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Climate-Energy-Economy Modeling:Scenarios for Designing a Sustainable Economy Transition

International Beirut Energy Forum 201925/9/2019

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Chiheb BoudenEcole Nationale d’Ingénieurs de Tunis

NATIONAL PRODUCTION TREND [KTOE]

Projections of the electricity demand

3

4

Energy Surplus

* Development of the demand* Implementation of the institutional framework, i.e. SOEs (STEG, ETAP, STIR)

* Growing awareness about the predictable energy deficit* Integration of Energy management and creation of specialized entity

* Energy transition in the industry* Institutional reform* Integration of IPP in electricity

Promoting:* Energy Efficiency* Renewable Energies* Natural Gas

Mto

e

Evolution of Energy Balance

95

8781

72

6056

59

5148

0

10

20

30

40

50

60

70

80

90

100

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Energy Independance rate

FOCUS ON THE ELECTRICITY PRODUCTION SYSTEM

• Based on the voltage’s type, customers are connected to:Low Voltage customers (230/400V) primarily residential sectorMedium Voltage (10 kV, 15 kV et 30 kV)High Voltage (90 kV, 150 kV, 225 kV et 400 kV)

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Services12%

Tourism5%

Transport4%

Pumping and sanitary services

9%

Agriculture8%

Industries: extractive, food,

textile, paper and publishing, chemical,

construction materials, metal

62%HV & MV Electricity Consumption in 2016

ELECTRICITY DEMAND

471 MW / 9%

1 639 MW30%

1 040 MW19%

2 024 MW37%

302 MW / 5%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5 476 MW

Cyclescombinés

Thermiques à vapeur

Turbines à combustion

Renouve-lables

IPP

3,3 TWh19%

8,4 TWh46%

3,7 TWh20%

2,2 TWh12%

0,5 TWh / 3%

18,1 TWh

677 ktep18%

1 480 ktep38%

994 ktep26%

687 ktep18%

3,8 Mtep

308 tep/GWh

272 tep/GWh

184 tep/GWh

218 tep/GWh

3 % d’ER

Electricity Supply by Technology (2016)

Electricity Demand

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Load Curve For 2009

Ele

ctric

ityD

eman

din

MW

Annual Electricity Demand

10

9 july 2019Night Peak

Day Peak

LOAD CURVE (SUMMER 2019)

Evolution of the demand daily profile

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Evolution amongst years

Tunisian Electricity Strategy

Enhence Electricity Efficiency (improve specific consumption)

Diversify the primary resources & create an balanced electricity MixObjectives:

• 12% Renewable Electricity by 2020• 30% Renewable Electricity by 2030

Re-create a reserve margin (10 to 15%)

Strengthen the grid to allow absorbtion of Renewable Electricity

Carry-out the project of Interconnection with Europe

Develop a pumed-Storage Hydroelectricity system (Large scale Storage)

Implement the smart grid

Wind Energy Potential8 GW

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PV Potential:900 GW

Renewable energies potentials

Biomass PotentialMillions Tons6

SDHWS Potential2millions m4

The Tunisian Solar Plan: 30% of Renewable Electricity by 2030

245

775

1305

1755250

555

868

30

155

392

642

150

450

45

80

100

0

500

1000

1500

2000

2500

3000

3500

4000

4500

2015 2020 2025 2030

E. Eolienne Centrales PV PV Individuel CSP Biomasse

257

MW

4%

1225

MW

14% 24

82M

W 2

4%

3815

MW

30%

Secteur Filière Capacité Période prévue de mise en service

STEGPV Systems 300 MW

South of Tunisia

2017-2020Wind Energy 80 MW

Tbaga (Kébili)

PPP & Self consumption

PV Systems 350 MW

Wind Energy 270 MW

All All sectors 1250 MW 2021-2025

All All sectors 1250 MW 2026-2030

The Tunisian Solar Plan

Expected Objectives: Contribute to achieve the objectives of 12% and 30% of

Renewable Electricity respectively by 2020 and 2030

Planned Projects

What are Next Steps?

• Presentation of APPLIED insights about long-term impacts of the national strategy

• Governmental inquiries (effects on energy budget, security of supply, …) • Economic impacts (GDP, evolution of the demand, prices,…)• Social considerations (job creation, development of the industry,…)• Environmental impacts (emissions gains and adaptation to NAMA)

• Need for a Modelling tool and development of Insights

CREATION OF A MODELLING GROUP

• OSeMOSYS and TEMOA could be considered as the most suitable models for our case Easy access to source code and different developed versions Effective information dissemination actions Accessibility to manuals Availability of capacity building and training Rapid growth: Wide range of flexibility and variability

Some Characteristics• A bottom-up, dynamic and linear optimisation model generator for long-run integrated assessment and energy

planning

• Aimed to calculate the lowest net present cost of an energy system to meet given demands and constraints

• Programming language: GNU MathProg (GMPL) language

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CHOICE:OSeMOSYS to model the Tunisian power system

Open Source energy Modelling SYStems « OSeMOSYS »

Supply Shares and Impacts: Reference Energy System

Natural Gas Imports

Natural Gas Production

Wind

New CCGT/GT

Hydro run‐of‐river

Hydro Dam

T&D

Dist. Solar PV

Dist. Solar PV with battery

Grid Expansion

Backstop 1

Backstop 2

Traditional stove

Electric stove

Gas stove

Diesel car

Electric car

Air conditioning

Biomass collection

Diesel import Basktop Air Conditioning

Backstop Transport

Backstop Cooking

Gas distribution

Recharge

Electricity generation from Biomass

Electricity generation from CSP

OSeMOSYS features

emissionsRE integration

Techno-economic

Reserve margin

Start-up costs

Online capacitiesDemand modelling

Subsidies

End-users

Interconnections

Socio-economic

System reliability

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Open Source energy Modelling SYStems « OSeMOSYS »

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BAU: 18684.81 M$-2010 vs. RE: 19015.37 M$-2010

Supply Shares and Impacts: Discounted Costs

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BAU Scenario RE Scenario

Supply Shares and Impacts: Capacities Shares

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BAU Scenario RE Scenario

Supply Shares and Impacts: Electricity Mix

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Supply Shares and Impacts: Job Creation

BAU Optimal Capacity Shares by 2030 RE Optimal Capacity Shares by 2030

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Supply Shares and Impacts: Job Creation

• Train a multi-diciplinary group on « energy-Economy Modeling »

• Develop Energy Scenarii

• Develop energy-climate-Economy insights

• Create an « Energy-Climate-Economy » research & Consulting group

OBJECTIVES

GIZ Bureau Expert International

International Trainers /

International Experts / Scientists

Other Team Members: 2 Engineers

Modeling Group

5 PhD Students + 12 Master Students

Senior Researchers(Multidisciplinary)

Ministries /

Official Institutions(STEG, ANME, ITCEQ, INS, ……)

CapacityBuilding

Consulting / technical Support

Identification of the Research Subjects

Supply of Data

Answer to the ResearchQuestions

Comments / Analysis

Training

Tutoring / Supervision

FormationApprovisionnement en données

Consulting / Scientific Support

Co-Supervision

Co-Supervision

THE GROUP ORGANIZATION

THANK YOU FOR YOUR KIND ATTENTION