RENEWABLES ON THE RIGHT SPOT: SPATIAL MATCHING MODELS FOR LOW CARBON ENERGY SYSTEM DESIGN

DIEGO SILVA HERRAN NIES, JAPAN

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AIM INTERNATIONAL WORKSHOP

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.

Future steps in research

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INTRODUCTION: “RENEWABLES ON THE RIGHT SPOT”

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Location

Cost

Resource flow

Efficiency of technologies

Other

WHY IS NEEDED? PURPOSE?

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Renewable energy targets

Resource

Technology

Demand

Energy system

Source: Asia LCS Research Project leaflet (NIES, 2011)

Low carbon society/energy

system

Global emissions (BAU scenario)

Asia emissions (LCS scenario)

Global emissions (LCS scenario)

Mitigation contribution (Asia)

Renewables

Aspects in assessment of resource potential

Spatial matching

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)

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GHG

Past 2010 2050 2100

Renewables

Resource availability (Potential)

Renewable energy system

Local

Global

Protected areas Population Urban areas

Local area features

GLOBAL RENEWABLE ENERGY POTENTIAL MODEL

Technical energy potential

Renewable energy

• Solar radiation: solar PV • Wind speed: onshore wind turbines • Forest biomass (natural growth, residues):

direct combustion in boilers

35 world regions (focus on Asia)

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Geo-referenced (GIS) data

Input

Renewable Energy Potential Model

Parameters Restriction boundaries Land suitability Yields

General data Regions Grid area

Resource data Insolation Wind speed Panel inclination angle

General data Land cover Slope Altitude Wilderness

Resource specific data Wind power curve Forest removals

Output

Aggregated Potential Cost Cost-supply curve

Maps Potential

Theoretical potential

Resource specific restrictions

Technical potential

Area

Quantity

Technology specific restrictions

Spatial restrictions

- Access (wilderness)

- Conservation

- Geographic

ACCOUNTING FOR NATURAL CONSERVATION (PROTECTED AREAS)

“Loss” in technical potential [MWh/yr]

Solar PV = 11%

Onshore wind = 10%

Forest biomass = 17%

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SUPPLY (POTENTIAL) COST CURVES

Energy supply

(technical potential) [TWh/yr]

Unit electricity costs [USD/kWh]

0.00

0.05

0.10

0.15

0.20

0.25

0.30

- 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000U

nit c

ost [

USD

/kW

h]

Technical potential - Solar PV electricity [TWh/yr]

JPN

CHN

IND

IDN

KOR

THA

MYS

VNM

XSE

PHL

SGP

XSA

XEA

TWN

XCS

XME

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 1,000 2,000 3,000 4,000 5,000 6,000 7,000

Uni

t cos

t [U

SD/k

Wh]

Technical potential - Onshore wind electricity [TWh/yr]

JPN

CHN

IND

IDN

KOR

THA

MYS

VNM

XSE

PHL

SGP

XSA

XEA

TWN

XCS

XME

0

0.1

0.2

0.3

0.4

- 20,000 40,000 60,000 80,000 100,000

Uni

t cos

t [U

SD

/kW

h]

Technical energy potential [TWh/yr]

Solar PV

Onshore wind

Forest

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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)

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Land cover (urban)

Distance threshold

Transmission losses

GIS (grid cell) model

Technical potential with spatial

matching (urban areas)

Technical potential

INPUT OUTPUT

SPATIAL MATCHING IN GLOBAL TECHNICAL POTENTIAL

Technical potential of PV and Wind

down by 12% and 15% in China

0

1,000

2,000

3,000

4,000

5,000

6,000

Japan China India Japan China India

Solar PV Onshore wind

Ele

ctric

ity s

uppl

y [T

Wh/

yr]

TD_LossTDMaxNet potentialElec.dmd.

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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)

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INPUT

GIS data

Scalars

Energy Supply Potential

Distance

Demand (target) Variable costs

Fixed costs

Optimization model

- Min Cost - s.t. Constraints

OUTPUT

Renewable energy potential model

Distance-to-Closest-Feature (DistCF) tool

Plant location - Coordinates - Map

Energy system performance - Costs - Area

Energy mix - Rsc. allocation - Spl. allocation

Structure of local energy system model

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

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Solar PV + Wind = 5% elec.demand

Forest biomass = 100% forest potential 0

2

4

6

8

10

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 100 200 300 400 500 600

CO2

Emis

sion

s re

duct

ion

[%]

Uni

t cos

t [U

SD/k

Wh]

Energy supply target [GWh/yr]

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 • Scenarios (e.g. land use)

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THANK YOU VERY MUCH!

COMMENTS AND QUESTIONS ARE WELCOME!

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