RENEWABLES ON THE RIGHT SPOT:SPATIAL MATCHING MODELS FOR LOW CARBON
ENERGY SYSTEM DESIGNDIEGO SILVA HERRANNIES, JAPANAIM INTERNATIONAL WORKSHOP

1

NIES, TSUKUBA, DECEMBER 2012

CONTENTS
Introduction: “Renewables on the right spot”
Outline of research
Description of models• Global renewable energy potential model: protected areas, supply-cost curves,spatial matching.
• Local energy system model: plant location, resource allocation.

2

Future steps in research

3

INTRODUCTION: “RENEWABLES ON THE RIGHT SPOT”

WHY IS NEEDED? PURPOSE?
Low carbon
society/energysystem
Renewableenergytargets

Global emissions(BAU scenario)

Source: Asia LCS Research
Project leaflet (NIES, 2011)
Global emissions(LCS scenario)
Mitigation
contribution(Asia)

Asia emissions (LCSscenario)

Renewables

Aspects in assessment of
resource potential
Resource flow

Resource

Efficiency oftechnologies

Technology

Cost
Location

DemandOther

Spatialmatching

4

Energy system

OUTLINE OF RESEARCH

Renewable energy supply using GIS (gridded) data
Global technical potential• Outputs: technical potential world x35 regions, supply cost curves, maps
• Contribution: integrated models considering renewables, S-6 project

Local energy system model using renewables• Outputs: optimal mix of renewables in local region,• Contribution: feasibility of renewable energy targets to policy makers in local areas, Iskandar Malaysia(SATREPS project)
Population
Urban areas

Protected areas

GlobalGHG
Resource availability (Potential)

Renewables
Local

2010

2050

2100

Local area features

Renewable energy system

5Past

GLOBAL RENEWABLE ENERGY POTENTIAL MODEL
Quantity

Renewable energy• Solar radiation: solar PV• Wind speed: onshore wind turbines• Forest biomass (natural growth, residues):
direct combustion in boilers

Theoretical potential
Resource specificrestrictions

Spatial restrictions

Technology specificrestrictions- Access (wilderness)

Technical potential- Geographic- Conservation

35 world regions (focus on Asia)

Area

Input
Parameters
Restriction boundaries
Land suitabilityYields
General dataRegions
Grid area
Resource specific data
Wind power curve
Forest removals

OutputRenewable Energy
Potential Model

AggregatedPotential
Cost
Cost-supply curve
Maps
Potential

Geo-referenced (GIS) data
Resource data
Insolation
Wind speed
Panel inclination
angleGeneral data
Land cover
Slope
Altitude
Wilderness

6

Technical energy potential

ACCOUNTING FOR NATURAL CONSERVATION (PROTECTED AREAS)“Loss” in technical potential [MWh/yr]
Solar PV = 11%
Onshore wind = 10%

7

Forest biomass = 17%

SUPPLY (POTENTIAL) COST CURVES
0.30
JPN
CHN

0.25

Energy supply

IND(technical potential) [TWh/yr]
Unit electricity costs [USD/kWh]Unit cost [USD/kWh]

IDN
KOR

0.20

THA
MYS

0.15

VNM
XSE
PHL

0.10

SGP
XSA
0.05

XEA
TWN

0.00
0.40 -0.4

XCS
1,000

2,000

3,000

4,0005,000

6,000

7,000

8,000

9,000

Technical potential - Solar PV electricity [TWh/yr]

CHN
IND

0.30

IDN

Forest
0.2

0.1

KOR
0.25

THAMYS

0.20

VNM
XSE

0.15

PHL
SGP

0.10

0-

20,00040,00060,00080,000Technical energy potential [TWh/yr]

100,000

XSA
XEA

0.05

TWN
XCS

0.00
-

1,000

2,000

3,000

4,000

5,000

Technical potential - Onshore wind electricity [TWh/yr]

6,000

7,000

XME

8Onshore wind
Unit cost [USD/kWh]Unit cost [USD/kWh]

0.3

JPN

0.35

Solar PVXMESPATIAL MATCHING IN GLOBAL TECHNICAL POTENTIAL
Spatial matching = Proximity to urban areas
Threshold for distance to urban areas; Transmission losses
Solar PV and onshore wind electricity generation
Neglect current electricity transmission networks (grids)
INPUT
Technicalpotential
Land cover(urban)
Distance
threshold

OUTPUT

GIS (grid cell)
modelTechnical potential
with spatialmatching(urban areas)

9Transmissionlosses

SPATIAL MATCHING IN GLOBAL TECHNICAL POTENTIAL
Technical potential of PV and Wind
down by 12% and 15% in China

6,000
TD_Loss
TDMax
Net potential
Elec.dmd.

4,000
3,0002,0001,0000Japan

China

India

Solar PVJapan

China

India

Onshore wind

10Electricity supply [TWh/yr]

5,000LOCAL ENERGY SYSTEM MODEL USING RENEWABLES
Technology (plant site) location + Resource allocation
Optimization: MIP (mixed integer programming)
Objective function: Minimize Total cost
Solved using GAMS (General Algebraic Modeling System)OUTPUT

Renewableenergy potential
model
Distance-toClosest-Feature(DistCF) tool

GIS data
Energy Supply
Potential
Fixed costs
Distance

Optimization model- Min Cost- s.t. Constraints

Scalars
Demand (target)
Variable costs

Structure of local energy system model

Plant location- Coordinates- Map
Energy mix- Rsc. allocation- Spl. allocation
Energy system
performance- Costs- Area11

INPUT

LOCAL ENERGY SYSTEM MODEL – OUTCOMES
Demand = 550 GWh (i.e. 5% electricity demand)• PV supply = 91 % (1,941-1,981 MWh/yr/cell)• Wind supply = 9 % (73 MWh/yr/cell)

Demand = All forest potential (157 GWh)
• 1.5% of electricity demand
0.40

Solar PV + Wind = 5% elec.demand

1080.30
0.25

60.20
40.15
0.10

CO2 Emissions reduction [%]20.05
0.00

0-100

200

300

400

Energy supply target [GWh/yr]

500

600

Forest biomass = 100% forest potential

12Unit cost [USD/kWh]

0.35

FUTURE STEPS
Spatial matching in global model• Proximity to urban areas: Impact on costs?
• Incorporate population and consumption per capita data
• Deployment of renewables based on spatial matching: On-site vs off-site• Load (electricity demand) matching: compare size of supply and demand for locating plants

Spatial matching in local model• Generic model formulation• Incorporate detailed data: land use, renewable resources• Model application to other regions in Asia

Focus on biomass• Energy crops

Dynamic aspects of renewable supply

13• Scenarios (e.g. land use)

THANK YOU VERY MUCH!

14COMMENTS AND QUESTIONS ARE WELCOME!