Renewable on the right spot: spatial matching in renewable energy … · 2020. 2. 6. · Project...
Transcript of Renewable on the right spot: spatial matching in renewable energy … · 2020. 2. 6. · Project...
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
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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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