Cost-optimal regional deployment of renewable

energy in the Mexican electric power system

Anahi Molar Cruz, Beatriz Guillén, Thomas Hamacher

Technical University of Munich

Faculty of Electrical and Computer Engineering

Chair of Renewable and Sustainable Energy Systems

International Energy Workshop 2018

Gothenburg, 20.06.2018

Context

2

1. Transformation of the Mexican power

sector to a low-carbon system

2. Clean energy policy (2015)

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

2018

25%

2021

30%

2024

35%

2050

50%

Clean* Energy targets

3. Long-term auction prices of RE projects in

2017 (with financial incentives):

Solar: 21 USD/MWh (avg 27 USD/MWh)

Wind: 19 USD/MWh (avg 32 USD/MWh)

4. Massive national RE potential

5. High dependency on imported natural gas

6. Very few literature on the gradual

transformation of the power system until

2050

*RE, nuclear, efficient cogeneration, waste-based generation

Methodology

3

Regional model in urbs

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

BC

BCS

NW

N NE

W

C

E

P

• Nine regions + interconnections

• 2016-2050 in four years: 2016, 2020, 2030, 2050

• Input data:

Hourly time series of electricity demand

Hourly time series of RES

Power plant list

Storage capacities

Transmission capacities

Restrictions for grid and generation capacity expansion

• Scenarios: BASE, CLEAN-ENERGY, COST-OPTIMAL

Control regions and demand

4Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

Annual

average

growth

rate

Annual electricity

consumption [TWh]

2016* 2020 2030 2050

BC 2.2% 13 15 20 27

BCS 3.0% 3 3 5 7

C 1.6% 59 64 78 100

E 2.1% 48 53 69 94

N 2.3% 25 28 37 51

NE 2.5% 52 59 80 117

NW 2.5% 23 27 36 52

P 3.0% 12 14 20 32

W 2.5% 63 72 99 142

Mexico 2.4% 299 334 445 623

The Mexican power system in 2016

5

Installed capacity by technology and region in GW - TOTAL: 70 GW

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

0

10

20

30W

P

NW

NE

N

E

C

BCS

BC

17% power

10% energy

38% power

50% energy

Untapped renewable potential

6Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

Bio CHP GeoLarge

Hydro

Small

HydroWind PV PSHP

BC 0 0.1 0.8 0 0.2 0.8 2.9 0.3

BCS 0.1 0 0 0 0 0.1 6.4 0

C 0.3 0.9 0.4 0.2 0.7 1.3 7.0 2.8

E 0.4 3.2 0.1 1.0 1.8 8.0 13.0 0

N 0.5 0.2 0 0 0.1 10.6 47.4 0

NE 0.2 2.1 0 0 0.2 8.4 21.8 0.2

NW 0.2 0.1 0.4 0 0.6 0.8 35.7 0

P 0.1 0 0 0 0 0.8 19.1 0

W 0.5 0.5 1.9 0 0.2 3.0 29.5 0

MEX 2.3 7.0 3.6 1.2 3.8 33.7 183 3.3

• Cogeneration in the

industrial sector

• Hydro has been

exploited already

• Geothermal potential

with temperatures

above 130°C

• Share of available

considered:

• Wind: 2.5%

• PV: 1%

Average load factors for RE

7Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

0% 20% 40% 60%

BCN

BCS

C

E

N

NE

NW

PEN

W

Solar

Wind

Large hydro

Small hydro

National averages

Solar: 21%

Wind: 35%

Large hydro: 27%

Small hydro: 44%

Validation – Electricity generation mix

8

Real generation mix from PRODESEN 2017 vs BASE 2016

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

PRODESEN BASE 2016

0

80

160

240

320PV

Wind

Bioenergy

Geothermal

Hydro

Nuclear

CHP

IC

GT

CCGT

ST Fossil

ST Coal

TWh

Evolution of installed capacity at the country level

9

2016-2050 (CLEAN-ENERGY)

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

0

50

100

150

200

2016 2020 2030 20500

200

400

600

2016 2020 2030 2050

Installed capacity [GW] Electricity generation [TWh]

50%

28%

44%

27%

Evolution of installed capacity at the regional level

10

2016-2050 (CLEAN-ENERGY)

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

0

10

20

30

40

50

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

201

6

202

0

203

0

205

0

BC BCS C E N NE NW P W

BatteryPSHPPVWindBioenergyGeothermalLarge HydroSmall HydroNuclearCHPIC GasGT GasST GasCCGTIC DieselGT DieselIC Fuel OilST Fuel OilST Coal

GW

BC

BCS

NW

N NE

W

C

E

P

Regional clean energy share

11

2016-2050 (CLEAN-ENERGY)

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

2016

2050

2016

2050

2016

2050

2016

2050

2016

2050

2016

2050

2016

2050

2016

2050

2016

2050

2016

2050

BC

BC

SC

EN

NE

NW

PW

ME

X

Conventional Clean

100% 100%50% 50%0%

Comparison with the national sector plan (PRODESEN)

12

2030 (CLEAN-ENERGY vs BASE)

0

40

80

120

BASE 2030 CLEANENERGY

2030

0

150

300

450

BASE 2030 CLEANENERGY

2030

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

Installed capacity [GW] Electricity generation [TWh]

• Geothermal energy

could play a major role

• CHP fraction is

overestimated

• Storage allows the

integration of PV

The cost of the clean energy targets

13

CLEAN-ENERGY vs COST-OPTIMAL

Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

$0 $5 $10 $15 $20 $25

BASE 2016

COSTOPTIMAL

CLEANENERGY

COSTOPTIMAL

CLEANENERGY

COSTOPTIMAL

CLEANENERGY

Billions

Invest

Fuel

Fixed

Variable

20

50

20

20

20

30

Additional

$2.4 USD billions per year

or 0.38 ¢/kWh

to reach the targets

Conclusions

14Anahi Molar-Cruz (TUM ENS) | IEW 2018 | 20.06.2018 Gothenburg

• 50% share of clean energy by 2050 is an attainable goal

• In the scenarios analyzed:

• Short term: geothermal

• Medium term: wind + PHSP

• Long term: solar + nuclear + battery + small hydro

• The interconnection of regions is strengthened

• Even with low costs of RES-based generation, an additional 0.38 c/kWh is required

• Results are highly sensible to the future demand and the costs of the technologies

Anahi Molar-Cruz

Technical University of Munich

Chair of Renewable and Sustainable Energy Systems

anahi.molar-cruz@tum.de

15