TECHNICAL!NOTE! SelectionofLong5RangeEnergy!Systems! … · 2016. 11. 19. ·...

TECHNICAL NOTE

Selection of Long-‐Range Energy Systems Modelling Platforms

The MAPS Chile experience

ISSUE 22

Selection of Long-‐Range Energy Systems Modelling Platforms

The MAPS Chile experience

Date: 12/06/2014

Country: Chile

Authors:

Carlos Benavides Farías, Energy Research Centre, University of Chile Manuel Diaz Romero, Energy Research Centre, University of Chile

© MAPS 2014

Disclaimer: the contents of these briefings are the responsibility of the authors, and the views expressed therein those of the author alone.

The following citation should be used for this document:

Benavides Farías, C., and Diaz Romero, M., 2014. Selection of Long-‐Range Energy Systems Modelling Platforms: MAPS Chile experience.

Cape Town. MAPS

Selection of Long-‐Range Energy Systems Modelling Platforms: MAPS Chile experience

Table of Contents

Introduction 1

The sectoral models 1

Commercial, public and residential (CPR) 1

Transport 2

Electricity generation 2

Industry and mining 3

Agriculture 4

Forestry 4

Waste 4

Challenges in finding baseline data 5

Motivation for model selection 7

Lessons for future studies 8

Conclusions 9

References 10

1 Selection of Long-‐Range Energy Systems Modelling Platforms: MAPS Chile experience

INTRODUCTION

MAPS Chile is a government project, which, through a process of research and stakeholder participation, aims to identify

options for reducing greenhouse gases emissions in Chile, through providing a sound evidence base. The project is divided

into three phases. In Phase 1, the team generated the 2007-‐2030 Baseline scenario (PRIEN 2013; POCH Ambiental 2013a;

SISTEMAS SUSTENTABLES 2013a; AGRIMED 2013; POCH Ambiental 2013b; FUNDACIÓN CHILE 2013a; POCH Ambiental

2013c). Phase 2 involved the preparation of the Baseline 2013-‐2050 scenario and the development of mitigation actions

(Centro Cambio Global-‐UC 2013; SISTEMAS SUSTENTABLES 2013; INFOR 2013; UNTEC 2013; FUNDACION CHILE 2013;

GreenLab UC 2013. Phase 3 will build on and refine outputs and results from Phase 2 including the bridge to an ambitious

2050 vision, and include the analysis of co-‐benefits.

The research team, led by an engineering group from the University of Chile and an economic group from the Pontifical

Catholic University, plans and coordinates the research process and commissioned external researchers to conduct seven

sectoral studies to inform the baseline scenarios. The consultants for these studies were selected through a competitive

bidding process led by United Nations Development Programme (UNDP). The consultants selected in Phase 1 were not

necessarily involved in Phase 2 and as a result some of the models used in Phase 1 were different from those used in Phase

2.

This note is part of a series that aims to support stakeholders and research groups in the selection of long-‐range energy

systems modelling platforms, to inform decision making for public policy options which are compatible with national

development goals. It documents and provides brief reflections on the development of the baseline scenarios, based on the

observations of the consultants involved in the two phases of the project, with a view to sharing experiences with

modellers in the other MAPS country teams. The note assumes a basic understanding of models and the modelling

terminology.

THE SECTORAL MODELS

In general the modellers used one or a combination of econometric, optimisation and bottom-‐up end-‐use models. The

focus of this note is on the energy models that were used for the commercial, public and residential (CPR), transport,

electricity generation, industry and mining sectors. Brief descriptions of the models used in the non-‐energy sectors

(forestry, agriculture and waste) are also given, as despite not being the focus of this note, they were also included in the

scenario building exercise.

Commercial, public and residential (CPR)

For the commercial and public sectors an econometric model was used in Phase 1, using GDP as the key driver. For the

residential sector, a bottom-‐up, end-‐use model was developed. Here, drivers were related to economic parameters (GDP

per capita) and population (numbers of households and people per household, based on the relationship between GDP per

capita and people per household). The econometric model was developed using EViews and implemented in Excel

spreadsheets. GHG emissions were calculated by multiplying energy consumption in a particular year by the relevant

emission factors, using the IPCC 2006 guidelines as a basis.


The residential sector methodology considered heating, hot water, cooking and electrical uses. It took a base-‐year fuel

share based on statistical information from a single year which was gathered through surveys, and technical considerations

and fuel substitution according to future fuel price series. For residential heating, one of the most important consumers of

energy in this sector and the country -‐ a thermal comfort assumption at a high GDP per capita level -‐ was established as a

saturation parameter. A criterion of saturation of household appliances according to international experience and GDP per

capita was also considered.

In Phase 2, bottom-‐up end-‐use models were used in each sector. Heating, electrical, air conditioners, and other uses were

estimated for this sector in addition to the allocation of the share of different fuel types. This approach allowed for

mitigation actions to be easily modelled. The model was developed in a software tool called LEAP (The Long-‐Range Energy

Alternatives Planning system). In the commercial and public sector a new approach was developed considering drivers

related to population, GDP per capita and benchmarked against international indices by subsectors (hospitals, schools,

universities, public buildings, shopping centres, supermarkets, banks, and clinics). These international sub-‐indices play a

role in determining the saturation parameter in the future. The residential sector was modelled similarly to Phase 1.

Transport

Several econometric models were used during Phase 1 and Phase 2 in this sector. The main variables projected by these

econometric models are; passenger kilometres (PKM) for passenger transport, tonne kilometres (TKM) for freight transport,

and energy consumption for aviation and shipping transport. The econometric models were built using EViews statistical

software and implemented in Excel spreadsheets. No modelling or software package (such as LEAP or MARKAL was used) to

build the transport model, and all the calculations were done in Excel spreadsheets, which were similar in structure to the

UK 2050 Pathways Calculator1. The main drivers were GDP, GDP per capita and population. GHG emissions were calculated

by multiplying energy consumption in particular year by the relevant emission factors. Equations (1) and (2) show the

relationship between energy consumption and TKM and PKM, respectively.

𝐸𝐶(𝑙𝑡) = !"# (!"#!!")!"(!"

!")∗!( !"#

!"!!"#$) (1), 𝐸𝐶(𝑇𝐽) = 𝑀! % ∗

!"# !"!ñ!

/!"# ×![!"#]

!" !"!!"!!"

×!" !"#!"!

(2)

Where EC is energy consumption, FE is fuel efficiency, L is average load by vehicle, OC is average occupancy, P is the

population and M is the modal share (bus, private vehicle, taxi, etc.). During Phase 1 the modal share was projected based

on expert opinion and historical information. A preliminary model to project modal share was developed during Phase 2. In

addition, fuel efficiency improvements were projected based on expert opinion.

Electricity generation

An optimisation model was used during Phase 1 and Phase 2 for the electricity generation sector. The objective function

minimised the investment cost, operation cost and the unserved energy cost. The problem was subject to several

constraints: energy balance between electricity generation and demand, appropriate upper and lower bounds to electricity

generation, maximum feasible amount of investment for each kind of technology that could happen during every year,

quota obligation to renewable energy generation, etc. GHG emissions were calculated by multiplying primary energy

1 Available online at http://2050-‐calculator-‐tool.decc.gov.uk/pathways/.


consumed to produce electricity in a particular year by the relevant emission factors. The MESSAGE and LEAP software

packages were used in Phase 1 and Phase 2, respectively.

Hydroelectric generation is one of the main sources of electricity in Chile. Between 1996 and 2010 this represented an

average of 50% of total generation. Although Chile has suffered some droughts in the last three years, this energy source

has contributed up to the 30% of the total generation. MESSAGE and LEAP do not accommodate hydroelectric uncertainties

due to climate conditions hence an exogenous approach was used to deal with this. Five hydrological scenarios were

projected: wet, middle wet, normal, middle dry, and dry. Historic information was used to develop these projections. For

example, for the wet scenario, rainfall records from 1972 were used to project for the first year, those from 1973 were used

to project the second year, and so on. Capacity factors for hydroelectric plants were calculated according these hydrological

scenarios. For every scenario an optimum expansion plant was obtained. The expansion plant which minimises the average

cost was selected.

Electricity demand was projected by the sectoral study teams and passed manually to the electricity generation model.

Industry and mining

In Phase 1, an econometric model was used as the basis from which to project emissions in industry and mining.

Researchers defined drivers related to economic parameters (national and global GDP), mainly in the ‘Other Industries’

sector. They defined production parameters (production functions) and technology penetration as the main drivers in

sectors including copper, pulp and paper, and cement. The econometric model was developed in EViews and implemented

in Excel spreadsheets.

In the case of the Industry sector, the projection was based on models of the type:

𝑌! = 𝑎!𝑥!"!"

!

Where Yt: Energy consumption at time t; ai: Constant; xit: consumption explanatory variable Y i at time t; bi: elasticity with

respect to consumption and the explanatory variable i at time t. The consumption explanatory variable is the driver which

in most cases is the GDP projection.

In Chile, one of the main economic sectors is the copper industry. This sector’s energy consumption is modelled according

to the following expression:

Energy Consumption = Unit power coefficient×CopperProduction

Where Unit power coefficient is the amount of energy required in producing a fine metric ton (TMF) of copper.

Additionally, GHG emissions from Industrial Processes are estimated in this model. They correspond to the GHG emissions

generated by energy use in production processes and the physical and chemical transformation of raw materials. They

include emissions from the production process of cement and lime, and steel production cycle.

During Phase 2, bottom-‐up (useful energy approach) end-‐use models were used. Motor, electrical and thermal

consumption were estimated for each sector in addition to the allocation of fuel share. This approach allows for mitigation


actions to be modelled easily. The model was developed using LEAP software. The useful energy approach is represented in

Figure 1:

Figure 1: Useful energy approach in the industrial sector

Modelling considers projections of production with specific criteria for each sector and the projection of energy intensities

by product and subsector, which takes into account improving efficiencies by international standards in the long term and

fuel substitution according to future fuel price series. In this case, the mining sector is modelled according to Processed

Mineral (for extraction and concentration) and TMF (for refining), not just TMF as in Phase 1.

During Phase 2, Other Industries and Industrial Processes were modelled similarly to Phase 1.

Agriculture

During Phase 1 expert opinion (based on historical tendencies) was used to project land use for the agriculture sector. A

similar approach was used to project the livestock population. The model was implemented in an Excel spreadsheet. In

Phase 2 a more robust model was used with different econometric models implemented to project these variables.

Forestry

In Phase 1 a distinction was made between two types of forests: native forests and plantations. A simulation model of

forestry growth was used to project carbon capture. In the case of native forests only new native forest were considered to

capture carbon. The number of hectares in the first year and the rate of growth for different forest species were the

parameters used to simulate the future capture. The emissions associated with cutting down plantations were projected

using the information on the pulp industries demand which was published in public reports. The model was implemented in

an Excel spreadsheet. In addition, emissions related to fires were projected according to historical averages.

Waste

An econometric model was used to project the total per capita waste generation for the solid waste category. Expert

opinion was considered to project the composition (food, paper, textile, wood, and others). In Phase 1 the econometric

models were estimated using the statistical software (EViews) and implemented in Excel spreadsheets. In Phase 2 the

econometric model was implemented in Analytica.

Final Ene

rgy

Final EnergyMotor Use

Efficiency(%)

Final EnergyThermal Use

Efficiency (%)

Final EnergyElectrical Use Efficiency (%)

UsefulEne

rgy


CHALLENGES IN FINDING BASELINE DATA

The main source of energy consumption data is the National Energy Balance (NEB) (Ministry of Energy, 2012). This

information is available to the public from 1991 in an electronic format and from 1973 in a physical format. The following

table shows the main issues and challenges related to baseline data.

Table 1: Baseline data issues and challenges

Sector Issues and challenges

Transport Historical information: PKM, TKM. There is no information about PKM and TKM for road freight transport and

passenger transport, respectively. This information is critical to calibrating the econometric models. The

historical variables PKM and TKM were calculated indirectly using historical energy consumption (obtained from

NEB) and equation (1) and (2). Many assumptions about critical parameters (FE, L, OC and M) were made.

Modal share: The main information source is the Origin-‐Destination Survey (ODS). Unfortunately, this survey is

only done every six or ten years. In some regions there is only one data point available. Therefore, there are no

trends in modal share per year available.

Main parameters: Apart from the FE parameter, there is a lack of information for the main parameters. The ODS

is also the main information source for the OC parameter. With regard to freight transport, there is a lack of

information for the L parameter for the different kinds of freight. In addition, there is little information of the

freight trip length which is useful to calculate TKM.

Electricity generation

For the electricity generation problem, apart from information about capacity factor, the main issues are in the

future projections.

Investment cost: It is possible to find information about investment costs in the press or in public sources, such

as the Environmental Evaluation System (Environmental Assessment Service n.d.). However these values have

uncertainties because there is no a policy or law to compel the owners to publish the real costs. It is likely that

only the owners or manufacturers know the real investment costs of new plants. In addition, current costs are as

important to know as future investment cost. For example, there is uncertainty about projections of solar energy

investment cost.

Fuel prices: The information on current fuel prices or variable costs of generation is available, but as with the

investment cost, there are uncertainties about future prices. While coal is used by base-‐load plants, and diesel or

fuel oil generation meet peak demand, it is not clear what will happen with natural gas plants due to fuel price

uncertainties. In comparison to other Latin American countries, Chile imports almost all the required natural gas

(LNG). While in other Latin American countries the price of natural gas is 3-‐5 US$/MMBTU, in Chile this price is

up to 8 US$/MMBTU. It is not easy to project if new investors in LNG plants will be able to provide supply of

natural gas at competitive prices.

Capacity factor of renewable sources: During the last year the public sector has made an effort to improve the

information available on renewable energy sources (Solar energy explorer n.d.; Wind energy explorer n.d.).

However, this information is not still sufficient. To have a capacity factor equal to 0.2 for a wind plant instead of

0.3 can have a big impact on the economic evaluation.

Annual capacity potential: There are some studies which have estimated technical potentials of different kinds of

technologies, but the main challenge is to know or project the maximum feasible capacity that can be built every

year. Therefore, it is necessary to achieve consensus among stakeholders because the results are very sensitive

to these assumptions.

CPR Information on end uses is only available for one year for the residential sector (the most energy-‐intensive) so it


is very difficult to visualise trends and change the fuel share over the evaluation horizon. For the commercial and

public sectors there is no end use information.

There is not enough historical information for the characterisation of technologies such as appliances,

households, and electronic devices, required for the bottom up analysis, and the available data is not always

reliable. A lot of assumptions are required to characterise the base year

Mining and Industries

Information on end uses is available for one year for the main sectors and industries. Hence it is very difficult to

visualise trends and change fuel shares over the evaluation horizon. In small and medium enterprises there is no

end use information.

Regarding other industries (the remaining industries in the most important subsectors), historical information on

energy consumption is not disaggregated and it is not possible to deduce shares and drivers for medium or small

subsectors, nor can end uses of energy be identified.

There is not enough historical information on the characterisation of technologies such as machinery, trucks,

engines, and processes, required for the bottom-‐up analysis, and existing information is not always reliable. A lot

of assumptions are required to determine the base year.

Waste Waste generation: A long series of data is not available. There is data only for some specific years that is a

disadvantage if researchers want to calibrate econometric models.

Waste composition: There is no validated source about the organic composition of waste generation or disposal.

There is also no historical information.

Recycling, compost: There is no historical information on recycling and composting. There are some specific

studies but there is not a long time series of data.

Agriculture There are no long series of historical land use and livestock population data. The agriculture census is made

every ten years. This information is complemented with inter-‐census data (a reduced census done every year),

however, there are uncertainties about the historical information. This is critical when researchers try to use

econometric models. In addition, there is a lack of information on manure management and unit fertilizer

consumption.

Forestry There are many uncertainties surrounding critical parameters in the model. The main ones are the following:

number of hectares of forest area (initial condition), rate of growth of different forest species, carbon content of

the biomass and biomass expansion factor. The sectoral results (and national results) are very sensitive to these

parameters.


MOTIVATION FOR MODEL SELECTION

The TORs of the sectoral studies did not require the use of any specific model or software, but included several technical

requirements. For example, a general requirement for every Baseline model (2007 and 2013) was that it must be able to

integrate different mitigation actions. In addition, there were budget and time restrictions to select and develop the

models.

As explained above, the main source of energy consumption data is the National Energy Balance. The availability and extent

of historical data offered in the Energy Balances is critical in selection of econometric models. For the industrial, CPR and

transport sectors, one of the main advantages of using this data is that the models are calibrated according to historical

energy consumption.

The main drivers of the sectoral models are GDP, GDP per capita and population. These variables are modelled exogenously

by the economic research team and approved by stakeholders through the participatory MAPS Chile process. They are

common to all sectors and consistent with the macroeconomic scenarios.

Some specific advantages of the models are described below.

Transport sector: This approach projects transport demand (PKM or TKM) which is useful to model modal shift, for

example, from private vehicle to bus or non-‐motorised transport. Other kinds of mitigation actions can be modelled by

modifying the main parameters of equations (1) and (2). Previous work in Chile projected the number of vehicles instead of

transport demand. The problem with this approach is the difficulty in simulating mitigation action as modal share changes.

Another benefit of this approach is that the drivers used (GDP, GDP per capita, and population) are available and are

common to all sectors. In general, this last advantage is common to other sectors.

Electricity Generation sector: In Chile the daily dispatch of the electricity generation units of the two main power systems

(SIC and SING) is coordinated by two Independent System Operators (ISO). These ISOs try to minimise the operation cost to

dispatch the electricity generation units. Therefore, to use an optimisation problem to project the dispatch and emissions is

an acceptable approach in terms of replicating the ISO’s rules. However, as we explain below, the investment in new

generation capacity is made by the private sector.

Waste sector: The drivers used are GDP per capita, and population which are available and are common to all sectors.

There is international information which allows for the comparison of the Chilean waste per capita generation with data of

other countries with similar GDP.

Mining and Industry and CPR sectors: Microsoft Excel models were chosen in Phase 1 because of its simplicity and data

reproducibility. This software allows for developing a robust model and good visualisation of the results. A LEAP model was

used in Phase 2. LEAP allows for the modelling of mitigation actions and alternative scenarios. Econometrics models were

used in sectors where long data series were available. A data mining approach was used in this case.


LESSONS FOR FUTURE STUDIES

The following are some reflections on the different approaches that could have been adopted and may be preferable for

future studies. Researchers found that the electricity generation models, in particular, could be improved in the future.

General (all sectors):

Most models use GDP and population as drivers. The results are very sensitive to these assumptions.

Therefore, more discussions amongst stakeholders and technical experts on these main drivers are necessary.

Lack of information has been identified in all sectors. To improve the quality of the models it is necessary to

improve the available information. However, for an econometric approach the benefits of additional data can

only be realised in the long term due to the requirement of long data series.

It is recommended that more sensitivity analyses be conducted to quantify the impact of a lack of information

on the results. Once the parameters or data which produce the greatest dispersion of results have been

identified, the researchers or stakeholders should try to improve the quality of this information before

developing the models.

In addition to the previous point, a probabilistic uncertainty analysis is recommended.

Including energy price as an endogenous variable is useful to evaluate mitigations actions such as the carbon

tax. With the exception of the electricity generation models, all of the sectoral models do not include the price

of energy as endogenous variable. However, this can be a big challenge due to lack of information. For

example, to include the fuel price as a variable to project the modal share in the transport sector model

requires having historical information of modal share behaviour of people.

Benchmarking with international parameters to compare results can be useful.

Electricity generation models:

More sensitivity or probabilistic uncertainty analysis for the main projected parameters, for example natural

gas price, investment cost, and annual capacity potential.

Complementing the long-‐term models with medium term and short term models, especially when a high

capacity of renewable energy sources is projected. Power systems with a high proportion of variable

renewable resources such as solar energy and wind energy require more reserves which are normally satisfied

by conventional electric plants, and affect the dispatch of power plants and projected emissions. This kind of

phenomenon is not possible to analyse using long-‐term models.

Attempting to model private decisions instead of centralised decisions while the effect on the results is not

clear, an analysis would add depth and credibility to the models.

Allowance for flexibility to add specific constraints: hydraulic net, natural gas constraint, transmission system,

etc.


CONCLUSIONS

The objective of this paper was to collect reflections from the teams during the two phases of the MAPS-‐Chile process in

order to form a basis on which to build future models and approaches. It has been shown that an integrated model that

covers all energy sectors (transport, electricity generations, industries and mining, and CPR) has not been used or

developed. Different models and software packages have been developed or used for the different sectors, according to the

expertise of the researchers and/or the information available. In addition to this, different consultants were involved in the

preparation of scenarios during Phase 1 and Phase 2 of the project which led to additional issues in models used.

However, regardless of these limitations, it is suggested that the credibility of the results has not been compromised. The

quality of the mathematical and econometric models in the provision of results has been high. The coherence between

sectors has been guaranteed by having periodical meetings with the different research groups or consultants to ensure

consistency of inputs. For example, all sectors used the same GDP and population projections, and the electricity

generation model used the electric demand projected by the sectoral models. This information has been shared manually.

In some cases the assumptions like GDP, population, fuel prices, etc. are as important as the models or software package

used because these parameters are drivers of many of the models.

To improve the quality of the models it is necessary to improve the available information. However, for an econometric

approach the benefits of this strategy can only be observed in long term due to these kinds of models requiring long data

series.


REFERENCES

Centro Cambio Global-‐UC, ["Scenario for 2013 Baseline and Mitigation Scenarios for Electricity Generation and Transmission of Electricity Sector"], (on-‐going), Estudio Proyecto MAPS Chile, (licitado a través de PNUD). (In Spanish) [Environmental Assessment Service](Online). Available: http://www.sea.gob.cl/ (In Spanish) FUNDACIÓN CHILE, ["Baseline Emissions Scenario Retail Sector Public and Residential"], Estudio Proyecto MAPS Chile, mayo 2013a (licitado a través de PNUD SDP 111/2010). (In Spanish) FUNDACION CHILE, ["Scenario for 2013 Baseline and Mitigation Scenarios Commercial, Residential and Public Sector"] (on-‐going), Estudio Proyecto MAPS Chile, (licitado a través de PNUD). (In Spanish) [Government of Chile, Ministry of Energy, National Energy Balance](Online). Available: http://antiguo.minenergia.cl/minwww/opencms/14_portal_informacion/06_Estadisticas/Balances_Energ.html (In Spanish) [Government of Chile, Ministry of Energy, Solar energy explorer](Online). Available: http://ernc.dgf.uchile.cl/Explorador/Solar2/(In Spanish) [Government of Chile, Ministry of Energy, Wind energy explorer](Online). Available: http://ernc.dgf.uchile.cl/Explorador/Eolico2/(In Spanish) GreenLab UC, ["Scenario for 2013 Baseline and Mitigation Scenarios Anthropic Waste Sector"] (on-‐going), Estudio Proyecto MAPS Chile, (licitado a través de PNUD). (In Spanish) INFOR, ["Scenario for 2013 Baseline and Mitigation Scenarios Forestry and Agricultural Sector"] (on-‐going), Estudio Proyecto MAPS Chile, (licitado a través de PNUD). (In Spanish) Government of Chile, Ministry of Environment. Reference Scenarios for Climate Change Mitigation in Chile -‐ Phase 1 Results[Online]. Available: http://www.mapschile.cl/files/Chile_Results_Phase_I_Final_English_29042014-‐1.pdf POCH Ambiental, ["Baseline Scenario Emission Mining and Other Industries"], Estudio Proyecto MAPS Chile, mayo 2013a (licitado a través de PNUD SDP 110/2012). (In Spanish) POCH Ambiental, ["Baseline Scenario Emission Forestry and Land Use Change"], Estudio Proyecto MAPS Chile, mayo 2013b (licitado a través de PNUD SDP 112/2012). (In Spanish) POCH Ambiental, ["Baseline Scenario GHG Emissions from Waste Sector Anthropic"], Estudio Proyecto MAPS Chile, mayo 2013c (licitado a través de PNUD SDP 114/2012). (In Spanish) SISTEMAS SUSTENTABLES, ["Scenario Baseline Emissions and Urban Transport Sector"], Estudio Proyecto MAPS Chile, mayo 2013a (licitado a través de PNUD SDP 109/2012). (In Spanish) SISTEMAS SUSTENTABLES, ["Scenario for 2013 Baseline and Mitigation Scenarios and Urban Transport Sector"] (on-‐going), Estudio Proyecto MAPS Chile, (licitado a través de PNUD). (In Spanish) PRIEN, University de Chile, ["Baseline Scenario GHG Emissions Sector Generation and Transmission of Electricity"], Estudio Proyecto MAPS Chile, mayo 2013 (licitado a través de PNUD 108/2012). (In Spanish) AGRIMED, University de Chile, ["Baseline Emissions Scenario Agricultural Sector and Land Use Change"], Estudio Proyecto MAPS Chile, mayo 2013 (licitado a través de PNUD SDP 113/2012). (In Spanish) UNTEC, ["Scenario for 2013 Baseline and Mitigation Scenarios Mining Industry and Other Industries"] (on-‐going), Estudio Proyecto MAPS Chile, (licitado a través de PNUD). (In Spanish)

TECHNICAL!NOTE! SelectionofLong5RangeEnergy!Systems! … · 2016. 11. 19. ·...

Documents

Transcript of TECHNICAL!NOTE! SelectionofLong5RangeEnergy!Systems! … · 2016. 11. 19. ·...