RENEWABLES ON THE RIGHT SPOT: SPATIAL MATCHING MODELS FOR LOW CARBON - - PowerPoint PPT Presentation

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

DIEGO SILVA HERRAN

NIES, JAPAN 1

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 9,000

Unit cost [USD/kWh] 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

Unit cost [USD/kWh] Technical potential - Onshore wind electricity [TWh/yr]

JPN CHN IND IDN KOR THA MYS VNM XSE PHL SGP XSA XEA TWN XCS XME

0.1 0.2 0.3 0.4

• 20,000

40,000 60,000 80,000 100,000 Unit cost [USD/kWh] 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

1,000 2,000 3,000 4,000 5,000 6,000 Japan China India Japan China India Solar PV Onshore wind

Electricity supply [TWh/yr]

TD_Loss TDMax Net potential Elec.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

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 Emissions reduction [%]

Unit cost [USD/kWh] 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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