socio-economic assessment and energy system analysis d6.1_ economic assessment and... ·...

Socio-economic assessment and energy system

analysis

Deliverable nº: D6.1

July 2016

EC-GA nº: 308912 Project full title: Innovative Configuration for a Fully

Renewable Hybrid CSP Plant WP: 6 Responsible partner: DTU-ME Authors: Cristian Cabrera and Lars Henrik Nielsen Dissemination level: Public

D6.1: Socio-economic assessment and energy system analysis

2

Table of contents

ACKNOWLEDGEMENTS .......................................................................................................... 7

CONTACT .............................................................................................................................. 8

1 EXECUTIVE SUMMARY ................................................................................................... 9

2 INTRODUCTION............................................................................................................ 11

3 SOCIO-ECONOMIC FEASIBILITY ASSESSMENT ................................................................ 12

3.1 FRAMEWORK CONDITIONS FOR THE COUNTRIES ANALYSED ...................................................... 12

3.2 THE HYSOL ALTERNATIVE AND COMPETING TECHNOLOGY ...................................................... 12

3.3 APPROACH AND BASIC ASSUMPTIONS .................................................................................. 12

3.3.1 Economic indicator ................................................................................... 12

3.3.2 Base Case assumptions ............................................................................. 13

3.3.3 Base Case overview and issues addressed via sensitivity analyses .......... 13

3.4 KSA............................................................................................................................... 14

3.4.1 Base Case for KSA HYSOL plant................................................................. 14

3.4.2 Electricity costs as function of load factor and NG price .......................... 14

3.4.3 Design Point assumptions ......................................................................... 15

3.4.4 HYSOL relative to OCGT and CCGT ........................................................... 15

3.4.5 CO2 emission costs .................................................................................... 15

3.4.6 Results: HYSOL compared to OCGT........................................................... 16

3.4.7 Results: HYSOL compared to CCGT ........................................................... 17

3.5 MEXICO ......................................................................................................................... 19

3.5.1 Base Case for MEX HYSOL plant ............................................................... 19

3.5.2 Assumption on NG and biogas price relation ........................................... 20



3.6 CHILE ............................................................................................................................. 24

3.6.1 Base Case for CHI HYSOL plant ................................................................. 24

3.6.2 Assumption on NG and Biogas price relation ........................................... 25



3.7 RSA .............................................................................................................................. 29

3.7.1 Base Case for RSA HYSOL plant ................................................................ 29




3.8 SENSITIVITY ANALYSES ...................................................................................................... 34

3.8.1 KSA ............................................................................................................ 34

3.8.2 Mexico ...................................................................................................... 36


3

3.8.3 Chile .......................................................................................................... 37

3.8.4 RSA ............................................................................................................ 39

3.9 KEY FINDINGS .................................................................................................................. 41

4 ENERGY SYSTEMS ANALYSIS ......................................................................................... 42

4.1 OBJECTIVE ...................................................................................................................... 42

4.2 METHOD ........................................................................................................................ 42

4.3 ETSAP-TIAM ................................................................................................................. 43

4.3.1 Times Architecture Background ................................................................ 43

4.3.2 Regions ..................................................................................................... 43

4.3.3 Time Frame ............................................................................................... 44

4.3.4 Model Structure ........................................................................................ 44

4.4 SCENARIOS ..................................................................................................................... 44

4.4.1 Carbon Price .............................................................................................. 45

4.5 ENERGY SYSTEM ASSESSMENT ............................................................................................ 46

4.5.1 Availability of resources ............................................................................ 46

4.6 MODELLING .................................................................................................................... 49

4.6.1 CSP technology overview .......................................................................... 49

4.6.2 HYSOL implemented in ETSAP-TIAM......................................................... 49

4.7 RESULTS ......................................................................................................................... 50

4.7.1 Annual electricity production.................................................................... 50

4.7.2 Total system cost ...................................................................................... 53

4.7.3 Direct CO2 emissions ................................................................................. 54

4.8 KEY FINDINGS .................................................................................................................. 55

5 CONCLUSION ............................................................................................................... 56

6 REFERENCES ................................................................................................................. 57

7 APPENDIX .................................................................................................................... 60

7.1 SECTION A: ENERGY SYSTEM ANALYSIS ................................................................................ 60

7.1.1 Calibration of TIAM model........................................................................ 60

7.1.2 Model Structure ........................................................................................ 60

7.1.3 CO2 tax in ETSAP-TIAM ............................................................................. 62

7.1.4 Flow diagram in ETSAP -TIAM .................................................................. 63

7.2 SECTION B: SOCIO-ECONOMIC FEASIBILITY ASSESSMENT ......................................................... 64

7.2.1 Power price composition Mexico .............................................................. 65

7.2.2 Power price composition for Chile ........................................................... 67

7.2.3 Power price composition for KSA .............................................................. 68

7.2.4 Power price composition for RSA .............................................................. 69


4

List of tables

Table 3.1: General assumptions .................................................................................................. 13

Table 3.2: Parameters analysed for base and sensitivity cases .................................................. 13

Table 3.3: Assumptions for KSA .................................................................................................. 14

Table 3.4: General assumptions for Mexico................................................................................ 19

Table 3.5: Assumptions for Chile ................................................................................................. 24

Table 3.6: General assumptions for RSA ..................................................................................... 29

Table 4.1: Current carbon prices in 2005 USD/tCO2eq ............................................................... 45

Table 4.2: IRENA's Renewable Energy Roadmap - REmap Countries Renewable Energy Targets,

2014 ............................................................................................................................................. 46

Table 4.3: Comparison of criteria between Biberacher (2010) and Trieb et al (2009) study ..... 48

Table 4.4: Comparison between Biberacher (2010) and Trieb et al (2009) study ...................... 48

Table 4.5: Inputs parameters for the HYSOL in ETSAP-TIAM ...................................................... 49

Table 4.6: Total system cost in billion USD2005 ............................................................................ 54

Table 4.7: Total direct CO2 emissions in Gt of CO2. .................................................................... 54

List of figures

Figure 3.1: Electricity production costs for OCGT and KSA HYSOL.............................................. 16

Figure 3.2: Electricity production costs for OCGT andKSA HYSOL .............................................. 17

Figure 3.3: Electricity production costs for CCGT and KSA HYSOL .............................................. 18

Figure 3.4: Electricity production costs for CCGT and KSA HYSOL .............................................. 19

Figure 3.5: Electricity production costs for OCGT, MEX HYSOL .................................................. 21

Figure 3.6: Electricity production costs for OCGT and MEX HYSOL ............................................ 22

Figure 3.7: Electricity production costs for CCGT and MEX HYSOL ............................................. 23

Figure 3.8: Electricity production costs for CCGT and MEX HYSOL ............................................. 24

Figure 3.9: Electricity production costs for OCGT and CHI HYSOL ............................................. 26

Figure 3.10: Electricity production costs for OCGT and CHI HYSOL ............................................ 27

Figure 3.11: Electricity production costs for CCGT andCHI HYSOL ............................................. 28

Figure 3.12: Electricity production costs for CCGT, CHI HYSOL ................................................... 29

Figure 3.13: Electricity production costs for OCGT and RSA HYSOL ........................................... 31

Figure 3.14: Electricity production costs for OCGT and RSA HYSOL ........................................... 32

Figure 3.15: Electricity production costs for CCGT and RSA HYSOL ............................................ 33

Figure 3.16: Electricity production costs for CCGT and RSA HYSOL ............................................ 34

Figure 3.17: Sensitivity relative to base case assumptions KSA HYSOL ...................................... 35

Figure 3.18: Sensitivity relative to base case assumptions KSA OCGT ........................................ 35

Figure 3.19: Sensitivity relative to base case assumptions KSA CCGT ........................................ 36

Figure 3.20:Sensitivity relative to base case assumptions MEX HYSOL ...................................... 36

Figure 3.21: Sensitivity relative to base case assumptions MEX OCGT ...................................... 37

Figure 3.22: Sensitivity relative to base case assumptions MEX CCGT ....................................... 37

Figure 3.23: Sensitivity relative to base case assumptions CHI HYSOL ....................................... 38


5

Figure 3.24: Sensitivity relative to base case assumptions CHI OCGT ........................................ 38

Figure 3.25: Sensitivity relative to base case assumptions CHI CCGT ......................................... 39

Figure 3.26: Sensitivity relative to base case assumptions RSA HYSOL ...................................... 39

Figure 3.27: Sensitivity relative to base case assumptions RSA OCGT ........................................ 40

Figure 3.28: Sensitivity relative to base case assumptions RSA CCGT ........................................ 40

Figure 4.1: Diagram of framework for analysis and work flow ................................................... 43

Figure 4.2: Fifteen regions of the ETSAP-TIAM ........................................................................... 44

Figure 4.3: Annual average DNI (KWh/m2 year) .......................................................................... 46

Figure 4.4: Worldwide exclusion of sites for CSP plant construction ......................................... 47

Figure 4.5: Overall learning curve and the contributions of the main parts for CSP plant in Spain

..................................................................................................................................................... 50

Figure 4.6: Electricity production by fuel in Mexico, reference scenario ................................... 51

Figure 4.7: Electricity production by fuel in Mexico, HYSOL high penetration scenario ............ 51

Figure 4.8: Electricity production by fuel in Western Europe, reference scenario ..................... 52

Figure 4.9: Electricity production by fuel in Western Europe, HYSOL high penetration scenario

..................................................................................................................................................... 52

Figure 4.10: Electricity production by fuel in Africa, reference scenario .................................... 53

Figure 4.11: Electricity production by fuel in Africa, HYSOL high penetration scenario ............. 53


6

Abbreviations

AEEI Autonomous Energy Efficiency Improvement

AFR Africa

CAPEX Capital Expenditure

CCGT Combined Cycle Gas Turbines

CSP Concentrate Solar Power

DNI Direct Normal Irradiance ETSAP-TIAM Energy Technology System Analysis Program TIMES Integrated Assessment Model

ETS Emission Trading Schemes

GT Gas Turbine

HFLH Annual Full Load Hours

HTS High Temperature Molten Salt

HYSOL HYbrid SOLar

IDIE Research Development Innovation and Energy

KSA The Kingdom of Saudi Arabia

LCOE Levelized Cost of Energy

LF Load Factor

MARKAL MARket Allocation mode

MEX Mexico

NG Natural Gas

O&M Operation and Maintenance

OCGT Open Cycle

OPEX Operational Expenditure

PH Assigned HYSOL capacity

p.a. Per annum (per year)

PV Photovoltaic

RE Renewable Energy

RES Renewable Energy Source

RSA Republic of South Africa

ST Steam Turbine

TIMES The Integrated MARKAL-EFOM System

WEU Western Europe


7

Acknowledgements

Within the Seventh Framework Programme, the deliverable D6.1 "Socio-economic assessment

and energy system analysis" is a collaborative work among the System Analysis Group of the

Management Engineering Department of DTU, ACS Cobra, and PSA-CIEMAT and UPM.

Special thanks to go to Klaus Skytte, Olexandr Balyk, Kenneth Karlsson Mattia Baldini and Poul

Erik Grohnheit from DTU-ME and Helena Cabal (CIEMAT), who have been supporting forces

behind this work.


8

Contact

Comments and questions are welcome and should be addressed to:

Cristian Cabrera

DTU-Management Engineering (ME)

[email protected]

Lars Henrik Nielsen

DTU- Management Engineering (ME)

[email protected]

mailto:[email protected]

mailto:[email protected]


9

1 Executive summary

The aim of HYSOL Project is to become the European reference in competition to initiatives

ongoing in the CSP/biomass global market. The HYSOL Project focusses on overcoming the CSP

technology limitations to increase its contribution in the global electric market, hybridising

with biomass energy to achieve 100 % renewable and sustainable energy, and providing a

stable and reliable power independently of meteorogical circumstances.

Socio-economic assessment

The aim of this analysis is to investigate the economic viability of HYSOL relative to

conventional reference firm power generation technologies. In particular the HYSOL

performance relative to new power plants based on natural gas (NG) such as Open Cycle or

Combined Cycle Gas Turbines (OCGT or CCGT) are in focus. Levelized Cost of Energy (LCOE) are

used as a benchmark for comparison among the mentioned technologies Furthermore, a

sensitivity analysis is performed to identify the critical parameters that make HYSOL more

attractive from a socio-economic viewpoint. The regions examined are the Kingdom of Saudi

Arabia (KSA hereafter), Mexico, the Republic of South Africa (RSA hereafter) and Chile. These

regions not only have a prominent solar potential but also interesting market conditions, for

more information refer to D.6.4 "Analysis of regulation and economic incentives".

From the socio-economic assessment the following key findings are outlined in the box below:

Key findings from the socio-economic assessment

General findings:

CO2 emission cost increase significantly the LCOE cost in both OCGT and CCGT cases, in particular the OCGT plant solution is strongly exposed to potential rising CO2 emission while this cost may impact positively the future scenario for HYSOL.

HYSOL is sensitive to the interest rate. In Base Case a rate of calculation of 4% per annum (p.a.) has been assumed, which correspond to typical socio-economic conditions. Assuming a higher rate of interest of 10% p.a. equivalent to corporate economic interest rate, the sensitivity analysis shows that power production costs (LCOE) are increased substantially. Therewith, HYSOL is very sensitive to changes in the interest rate.

Country-specific findings:

For Chile, HYSOL is economically competitive when it is compared vs. OCGT and CCGT options, while it is not cost-effective in the KSA and Mexico. The lack of competitiveness in these countries is due to the significantly low NG prices in comparison to Chile (Chile NG is about three times as much as in Mexico or KSA). Furthermore, the current NG price conditions discourage the economic attractiveness of HYSOL in these countries.

For RSA, HYSOL competes favourable relative to the OCGT reference as can be seen from comparing results when base case data are assumed. This conclusion holds even without taking into account an assumed cost on CO2 emission. When compared to a CCGT reference plant the RSA HYSOL alternative is less favourable. However, introducing an assumed CO2 emission costs of 40 USD/tCO2eq emitted, narrows the LCOE price difference considerable (down to a LCOE difference of less than 5 USD/MWh).


10

Energy system analysis

The objective of this analysis is to determine the impact of HYSOL roll-out on total system cost,

direct CO2 emissions and energy mix in regions with a high solar potential and attractive

market conditions, for example, Mexico, Africa and Western Europe. Chile and KSA were

excluded from this analysis due to modelling limitations.

From the energy system analysis the following key findings are summarized in the box below:

Key findings from the energy system analysis

General findings:

HYSOL will not be deployed under normal market conditions. This is due to the high investment cost of this technology. Therefore, HYSOL will need to be supported.

The deployment of HYSOL has neither negative nor positive effect in the total system cost, this is valid for all the regions analysed.

A high deployment of HYSOL has a negligible impact on direct C02 emissions at total energy system level. However, it may have an impact at local level because it can replace gas and oil fuelled power plants.


In Africa, the roll-out of HYSOL can help to phase-out gas and oil power plants in the long-term.

In Mexico, the deployment of HYSOL could contribute to decrease gas and oil power plants in the mid-term.

In Western Europe, a high penetration of HYSOL has negligible impact on the power system. This is due to the limited solar potential and land availability of the region.


11

2 Introduction

Concentrating Solar Power (CSP) is in its infancy in terms of deployment compared to the other

renewable power generation technologies, with only 5 GW of CSP installed worldwide at the

end of 2014; of this capacity, the CSP market is dominated by parabolic trough technologies

(around 85% of cumulative installed capacity) (IRENA, 2014b). Nevertheless, an increasing

numbers of solar towers are being built and offer the promise of lower electricity costs. CSP

can integrate low-cost thermal energy storage in order to provide dispatchable electricity to

the grid and capture peak market

CSP plants utilize thermal conversion of direct solar irradiation. A trough or tower

configuration focus solar radiation and heat up oil or molten salt that subsequently in high

temperature heat exchangers generate steam for power generation. High Temperature

Molten Salt can be stored (HTS) and the stored heat can thus increase the load factor and the

usability for a CSP plant, e.g. to cover night (peak) demand. In the HYSOL concept (HYbrid

SOLar) such configuration is extended further to include a gas turbine fuelled by upgraded

biogas or natural gas. The optimised integrated HYSOL concept, therefore, becomes a fully

dispatchable (offering firm power) and a fully Renewable Energy (RE) based power supply

alternative, offering CO2-free electricity in regions with sufficient solar resources and attractive

market conditions.

Under this framework the objectives of the study are:

1. To study the economic potential of HYSOL in comparison to competing technologies from

a socio-economic perspective, e.g. to compare HYSOL vs. Open/Combined Cycle Gas

Turbine (OCGT and CCGT) for KSA, Mexico, RSA and Chile.

2. To examine effect on the energy system, when HYSOL is deployed, in terms of total system

cost, direct CO2 emissions and energy mix under different scenarios in Africa, Western

Europe and Mexico.

This study contents mainly two analyses, a socio-economic and an energy system analysis. The

socio-economic study examines the future economic viability of HYSOL in KSA, Mexico, RSA

and Chile, from a socio-economic viewpoint. For each country, different scenarios are

investigated where HYSOL competes vs. OCGT and CCGT under different conditions, e.g.

Variable Natural Gas (NG) price and CO2 emission cost among others parameters are exposed

to sensitivity analysis to determine the key parameters that have a significant impact on the

economic feasibility of this technology, based on Levelized Cost of Energy (LCOE) differences.

In addition, an energy system analysis is carried out to foreseeing the future economic

potential of HYSOL under three different deployment scenarios: a low, a moderate and a high

HYSOL roll-out, for Western Europe, Africa, and Mexico. This analysis shows the overall impact

of HYSOL in terms of CO2 emissions reduction, energy mix and total system cost. Finally,

conclusions based on the key findings found in both socio-economic and energy system

analysis are drawing in the conclusion section.


12

3 Socio-economic feasibility assessment

The economic feasibility of HYSOL configurations is addressed. The HYSOL alternative is

discussed relative to conventional reference firm power generation technologies. In particular

the HYSOL performance relative to new power plants based on Natural Gas (NG) such as Open

Cycle or Combined Cycle Gas Turbines (OCGT or CCGT) are in focus. The feasibility of

renewable based HYSOL power plant configurations attuned to specific electricity consumption

patterns in KSA, Mexico, RSA and Chile, where promising solar energy potentials are discussed.

3.1 Framework conditions for the countries analysed

The analytical approach used is illustrated from an example where a HYSOL configuration is

optimised to conditions seen in the countries studied. Thus, the HYSOL power plant studied

has been attuned to solar potentials and power system characteristics resembling conditions

in the countries analysed.

HYSOL plant configuration particularizes the basic outline by the choices:

For all the countries analysed, a CSP Tower configuration has been assumed. HYSOL

configurations can also be applied with CSP trough design;

The HYSOL plant investments do not include investments in biogas plants. The HYSOL plant

is assumed to purchase biogas at a price that equals the price of natural gas (NG) plus the

value of the reduced CO2 emission when biogas is used. HYSOL’s 100% renewable

configuration use biogas upgraded to NG quality.

As the HYSOL configuration analysed uses natural gas (NG) and not biogas based methane, the

plant may not be termed fully renewable, though being a firm, fully dispatchable and mainly

renewables based power plant.

Note: The data used for this analysis was provided by IDIE (Research Development Innovation

and Energy) unless otherwise indicated.

3.2 The HYSOL alternative and competing technology

This analysis compares electricity production costs for a HYSOL plant alternative to production

cost for conventional power plant solutions or reference plants.

For the KSA, Chile, Mexico and RSA cases it has been assumed that the main competing

reference technologies are an OCGT and a CCGT using NG.

3.3 Approach and basic assumptions

3.3.1 Economic indicator

A socio-economic approach is applied focusing on the economic indicator Levelized Cost of

Energy (LCOE), and on the sensitivity of the LCOE in particular to variations in the two

parameters:

• Load factor or the number of full load hours per year, and the


13

• price of natural gas (given as the levelized NG price covering the period analysed).

The solar potential and the annual power production heavily impact the HYSOL power plant

economy. And for fossil based competing reference technologies fuel cost and CO2 emission

cost developments constitute important framework conditions. LCOE dependency on in

particular these major parameters will be in focus in this study of HYSOL solutions (based

predominantly on a Renewable Energy Source (RES)) relative to fossil based conventional

reference power plant solutions.

3.3.2 Base Case assumptions

For the present socio-economic analysis1 the following general assumptions have been

adopted as 'Base Case' as outlined in Table 3.1.

Table 3.1: General assumptions

General assumptions

Price level Year 2015

Socio economic rate of calculation (rate of interest)

4 % p.a.

Project base year 2020

Period analysed 2021-2045

Period in years 25 years

Note: Assumptions are valid for all the countries studied.

3.3.3 Base Case overview and issues addressed via sensitivity analyses

Electricity production costs (LCOE) are furthermore analysed for its dependence on or

sensitivity to variations in the parameters outlined in Table 3.2.

Table 3.2: Parameters analysed for base and sensitivity cases

General assumptions & sensitivities

Natural Gas price Sensitivity Base Case -/+40%

CO2 emission quota market price

Base case-Sensitivity 0-40 USD/tCO2eq2

Capacity assignment Base case-Sensitivity 1303-

1504 MW

5-100MW <--> 180MW

1 Socio-economic analyses are used to assess how the objectives of energy policy are achieved in the

most appropriate way. The objective of socio-economic analysis of projects is to improve the basis for a qualified social prioritization of scarce resources. A sensible social prioritization of resources across sectors with varying time horizons require that assessments are made based on consistent and transparent methods, while special issues and consequences are described as best as possible. The result will always be a balance of both economic and non-economic considerations, including social, ethical and others. http://www.ens.dk/en/info/facts-figures/scenarios-analyses-models/socio-economic-method-analyses 2 Tonnes of carbon dioxide equivalents.


14

Lifetime of initial investment

Base case-Sensitivity 25-20 years

Interest rate Base case-Sensitivity 4.0-10% p.a.

Initial investment (CAPEX)

Sensitivity Base Case +/- 20%

3.4 KSA

3.4.1 Base Case for KSA HYSOL plant

Chosen Base Case for KSA HYSOL plant annual production, assigned capacity, load factor and

NG price (including sensitivity) are outlined in Table 3.3.

Table 3.3: Assumptions for KSA

KSA assumptions

Annual electricity production 812.7 GWh6/year

Assigned HYSOL capacity (PH) 130 MW

Annual full load hours7 (HFLH) 6 251 hours/year

Load factor8 (LF) 0.714

NG price 13.65 USD/MWh9 (4USD/MMBtu

10)

Sensitivity +/- 20%, +/-40% (Base Case)

Note: Data on investments, operation and maintenance costs for the KSA HYSOL configuration

are found in the Appendix.

3.4.2 Electricity costs as function of load factor and NG price

In Figure 3.1-Figure 3.4, results on the LCOE (given along the y-axis) are shown as a function of

the annual load. The annual load or electricity production, here expressed through its

equivalent, the number of full load hours per year, is shown along the x-axis.

HYSOL plant operation at different load factors is assumed to maintain the relative ST and GT

contribution to the electricity production. Thus, even the annual power production may differ

from the Base Case assumption the %-split of production contributions from the ST and GT

HYSOL plant components is assumed constant. And the share of the annual production based

on gas (via the GT directly and indirectly via GT flue gas heat recovered and utilized by the ST)

is kept constant.

3 130 MW is assigned to KSA.

4 150 MW is assigned to Mexico, RSA and Chile.

5 Megawatt.

6 Gigawatt-hours.

7 HFLH=812.7 GWh/year/130 MW=6 251 hours/year.

8 LF= 6 251/8 760 = 0.714

9 Megawatt-hours.

10 Million British Thermal Units.


15

Furthermore, for this feasibility analysis the HYSOL plant operation efficiency is assumed

constant, - even at e.g. lower annual production levels. And gas consumption per MWh

electricity generated, accordingly, is assumed constant and independent of the production.

This may be a somewhat rough assumption.

3.4.3 Design Point assumptions

Assumptions used as basis for optimizing and configuring the HYSOL plant, will in the following

be termed the 'Design Point' data assumptions. Yellow points, 'Design Points', shown in Figure

3.1-Figure 3.4 represent results for the KSA HYSOL plant based on Base Case assumptions.

Black points, correspondingly, represent (OCGT or CCGT) reference technology results based

on equivalent assumptions. Other results presented may thus be considered as sensitivity and

parameter analyses.

3.4.4 HYSOL relative to OCGT and CCGT

In what follows the KSA HYSOL plant alternative is compared to competing 'conventional' or

reference plant solutions based on equivalent system framework condition. Benchmarked via

the LCOE the competing technologies are evaluated using equivalent general assumptions. The

so-called Base Case data assumptions form the core for this feasibility comparison. For

selected key parameters LCOE consequences of data deviating from Base Case are covered via

sensitivity analysis.

For consistency of the comparison it is assumed, that the average annual electricity production

is the same for the HYSOL alternative and for the reference plants. Furthermore, plants being

compared are assumed to have the same capacity value in the KSA power system, and the

plants are assumed to be fully dispatchable (firm power). Thus, all plants are assumed to be

able to occupy the same position in the overall power system dispatch.

Data for the KSA HYSOL alternative and for the assumed KSA OCGT and KSA CCGT reference

power plants are found in the Appendix.

It can be observed from Figure 3.1-Figure 3.4 that the annual number of full load operation

hours for the HYSOL plant, shown along the x-axis, is extremely important for the electricity

production cost achieved, - and the plant economy. Low annual power production results in

high production costs. For the overall economy of a HYSOL plant, therefore, it is very important

to achieve high annual power production, as the total production costs are much dominated

by high initial investments. NG prices, however, have minor impact on the HYSOL power

production cost due to the relatively low electricity production contribution via the GT part of

the KSA HYSOL configuration.

3.4.5 CO2 emission costs

Comparison of HYSOL solutions relative to conventional OCGT and CCGT power plant solutions

are carried out for cases with and without inclusion of an assumed CO2 emission cost. For this

sensitivity analysis it has been assumed, as an example, that CO2 emission costs amounts to 40

USD/tCO2eq emitted. For natural gas (NG) this CO2 emission cost is equivalent to 8.17


16

USD/MWh NG. The CO2 emission cost assumed thus rises the NG price with an extra 8.17

USD/MWh NG.USD

3.4.6 Results: HYSOL compared to OCGT

Figure 3.1 shows the electricity production costs for OCGT and KSA HYSOL configuration, as

function of load factor and NG price.

Figure 3.1: Electricity production costs for OCGT and KSA HYSOL

Note: Assumed CO2 costs = 0 USD/tCO2eq, R=4%p.a., Lifetime=25years. Unit: USD/MWh el.

Figure 3.2 shows the electricity production costs for OCGT and KSA HYSOL configuration, as



17

Figure 3.2: Electricity production costs for OCGT and KSA HYSOL

Note: Assumed CO2 costs = 40USD/tCO2eq, R=4%p.a., Lifetime=25years. Unit: USD/MWh el.

3.4.7 Results: HYSOL compared to CCGT

Figure 3.3 shows the electricity production costs for CCGT and KSA HYSOL configuration, as



18

Figure 3.3: Electricity production costs for CCGT and KSA HYSOL


Figure 3.4 shows the electricity production costs for CCGT and KSA HYSOL configuration, as



19

Figure 3.4: Electricity production costs for CCGT and KSA HYSOL


For details about LCOE price composition see the Appendix section.

3.5 Mexico

3.5.1 Base Case for MEX HYSOL plant

Chosen Base Case for the MEX HYSOL plant annual production, assigned capacity and load

factor are outlined in Table 3.4.

Table 3.4: General assumptions for Mexico

Mexico assumptions

Annual electricity production 929.2 GWh/year


Annual full load hours11

(HFLH) 6 195 hours/year

Load factor12

(LF) 0.707

NG price 13.31 USD/MWh (3.9 USD/MMBtu)


11

HFLH = 929.2 GWh / 150MW = 6 195 hours/year. 12

LF= 6 251/8 760= 0.707.


20

Note: Data on investments, operation and maintenance costs for the MEX HYSOL configuration


3.5.2 Assumption on NG and biogas price relation

It has been assumed that the price of biogas can be estimated to equal the price of natural gas

(NG) plus the cost for the CO2 emission using the NG.

A CO2 emission cost, as assumed in our case study, of 40USD/tCO2eq emitted corresponds to a

rise of the NG price with an extra 8.17 USD/MWh NG. Thus, for the case of 40 USD/tCO2eq

emitted this means that the Biogas price will equal the NG price plus 8.17 USD/MWh NG.

With a NG price of 13.65 USD/MWh NG the assumption thus implies:

Biogas price = NG price + 8.17USD/MWh = 13.31 USD/MWh + 8.17 USD/MWh = 21.48

USD/MWh NG

If it is furthermore assumed that biogas has zero CO2 emission the economic consequence of

the use of biogas as fuel in HYSOL plant solutions will correspond to fuel costs as for NG plus its

CO2 cost. The fuel price relations for HYSOL, OCGT and CCGT solutions thus correspond to the

NG price including CO2 costs. However in this case the HYSOL solution using biogas has no CO2

emission.

The economic calculations shown in Figure 3.5 and Figure 3.6 showing power production costs

(LCOE) for the HYSOL solution relative to the OCGT and CCGT solutions, therefore, will hold

also for the case where HYSOL use biogas (and thus has no CO2 emission) and the OCGT and

CCGT use NG and emit CO2 at a cost of 40USD/tCO2eq emitted.


In Figure 3.5 it has been assumed that the CO2 emission costs are 0 USD/tCO2eq emitted. In

such scenario the CO2 reduction achieved by using (CO2 emission free biogas) thus has no

value. Therefore, in the 0 USD/tCO2eq emitted scenario, it has been assumed that both the

HYSOL plant and the OCGT plant use NG.


21

Figure 3.5: Electricity production costs for OCGT and MEX HYSOL



this case it has been assumed that the HYSOL plant use (CO2 emission free) biogas. The price of

biogas has been assumed to equal the price of NG plus the value of CO2 emission reduction

achieved by using biogas substituting NG.

However, the reference OCGT plant that solely relies on gas as fuel has been assumed use NG

priced as the NG price plus the cost of the CO2 emitted. (A cost of 40 USD/tCO2eq emitted

equals a price increase for the NG with an extra 8.17 USD/MWh NG.)


22

Figure 3.6: Electricity production costs for OCGT and MEX HYSOL



23


Figure 3.7 compares MEX HYSOL vs. CCGT in terms of their electricity production costs under

different NG prices and a carbon cost of 0 USD/tCO2eq.

Figure 3.7: Electricity production costs for CCGT and MEX HYSOL


Figure 3.8 shows MEX HYSOL vs. CCGT in terms of their electricity production costs under



24

Figure 3.8: Electricity production costs for CCGT and MEX HYSOL


For details about LCOE price composition see the Appendix section.

3.6 Chile

3.6.1 Base Case for CHI HYSOL plant

Chosen Base Case for the CHI HYSOL plant annual production, assigned capacity and load


Table 3.5: Assumptions for Chile

Chile assumptions

Annual electricity production 868.5 GWh/year




Load factor14

(LF) 0.661

NG price 44.36 USD/MWh (4 USD/MMBtu)

13

HFLH = 868.48GWh / 150MW = 5 790 h/year. 14

LF= 5790/8760= 0.661.


25


Note: Data on investments, operation and maintenance costs for the CHI HYSOL configuration


3.6.2 Assumption on NG and Biogas price relation

It has been assumed that the price of Biogas can be estimated to equal the price of natural gas


A CO2 emission cost, as assumed in our case study, of 40USD/tCO2eq emitted corresponds to a


emitted this means that the Biogas price will equal the NG price plus 8.17 USD/MWh NG.


Biogas price = NG price + 8.17 USD/MWh = 44.36 USD/MWh + 8.17 USD/MWh= 52.53

USD/MWh NG.





emission.

The economic calculations shown in Figure 3.10 and ¡Error! No se encuentra el origen de la

referencia. showing power production costs (LCOE) for the HYSOL solution relative to the

OCGT and CCGT solutions, therefore, will hold also for the case where HYSOL use biogas (and

thus has no CO2 emission) and the OCGT and CCGT use NG and emit CO2 at a cost of 40

USD/tCO2eq emitted.




value. Therefore, in the 0 USD/tCO2eq emitted scenario, it has been assumed that both the



26

Figure 3.9: Electricity production costs for OCGT and CHI HYSOL

Note: Assumed: CO2 costs = 0 USD/tCO2eq, R=4%p.a., Lifetime=25years. Unit: USD/MWh el.


this case it has been assumed that the HYSOL plant use (CO2 emission free) biogas. The price of




priced as the NG price plus the cost of the CO2 emitted. (A cost of 40 USD/tCO2eq emitted

equals a price increase for the NG with an extra 8.17 USD/MWh NG.)


27

Figure 3.10: Electricity production costs for OCGT and CHI HYSOL



Figure 3.11 compares CHI HYSOL vs. CCGT in terms of their electricity production costs under



28

Figure 3.11: Electricity production costs for CCGT and CHI HYSOL


Figure 3.12 shows CHI HYSOL vs. CCGT in terms of their electricity production costs under

different NG prices and a carbon cost of 40USD/tCO2eq.


29

Figure 3.12: Electricity production costs for CCGT, CHI HYSOL


3.7 RSA

3.7.1 Base Case for RSA HYSOL plant

Chosen Base Case for the RSA HYSOL plant annual production, assigned capacity and load


Table 3.6: General assumptions for RSA

RSA assumptions

Annual electricity production 1 014 GWh/year




Load factor16

(LF) 0.772

NG price 23.88 USD/MWh (7 USD/MMBtu)


Note: Data on investments, operation and maintenance costs for the RSA HYSOL configuration


15

HFLH = 1 014 GWh / 150 MW = 6 760 hours/year. 16

LF= 6 760/8 760= 0.772.


30


It has been assumed that the price of biogas can be estimated to equal the price of natural gas


A CO2 emission cost, as assumed in our case study, of 40 USD/tCO2eq emitted corresponds to a


emitted this means that the biogas price will equal the NG price plus 8.17 USD/MWh NG.


Biogas price = NG price + 8.17 USD/MWh = 23.88 USD/MWh + 8.17 USD/MWh = 32.05

USD/MWh NG.





emission.

The economic calculations shown in Figure 3.14 and Figure 3.16 showing power production

costs (LCOE) for the HYSOL solution relative to the OCGT and CCGT solutions, therefore, will

hold also for the case where HYSOL use biogas (and thus has no CO2 emission) and the OCGT

and CCGT use NG and emit CO2 at a cost of 40 USD/tCO2 eq emitted.


31


Figure 3.13 shows the LCOE of RSA HYSOL vs. OCGT as a function of the annual full load hours

and NG prices. HYSOL is competitive at about 24 USD/MWh NG price, because at design point,

HYSOL LCOE is 67 USD/MWh vs. 84 USD/MWh of OCGT.

Figure 3.13: Electricity production costs for OCGT and RSA HYSOL


Figure 3.14 illustrates the LCOE of RSA HYSOL vs. OCGT as a function of the annual full load

hours and NG prices. HYSOL is competitive at about 24 USD/MWh NG, because at design point,

HYSOL LCOE is 72 USD/MWh vs. 110 USD/MWh of OCGT.


32

Figure 3.14: Electricity production costs for OCGT and RSA HYSOL



33


Figure 3.15 illustrates the LCOE of RSA HYSOL vs. CCGT as a function of the annual full load

hours and NG prices. HYSOL is not competitive at design point (approximately 24 USD/MWh),

because HYSOL LCOE corresponds to 67 USD/MWh vs. 54 USD/MWh of CCGT.

Figure 3.15: Electricity production costs for CCGT and RSA HYSOL


Figure 3.16 shows the LCOE of RSA HYSOL vs. CCGT as a function of the annual full load hours

and NG prices. HYSOL is not competitive at design point (approximately 24 USD/MWh),

because HYSOL LCOE corresponds to 72 USD/MWh vs. 68 USD/MWh of CCGT.


34

Figure 3.16: Electricity production costs for CCGT and RSA HYSOL


3.8 Sensitivity analyses

3.8.1 KSA

Figure 3.17 illustrates KSA HYSOL results in overview: Electricity production costs (LCOE) -

Sensitivity relative to Base Case Assumptions. Units: USD/MWh el.


35

Figure 3.17: Sensitivity relative to base case assumptions KSA HYSOL

Figure 3.18 shows KSA OCGT results in overview: Electricity production costs (LCOE) -


Figure 3.18: Sensitivity relative to base case assumptions KSA OCGT

Figure 3.19 shows KSA CCGT results in overview: Electricity production costs (LCOE) -



36

Figure 3.19: Sensitivity relative to base case assumptions KSA CCGT

3.8.2 Mexico

Figure 3.20 shows MEX HYSOL results in overview: Electricity production costs (LCOE) -


Figure 3.20:Sensitivity relative to base case assumptions MEX HYSOL

Figure 3.21 illustrates MEX OCGT results in overview: Electricity production costs (LCOE) -



37

Figure 3.21: Sensitivity relative to base case assumptions MEX OCGT

Figure 3.22 shows MEX CCGT results in overview: Electricity production costs (LCOE) -


Figure 3.22: Sensitivity relative to base case assumptions MEX CCGT

3.8.3 Chile

Figure 3.23 illustrates CHI HYSOL results in overview: Electricity production costs (LCOE) -



38

Figure 3.23: Sensitivity relative to base case assumptions CHI HYSOL

Figure 3.24 shows CHI OCGT results in overview: Electricity production costs (LCOE) -


Figure 3.24: Sensitivity relative to base case assumptions CHI OCGT

Figure 3.25 shows CHI CCGT results in overview: Electricity production costs (LCOE) - Sensitivity

relative to Base Case Assumptions. Units: USD/MWh el.


39

Figure 3.25: Sensitivity relative to base case assumptions CHI CCGT

3.8.4 RSA

Figure 3.26 illustrates RSA HYSOL results in overview: Electricity production costs (LCOE) -

Sensitivity relative to Base Case Assumptions. Units: $/MWh el.

Figure 3.26: Sensitivity relative to base case assumptions RSA HYSOL


40

Figure 3.27 shows RSA OCGT results in overview: Electricity production costs (LCOE) -


Figure 3.27: Sensitivity relative to base case assumptions RSA OCGT

Figure 3.28 depicts RSA CCGT results in overview: Electricity production costs (LCOE) -


Figure 3.28: Sensitivity relative to base case assumptions RSA CCGT


41

3.9 Key findings

General findings:

The price of natural gas (NG) and its expected development strongly impacts the economic

attractiveness of HYSOL solutions relative to NG based competing technologies, such as

OCGT and CCGT power plants.

CO2 emission costs acts significantly in favour of HYSOL solutions. As seen from the

sensitivity analysis, in particular an OCGT plant solution is strongly exposed to potential

rising CO2 emission costs.

The capacity of a HYSOL plant is defined by the size of firm capacity it may substitute being

part of the power system in question. This impacts the required capacity investments for

competing solutions (OCGT or CCGT) matching the HYSOL plant in the system. The

economic implication of different capacity assignments, however, as seen from the

sensitivity analysis, is relatively minor. This due to the relative low initial investment

component for OCGT and CCGT plants, which may be seen comparing power price

composition results shown in the Appendix.

The period analysed and the lifetime of the initial investments has minor impact on the

electricity production cost for the OCGT and CCGT plant solutions. Being an initial

investment intensive RES based technology the HYSOL solution is seen to be impacted,

though moderately, from changes in lifetime of the investment.

The interest rate or the rate of calculation is important for investment intensive plants,

such as the HYSOL solution. In Base Case a rate of calculation of 4% p.a. has been assumed,

which may correspond to typical socio-economic conditions. Assuming a higher rate of

interest of 10% p.a., that may resemble a corporate economic situation, it is seen from the

sensitivity analysis that power production costs (LCOE) are increased substantially. In

particular the HYSOL solution is very sensitive to changes in the interest rate.

HYSOL solutions, being investment intensive are very sensitive to changes in the overall

investment costs, and the rate of interest, whereas the OCGT and CCGT solutions are

considerable less exposed to changes in the overall investment.


For Chile, HYSOL is economically competitive when compared to OCGT and CCGT options,

while this HYSOL solution is not competitive in the KSA and Mexico cases. The lack of

competitiveness in these countries could be attributing to the significantly low NG prices.

(NG price in Chile is about three times as much as in Mexico or KSA).

The HYSOL solution in RSA competes favourable relative to the OCGT reference as can be

seen from comparing results when base case data are assumed. This conclusion holds

without taking into account an assumed cost on emission of CO2. When compared to a

CCGT reference plant the RSA HYSOL alternative is less favourable. However, introducing

an assumed CO2 emission costs of 40 USD/tCO2eq emitted, narrows the LCOE price

difference considerable (down to a LCOE difference of less than 5 USD/MWh).


42

4 Energy Systems Analysis

4.1 Objective

The general objective of the energy systems analysis is to assess the effects of introducing the

HYSOL technology into the target markets. I.e. how a hybrid CSP technology will affect the

surrounding energy system while taking into account the availability of resources for each

region/country selected.

More specific, this exercise will help to understand the possible contribution in terms of CO2

abatement, system cost reduction and energy mix diversification.

4.2 Method

Energy-economic modelling (bottom-up) is the main method for this study, using the Energy

Technology System Analysis Program TIMES Integrated Assessment Model (ETSAP-TIAM). The

framework for the analysis is presented in Figure 4.1.

First, a reference scenario is created based on a default reference energy system

(described below);

Then an evaluation criteria is created for analysis, and based on these criteria, a HYSOL

scenario is created that represents different technological and policy constraints and

country/region specific CSP targets;

Furthermore, a high penetration HYSOL scenario will emphasize the effects on the

surrounding energy system.

These scenarios will be modelled in ETSAP-TIAM and compared to a reference scenario to see

the effect that the alternative technological and political factors have on the resulting

implementation of the HYSOL. The methodology used for analysing the energy systems is

based on linear programming.


43

Figure 4.1: Diagram of framework for analysis and work flow

4.3 ETSAP-TIAM

4.3.1 Times Architecture Background

The TIMES (The Integrated MARKAL-EFOM System) model generator, is an evolved version of

MARKAL (MARket Allocation model), developed under the IEA implementing agreement,

ETSAP. TIMES is a model generating set of optimization equations17 that computes an inter-

temporal dynamic partial equilibrium on energy and emission markets based on the

maximization of total surplus (defined as the sum of supplier and consumer surpluses). In

essence, a model generated by TIMES finds the least-cost solution for the entire energy system

with flexibility in terms of time resolution and sectorial focus.

4.3.2 Regions

ETSAP-TIAM is a global technology-rich model of the entire energy/emission system of the

world based on the TIMES model architecture. The model is set up to explore the development

of the world energy system for the 21st century, representing the energy system of the world,

divided into 15 regions (Figure 4.2). ETSAP-TIAM models the procurement, transformation,

trade, and consumption of a large number of energy forms. Results from applicable processes

are also aggregated to the global level.

17 A complete description of the TIMES equations appears in: http://www.iea-

etsap.org/web/Documentation.asp.

No

Reference scenario HYSOL scenarios

ETSAP-TIAM

Evaluation criteria: E.g.

does HYSOL deploy

under normal market

conditions?

Energy system

assessment

Input data

Output data

Yes

New input data

http://www.iea-etsap.org/web/Documentation.asp

http://www.iea-etsap.org/web/Documentation.asp


44

Figure 4.2: Fifteen regions of the ETSAP-TIAM18

4.3.3 Time Frame

Our analysis considers from 2010 to 2050. We conduct the analysis using 2010 as a base year,

and use 10-year time steps. In which 2030 is considered as midterm and 2050 is considered as

long term.

4.3.4 Model Structure

As ETSAP-TIAM is based on the TIMES equations, it is a perfect foresight, linear optimization

model (ETSAP-TIAM optimizes all time periods simultaneously). The objective function that is

maximized is the discounted net present value of the total surplus for the entire world. The

surplus maximization can be subject to many exogenously-defined constraints on a regional,

sectoral or global basis, such as supply bounds (in the form of detailed supply curves) for the

primary resources, technical constraints governing the creation, operation, and abandonment

of each technology, balance constraints for all energy forms and emissions, timing of

investment payments and other cash flows, and the satisfaction of a set of demands for energy

services in all sectors of the economy. For more information see the Appendix section.

4.4 Scenarios

First a reference scenario is run in ETSAP-TIAM that assumes no additional energy efficiency

improvements beyond currently adopted policies. It also contains no additional climate

polices, and no renewable energy targets. HYSOL scenario is then run in ETSAP-TIAM. In both

scenarios, ETSAP-TIAM will then optimize the energy systems based on resource availability,

existing infrastructure stock, and prices given the exogenous constraints.

18

Energy Technology System Analysis Program TIMES Integrated Assessment Model (ETSAP-TIAM).

ETSAP-TIAM Regions

AFR Africa

AUS Australia & NZ

CAN Canada

CHI China

CSA Central and South America

EEU Eastern Europe

FSU Former Soviet Union

IND India

JPN Japan

MEA Middle East

MEX Mexico

ODA Other Developing Asia

SKO South Korea

USA United States

WEU Western Europe


45

The following scenarios are constructed:

Reference: This scenario reflects the development of the global, regional and sectoral

energy demand if current technological trends and policies are continued. This pathway

will take into account current technological mixes, performance and cost data for

conventional technologies, and default assumptions for Autonomous Energy Efficiency

Improvement (AEEI). It also takes into account the current carbon price, holding it constant

until 2050. It does not take into consideration any major energy efficiency improvements

and policy interventions beyond what have already been planned.

HYSOL: This scenario considers the elements mentioned in the reference scenario,

additionally, it will explicitly take into account the CSP installed capacity targets for Mexico

(MEX), Africa (AFR) and Western Europe (WEU), in this way we will study how the energy

system react under a forced implementation of the HYSOL.

HYSOL high penetration: This scenario multiplies the CSP installed capacity targets of each

region/country studied by factor ten, except for WEU which its CSP target is multiplied by

factor 6, this is due to the lack of resources available in this region, see section 4.5.1. This

scenario is interesting because it will show, for instance, which power plants will be

replaced when a high penetration of HYSOL comes into place.

4.4.1 Carbon Price

The (World Bank, 2014) released a report that documented the current state of carbon taxes

and carbon Emission Trading Schemes (ETS) and their price levels. Further information on ETS

was taken from the International Carbon Action Partnership (ICAP, 2015).

Carbon taxes are summarized in Table 4.1, and are applied in ETSAP-TIAM for the periods

2015-2050 in all the scenarios. See more about the assumptions behind carbon pricing in the

Appendix.

Table 4.1: Current carbon prices in 2005 USD/tCO2eq

Region Industry Power Heat Buildings Transport (excluding Aviation)

Oil Coal

MEX - - - - - 0.62 1.00

WEU 7.02 11.35 7.02 - 5.43 - -

AFR - - - - - - -

Sources: (ICAP, 2015) and (World Bank, 2014).

For the HYSOL scenario, the following approach was considered: Identical carbon prices as in

the Reference scenario were considered. Additionally, specific renewable energy targets were

taken into account for the studied regions/countries. These targets focus on the installed

capacity for concentrated solar thermal technologies as outlined in

Table 4.2.


46

Table 4.2: IRENA's Renewable Energy Roadmap - REmap Countries Renewable Energy Targets, 2014

Country Installed capacity of CSP (GW19

) Year

Mexico20

0,63 2018

Western Europe21

1,14 2020

Africa22

5,1 2020

Source: (IRENA, 2014a).

4.5 Energy system assessment

4.5.1 Availability of resources

The future deployment of CSP technologies will be limited to regions with at least an average

Direct Normal Irradiance class (DNI) of 2 000 kWh/m2 year (Trieb et al., 2009), see Figure 4.3.

Additionally, the CSP potential will be constrained even further due to land availability, e.g.

Farming land, protected areas and other areas of exclusion which could eventually be

considered as competitors of CSP plants, in terms of land use, see Figure 4.4.

Figure 4.3: Annual average DNI (KWh/m2 year)

19

Gigawatt. 20

According to IRENA, (2014a) this value represent both CSP and PV capacities aggregated. Here we assumed that 0,63GW corresponds to only CSP installed capacity. 21

The CSP targets of Western Europe correspond to Italy and France respectively with 0.60 and 0.54 GW of installed capacity in 2020. 22

To estimate the total installed capacity target for Africa, target values from Egypt (1.1GW), Morocco (2GW assumed only for CSP) and Nigeria (2GW) were aggregated.


47

Note: Resulting map of the annual sum of direct normal irradiance for potential global CSP

sites as identified within the EU-project REACCESS.

Source: (Trieb et al., 2009).

According to (Trieb et al., 2009) Mexico has a technical CSP potential of 146 430 PJ23/year

while India has a technical CSP potential of 39 341 PJ/year. Africa has the larger technical CSP

potential among the REACCESS24 world regions.

Figure 4.4: Worldwide exclusion of sites for CSP plant construction

Note: Dark areas indicate suitable sites from the point of view of land suitability.

Source: (Trieb et al., 2009).

This section compares solar energy and land resources availability based on the analysis made

by (Trieb et al., 2009) and by (Biberacher, 2010). For the first case, the methodology of site

exclusion was described in (Trieb et al., 2005). Exclusion criteria comprise: slope > 2.1 %, land

cover like permanent or non-permanent water, forests, swamps, agricultural areas, shifting

sands including a security margin of 10 km, salt pans, glaciers, settlements, airports, oil or gas

fields, mines, quarries, desalination plants, protected areas and restricted areas. Spatial

resolution of the data was 1 km². Similar approach was used in (Biberacher, 2010) where the

main differences are described as follows: instead of constraint the CSP availability to DNI

above 2 000 kWh/m2 year, it was constraint to above 1 800 kWh/m2 year. Additionally,

suitable areas in each of the regions of the model and maximum production of solar electricity

23

Petajoule. 24

REACCESS project, Risk of Energy Availability: Common Corridors for Europe Supply Security

http://reaccess.epu.ntua.gr/Home.aspx

http://reaccess.epu.ntua.gr/Home.aspx


48

in these areas was considering a 16% solar to electricity efficiency while in (Trieb et al., 2009)

solar to electricity efficiency considered corresponds to 12%, moreover, this factor was

multiplied by the land use factor (37% in average for CSP parabolic trough). Therefore, the land

use efficiency calculated is 4.4% as shown in Table 4.3. These differences in the calculations of

land availability could explain the difference of technical potential for the CSP technologies as

outlined in Table 4.4.

Table 4.3: Comparison of criteria between Biberacher (2010) and Trieb et al (2009) study

Unit (RSA, 2010) (Trieb et al., 2009)

Minimum DNI (kWh/m2) >1 800 >2 000

Land use criteria % >2.1 >2.1

Electric efficiency % 16 12

Land use factor % N.A 37

Land use efficiency % N.A 4.4

Sources: (Biberacher, 2010) and (Trieb et al., 2009).

Table 4.4: Comparison between Biberacher (2010) and Trieb et al (2009) study

Region

DNI Potential

(PJ/year)25

DNI Potential

(PJ/year)26

Difference

(%)

Mexico 146 430 161 000 -9.95

India 39 341 8 700 77.89

USA 373 334 30 000 91.96

China 453 006 217 000 52.10

Africa 5 253 732 1 758 000 66.54

Australia 2 511 360 819 000 67.39

Central and South America 446 371 185 000 58.55

Central Asia, Caucasus 54 695 N.A N.C

Canada 0 142 000 N.C

Japan 0 80 N.C

Middle East 1 046 300 570 000 45.52

Other Developing Asia 272 020 143 000 47.43

Other East Europe 76 130 -71.96

Russia 0 539 000 N.C

EU27 8 672 240 97.23

Total 10 605 337 4 573 150 56.88

Sources: (Biberacher, 2010) and (Trieb et al., 2009).

25

Trieb et.al 2009. 26

Research Studies Austria (RSA) 2010.


49

N.C: Not Comparable

N.A: Not Available

Biberacher's approach was used in ETSAP-TIAM because is considered to be more conservative

than Trieb's approach. Therefore, these potentials are considered to be upper bound for our

modelling exercise.

4.6 Modelling

4.6.1 CSP technology overview

CSP is a power generation technology that uses mirrors to concentrate the sun’s rays and, in

most of today’s CSP systems, to heat a fluid that is used to produce steam. The steam is then

used to drive a conventional steam turbine and generate power in the same way as

conventional thermal power plants that use steam cycles. However, other concepts are being

explored and not all future CSP plants will necessarily use a steam cycle. CSP is at its infancy in

terms of deployment, with total installed capacity at the end of 2014 of around 5 gigawatts

(GW). New capacity additions in 2013 were estimated to have reached 0.9 GW, a new record.

Total installed capacity has grown rapidly since 2010, but policy uncertainty has reduced

growth prospects in key markets (IRENA, 2014b).

4.6.2 HYSOL implemented in ETSAP-TIAM

The HYSOL is a hybrid CSP parabolic trough plant in which the input parameters for the model

are outlined in Table 4.5.

Table 4.5: Inputs parameters for the HYSOL in ETSAP-TIAM

Input parameters

Start 2015

Life 25 years

Annual availability factor 0.99

Investment cost 2015 7 795 MUSD2014/GW




Fix O&M cost 2015 106 MUSD2014/GW




Note: These values of investment and fix O&M cost are input in the model for all the regions

studied.

The learning curve cost for HYSOL was calculated based on (Viebahn et al., 2011). Multipliers

for the total investment and fix O&M costs were obtained by assuming linearity in the curves


50

depicted in the Figure 4.5. Then these multipliers were used to obtain the total investment and

fix O&M costs for the HYSOL as outlined in Table 4.5.

Figure 4.5: Overall learning curve and the contributions of the main parts for CSP plant in Spain

Source: (Viebahn et al., 2011).

4.7 Results

This section presents results on annual electricity production by fuel, total system cost and

direct CO2 emissions for Mexico, Western Europe and Africa.

4.7.1 Annual electricity production

In the Mexican reference scenario, HYSOL will not be deployed under normal market

conditions as depicted in Figure 4.6.

In HYSOL high penetration scenario, HYSOL replaces gas and oil power plants as illustrated in

Figure 4.7, this is valid for the midterm. Additionally, in the long term the model goes for

nuclear energy and geothermal, this is mainly due to the fact that Mexico has a large

geothermal potential and its decreases in future technology cost.


51

Figure 4.6: Electricity production by fuel in Mexico, reference scenario

Figure 4.7: Electricity production by fuel in Mexico, HYSOL high penetration scenario

In the reference scenario, HYSOL will not be deployed in Western Europe under normal market

conditions. In the midterm and long term, a significant deployment of wind and geothermal

plants are envisaged, while coal, nuclear, gas and oil power plants will be considerably reduced

as illustrated in Figure 4.8. HYSOL deployment is limited in WEU due to lack of land and DNI

availability. Therefore, HYSOL has a negligible impact on primary electricity production in this

region as shown in Figure 4.9.

0

500

1000

1500

2000

2500

3000

2010 2020 2030 2050

Pri

mar

y e

lect

rici

ty p

rod

uct

ion

(P

J)

Reference MEX

Wind

Solar Thermal

Solar PV

Nuclear

Hydro

Geo, Tidal and Wave

Gas and Oil

Coal

CH4 Options

Biomass

0

500

1000

1500

2000

2500

3000

2010 2020 2030 2050

Pri

mar

y e

lect

rici

ty p

rod

uct

ion

(P

J)

HYSOL high penetration MEX

Wind

Solar Thermal

Solar PV

Nuclear

Hydro

Geo, Tidal and Wave

Gas and Oil

Coal

CH4 Options

Biomass


52

Figure 4.8: Electricity production by fuel in Western Europe, reference scenario

Figure 4.9: Electricity production by fuel in Western Europe, HYSOL high penetration scenario

In the reference scenario, HYSOL will not be deployed in Africa under normal market

conditions as shown in Figure 4.10.

In HYSOL high penetration scenario, the deployment of HYSOL will help to phase-out gas and

oil power plants in the long term. In addition, HYSOL will partially replace hydro, geothermal

and wind power as illustrated in Figure 4.11.

0

2000

4000

6000

8000

10000

12000

14000

16000

2010 2020 2030 2050

Pri

mar

y e

lect

rici

ty p

rod

uct

ion

(P

J)

Reference WEU

Wind

Solar Thermal

Solar PV

Nuclear

Hydro

Geo, Tidal and Wave

Gas and Oil

Coal

CH4 Options

Biomass

0

2000

4000

6000

8000

10000

12000

14000

16000

2010 2020 2030 2050

Pri

mar

y e

lect

rici

ty p

rod

uct

ion

(P

J)

Hysol high penetration WEU

Wind

Solar Thermal

Solar PV

Nuclear

Hydro

Geo, Tidal and Wave

Gas and Oil

Coal

CH4 Options

Biomass


53

Figure 4.10: Electricity production by fuel in Africa, reference scenario

Figure 4.11: Electricity production by fuel in Africa, HYSOL high penetration scenario

4.7.2 Total system cost

The deployment of HYSOL has neither negative nor positive effect in the total system cost, this

is valid for all the regions analysed as outlined in

0

1000

2000

3000

4000

5000

6000

7000

2010 2020 2030 2050

Pri

mar

y e

lect

rici

ty p

rod

uct

ion

(P

J)

Reference AFR

Wind

Solar Thermal

Solar PV

Nuclear

Hydro

Geo, Tidal and Wave

Gas and Oil

Coal

CH4 Options

Biomass

0

1000

2000

3000

4000

5000

6000

7000

8000

2010 2020 2030 2050

Pri

mar

y e

lect

rici

ty p

rod

uct

ion

(P

J)

Hysol high penetration AFR Wind

Solar Thermal

Solar PV

Nuclear

Hydro

Geo, Tidal and Wave

Gas and Oil

Coal

CH4 Options

Biomass


54

Table 4.6. However, it may have an impact at power system level which it needs to be further

study.

Table 4.6: Total system cost in billion USD2005

Region Scenario Total Index27

AFR REFERENCE 19 717 1.00

AFR HYSOL 19 735 1.00

AFR HYSOL10 19 856 1.01

MEX REFERENCE 6 511 1.00

MEX HYSOL 6 508 1.00

MEX HYSOL1028

6 520 1.00

WEU REFERENCE 33 957 1.00

WEU HYSOL 33 961 1.00

WEU HYSOL629

33 968 1.00

4.7.3 Direct CO2 emissions

At system level, the impact of HYSOL on total direct CO2 emissions abatement is negligible as

outlined in Table 4.7.

Table 4.7: Total direct CO2 emissions in Gt of CO2.

Region Scenario 2050 Index205030

AFR REFERENCE 2 1.00

AFR HYSOL 2 1.00

AFR HYSOL10 2 1.00

MEX REFERENCE 1 1.00

MEX HYSOL 1 1.00

MEX HYSOL10 1 0.98

WEU REFERENCE 4 1.00

WEU HYSOL 4 0.99

WEU HYSOL6 4 0.99

27

This index shows total system cost from the HYSOL scenarios divided by the base scenario. 28

HYSOL10 stands for multiply country specific installed capacity CSP targets by ten. 29

HYSOL6 stands for multiply country specific installed capacity CSP targets by six (valid just for WEU). 30

Index2050 indicate the impact of HYSOL on direct CO2 emissions abatement.


55

4.8 Key findings

Despite the limitations of the ETSAP-TIAM model regarding time and geographical resolution,

there are interesting findings from this exercise which are highlighted below.

General findings:

The base scenario shows that HYSOL will not be deployed under normal market conditions.

This is due to the high investment cost of this technology. Therefore, HYSOL will need to be

supported;

the deployment of HYSOL has neither negative nor positive effect in the total system cost,

this is valid for all the regions analysed;

high deployment of HYSOL has a negligible impact on direct C02 emissions at total energy

system level.


In Africa, the deployment of HYSOL will help to phase-out gas and oil power plants in the

long-term;

in Mexico, the deployment of HYSOL will help to decrease gas and oil power plants in the

mid-term;

in Western Europe, the deployment of HYSOL will have negligible impact on the power

system. This is due to the limited solar potential and land availability of the region.


56

5 Conclusion

The socio-economic analysis has shown that HYSOL is not economically feasible when it is

compared vs OCGT/CCGT under normal market conditions in KSA and Mexico, while this

technology is economically feasible for Chile in all the scenarios investigated, and it is partially

feasible for RSA when it competes with OCGT. This is due to the significantly high NG price in

Chile and in RSA respectively, which it corresponds to approximately three times as much as in

KSA or Mexico, which makes Chile and RSA attractive markets for the investment in HYSOL.

This analysis also has shown that the interest rate is critical for HYSOL solutions due to the high

initial investment. In Base Case a rate of calculation of 4% p.a. has been assumed, that

corresponds to typical socio-economic conditions. When assuming a higher rate of interest of

10% p.a., similar to a corporate economic situation, it is seen from the sensitivity analysis that

power production costs (LCOE) are increased substantially. In particular the HYSOL solution is

very sensitive to changes in the interest rate.

CO2 emission costs acts significantly in favour of HYSOL solutions. As seen from the sensitivity

analysis, in particular an OCGT plant solution is strongly exposed to potential rising CO2

emission costs.

Additionally, the energy system analysis has shown that HYSOL is not economically feasible

under current market conditions. However, when this technology is highly deployed, it

contributes to phase-out gas and oil power plants within the energy systems studied.


57

6 References

Anon., 28. Diario Oficial de la Federación. [Online]

Available at: http://www.dof.gob.mx/nota_detalle.php?codigo=5342501&fecha=28/04/2014

[Accessed 2015 April 9].

Anon., n.d. Powermag. [Online]

Available at: http://www.powermag.com/mexicos-electricity-sector-reform-in-

perspective/?pagenum=1


Anon., n.d. SENER. [Online]

Available at: http://sie.energia.gob.mx/bdiController.do?action=temas


Bataille, 2005. Design and application of a technologically explicit hybrid energy-economy

policy model with micro and macro-economic dynamics, s.l.: School of Resource and

Environmental Management, Simon Fraser University.

Biberacher, 2010. Model benchmark with GIS data, Salzburg: Research Studios Austria

Forschungsgesellschaft mbH.

Bierzwinsky, R. J. D. F. J., 2014. Chadbourne. [Online]

Available at: http://www.chadbourne.com/files/Publication/cfbf25f4-52c1-4a46-8675-

5298e9836121/Presentation/PublicationAttachment/fba22689-74b6-4d28-b623-

02318254c978/New_Power_Market_Mexico_0914.pdf

Brand et al., 2012. The value of dispatchability of CSP plants in the electricity systems of

Morocco and Algeria. Energy Policy, Volume 47, p. 321–331.

Centro Nacional de Control de Energía, n.d. [Online]

Available at: http://www.cenace.gob.mx/GraficaDemanda.aspx

[Accessed July 2015].

Consejo Nacional de Población (CONAPO), n.d. [Online]

Available at: http://www.conapo.gob.mx/es/CONAPO/Proyecciones

[Accessed 9 September 2013].

Electricidad, C. F. d., n.d. [Online]

Available at:

http://app.cfe.gob.mx/Aplicaciones/OTROS/costostotales/ConsultaArchivoCostosyCapacidade

s.aspx

[Accessed April 2015].


58

ICAP, 2015. ETS Map. [Online]

Available at: https://icapcarbonaction.com/ets-map


IEA, 2010a. International Energy Agency Statistics. [Online]

Available at:

http://www.iea.org/statistics/statisticssearch/report/?year=2010&country=INDIA&product=El

ectricityandHeat


IEA, 2010b. International Energy Agency Statistics. [Online]

Available at:

http://www.iea.org/statistics/statisticssearch/report/?country=MEXICO&product=electricityan

dheat&year=2010


IRENA, 2014a. REMAP 2030 - A Renewable Energy Roadmap, Abu Dhabi: IRENA.

IRENA, 2014b. Renewable Power Generation Costs in 2014, s.l.: IRENA.

Joskow, 2011. Comparing the Costs of Intermittent and Dispatchable Electricity Generating

Technologies. American Economic Review, 100(3), p. 238–241.

Kost & Schlegel, 2010. StudieStromgestehungskostenErneuerbareEnergien..

Labriet et al., 2012. Climate mitigation under an uncertain technology future: A TIAM-World

analysis. Energy Economics, Volume 34, pp. S366-S377.

Practical Law, 2014. Practical Law. [Online]

Available at: http://us.practicallaw.com/9-524-0279

Sasse, D., 2009. [Online]

Available at: http://goodrichriquelme.com/wp-content/uploads/2011/05/Power-Generation-

Law-for-the-Use-of-Renewable-Energies-and-the-Financing-of-Energy-Transition.pdf

Scheper & Kram, 1994. Comparing MARKAL and MARKAL-MACRO for The Netherlands. s.l.,

ECN Policy Studies.

Secretaría de Energía, 2014. Prospectivas del Sector Eléctrico 2014-2028, s.l.: s.n.

Sioshansi & Denholm, 2010. The Value of Concentrating Solar Power and Thermal Energy

Storage, Colorado: NREL.

Trieb et al., 2005. Concentrating Solar Power for the Mediterranean Region (MED-CSP),

Stuttgart: German Aerospace Center (DLR).


59

Trieb et al., 2009. Global Potential of Concentrating Solar Power. Berlin, German Aerospace

Center.

U.S. Energy Information Administration, 2014. EIA. [Online]

Available at: http://www.eia.gov/countries/analysisbriefs/Mexico/mexico.pdf

Viebahn et al., 2011. The potential role of concentrated solar power (CSP) in Africa and

Europe—A dynamic assessment of technology development, cost development and life cycle

inventories until 2050. Energy Policy, 39(8), p. 4420–4430.

World Bank, 2014. State and Trends of Carbon Pricing 2014, Washington, DC: s.n.


60

7 Appendix

7.1 Section A: Energy system analysis

7.1.1 Calibration of TIAM model

The calibration of the ETSAP-TIAM model was done by comparing the reference scenario

versus historical data from the International Energy Agency (IEA) for Mexico and Western

Europe.

For Mexico, the total electricity production difference is negligible, approximately 1 % of

difference in the results obtained by the ETSAP-TIAM model compare with the data from IEA

2010. Therefore, the ETSAP-TIAM model is well calibrated since the values for electricity

production are in a match with the historical values, as it is depicted in the figures below.

Figure A: comparison between historical data from (IEA, 2010b) vs. ETSAP-TIAM model, Mexico

7.1.2 Model Structure

As ETSAP-TIAM is based on the TIMES equations, it is a perfect foresight, linear optimization

model (ETSAP-TIAM optimizes all time periods simultaneously). The objective function that is

maximized is the discounted net present value of the total surplus for the entire world. The

surplus maximization can be subject to many exogenously-defined constraints on a regional,

sectoral or global basis, such as supply bounds (in the form of detailed supply curves) for the

primary resources, technical constraints governing the creation, operation, and abandonment

of each technology, balance constraints for all energy forms and emissions, timing of

investment payments and other cash flows, and the satisfaction of a set of demands for energy

services in all sectors of the economy.

As an integrated energy system model, ETSAP-TIAM is built to represent the total energy

chain, including energy extraction, conversion and demand (e.g., fossil and renewable

0

200

400

600

800

1000

1200

TIAM IEA

An

nu

al e

lect

rici

ty p

rod

uct

ion

[P

J]

Electricity production by fuel in Mexico 2010 Wind

Solar Thermal

Solar PV

Nuclear

Hydro

Geo and Tidal

Gas and Oil

Coal

CH4 Options

Biomass


61

resources), potentials of storage of CO2 (which comes into play with a carbon price and can be

adjusted via cost parameters) and region- specific demand developments. The region and

sector-specific demands for end-use energy and industrial products are driven by socio-

economic parameters which are described below. The model contains explicit detailed

descriptions of hundreds of technologies as well as hundreds of energy, emission and demand

flows within each region (region-specific parameters can be defined), logically interconnected

to form a Reference Energy System (Figure B). Such technological detail allows precise tracking

of optimal capital turnover, and provides a precise description of technology and fuel

competition. The long-distance trade of energy between the regions of ETSAP-TIAM is

endogenously modelled for coal, natural gas (gaseous or liquefied), crude oil, various refined

petroleum products, and biofuels. Global and regional (partial agreement) GHG emission

trading is also possible. ETSAP-TIAM is driven by a set of demands for energy services in

agriculture, residential buildings, commercial buildings, industry, and transportation. Each

technology has a hurdle rate that varies from 5% to 20%, depending on the sector. The hurdle

rate is used to convert the capital cost in an annual cash flow: discounted multi-year interest

rate payments are included when calculating an annual payment for an investment and

payback time (a technology with a high hurdle rate means a short payback rate is required,

while a technology with a low hurdle rate allows a longer payback time. Demands for energy

services are specified by the user in the Reference (BAU) scenario, and each have its own price

elasticity. Each demand may vary endogenously in alternative scenarios, in response to

endogenous price changes. Because energy services respond to changes in their respective

prices through end-use price elasticities within ETSAP-TIAM, savings of energy demand and

corresponding cost variations are accounted for in the objective function as well.

The model's variables include the investments, capacities, and activity levels of all technologies

at each period of time, as well as the amounts of energy, material, and emission flows in and

out of each technology, and the quantities of traded energy between all pairs or regions. For

sectors that use non-storable energy (electricity, heat), the flow variables are defined for each

of six time-slices: three seasons (summer, winter, autumn/spring) times two diurnal (day,

night) divisions. ETSAP-TIAM is a partial equilibrium model, and although it does not include

macroeconomic variables beyond the energy sector, there is evidence that accounting for

price elasticity of demands captures the majority of the feedback effects from the economy to

the energy system (Bataille, 2005) (Labriet et al., 2012) (Scheper & Kram, 1994).


62

Figure B. Reference Energy System within ETSAP-TIAM. Technological efficiencies are included in the Industrial, Agriculture, Commercial, Residential, and Transport Technology boxes. Other efficiency adjustments are possible within the fuel production chains

7.1.3 CO2 tax in ETSAP-TIAM

While ETSAP-TIAM is capable of simulating cap-and-trade carbon markets such as the ETS, for

simplicity, carbon markets were modelled as a tax by taking the current carbon price. Some

regions have several carbon prices applying to different sectors, and this was retained in the

ETSAP-TIAM input. Mexico has a carbon tax applying to fossil fuels, where the tax is the

difference between the emissions versus emissions from natural gas, in effect, creating a tax

on emissions from petroleum and coal. For Mexico, we applied a 25% ratio for petroleum, and

a 40% ratio for coal, representative of the approximate ratios in emissions per unit of energy

relative to natural gas. In the case where a country has both an upper and lower bound for

carbon, then the upper bound was used.

The carbon prices were then aggregated to the ETSAP-TIAM regions. This aggregation was

done by computing the nation’s share of energy (and cement production) carbon emissions

Climate

Module

Atm. Conc.

ΔForcing

ΔTemp

Used for

reporting &

setting

targets

Biomass

Potential

Renewable

Potential

Nuclear

Fossil Fuel

Reserves

(oil, coal, gas)

ExtractionUpstream

Fuels

Trade

Secondary

Transformation

OPEC/

NON-OPEC

regrouping

Electricity

Fuels

Electricity

Cogeneration

Heat

Hydrogen production

and distribution

End Use

Fuels

Industrial

Service

CompositionAuto Production

Cogeneration

Carbon

captureCH4 options

Carbon

sequestration

Terrestrial

sequestration

Landfills ManureBio burning, rice,

enteric fermWastewater

CH4 options

N2O options

CH4 options

OI****

GA****

CO****

Trade

ELC***

WIN SOL

GEO TDL

BIO***

NUC

HYD

BIO***

HETHET

ELCELC

SYNH2

BIO***

CO2

ELC

GAS***

COA***

Industrial

Tech.

Commercial

Tech.

Transport

Tech.

Residential

Tech.

Agriculture

Tech.I***

I** (6)T** (16)R** (11)C** (8)A** (1)

INDELC

INDELC

IS**

Demands

IND*** COM***AGR*** TRA***RES***

Non-energy

sectors (CH4)

OIL***


63

relative to the total emissions from it corresponding ETSAP-TIAM region. The carbon price was

then converted to 2005 US dollars31 and scaled by this amount.

7.1.4 Flow diagram in ETSAP -TIAM

The Figure C represents the flow diagram for the HYSOL, where the input commodities are

electricity from the sun (ELCSOL) and electricity from biogas (ELCBGS) while the output

commodity is electricity centralized (ELCC). From a modelling viewpoint, the HYSOL has been

implemented as a CSP parabolic trough plus a thermal storage unit of 12 hours, and a backup

capacity (50 MW gas turbine), an intermediate unit that aggregate the output commodities

from the CSP plus storage (ELCCSP) and the backup capacity (ELCB) was considered, the output

commodity of this "non-physical" unit corresponds to ELCC. Thus, it will be possible to

determinate the electricity generation contribution of the backup capacity during the lifetime

of the HYSOL.

Figure C: Flow diagram for the HYSOL (own source)

Note: This flow diagram represents the electricity flows in ETSAP-TIAM, and is not

representative of a process flow. Thus, heat flows such as heat recovered from the gas turbine

which it is sent later on to the heat storage is not supposed to be represented on the flow

diagram.

There are three mathematical relations that the HYSOL model must comply with in ETSAP-

TIAM, these are the following:

𝑪𝑨𝑷𝑪𝑺𝑷+𝒔𝒕𝒐𝒓𝒂𝒈𝒆 = 𝟐 ∗ 𝑪𝑨𝑷𝑩𝒂𝒄𝒌𝒖𝒑 1

31 Exchange rates from:

https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html http://www.xe.com/ http://www.bankofcanada.ca/rates/exchange/daily-converter/

Intermediate Backup capacity

CSP + storage

ELCSOL

ELCBGS

ELCCSP

ELCB

ELCC

ELCNGA

https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html

http://www.xe.com/

http://www.bankofcanada.ca/rates/exchange/daily-converter/


64

𝑨𝑪𝑻𝑪𝑺𝑷+𝒔𝒕𝒐𝒓𝒂𝒈𝒆 ≤ 𝑴𝒂𝒙𝑷𝒐𝒕𝒆𝒏𝒕𝒊𝒂𝒍 2

𝑨𝑪𝑻𝑩𝒂𝒄𝒌𝒖𝒑 ≤ 𝑴𝒂𝒙𝑩𝒊𝒐𝒈𝒂𝒔 𝒑𝒐𝒕𝒆𝒏𝒕𝒊𝒂𝒍 3

The equation 1 establishes a relation between the power block capacity (100 MW steam

turbine), which it is been feed by the thermal storage and the solar field, and the backup

capacity (50 MW gas turbine). The equation 2 limits the generation of the CSP plus storage

plant to a maximum annual value by taking into account both land availability and direct

normal solar irradiance (Trieb et al., 2009). Finally, the equation 3 limits the generation of the

biogas turbine (backup capacity) to an upper limit.

7.2 Section B: Socio-economic feasibility assessment


65

7.2.1 Power price composition Mexico

LCOE results based on design point assumptions are presented below with a breakdown or

split into its components related to respectively Investment, O&M, and fuel cost parts.

CO2 emission costs of 0 USD/tCO2eq emitted is assumed:

HYSOL Table M MEX HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for

'design basis' assumptions split on contributions from the Investment, O&M, and Fuel Cost

parts to the total cost. Natural gas has been assumed for the HYSOL GT component.

LCOE on socio-economic basis for MEX HYSOL

OCGT Table O MEX 150MW OCGT reference: Electricity production cost (LCOE on socio economic

basis) for 'design basis' assumptions split on contributions from the Investment, O&M, and

Fuel Cost parts to the total cost. OCGT capacity: 150MW.

CCGT Table P MEX 150MW CCGT reference: Electricity production cost (LCOE on socio economic


Fuel Cost parts to the total cost. CCGT capacity: 150MW.

CO2 emission costs of 40 USD/tCO2eq emitted is included in the NG fuel costs shown:

HYSOL Table Q MEX HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for

'design basis' assumptions split on contributions from the Investment, O&M, and Fuel Cost parts

Electricity production costs (LCOE) split on cost components

$/MWh el % of tot $/MWh el % of tot $/MWh el % of tot $/MWh el % of tot

75.55 100.0% 53.70 71.1% 8.76 11.6% 13.09 17.3%

at 'design basis point' data Investment O & M Fuel costs

Total



51.61 100.0% 7.89 15.3% 1.29 2.5% 42.43 82.2%


Total



35.95 100.0% 9.48 26.4% 1.92 5.3% 24.55 68.3%


Total


66

to the total cost. Biogas use has been assumed for the HYSOL GT component.

OCGT Table R MEX 150MW OCGT reference: Electricity production cost (LCOE on socio economic

basis) for 'design basis' assumptions split on contributions from the Investment, O&M, and Fuel

Cost parts to the total cost. OCGT capacity: 150MW. CO2 emission costs are included in the

fuel costs shown.

CCGT Table S MEX 150MW CCGT reference: Electricity production cost (LCOE on socio economic


Cost parts to the total cost. CCGT capacity: 150MW. CO2 emission costs are included in the

fuel costs shown.



83.58 100.0% 53.70 64.2% 8.76 10.5% 21.12 25.3%


Total



77.65 100.0% 7.89 10.2% 1.29 1.7% 68.47 88.2%


Total



51.02 100.0% 9.48 18.6% 1.92 3.8% 39.62 77.7%


Total


67

7.2.2 Power price composition for Chile

LCOE results based on Design Point assumptions are presented below with a breakdown or

split into its components related to respectively Investment, O&M, and Fuel cost parts.

CO2 emission costs of 0 USD/tCO2eq emitted is assumed:

HYSOL Table T CHI HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for



OCGT Table U CHI 150MW OCGT reference: Electricity production cost (LCOE on socio economic



CCGT Table V CHI 150MW CCGT reference: Electricity production cost (LCOE on socio economic



CO2 emission costs of 40 USD/tCO2eq emitted is included in the NG fuel costs shown:

HYSOL Table W CHI HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for





89.05 100.0% 54.51 61.2% 9.11 10.2% 25.43 28.6%


Total



160.38 100.0% 8.85 5.5% 1.20 0.8% 150.33 93.7%


Total



99.79 100.0% 10.47 10.5% 1.75 1.7% 87.58 87.8%


Total


68

OCGT Table X CHI 150MW OCGT reference: Electricity production cost (LCOE on socio economic


Cost parts to the total cost. OCGT capacity: 150MW. CO2 emission costs are included in the fuel

costs shown.

CCGT Table Y CHI 150MW CCGT reference: Electricity production cost (LCOE on socio economic


Cost parts to the total cost. CCGT capacity: 150MW. CO2 emission costs are included in the fuel

costs shown.

7.2.3 Power price composition for KSA

HYSOL Table Z KSA HYSOL alternative: Electricity production cost (LCOE on socio economic basis)

for 'design basis' assumptions split on contributions from the Investment, O&M, and Fuel

Cost parts to the total cost.



93.73 100.0% 54.51 58.2% 9.11 9.7% 30.11 32.1%


Total



188.07 100.0% 8.85 4.7% 1.20 0.6% 178.02 94.7%


Total



115.92 100.0% 10.47 9.0% 1.75 1.5% 103.71 89.5%


Total



81.09 100.0% 60.91 75.1% 12.13 15.0% 8.05 9.9%


Total


69

OCGT Table Z.1 KSA 130MW OCGT reference: Electricity production cost (LCOE on socio

economic basis) for 'design basis' assumptions split on contributions from the Investment,

O&M, and Fuel Cost parts to the total cost. OCGT capacity: 130MW.

CCGT Table Z.2 KSA 130MW CCGT reference: Electricity production cost (LCOE on socio

economic basis) for 'design basis' assumptions split on contributions from the Investment,

O&M, and Fuel Cost parts to the total cost. CCGT capacity: 130MW.

Table Z illustrates, as expected, that power production costs from the KSA HYSOL plant are

dominated by the investment cost component. On average for the period analysed of about

75% of the total electricity costs relates to the initial investment, whereas the fuel cost

component only contributes about 10% to the total costs. Compared to results for OCGT and

CCGT plants shown in Table Z.1 and Table Z.2, this illustrates that HYSOL plants are less

exposed and less vulnerable to gas price (and CO2 emission cost) uncertainty.

7.2.4 Power price composition for RSA

LCOE results based on Design Point assumptions (shown as yellow and black points in Figures

1-4) are presented below with a breakdown or split into its components related to respectively

Investment, O&M, and Fuel cost parts.

CO2 emission costs of 0 $/ton CO2 emitted is assumed:

HYSOL Table 8 RSA HYSOL alternative: Electricity production cost (LCOE on socio economic basis)



OCGT Table 9 RSA 150MW OCGT reference: Electricity production cost (LCOE on socio economic



52.66 100.0% 8.31 15.8% 2.30 4.4% 42.05 79.8%


Total



39.93 100.0% 10.16 25.4% 3.41 8.6% 26.36 66.0%


Total



Total


66.78 100.0% 48.27 72.3% 2.12 3.2% 16.39 24.5%


70



CCGT Table 10 RSA 150MW CCGT reference: Electricity production cost (LCOE on socio economic



CO2 emission costs of 40 $/ton CO2 emitted are included in the NG fuel costs shown:

HYSOL Table 11 RSA HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for






fuel costs shown.



84.39 100.0% 7.23 8.6% 1.00 1.2% 76.15 90.2%


Total



53.52 100.0% 8.69 16.2% 1.49 2.8% 43.34 81.0%


Total



Total


72.39 100.0% 48.27 66.7% 2.12 2.9% 22.00 30.4%



110.44 100.0% 7.23 6.6% 1.00 0.9% 102.20 92.5%


Total


71




fuel costs shown.

Table illustrates, as expected, that power production costs from the RSA HYSOL plant are




CCGT plants shown in Table and Table , this illustrates that HYSOL plants are less exposed and

less vulnerable to gas price (and CO2 emission cost) uncertainty.



68.35 100.0% 8.69 12.7% 1.49 2.2% 58.17 85.1%


Total

Kingdom of Saudi Arabia (KSA): Economic

assessment and energy system analysis

Deliverable nº: 6.1.1


Renewable Hybrid CSP Plant WP: Responsible partner: DTU/MAN/SYS Dissemination level:

Name of deliverable

2

TABLE OF CONTENTS

1 DOCUMENT HISTORY ..................................................................................................... 5


2.1 ABSTRACT ......................................................................................................................... 5

3 FEASIBILITY STUDY ON HYSOL CSP .................................................................................. 6

3.1 INTRODUCTION .................................................................................................................. 6

3.1.1 Example studied .......................................................................................... 6

3.1.2 The HYSOL alternative and competing technology .................................... 7

4 APPROACH AND BASIC ASSUMPTIONS ........................................................................... 7

4.1 ECONOMIC INDICATOR ........................................................................................................ 7

4.2 BASE CASE ASSUMPTIONS ................................................................................................... 7

4.3 BASE CASE FOR KSA HYSOL PLANT ...................................................................................... 8

4.4 BASE CASE OVERVIEW AND ISSUES ADDRESSED VIA SENSITIVITY ANALYSES ................................... 8

4.5 ELECTRICITY COSTS AS FUNCTION OF LOAD FACTOR AND NG PRICE ............................................. 9

4.6 DESIGN POINT ASSUMPTIONS .............................................................................................. 9

5 HYSOL RELATIVE TO OCGT AND CCGT ........................................................................... 10

5.1 BASIC PRESENTATIONS ...................................................................................................... 10

5.1.1 Assumption on CO2 emission costs .......................................................... 10


5.2 RESULTS: HYSOL COMPARED TO OCGT .............................................................................. 11

5.3 RESULTS: HYSOL COMPARED TO CCGT .............................................................................. 13

5.4 POWER PRICE COMPOSITION .............................................................................................. 14

6 SENSITIVITY ANALYSES AND CONCLUSIONS .................................................................. 16

6.1 OVERVIEW OF SENSITIVITY ANALYSES ................................................................................... 16

6.2 CONCLUSIONS ................................................................................................................. 18

7 APPENDIX .................................................................................................................... 19

7.1 ASSUMPTION ON NG AND BIOGAS PRICE RELATION ............................................................... 19

Name of deliverable

3

Name of deliverable

4

Acronyms

Name of deliverable

5

1 Document History

Version Status Date

vX.Y Draft day/month/year

vX.Y Final day/month/year

Approval Name Date

Prepared day/month/year

Reviewed day/month/year

Authorised day/month/year

2 Executive Summary

2.1 Abstract

Concentrating Solar Power (CSP) plants utilize thermal conversion of direct solar irradiation. A

trough or tower configuration focuses solar radiation and heats up oil or molten salt that

subsequently in high temperature heat exchangers generate steam for power generation.

High temperature molten salt can be stored and the stored heat can thus increase the load

factor and the usability for a CSP plant, e.g. to cover evening peak demand. In the HYSOL

concept (HYbrid SOLar) such configuration is extended further to include a gas turbine fuelled

by upgraded biogas or natural gas. The optimised integrated HYSOL concept, therefore,

becomes a fully dispatchable (offering firm power) and fully renewable energy source (RES)

based power supply alternative, offering CO2-free electricity in regions with sufficient solar

resources.

The economic feasibility of HYSOL configurations is addressed in this report. The CO2 free

HYSOL alternative is discussed relative to conventional reference firm power generation

technologies. In particular the HYSOL performance relative to new power plants based on

natural gas (NG) such as open cycle or combined cycle gas turbines (OCGT or CCGT) are in

focus. The feasibility of renewable based HYSOL power plant configurations attuned to specific

electricity consumption patterns in selected regions with promising solar energy potentials are

discussed

Name of deliverable

6

3 Feasibility study on HYSOL CSP

Feasibility study on HYSOL CSP configurations with High Temperature Storage and NG/Bio-gas

fuelled Gas Turbine providing Fully Dispatchable and Renewable Power Supplies.

3.1 Introduction


trough or tower configuration focus solar radiation and heat up oil or molten salt that


High temperature molten salt can be stored (HTS) and the stored heat can thus increase the

load factor and the usability for a CSP plant, e.g. to cover night (peak) demand. In the HYSOL



becomes a fully dispatchable (offering firm power) and a fully renewable energy (RES) based

power supply alternative, offering CO2-free electricity in regions with sufficient solar

resources.

The economic feasibility of HYSOL configurations is addressed. The CO2 free HYSOL alternative

is discussed relative to conventional reference firm power generation technologies. In

particular the HYSOL performance relative to new power plants based on natural gas (NG) such

as open cycle or combined cycle gas turbines (OCGT or CCGT) are in focus. The feasibility of


patterns in selected regions with promising solar energy potentials are discussed.

3.1.1 Example studied

The analytical approach used is illustrated for a HYSOL configuration optimised to conditions

seen in the Kingdom of Saudi Arabia (KSA). The HYSOL Power Plant studied has been attuned

to solar potentials and power system characteristics resembling conditions in the Kingdom of

Saudi Arabia (KSA).

The KSA HYSOL plant configuration particularizes the basic HYSOL outline by the choices:

- A CSP Tower configuration has been assumed. HYSOL configurations can also be

applied with CSP trough design.

- No biogas plant and biogas supply have been assumed for this KSA case. HYSOL’s

100% renewable configuration would have a biogas plant included and would use biogas

upgraded to NG quality.

The KSA HYSOL configuration analysed uses natural gas (NG) and not biogas based methane,

and may thus not be termed fully renewable, - though being a firm, fully dispatch-able and

mainly renewables based power plant.

Name of deliverable

7

3.1.2 The HYSOL alternative and competing technology

This present analyses compare electricity production costs for a HYSOL plant alternative to

production cost for conventional power plant solutions or reference plants.

In this KSA case it has been assumed that the main competing reference technologies are an

Open Cycle Gas Turbine (OCGT) and an

Combined Cycle Gas Turbine (CCGT)

using natural gas (NG).

4 Approach and basic assumptions

4.1 Economic indicator

Basically a socio-economic approach is applied. And generally main focus is placed on the

economic indicator LCOE (the levelized cost of electricity), and on the sensitivity of the LCOE in

particular to variations in the two parameters:

• load factor or the number of full load hours per year, and the

• price of natural gas (given as the levelized NG price covering the period analysed)




particular these major parameters will be in focus in this study of (predominantly) renewable

energy source (RES) based HYSOL solutions relative to fossil based conventional reference

power plant solutions.

4.2 Base Case assumptions

For the present socio-economic analyses the following general assumptions have been

adopted as 'Base Case':

Price level: Year 2015

Socio economic rate of calculation (rate of interest): 4 % p.a.

Project base year: 2020

Period analysed: Time period: 2021-2045

Period in years: 25 years

Name of deliverable

8

4.3 Base Case for KSA HYSOL plant

Chosen Base Case for the KSA HYSOL plant annual production, assigned capacity and load

factor are:

Annual electricity production: 812.7 GWh/year

Assigned HYSOL capacity (PH): PH = 130MWel

Annual full load hours (HFLH) and Load factor (LF):

HFLH = 812.7GWh / 130MW = 6251 hours/year

and LF= 6251/8760= 0.714

As mentioned, gas consumed in the KSA HYSOL gas turbine (GT) component is assumed to be

natural gas (NG). The KSA Base Case NG price and the sensitivity variations analysed for the

NG price are:

NG price Base case: 13.65 $/MWh (4$/MMBtu)

Sensitivity: Base Case +/- 20%, +/-40%

Data on investments, operation and maintenance costs for the KSA HYSOL configuration are

found in the Appendix.

4.4 Base Case overview and issues addressed via sensitivity analyses


sensitivity to variations in the following parameters:

• Natural Gas price: Sensitivity Base Case -/+40%

• CO2 emission quota market price Base case: 0 $ / ton CO2

Sensitivity: 40 $ / ton CO2

• Capacity assignment: assignment Base case: 130 MW

Sensitivity: 100MW <--> 180MW

• Lifetime of initial investment: Base case: 25 years

Sensitivity: 20 years

• Rate of calculation (interest rate) Base case: 4.0 % p.a.

Sensitivity: 10.0 % p.a.

Name of deliverable

9

• Initial investment (CAPEX) Sensitivity: Base Case +/- 20%

The combined steam turbine (ST) and gas turbine (GT) capacity in the KSA HYSOL configuration

plant has been assigned a total combined capacity of 130MW. The peak power generated by

the plant is limited to 130 MW, and the plant is made to follow a demand curve congruent or

analogous to that of country altogether. This implies that the number of full load hours for the

combined KSA HYSOL configuration can be calculated as 812.7GWh/130MW = 6251

hours/year, and the demand coverage rate is above 99.9%.

4.5 Electricity costs as function of load factor and NG price

In Figures 1-4 results on the LCOE (given along the y-axis) are shown as a function of the

annual load. The annual load or electricity production, - here expressed through its equivalent,

the number of full load hours per year, is shown along the x-axis.






is kept constant.



electricity generated, accordingly, is assumed constant and independent of the annual

production. This may be a somewhat rough assumption.

4.6 Design Point assumptions


be termed the 'Design Point' data assumptions. Yellow points, 'Design Points', shown in Figures

1-4 represent results for the KSA HYSOL plant assuming Base Case operation conditions. Black

points, correspondingly, represent (OCGT or CCGT) reference technology results based on

equivalent assumptions. Other results presented may thus be considered as sensitivity and

parameter analyses.

Name of deliverable

10

5 HYSOL relative to OCGT and CCGT

5.1 Basic presentations

In what follows the KSA HYSOL plant alternative is compared to competing 'conventional' or





sensitivity analyses.

As mentioned above the competing reference technologies assumed are the Open Cycle Gas

Turbine (OCGT) and the Combined Cycle Gas Turbine (CCGT).



compared are assumed to have the same capacity value in the Saudi Arabian power system,

and the plants are assumed to be fully dispatchable (firm power). Thus, all plants are assumed

to be able to occupy the same position of operation in the overall power system dispatch.

Data for the KSA HYSOL alternative and for the assumed KSA OCGT and KSA CCGT reference


It can be observed from Figures 1-4 that the annual number of full load operation hours for the

HYSOL plant, shown along the x-axis, is extremely important for the electricity production cost

achieved, - and the plant economy. Low annual power production results in high production

costs. For the overall economy of a HYSOL plant, therefore, it is very important to achieve high

annual power production, as the total production costs are much dominated by high initial

investments. Natural gas prices, however, have minor impact on the HYSOL power production

cost due to the relatively low electricity production contribution via the GT part of the KSA

HYSOL configuration.

5.1.1 Assumption on CO2 emission costs



Name of deliverable

11

sensitivity analysis it has been assumed, as an example, that CO2 emission costs amounts to

40$/tonCO2 emitted. For natural gas (NG) this CO2 emission cost is equivalent to 8.17$/MWh

NG. The CO2 emission cost assumed thus rises the NG price with an extra 8.17$/MWh NG.




A CO2 emission cost, as assumed in our case study, of 40$/ton CO2 emitted corresponds to a

rise of the NG price with an extra 8.17$/MWh NG. Thus, for the case of 40$/ton CO2 emitted

this means that the Biogas price will equal the NG price plus 8.17$/MWh NG.

With a NG price of 13.65 $/MWh NG the assumption thus implies:

Biogas price = NG price + 8.17$/MWh

= 13.65 $/MWh + 8.17$/MWh = 21.82$/MWh NG

If it is furthermore assumed that Biogas has zero CO2 emission the economic consequence of



NG price including CO2 costs. However in this case the HYSOL solution using Biogas has no

CO2 emission.

The economic calculations shown in Figure 2 and Figure 4 showing power production costs


also for the case where HYSOL use Biogas (and thus has no CO2 emission) and the OCGT and

CCGT use NG and emit CO2 at a cost of 40$/ton CO2 emitted.

5.2 Results: HYSOL compared to OCGT

Name of deliverable

12

HYSOL and OCGT: Assuming 0 $/ton CO2 emitted

Figure 1 Electricity production costs for Open Cycle Gas Turbine (OCGT) and KSA HYSOL

configuration, as function of load factor and NG price. Assumed: CO2 costs = 0$/tonCO2,

R=4%p.a., Lifetime=25years. Unit: $/MWh el.


Figure 2 Electricity production costs for Open Cycle Gas Turbine (OCGT) and KSA HYSOL



Name of deliverable

13

5.3 Results: HYSOL compared to CCGT

HYSOL and CCGT: Assuming 0 $/ton CO2 emitted

Figure 3 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and KSA HYSOL




Figure 4 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and KSA HYSOL



Name of deliverable

14

5.4 Power price composition





HYSOL Table 1 KSA HYSOL alternative: Electricity production cost (LCOE on socio economic basis)



OCGT Table 2 KSA 130MW OCGT reference: Electricity production cost (LCOE on socio economic



CCGT Table 3 KSA 130MW CCGT reference: Electricity production cost (LCOE on socio economic





81.09 100.0% 60.91 75.1% 12.13 15.0% 8.05 9.9%


Total



52.66 100.0% 8.31 15.8% 2.30 4.4% 42.05 79.8%


Total

Name of deliverable

15


HYSOL Table 4 KSA HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for



OCGT Table 5 KSA 130MW OCGT reference: Electricity production cost (LCOE on socio economic



fuel costs shown.

CCGT Table 6 KSA 130MW CCGT reference: Electricity production cost (LCOE on socio economic



fuel costs shown.

Table 1 illustrates, as expected, that power production costs from the KSA HYSOL plant are




CCGT plants shown in Table 2 and Table 3, this illustrates that HYSOL plants are less exposed

and less vulnerable to gas price (and CO2 emission cost) uncertainty.

Name of deliverable

16

6 Sensitivity analyses and conclusions

6.1 Overview of sensitivity analyses

Sensitivity analyses shown in Tables 7-9 describe how power productions costs (LCOE) deviate

from results based on Base Case and 'design point' assumptions, if one parameter only is

changed at a time.

Blue vertical lines in Tables 7-9 represent the LCOE calculated from Base Case assumptions.

Tables 1-3, shown above, thus give details on the Base Case results, that are 'starting points'

for the sensitive analysis results shown below, - for the KSA HYSOL, KSA OCGT and KSA CCGT

plants respectively.

KSA HYSOL

Table 7 KSA HYSOL results in overview: Electricity production costs (LCOE) - Sensitivity relative

to Base Case Assumptions. Units: $/MWh el.

Name of deliverable

17

KSA OCGT

Table 8 KSA OCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative


KSA CCGT

Table 9 KSA CCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative


Name of deliverable

18

6.2 Conclusions


attractiveness of HYSOL solutions relative to NG based competing technologies, such as OCGT

and CCGT power plants.

CO2 emission costs acts heavily in favour of HYSOL solutions. As seen from Tables 4-6 (as

expected) in particular an OCGT plant solution is strongly exposed to potential rising CO2

emission costs.


part the power system in question (KSA). This impacts the required capacity investments for

competing solutions (OCGT or CCGT) matching the HYSOL plant in the system. The economic

implication of different capacity assignments, however, as seen from Tables 4-6, is relatively

minor. This due to the relative low initial investment component for OCGT and CCGT plants,

which may be seen comparing power price composition results shown in Tables 1-3.


electricity production cost for the OCGT and CCGT plant solutions. Being an initial investment

intensive RES based technology the HYSOL solution is seen to be impacted, though

moderately, from changes in lifetime of the investment.

The interest rate or the rate of calculation is important for initial investment intensive plants,


which may correspond to typical socio-economic conditions. Assuming a higher rate of interest

of 10% p.a., that may resemble a corporate economic situation, it is seen from Table 4 that

power production costs (LCOE) are increased substantially. Thus, in particular the HYSOL

solution is very sensitive to changes in the interest rate.

HYSOL solutions, being investment intensive are as such very sensitive to changes in the

overall investment costs, and the rate of interest, whereas the OCGT and CCGT solutions are


Name of deliverable

19

7 Appendix

7.1 Assumption on NG and Biogas price relation

It has been assumed that the price of Biogas can be estimated to equal the price of natural

gas (NG) plus the cost for the CO2 emission using the NG.


rise of the NG price with an extra 8.17$/MWh NG. For the case of 40$/ton CO2 emitted this

means that the Biogas price will equal the NG price plus 8.17$/MWh NG.


Biogas price = NG price + 8.17$/MWh = 13.65 $/MWh + 8.17$/MWh = 21.82$/MWh NG


the use of biogas in a HYSOL plant solutions will correspond to the cost relations to the OCGT

and CCGT solutions assuming 40$/ton CO2 emitted . However in this case the HYSOL solution

using Biogas has no CO2 emission.


(LCOE) for the HYSOL solution relative to the OCGT and CCGT solutions will hold also for the

case where HYSOL use Biogas and thus has no CO2 emission and the OCGT and CCGT use NG

and emit CO2 at a cost of 40$/ton CO2 emitted.

Chile: Economic assessment and energy system

analysis




Name of deliverable

2

TABLE OF CONTENTS



2.1 ABSTRACT ......................................................................................................................... 4


3.1 INTRODUCTION .................................................................................................................. 5

3.1.1 Example studied .......................................................................................... 5





4.3 BASE CASE FOR CHI HYSOL PLANT ....................................................................................... 7




5 HYSOL RELATIVE TO OCGT AND CCGT ............................................................................. 9

5.1 BASIC PRESENTATIONS ........................................................................................................ 9

5.1.1 Assumption on CO2 emission costs ............................................................ 9







6.2 CONCLUSIONS ................................................................................................................. 18

7 APPENDIX .................................................................................................................... 20

Name of deliverable

3

Acronyms

Name of deliverable

4

1 Document History

Version Status Date



Approval Name Date




2 Executive Summary

2.1 Abstract










resources.







discussed

Name of deliverable

5




3.1 Introduction









power supply alternative, offering CO2-free electricity in regions with sufficient solar resources.









optimised to conditions seen in the state of Chile (CHI). Thus, the HYSOL Power Plant studied

has been attuned to solar potentials and power system characteristics resembling conditions

in Chile (CHI).

The CHI HYSOL plant configuration particularizes the basic HYSOL outline by the choices:



- Biogas supply have been assumed for this CHI case. The HYSOL plant investments do

not include investments in biogas plants. The HYSOL plant is assumed to purchase biogas at a

price that equals the price of natural gas (NG) plus the value of the reduced CO2 emission

when Biogas is used. HYSOL’s 100% renewable configuration use biogas upgraded to NG

quality.

The CHI HYSOL configuration analysed uses natural gas (NG) and not biogas based methane,



Name of deliverable

6




In this CHI case it has been assumed that the main competing reference technologies are an

Open Cycle Gas Turbine (OCGT) and a
























Name of deliverable

7

4.3 Base Case for CHI HYSOL plant

Chosen Base Case for the CHI HYSOL plant annual production, assigned capacity and load

factor are:




HFLH = 868.48GWh / 150MW = 5790 h/year

and LF= 5790/8760= 0.661

As mentioned, gas consumed in the CHI HYSOL gas turbine (GT) component is assumed to be

natural gas (NG). The CHI Base Case NG price and the sensitivity variations analysed for the NG

price are:



Data on investments, operation and maintenance costs for the CHI HYSOL configuration are














Name of deliverable

8


The combined steam turbine (ST) and gas turbine (GT) capacity in the CHI HYSOL configuration


the plant is limited to 150 MW, and the plant is made to follow a demand curve congruent or

analogous to that of country altogether. This implies that the number of full load hours for the

combined CHI HYSOL configuration can be calculated as 868.48GWh/150MW = 5790

hours/year, and the demand coverage rate is above 99.9%.










is kept constant.








1-4 represent results for the CHI HYSOL plant assuming Base Case operation conditions. Black



parameter analyses.

Name of deliverable

9



In what follows the CHI HYSOL plant alternative is compared to competing 'conventional' or










compared are assumed to have the same capacity value in the Chilean power system, and the


able to occupy the same position of operation in the overall power system dispatch.

Data for the CHI HYSOL alternative and for the assumed CHI OCGT and CHI CCGT reference








cost due to the relatively low electricity production contribution via the GT part of the CHI





Name of deliverable

10










With a NG price of 44.36$/MWh NG the assumption thus implies:


= 44.36$/MWh + 8.17$/MWh = 52.53$/MWh NG





CO2 emission.






In Figure 1 below it has been assumed that the CO2 emission costs are 0 $/ton CO2 emitted. In


value. Therefore, in the 0 $/ton CO2 emitted scenario, it has been assumed that both the


Name of deliverable

11


Figure 1 Electricity production costs for Open Cycle Gas Turbine (OCGT) and CHI HYSOL




In Figure 2 it has been assumed that the CO2 emission costs are 40 $/ton CO2 emitted. In this

case it has been assumed that the HYSOL plant use (CO2 emission free) biogas. The price of




priced as the NG price plus the cost of the CO2 emitted. (A cost of 40 $/ton CO2 emitted

equals a price increase for the NG with an extra 8.17$/MWh NG.)

Name of deliverable

12

Figure 2 Electricity production costs for Open Cycle Gas Turbine (OCGT) and CHI HYSOL



Name of deliverable

13



Figure 3 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and CHI HYSOL




Figure 4 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and CHI HYSOL



Name of deliverable

14



2&4) are presented below with a breakdown or split into its components related to

respectively Investment, O&M, and Fuel cost parts.


HYSOL Table 1 CHI HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for



OCGT Table 2 CHI 150MW OCGT reference: Electricity production cost (LCOE on socio economic



CCGT Table 3 CHI 150MW CCGT reference: Electricity production cost (LCOE on socio economic





89.05 100.0% 54.51 61.2% 9.11 10.2% 25.43 28.6%


Total



160.38 100.0% 8.85 5.5% 1.20 0.8% 150.33 93.7%


Total



99.79 100.0% 10.47 10.5% 1.75 1.7% 87.58 87.8%


Total

Name of deliverable

15

CO2 emission costs of 40 $/ton CO2 emitted is included in the NG fuel costs shown:

HYSOL Table 4 CHI HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for



OCGT Table 5 CHI 150MW OCGT reference: Electricity production cost (LCOE on socio economic



fuel costs shown.

CCGT Table 6 CHI 150MW CCGT reference: Electricity production cost (LCOE on socio economic



fuel costs shown.

Table 4 illustrates, as expected, that power production costs from the CHI HYSOL plant are








93.73 100.0% 54.51 58.2% 9.11 9.7% 30.11 32.1%


Total



188.07 100.0% 8.85 4.7% 1.20 0.6% 178.02 94.7%


Total



115.92 100.0% 10.47 9.0% 1.75 1.5% 103.71 89.5%


Total

Name of deliverable

16





changed at a time.



for the sensitive analysis results shown below, - for the CHI HYSOL, CHI OCGT and CHI CCGT


CHI HYSOL

Table 7 CHI HYSOL results in overview: Electricity production costs (LCOE) - Sensitivity relative


Name of deliverable

17

CHI OCGT

Table 8 CHI OCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative


CHI CCGT

Table 9 CHI CCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative to

Base Case Assumptions. Units: $/MWh el.

Name of deliverable

18

6.2 Conclusions






emission costs.


part the power system in question (CHI). This impacts the required capacity investments for


















Summary CHI conclusion:

The HYSOL solution in Chile competes very favourable relative to the Open Cycle Gas Turbine

(OCGT) reference as can be seen from comparing results, when base case data are assumed.

The Base Case assumption on the level of future NG-prices in the region is an important factor

for the conclusion. This conclusion holds even without taking into account an assumed cost on

emission of CO2. When compared to a Combined Cycle Gas Turbine (CCGT) reference plant the

Name of deliverable

19

CHI HYSOL alternative is still favourable. And introducing an assumed CO2 emission costs of

40$/ton CO2 emitted, adds much in favour of the CHI HYSOL solution.

Sensitivity (or robustness) analyses carried out emphasize that HYSOL solutions, as expected,

are less exposed to CO2 emission cost uncertainty and fuel price uncertainty than the

reference OCGT/CCGT solutions. OCGT/CCGT solutions are more exposed to CO2 emission cost

uncertainty, and more exposed to NG-price uncertainty, but less exposed to investment cost

uncertainty.

However, as observed from Figures 1-4 the annual number of full load operation hours for

HYSOL solutions, and thus the annual power production, is very important the plant economy.

Name of deliverable

20

7 Appendix








Biogas price = NG price + 8.17$/MWh = 44.36 $/MWh + 8.17 $/MWh = 52.53 $/MWh NG









Mexico: Economic assessment and energy system

analysis




Name of deliverable

2

TABLE OF CONTENTS



2.1 ABSTRACT ......................................................................................................................... 4


3.1 INTRODUCTION .................................................................................................................. 5

3.1.1 Example studied .......................................................................................... 5





4.3 BASE CASE FOR MEX HYSOL PLANT ..................................................................................... 7













6.2 CONCLUSIONS ................................................................................................................. 18

7 APPENDIX .................................................................................................................... 19


Name of deliverable

3

Acronyms

Name of deliverable

4

1 Document History

Version Status Date



Approval Name Date




2 Executive Summary

2.1 Abstract










resources.







discussed

Name of deliverable

5




3.1 Introduction









power supply alternative, offering CO2-free electricity in regions with sufficient solar resources.









optimised to conditions seen e.g. in the state of Mexico (MEX). Thus, the HYSOL Power Plant

studied has been attuned to solar potentials and power system characteristics resembling

conditions in Mexico (MEX).

The MEX HYSOL plant configuration particularizes the basic HYSOL outline by the choices:



- Biogas supply have been assumed for this MEX case. The HYSOL plant investments do

not include investments in biogas plants. The HYSOL plant is assumed to purchase biogas at a

price that equals the price of natural gas (NG) plus the value of the reduced CO2 emission

when Biogas is used. HYSOL’s 100% renewable configuration use biogas upgraded to NG

quality.

The MEX HYSOL configuration analysed uses natural gas (NG) and not biogas based methane,



Name of deliverable

6




In this MEX case it has been assumed that the main competing reference technologies are an

























Name of deliverable

7

4.3 Base Case for MEX HYSOL plant

Chosen Base Case for the MEX HYSOL plant annual production, assigned capacity and load

factor are:




HFLH = 929.2 GWh / 150MW = 6195 hours/year

and LF= 6251/8760= 0.707

As mentioned, gas consumed in the MEX HYSOL gas turbine (GT) component is assumed to be

natural gas (NG). The MEX Base Case NG price and the sensitivity variations analysed for the

NG price are:

NG price Base case: 13.31 $/MWh (3.9$/MMBtu)


Data on investments, operation and maintenance costs for the MEX HYSOL configuration are














Name of deliverable

8


The combined steam turbine (ST) and gas turbine (GT) capacity in the MEX HYSOL

configuration plant has been assigned a total combined capacity of 150MW. The peak power

generated by the plant is limited to 150 MW, and the plant is made to follow a demand curve

congruent or analogous to that of country altogether. This implies that the number of full load

hours for the combined MEX HYSOL configuration can be calculated as 929.2GWh/150MW =

6195 hours/year, and the demand coverage rate is above 99.9%.










is kept constant.








1-4 represent results for the MEX HYSOL plant assuming Base Case operation conditions. Black



parameter analyses.

Name of deliverable

9



In what follows the MEX HYSOL plant alternative is compared to competing 'conventional' or










compared are assumed to have the same capacity value in the Mexican power system, and the


able to occupy the same position of operation in the overall power system dispatch.

Data for the MEX HYSOL alternative and for the assumed MEX OCGT and MEX CCGT reference








cost due to the relatively low electricity production contribution via the GT part of the MEX





Name of deliverable

10












= 13.31 $/MWh + 8.17$/MWh = 21.48$/MWh NG





CO2 emission.






In Figure 1 below it has been assumed that the CO2 emission costs are 0 $/ton CO2 emitted. In


value. Therefore, in the 0 $/ton CO2 emitted scenario, it has been assumed that both the



Name of deliverable

11

Figure 1 Electricity production costs for Open Cycle Gas Turbine (OCGT) and MEX HYSOL




In Figure 2 it has been assumed that the CO2 emission costs are 40 $/ton CO2 emitted. In this

case it has been assumed that the HYSOL plant use (CO2 emission free) biogas. The price of




priced as the NG price plus the cost of the CO2 emitted. (A cost of 40 $/ton CO2 emitted

equals a price increase for the NG with an extra 8.17$/MWh NG.)

Name of deliverable

12

Figure 2 Electricity production costs for Open Cycle Gas Turbine (OCGT) and MEX HYSOL



Name of deliverable

13



Figure 3 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and MEX HYSOL




Name of deliverable

14

Figure 4 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and MEX HYSOL





2&4) are presented below with a breakdown or split into its components related to

respectively Investment, O&M, and Fuel cost parts.


HYSOL Table 1 MEX HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for



OCGT Table 2 MEX 150MW OCGT reference: Electricity production cost (LCOE on socio economic



CCGT Table 3 MEX 150MW CCGT reference: Electricity production cost (LCOE on socio economic





75.55 100.0% 53.70 71.1% 8.76 11.6% 13.09 17.3%


Total



51.61 100.0% 7.89 15.3% 1.29 2.5% 42.43 82.2%


Total



35.95 100.0% 9.48 26.4% 1.92 5.3% 24.55 68.3%


Total

Name of deliverable

15

CO2 emission costs of 40 $/ton CO2 emitted is included in the NG fuel costs shown:

HYSOL Table 4 MEX HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for



OCGT Table 5 MEX 150MW OCGT reference: Electricity production cost (LCOE on socio economic



fuel costs shown.

CCGT Table 6 MEX 150MW CCGT reference: Electricity production cost (LCOE on socio economic



fuel costs shown.

Table 4 illustrates, as expected, that power production costs from the MEX HYSOL plant are








83.58 100.0% 53.70 64.2% 8.76 10.5% 21.12 25.3%


Total



77.65 100.0% 7.89 10.2% 1.29 1.7% 68.47 88.2%


Total



51.02 100.0% 9.48 18.6% 1.92 3.8% 39.62 77.7%


Total

Name of deliverable

16





changed at a time.



for the sensitive analysis results shown below, - for the MEX HYSOL, MEX OCGT and MEX CCGT


MEX HYSOL

Table 7 MEX HYSOL results in overview: Electricity production costs (LCOE) - Sensitivity relative


Name of deliverable

17

MEX OCGT

Table 8 MEX OCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative


MEX CCGT

Table 9 MEX CCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative


Name of deliverable

18

6.2 Conclusions






emission costs.


part the power system in question (MEX). This impacts the required capacity investments for


















Name of deliverable

19

7 Appendix








Biogas price = NG price + 8.17$/MWh = 13.31 $/MWh + 8.17$/MWh = 21.48$/MWh NG









Republic of South Africa (RSA): Economic

assessment and energy system analysis




Name of deliverable

2

TABLE OF CONTENTS



2.1 ABSTRACT ......................................................................................................................... 4


3.1 INTRODUCTION .................................................................................................................. 5

3.1.1 Example studied .......................................................................................... 5





4.3 BASE CASE FOR RSA HYSOL PLANT ...................................................................................... 7













6.2 CONCLUSIONS ................................................................................................................. 17

7 APPENDIX .................................................................................................................... 19


Acronyms

Name of deliverable

3

Name of deliverable

4

1 Document History

Version Status Date



Approval Name Date




2 Executive Summary

2.1 Abstract










resources.







discussed

Name of deliverable

5




3.1 Introduction









power supply alternative, offering CO2-free electricity in regions with sufficient solar

resources.








The analytical approach used is illustrated for a HYSOL configuration optimised to conditions

seen in the Republic of South Africa (RSA). The HYSOL Power Plant studied has been attuned to

solar potentials and power system characteristics resembling conditions in the Republic of

South Africa (RSA).

The RSA HYSOL plant configuration particularizes the basic HYSOL outline by the choices:



- No biogas plant and biogas supply have been assumed for this RSA case. HYSOL’s

100% renewable configuration would have a biogas plant included and would use biogas

upgraded to NG quality.

The RSA HYSOL configuration analysed uses natural gas (NG) and not biogas based methane,



Name of deliverable

6




In this RSA case it has been assumed that the main competing reference technologies are an

























Name of deliverable

7

4.3 Base Case for RSA HYSOL plant

Chosen Base Case for the RSA HYSOL plant annual production, assigned capacity and load

factor are:




HFLH = 1014.06GWh / 150MW = 6760.4 hours/year

and LF= 6760.4/8760= 0.772

As mentioned, gas consumed in the RSA HYSOL gas turbine (GT) component is assumed to be

natural gas (NG). The RSA Base Case NG price and the sensitivity variations analysed for the

NG price are:



Data on investments, operation and maintenance costs for the RSA HYSOL configuration are














Name of deliverable

8


The combined steam turbine (ST) and gas turbine (GT) capacity in the RSA HYSOL configuration


the plant is thus limited to 150 MW, and the plant is made to follow a demand curve

congruent or analogous to that of country altogether. This implies that the number of full load

hours for the combined RSA HYSOL configuration can be calculated as 1014.06GWh/150MW =

6760 hours/year, and the demand coverage rate is above 99.9%.










is kept constant.








1-4 represent results for the RSA HYSOL plant assuming Base Case operation conditions. Black



parameter analyses.

Name of deliverable

9



In what follows the RSA HYSOL plant alternative is compared to competing 'conventional' or










compared are assumed to have the same capacity value in the South African power system,

and the plants are assumed to be fully dispatchable (firm power). Thus, all plants are assumed

to be able to occupy the same position of operation in the overall power system dispatch.

Data for the RSA HYSOL alternative and for the assumed RSA OCGT and RSA CCGT reference








cost due to the relatively low electricity production contribution via the GT part of the RSA






Name of deliverable

10









With a NG price of 23.88$/MWh NG the assumption thus implies:


= 23.88$/MWh + 8.17$/MWh = 32.05$/MWh NG





CO2 emission.





Name of deliverable

11



Figure 1 Electricity production costs for Open Cycle Gas Turbine (OCGT) and RSA HYSOL




Figure 2 Electricity production costs for Open Cycle Gas Turbine (OCGT) and RSA HYSOL



Name of deliverable

12



Figure 3 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and RSA HYSOL




Figure 4 Electricity production costs for Combined Cycle Gas Turbine (CCGT) and RSA HYSOL



Name of deliverable

13






HYSOL Table 1 RSA HYSOL alternative: Electricity production cost (LCOE on socio economic basis)











Total


66.78 100.0% 48.27 72.3% 2.12 3.2% 16.39 24.5%



84.39 100.0% 7.23 8.6% 1.00 1.2% 76.15 90.2%


Total



53.52 100.0% 8.69 16.2% 1.49 2.8% 43.34 81.0%


Total

Name of deliverable

14


HYSOL Table 4 RSA HYSOL alternative: Electricity production cost (LCOE on socio economic basis) for






fuel costs shown.




fuel costs shown.

Table 1 illustrates, as expected, that power production costs from the RSA HYSOL plant are








Total


72.39 100.0% 48.27 66.7% 2.12 2.9% 22.00 30.4%



110.44 100.0% 7.23 6.6% 1.00 0.9% 102.20 92.5%


Total



68.35 100.0% 8.69 12.7% 1.49 2.2% 58.17 85.1%


Total

Name of deliverable

15





changed at a time.



for the sensitive analysis results shown below, - for the RSA HYSOL, RSA OCGT and RSA CCGT


RSA HYSOL

Table 7 RSA HYSOL results in overview: Electricity production costs (LCOE) - Sensitivity relative


Name of deliverable

16

RSA OCGT

Table 8 RSA OCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative


RSA CCGT

Table 9 RSA CCGT results in overview: Electricity production costs (LCOE) - Sensitivity relative


Name of deliverable

17

6.2 Conclusions






emission costs.


part the power system in question (RSA). This impacts the required capacity investments for


















Summary RSA conclusion:

The HYSOL solution in RSA competes favourable relative to the Open Cycle Gas Turbine (OCGT)

reference as can be seen from comparing results when base case data are assumed. This

conclusion holds even without taking into account an assumed cost on emission of CO2. When

compared to a Combined Cycle Gas Turbine (CCGT) reference plant the RSA HYSOL alternative

is less favourable. However, introducing an assumed CO2 emission costs of 40$/ton CO2

emitted, narrows the LCOE price difference considerable (- down to a LCOE difference of less

than 5$/MWh el). Sensitivity analyses shown illustrate the order of magnitude of LCOE price

impacts as consequence of potential uncertainties in the assumed base case data. And it can

Name of deliverable

18

be noted that the narrow price difference calculated (HYSOL versus CCGT) in base case is

relatively small compared to the span in price variations seen via sensitivity analyses.

Sensitivity (or robustness) analyses carried out emphasize that HYSOL solutions, as expected,

are less exposed to CO2 emission cost uncertainty and fuel price uncertainty than the

reference OCGT/CCGT solutions. OCGT/CCGT solutions are more exposed to CO2 emission cost

uncertainty, and more exposed to NG-price uncertainty, but less exposed to investment cost

uncertainty.

However, as observed from Figures 1-4 the annual number of full load operation hours for

HYSOL solutions, and thus the annual power production, is very important the plant economy.

Name of deliverable

19

7 Appendix








Biogas price = NG price + 8.17$/MWh = 23.88 $/MWh + 8.17 $/MWh = 32.05 $/MWh NG









socio-economic assessment and energy system analysis d6.1_ economic assessment and... ·...

Documents