decoupling observed in recent years, energy demand is also positively correlated to economic development (D. Streimikiene, 2016). Chinese energy consumption per capita is roughly 2.23 thousand Tons of Oil Equivalent per person per year, far behind developed countries ratios and saturation point values.

Since the accelerated Chinese economic growth is expected to continue, energy consumption in China is forecasted to grow, at least, until 2025. Sources differ in the quantification but agree on the tendency (Energy Resarch Institute, 2015) (British Petroleum, 2015).

Electricity is one of the most useful energy carriers because it can provide a wide range of services by means of affordable high efficiency devises. It is relatively easy and cheap to instantly transport over long distances, is silent and very transformable.

Electricity demand in China is expected to reach ~2.4 times the current consumption, equivalent to 15200 TWh, by 2050 (Energy Resarch Institute, 2015). At the same time, electricity can be produced from many sources (fossil fuels, nuclear, renewables) with relatively high levels of efficiency. Currently, the main sources of the Chinese grid are coal (~67%) and hydropower (~16%).

Emissions related to fossil fuels combustion have heavily affected the environment worldwide. The climate change issue has been acknowledged by world leaders and an agreement has been reached in Paris COP21 meeting. Most countries, including China, have locally ratified the agreement putting emissions reduction at the top of the agenda.

Additionally, in China, poor local air quality threatens the health of the population.

Small particulate matter (PM2.5) can cause respiratory diseases. PM2.5 levels in some Chinese cities are several times above the upper limit of 25 µg/m3 suggested by the World Health Organization. In Chengdu, for example, the average values for summer and autumn were 114 and 188 µg/m3 respectively. Other large cities like Beijing, Jinan, Nanjing, Taian, Tianjin are in the same situation (G. Chen, 2015) (B. Liu, 2016) (L. Wang, 2015) (H. Xu, 2016). Stationary sources, like thermal power plants running on coal, have been found to be among the major sources of PM2.5 in most cases.

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Consequently, if China desires to achieve sustainable development and meet international commitments, coal power generation should be replaced. If enough biomass was available, it could reduce emissions - CO2 neutral - but not improve the air quality problem. Solar and wind could tackle both issues which makes them very promising resources. China holds significant on and offshore wind resources (north and south) and areas with very high levels of irradiation (west). In a High Renewables Penetration Scenario (HRPS), solar is expected to provide the largest share of grid electricity as of 2050 followed by wind (Energy Resarch Institute, 2015). Together, they are expected to deliver over 63% of the total Chinese demand.

II. Renewable electricity generation and the cost of integration

Wind Power (WP) and PV are the pillars of today’s renewables deployment. WP is a relatively mature technology that has seen a significant improvement during its early stages and seems to have reached a plateau in terms of efficiency and costs. On the other hand, PV is going through a revolution. Efficiency is on the rise and, at the same time, costs are dramatically being reduced. Globally, its growth rate has surpassed WP and will likely keep doing so in the future. In China, practically all the new solar generation is PV. It is also forecasted to be, by far, the largest solar technology in the future Chinese grid (Chinese Academy of Sciences, 2009) (Sino-Danish RED, 2014) (Energy Resarch Institute, 2015).

Unlike traditional thermal power plants, both PV and WP are Variable Generation (VG) which means they only deliver electricity when the resource is available and both solar irradiation and wind are intermittent. This is a challenge for power grids where load and supply must be instantaneously equal. Besides, PV and WP use inverters or asynchronous generators respectively with a minor to negative contribution to grid stability (frequency and voltage control).

As early as 2005, California Energy Commission conducted a study to evaluate the impact of VG in the state’s electricity system. Despite the focus was not set in grid integration, they reached to the conclusion that power plants location was of key importance to grid balance. In the high penetration case (20% renewables), many transmission lines required upgrade but when “small” amounts (5%) of distributed PV were included, grid stress was reduced. As of VG the only mention to a possible issue was stated as “A more interesting observation is that seventy (70) percent of the capacity and forty-six (46) percent of the energy will be from intermittent resources […]

it will be interesting to observe […] the impact that intermittent resources could have on transmission operations” (Davis Power Consultants, 2005).

The effect of VG on the electric system begun to be better understood a few years

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3

later when WP gained momentum. A work concluded that “There is an increasing need for systematic, integrated planning processes that ensure energy adequacy and identify wind resources’ broader impact on grid security […]. An adequate level of security is critical to power systems operation. For example, in systems operated under competitive market regimes, poor transmission planning creates long-term congestion, price volatility, and opportunities for the exercise of market power” (S.

Grijalva, 2007).

A pseudo-sequential Monte Carlo simulation model was used to study nodal reliability in a theoretical deregulated power system (Q. Zhao, 2012). Nodal reliability was found to be compromised with a PV penetration of 20%. Because of the impact of PV intermittence, fast response reserves had to be increased to achieve the same levels of nodal reliability as with low PV penetration.

A glimpse of how intermittency affects nominal nameplate capacity can be observed in the estimation of PV capacity credit. A Chinese research has estimated that a 200 MW PV could replace 112 MW thermal generation. Although a heavily thermal grid was used for the comparison, it did make an interesting approach by considering displaced nameplate capacity (X. Fang, 2012).

Recently, a 5-minute time interval VG integration study was performed for the Eastern Interconnection power system in the United States of America (A. Bloom, 2016). Two high VG penetration cases -20% WP + 10% PV and 25% WP + 5% PV - were simulated by the National Renewable Energy Laboratory (NREL) arriving to the conclusion that traditional thermal power plants are significantly affected. Traditional generators are used less frequently due to higher variable costs and operate across a broader output range to compensate VG fluctuations. Among other conclusions the study states: “The ability of resources to obtain sufficient revenues from the new operating patterns will likely impact resource adequacy. Given the reduced utilization of many generators, it is unclear whether energy market revenue alone will be sufficient to keep units from retiring.” and “[…] high amounts of wind and PV, system and plant operators […] could expect to cycle or ramp their resources more frequently.

If adequate short term opportunities and regulatory structures are not in place to incentivize this flexibility, resources may exit the market […] compromising the ability of the system operator to manage the types of conditions simulated in this study”.

Unfortunately, the study did not investigate all aspects of the reliability but it was showed that curtailment, number of starts, ramp ups and idling time were higher even if inter-regional high voltage direct current transmission lines were built.

NREL also investigated the storage requirements in California to reach a 50% PV penetration (P. Denholm, 2016). The study recognizes that, even after the adoption of

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increased generator flexibility, demand response, additional export capacity and electric vehicles as storage, additional grid storage will probably be required to enable such penetration. The study also shows that storage requirements increase exponentially with levels of penetration. Such conclusion demonstrates how easy can be to accommodate VG while utilizing synergies with existing infrastructure but how expensive the required flexibility can become afterwards.

The most complete VG integration study was carried out during the last decade by the International Energy Agency, the Grid Integration of Variable Renewables (GIVAR) program. Their last report titled “The Power of Transformation: Wind, Sun and the Economics of Flexible Power Systems” was published in 2014 and assesses the flexibility options to integrate up to 45% of VG in 15 national grids. Although the report is not public, the author has revised the executive summary (IEA, 2014). This study found, among other things, that¹:

• the effect of 2 to 3% of VG penetration is not noticeable,

• geographical distribution, failure proof technology and forecasting is enough to include 5 to 10% of VG penetration,

• penetrations of 25% to 40% of VG can be achieved from a technical perspective, assuming current levels of system flexibility (sufficient grid capacity was assumed),

• shares of over 40% of VG require a system wide transformation to be integrated cost-effectively,

• if large shares of VG are integrated overnight, system cost increase would be around 30 $/MWh.

This study was performed in highly developed grids² mainly and performs a wonderful job in putting the LCOF quantification on the energy planning agenda.

However, its assumptions of sufficient grid capacity and VG widespread geographical distribution may not be realistic in a country like China or a Chinese region.

In Germany or Denmark, for example, early WP development took advantage of preexistent pumped storage, transmission, interconnection and thermal plants which allowed to balance their excess or defect. The whole system made the “extra effort” to accommodate the newcomers and LCOF went unnoticed while VG kept increasing.

The conflict aroused when thermal plants reached the end of their techno-economic life but the system could afford to lose them. Today, in Germany, capacity payments

1 Percentages are the share of variable generation production in the total power production.

2 Brazil, Texas (United States), Iberia (Portugal and Spain), India, Italy, Japan East (Hokkaido, Tohoku and Tokyo) and North West Europe (Denmark, Finland, France, Germany, Ireland, Norway, Sweden and United Kingdom.

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5

are being suggested to compensate the little number of running hours and new highly-flexible coal power plants are being built (Schultz, 2012).

The challenges of renewables integration to the Chinese national grid were identified as early as 2011 (Cheung, 2011). The work was performed at a nationwide level and included recommendations for grid flexibility to accommodate VG. These varied from grid development to market rules such as pricing and dispatch. It was understood that the cost of grid flexibility had to be assumed by the system to increase renewables and replace coal. No question was posed regarding to the convenience of such effort.

In December 2014, under the Sino-Danish Renewable Energy Development Programme, a renewable energy development forecast was made for China (Sino-Danish RED, 2014). By 2050 PV installed capacity was estimated to be 10 times the one of CSP. Very significant volumes of WP were also forecasted. No detail was provided on how to integrate this huge amount of VG to the grid. Intermittency seems to have been totally ignored.

The most recent work on Chinese renewable energy development is the HRPS made by the Chinese Energy Research Institute in collaboration with national and foreign institutions and local companies (Energy Resarch Institute, 2015). Forecasting was done considering socio-economic development goals and environment constraints. The outcome seems a realistic scenario in which energy sources, transmission and storage capacity are projected with regional and annual break down.

Costs and technology improvement assumptions are lead to a PV + storage development strategy. The analysis estimates a significant cost reduction for CSP which occurs very late in time and the achievement of a chemical storage solution as of 2040. This last assumption allows the integration of very significant volumes of VG into the grid by the increase of chemical storage. Besides, significant amounts of pumped hydro storage are built every year. As of 2050 both storage technologies amount to 300 TW, 36% of the current coal capacity.

III. Variable and non-variable solar technologies

CSP is a utility-scale power generation solar technology that works, essentially, in the same way that traditional thermal power plants. In CSP, radiation intermittency can be “easily” solved because it uses heat as an intermediate step between the solar resource and power production. This enables the use of low cost efficient heat storage and the combination with other thermal sources like coal, natural gas or biomass. It is called hybridization. Heat storage and hybridization are the keys by which CSP power plants can be designed as non-variable generation to supply baseload. If so, CSP

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capacity factor is significantly higher than that of PV since it works around the clock.

Besides, as it is based on synchronous generators it contributes to grid stability.

Moreover, although experience is limited, CSP technical life is estimated above 40 years which means it outlives PV. But in terms of upfront capital expenditures (CapEx), CSP is one of the most expensive renewable technologies while PV is among the most inexpensive. PV CapEx are roughly between a third and a fifth of those of CSP.

Although, LCOE-wise, both technologies were comparable in California as of 2013 (I.

Rhyne, 2015), PV CapEx have seen a much steeper decline than those CSP from 2013.

PV, because of its VG characteristic, requires the “extra integration effort” or LCOF which includes the need for curtailment, demand side management, additional transmission, increasing ramp up/down, storage, availability payments or other. In practice, LCOF is very difficult to estimate and is characteristic of each grid. For example, flat terrain regions would have smaller LCOF than irregular geographies due to reduced cost for new transmission. Besides, grid reinforcement may benefit various power plants making LCOF almost impossible to allocate. Even more, LCOF is an expense waiting further down the road, especially in developed countries.

Postponement of a solution is possible because, as said, the impact of VG on the grid is visible when their share is such that preexisting assets flexibility is saturated.

In some regions of China, existing infrastructure capacity cannot properly accommodate VG. Today, in China’s northern provinces of Jilin, Gansu, and Xinjiang, recently installed WP in suffered curtailment of over 30% during 2015, due to lack of grid capabilities to receive their output (Finamore, 2016). PV in Gansu and Xinjiang also suffered curtailment rates of 31% and 26% respectively. In 2015, national figures of WP and PV curtailment were much better, 15% and 9% respectively, but not minimal. An indicator of the severity of the issue is the proposal and enhancement of wind-to-heat project in the Northern provinces. The proposal relies on using WP electricity for space heating purposes (Zhou, 2012). If the highest quality energy carrier, electricity, is used for supplying the lowest possible service, heating, the cost of integration must be significant.

As said, LCOF is beginning to be understood and in some countries and regions of China, the VG impact on the grid has been overlooked. As elsewhere, there has been a rush down the road of renewable VG and now solutions must be found (Friederici, 2013). Governments eagerness to foster renewables and difficulties in LCOF visibility, estimation and allocation result in the disregard of LCOF in business schemes. Only in countries which are building their energy systems “from scratch” (few balancing plants and limited transmission capacity), like South Africa or Morocco, a high