Transcript Reforming Energy Subsidies for a Better

7th International Scientific Conference
Energy and Climate Change
Athens – Greece
9 October 2014
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Reforming Energy Subsidies for a
Better Implementation of
Renewable Energies in Tunisia
Asma DHAKOUANI
Dr. Essia ZNOUDA
Pr. Chiheb BOUDEN
Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis
Laboratory Materials, Optimization and Energy for Sustainability
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Content
• Energy Situation in Tunisia
 Overview
 Demand Evolution
 Supply Evolution
 Potentials and Effects
 Strategy
• Energy Systems Modelling
 Generalities
 Classification
 Characteristics
• Identification of suitable ESM
 Which models are suitable?
 Open Source Models
 Examples of Open Source Models
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Energy Situation in Tunisia
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Historical Overview
1960’s to 1980’s
• Development of the demand
• Implementation of the institutional framework, i.e. SOEs (STEG,
ETAP, STIR)
Mid 1980’s
• Growing awareness about the predictable energy deficit
• Integration of Energy management and creation of specialized
entity
During 1990’s
• Energy transition in the industry
• Institutional reform
• Integration of IPP in electricity
Starting from
2000
• Structural deficit in the energy balance
• Promoting energy conservation and natural gas
 The government intervention has tried to adapt to the different situations
through the implementation of institutional, legal and regulatory framework.
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Actual Challenges
High rate evolution of the peak
High dependency on gas
Declining national resources
High cost of Renewable Energy
Heavy Energy Subsidies
Lack of transparency and data
Proactive Objective (30% RE)
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Evolution of Energy Demand
Source: Ministry of Industry, Energy and Mines - Tunisia
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Evolution of Primary Energy Consumption
Petroleum
Products
Natural Gas
Others
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Evolution of Natural Gas Demand
End Uses
Electricity Production
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Source: Tunisian Company of Electricity and Gas
Evolution of Electricity Demand
• Annual growth rate: 5%
• 150 MW installed/year
• Rapid development of the peak 11%
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Evolution of Oil Production
Annual Production in
2012: 67 000 Barrels/day
Marginal
Fields
Decline in
resources
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Evolution of Natural Gas Supply
Production in
2012:
2.8 millions toe
Consumption in
2012:
5.4 millions toe
16% Fiscal rent
31% Imports
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Electric Mix in 2012
• Wind : 350 GWh
• Solar : 9 GWh
• Hydraulic : 59 GWh
• Part from production : 2.5 %
• Saved conventional : 100 ktoe (80 MTND)
7%
• CO2 avoided : 230 000 TCO2
40%
25%
28%
Gas turbine
Combined cycle
Steam turbine
Renewable
energy
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Costs of Renewable Energies
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Potentials: Other Energy Sources
High solar radiation
1800 kWh/m2 /year in the north
2600 kWh/m2 /year in the south
Speed > 6m/s
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Potentials: Electricity Access
 Length of lines (Low and Medium Voltage):
Around 150 000 Km
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Energy Deficit
Source: The Ministry of Industry, Energy and Mines - Tunisia
In 2013, the deficit has achieved 2.4 Mtoe:
• Evolution of primary energy consumption (doubled)
• Decline in domestic production
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Macro-economic effects: Energy Subsidies
The ministry of Industry, Energy and Mines estimated energy subsidies
in 2013: 5 200 millions TND ≈ 2261 millions €, including direct and
indirect subsidies, where:
 43% for petroleum products
 41% for electricity
 16% for natural gas
Evolution of direct Subsidies
Source: Ministry of Finance
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Macro-economic effects: Natural Gas
International Prices
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Other effects of Subsidizing Energy
• Economic efficiency losses :
Reduction in the incentives designated for more efficient energy.
• Social
Theeffects:
subsidies targeting consumption decrease energy suppliers’
the
benefits
reachin rural
areas % Producers,
rich in
and high
profitssocial
and their
abilitydon’t
to invest
new infrastructure
 Interest
consumers,
and
suppliers
whotechnologies
are most likely
benefit
investing into
cheap
and dirty
to
Energy
shortages
ES
ended up of
in imports
capital or
intensive
projects
causing
a creates
displacement
of
Augmentation
a decrease
in exports
which
a
communities
or affecting
the nations
health oforpoor
to move away,
high dependency
on other
theneighbours
spreading unable
of smuggling
• Environmental
effects:
alongside
with the improved conditions of power and infrastructure,
fuels

ES to hence
end-users
lower the prices
Increase the consumption 
reflecting
the contradictory
effects
of E.S.
Augmentation of emissions and pollution.
 ES to producers  Increase production  Raise pollution.
 ES for fuel decrease deforestation and lower carbon emissions 
Switching to modern forms (fuel and electricity) highly favourable.
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Main axes of the energy strategy
The strategy of Tunisia on energy sector is based on the following pillars:
1. The development of resources and energy infrastructure.
2. Enhancing Energy Efficiency and the Rational Utilization of Energy.
3. Diversification of energy resources:
A. Development of Renewable Energies
B. Intensification of Research in fossil fuelled energies
4. Strengthening interconnections between Maghreb countries and in
the Mediterranean.
5. The implementation and reorganization of institutional and budgetary
reforms in the energy sector.
6. Strengthening the North African and international cooperation
(training, research and development and technology transfer)
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30% RE and 70% FFE
Source: The Ministry of Industry, Energy and Mines - Tunisia
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Energy Systems Modelling
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Generalities
Definition: ‘‘Energy system models are formulated using theoretical and
analytical methods from several disciplines including engineering, economics,
operations research, and management science.’’ Hoffman and Wood (1976)
The models
that have
beenthat
designed
for industrialised
countries
Objective:
The various
models
have been
appearing lastly
aim incannot
generalbeto
transferred
for usage
and
application
developing
for the nonintroduce
a better
energy
supply
system in
design
with an countries
improved understanding
adequacy
due to
thefuture
severalinteractions
socio-economic
changes
of
the present
and
between
demand-supply, environment,
 Need of adapted models
and economy.
Energy modelling has a long history: Since the early 1970s, a wide variety of
models became available for analysing energy systems or sub-systems, such as
the power system
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Classification
• The classification of ESMs differ based on the perspectives and objectives
of the research.
 Presented Methodology: Historical evolution
• There are three periods which marked the development of ESMs:



Firstly: Following the oil crisis that occurred in the 1970’s, energy
demand management appeared
Secondly: During the 1980’s the shortage in energy supply has led to
the development of supply and demand management models
Lastly: With the appearance of the climate change concerns in the
1990’s, the models were targeting as well environmental concerns
alongside with the decrease in energy consumption.
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Classification
Aggregated
models
Descriptive analysis which:
economic parameters
growth rates, demand elasticity, energy intensities
Factor (or decomposition) analysis:
total change in energy consumption
Laspeyres method of decomposition, Divisia
Index Method
Analysis using physical indicators
Models for
energy
demand
management
techno-economic variable
Energy demand analysis
Disaggregated
models
econometric approach
sub-sector, process type, end-users through
decomposition
sectoral Energy Accounting
Simplistic
Forecasting
Models
simple indicators or trend analysis
bottom-up
Sophisticated
top-down
Energy
Models
Models for
supply and
demand
management
Financial
analysis
Private sectors
Economic
analysis
governmental
Optimization
bottom-up
Accounting
Integrated
Energy
Systems
structural accounting matrix
top-down
computational general equilibrium models
Hybrid
final demand module + power generation module + fossil fuel supply + emissions trading
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Classification
Descriptive analysis which:
economic parameters
Aggregated
models
growth rates, demand elasticity, energy intensities
Factor (or decomposition) analysis:
total change in energy consumption
Laspeyres method of decomposition, Divisia
Index Method
Analysis using physical indicators
Models for
energy
demand
management
techno-economic variable
Energy demand analysis
Disaggregate
d models
sectoral Energy Accounting
Simplistic
Forecasting
Models
econometric approach
sub-sector, process type, end-users through
decomposition
simple indicators or trend analysis
bottom-up
Sophisticated
top-down
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Classification
Models for
supply and
demand
management
Financial
analysis
Private sectors
Economic
analysis
governmental
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Classification
bottomup
Integrated
Energy
Systems
Optimization
Accounting
structural accounting matrix
top-down
Hybrid
computational general
equilibrium models
final demand module + power generation module + fossil fuel
supply + emissions trading
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Characteristics
Economic Models
Accounting
(Engineering &
Economic) Models
• suitable for short and
long-run
• Policy analysis
• Flexible
• Capture the effect of
price on energy
demand and energyeconomy interactions
• Require experienced
modellers, a high
quantity and quality of
data, government
intervention
• Do not capture
technologies
• Disaggregate the
demand into
homogeneous
modules and sectors
after link each module
to economic and
technical indicators.
• Emphasis on the role
of technology,
consumers’
behaviours and
economic
environment
• Suitable for medium
and long-term visions.
Input /Output Models
• Capture the
contributions of
activities through
inter-industry linkages
• Appropriate for
climate change and
energy efficiency
• Present the integral
part of the end-use
approach, capture
structural changes
explicitly
• Characterized by
disaggregation and
inclusion of traditional
energy and informal
activities.
Combined or Hybrid
Models
• Difficult to classify
since they reconcile
“efficiency gap” then
they estimate
parameters
• Capture technological,
micro, macro, and
economic details
• Take into
consideration induced
policies.
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Identification of suitable ESM
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Which models are suitable?
• Previous ESMs comparative studies: Bottom-up accounting models are the
most appropriate for developing countries
• Pros: Flexible + require limited skills + able to capture imperfections +
consideration of non-price policies.
 More suitable than econometric models which don’t capture
informal sector or traditional energy.
• Cons: Inability to analyse price induced effects + Problems of subsidies and
shortages are not adequately captured as the demand is not explicitly
covered in these models.
• End-use models suffer from information burden which is substantial,
especially that these models cover many fields, e.g. consumption, income,
location, end-use types…
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Which models are suitable?
• Econometric models: analyse the effects by identifying statistically
relationships using economic theories  These models didn’t capture
several of the characteristics of developing countries:
 Energy access
 Rural-urban divide
 Difference in consumption behaviour and supply conditions within
ESM is incapable
reflecting specific features of energy systems that are
incomeof
classes
affecting
the
results of
decision
making in developing countries.
Traditional
energy
usage
 Informal economies
 Technological diversities and inequity and inefficient technologies
 Misallocation of resources and choices
 Non-monetary transactions (inefficient institutional arrangements)
 Transition to modern energies
 Data limitation (quality and quantity)
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Which models are suitable?
• Decisions makers have to take into consideration some challenges that
play an important role in the diffusion of efficient and environmentally
sound technologies:
 Insufficient capital stock
 Tariff and non-tariff trade barriers
 Organization of international trade
 Inadequate R&D policy
• Modellers
use effective
software, introduce high quality of input
 Lack ofshould
institutions
for upgrade
data and be as a user well trained.
 Inadequate
human resources
• Economic,
environmental
and social barriers differ + There isn’t a fit-all
model
 Software sources
might be difficult to adopt, adapt and combine
 Infrastructure
development
for re-use in other contexts
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Open Source Models
Modern models: Complex interactions and high quality of analytical tools and
data
 Using them correctly require:
1. Validated models must be available and appropriate for the target
environment.
2. Suitable data must be available for input into the model and for
verifying model-based results.
3. Models must be operated by people trained in the use of the tools and
in interpreting the outcomes for local conditions.
Satisfying these conditions: Development of Open Source Software (OSS) +
Accessible data.
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Open Source Models
• Advantages:
 Set targets and monitor outcomes
 Design strategies and policies
 Make evidence-based decisions
 Enable citizens to make informed choices.
Open data sources are an application of transparency and avoid efforts of
replications and data collections.
• All OSSs represent a common paradigm: High adaptation + Promising
characteristics to be used in developing countries + Able to meet high
standards relative to the proprietary software sources.
• Capacity building and education in the field of energy modelling should be
accessible to satisfy the condition of trained users vision.
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Examples of Open Source Models
AIM/End-use
(Asian-Pacific Integrated Mode)
• A bottom-up model that
simulates:
• Flows of energy and materials
• Analysis of country-level
policies relating to GHG
mitigation
• Domestic emissions
mitigation, technology
• Energy selection
• Technology selection (Linear
programming)
• Stock transfers and Multiple year
simulations
• Extensive data requirement
• Outputs (Energy consumption,
CO2/SO2/NO2 emission ...)
• Scenario/countermeasures
analysis
• User-friendly interface
TEMOA
(Tools for Energy Model Optimization
and Analysis)
• An energy-economy
optimization model
• Technology explicit
• Designed for High Performance
Computing
• Written in Python
• Not tied to a particular solver
• User extendable
LEAP
(Long range Energy Alternatives
Planning System)
• Used for energy policy analysis and
climate change mitigation
assessment
• the demand side range from
bottom-up, end-use accounting
techniques to top-down
macroeconomic modeling
• Track energy consumption,
production and resource extraction
in all sectors of an economy
• Account for both energy sector and
non-energy sector (GHG) emission
sources
• Used to analyze emissions of local
and regional air pollutants
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Global Trade and Environment Model
GTEM
• This model was designed to capture harmful energy subsidies and
presents scenarios tackling this burden on the state budget.
• GTEM is characterized by multi-region and multi-sector coverage and
dynamic general equilibrium taking into consideration the world economy
reflected through global change policies.
• The origin of this model: MEGABAR and GETAP models
• The added value of GTEM: The interactions between different sectors of
an economy + Estimation of the impacts of policies on key economic
variables (price of consumer goods, inputs into production, sectoral and
regional outputs, trade and investment flows, regional income and
expenditure levels)
• Environmental aspect: Modelling emissions, i.e. CO2, methane and nitrous
oxides.
• GTEM uses a business as usual simulation and presents a commodity
disaggregation since it has been used in 45 regions and over 50 industries.
• The GDP used as an input is obtained from IMF.
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Open Source Energy Modelling System
OSEMOSYS
•
•
•
•
•
Flexibility: OSeMOSYS is modifiable depending on the target of the modelling, e.g. it has been
applied to South Africa for energy planning.
OSEMOSYS: is composed of three main parts
 Plain English description
 Algebraic formulation
 Detailed description of the model inputs, outputs and parameters.
OSeMOSYS is an optimization model and aims to calculate the lowest net present cost of
an energy system to meet given demands.
It is used for long-term energy planning by developed and developing economies’
researchers even though developing countries are characterized by high CO2 emissions,
high resource use and an elevated demand for energy services.
The model is easily updated and modified under the form of series of components “Blocks”
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Open Source Energy Modelling System
OSEMOSYS
Each block is composed of:
 a plain English description- of sets
 Parameters
 Variables
 Constraints
 Objectives
 The plain English description
helps on matching the policy
maker and energy system
analyst’s expectations.
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Conclusions
• Initially: ES were adapted to ensure that all the social classes have access
to energy and to assure local industrial growth.
• Nowadays: Subsidies distort price signals + Fail to reflect the true
economic cost of supply + Represent a burden on the government budget
+ Crowd out other necessary expenditures or investments.
• Results: Over-use, inefficient and wastage of energy + Extra-pollution.
• Keeping or implementing subsidies to overcome market failure  harmful
for the economic efficiency.
• The removal of energy subsidies: increases energy efficiency and
decreases environmental damages.
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Conclusions
• The removal of energy subsidies: increases energy efficiency and
decreases environmental damages.
• A reform of energy policy plans should prioritize ancillary services, such as
storage, long term energy generation planning projects with tax incentives
• The choice of a model is critical and should be fully scoped prior to
selecting the software to use, and the results of one model should be fully
validated by another similar model.
• Choosing the adequate model is based on the criteria characterizing the
energy planning  There are some general characteristics shared by all
models + specific ones that the developer judges necessary at a certain
situation.
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Conclusions
• The increasing number of programs and the huge criteria that could be
taken into consideration  Classifying ESM is very complex
• Selecting the proper model requires an overview of the different classes
 An overview covering all classes arbitrary is not yet examined by the
literature  Classification of models based on their evolution within time
and therefore adopting their main purposes, i.e. demand side, supply side,
or energy system.
• Bottom-up, accounting models are the most suitable for capturing
developing economies characteristics.
• Developing countries specifications differ greatly. Tunisia may be quoted
as an example for the dilemma of energy access which is presenting most
Depending
on the
flexibility
the model and its transparency, the case of
of developing
states
major of
problem.
open-source models, researchers might develop the adequate model
depending on the needs and conditions.
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Thank you for your attention
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