Power Generation Planning Toward Future Smart Electricity ...symbio-newsreport.jpn.org/.../presentation_1353767677.pdfPower Generation Planning Toward Future Smart Electricity Systems

Power Generation Planning Toward Future Smart Electricity Systems

Zhang Qi, Ph.D. GCOE Assistant ProfessorGraduate School of Energy ScienceKyoto University, Japan

シンビオ社会研究会２０１２年度第２回シンビオ研究談話会

Research Interests

• Electricity demand estimation based on bottom-up technology optimization selection

• Multi-objective optimization of power generation planning

• Hour-by-hour real-time simulation for designing future smartelectricity system

• Best mix and optimized operation in deregulated electricity markets

• Energy environment policy and strategy

Page 2

Outline of Presentation

• Background

• Modelling

• Case Study

• Summary

• Future work

Page 3

Technology Paradigm Shift in Infrastructure

Ener

gyTr

ansp

orta

tion

Centralized Decentralized

Industrial Revolution, The Depression, WWII

Pow

erC

omm

unic

atio

n

Decentralized, local, small

10’ onward

Radio, TV, Wireless Phone

Internet, Smart Phone

(Web 2.0 Technology)

Social Revolution, Technology Selection and Energy Consumption

More electricity at end use side

EnergySystem

Social Systemand Social

Value

+

+

Coal

Train

Oil & Gas

Stream Engine

Telegraph

Automobile EV

ElectricityICI, Motors

Renewable Nuclear Smart Grid

Page 4

Power Generation Composition by Source in Major Countries

Page 5Source：IEA “ENERGY BALANCES OF OECD COUNTRIES (2011 Edition)”/ “ENERGY BALANCES OF NON-OECD COUNTRIES (2011 Edition)”

Generating Capacity of Nuclear Power Plants in Major Countries

Page 6Source： JAIF, “World Nuclear Power Plants 2011”

Fukushima Nuclear Power Accident

Page 7

Fukushima Nuclear Accident(March.2011)Renewable Energy

Advanced Reactors

Source: The Wall Street Journal, 2012

OR

Blackout in Korea (Sept,2011) Blackout in India (July,2012)

Blackouts

Source: Photo-bolg, NBCNews, 2012

Page 8

Basic Concept of Smart Grid in Japan

Page 9Source: The Federation of Electric Power Companies "Environmental Action Plan by the Japanese Electric Utility Industry"

Power Generation Planning Toward Future Low-Carbon Smart Electricity System

Page 10

Generation PlanningLoad

Estimation

End Use

Grid Planning

Operation PlanningSystem Control

Grid (Transmission and Distribution )

Power Sources (Renewable,Hydro, Nuclear, Thermal,)

Electricity System

Electricity Flow Modeling Relationship

Cost or Price?

Keynote Speech at WNA Meeting about Nuclear Power for Smart Grids

Page 11

Nuclear power's development for futurelow‐carbon smart electricity systems 

ZHANG Qi, PhD, Assistant Professor, Kyoto University

Some Papers about Power Generation Toward Low-Carbon Smart Electricity Systems

Qi ZHANG, T. Tezuka, McLellan, B.C et al. Integration of PV power into Future Low-CarbonSmart Electricity Systems in Kansai Area, Japan, Renewable Energy, Vol.44, pp. 99–108,2012. (SCI: 000302821800012, IF=3.2)

Qi ZHANG, McLellan, B.C et al. Economic and Environmental Analysis of Power GenerationExpansion in Japan Considering Fukushima Nuclear Accident using a Multi-ObjectiveOptimization Model, Energy, Vol.44, pp.986-995, 2012. (SCI: 000308259300096; IF=3.9)

Qi ZHANG, K. Ishihara, McLellan, B.C, et al. Scenario Analysis on Electricity Supply andDemand in Future Electricity System in Japan, Energy, Vol. 38, pp.376-38, 2012. (SCI:000301273800036; IF=3.9)

Qi ZHANG, K. Ishihara, McLellan, B.C, et al, A Methodology of Integrating Renewable andNuclear Energy into Future Smart Electricity System, International Journal of EnergyResearch, DOI: 10.1002/er.2948, 2012. (SCI, IF=2.2)

Qi ZHANG, K. Ishihara, McLellan, B.C, et al. Long-term Planning for Nuclear Power’sDevelopment in Japan for a Zero-Carbon Electricity Generation System by 2100, FusionScience and Technology, Vol.61, pp.423-427, 2012. (SCI: 000299608100071; IF=1.12)

Page 12

Outline of Presentation

• Background

• Modelling

• Case Study

• Summary

• Future work

Page 13

1: Multi-Objective Multi-Period Generation Planning

Page 14

20102030

20502100

CostCost

Cost

CO2 Emission

Cost

CO2 Emission

CO2 Emission

CO2 Emission

Planning & Back-casting

1: Optimization Model

GDX file

Users……

Input Data

Execute Program

GDX: GAMS Data Exchange

31

GAMS: General Algebraic Modeling System

Page 15

Database2

Interface Data Model Solver Result Interface

Preconditions Results

Optimization

Future Smart Electricity Systems

PV

windmill

EV/battery

activated DR

DecentralizedEnergyManagement

Current Electricity System : stabilization : fluctuation

Nuclear/thermal/Hydro generation units

Supply-demand balance, more renewable and nuclear energy, less excess electricity, lower cost

Air-conditioner,HP, Others

Nuclear/thermal/Hydro generation units

Future Smart Electricity Systems

Source: CERIPPage 16

Heat Pump System

Page 17

Coefficient of Performance (COP) >3

-40%

-20%

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hotwater Consumption (%)Hotwater Production (%)Hotwater Storage (%)

Hours

Source: IBEC, Energy consumption calculation, 2009

Production, Consumption and Storage of Hot-WaterMechanism of HP for Hot-Water Production

Electric Vehicle

Page 18

The role of EV in the Smart Grid System

Time

Full

Discharge Depth

0%8:00 19:00

SOC（State of Charge）

Driving at morning

Discharge for daily peak

Charge at night

Drivingto home

Charge at night

Example of a Weekday Moving Pattern

Control Strategy Module of Battery

Page 19

Load

Battery

Grid

PV Panel

Nuclear/Thermal/Hydro

Max. SOC=95% Min. SOC=10% Charge<30% SOC/h Discharge<50% SOC/h Discharge actively

(SOC>60%)

Generation>Load?

Start

Read hourly demand data and calculate hourly supply from renewable and nuclear energy

End

Last Record?

YES

NO

Error Alarm

YES

Feasible?

YES

Read Data/rules(electricity mix, solar irradiation, Battery capacity, control strategy, etc.)

YES

Charge (SOC<95%)(Max 30% SOC/hour)

Excess?

NO

Excess Electricity Statistic

YES

NO

SOC>60%

YES

NO

YES

NOSOC<95%?

NO

YES

Discharge (SOC>10%)(Max 50% SOC/hour)

Peak Supply

Enough?

Enough?

Enough?

Discharge (SOC>10%)(Max 50% SOC/hour)Peak Supply

NO

YESNO

NO

Control Strategy Module of EV and HP

HPHWLoad

Hot WaterTank

EV

Nuclear/Thermal/Hydro

PV Panel

Priority of charging battery and making hot-water

Charge actively or passively Discharge actively or

passivelygeneration>load?

Start

Read hourly demand data and calculate hourly supply data

End

Last Data Record?

YES

YES

NO

SOC> 30%?NO

YES

Discharge

Enough?

NOError Alarm

YES

Feasible?

YES

Read Data/rules(electricity mix, solar irradiation, EV and HP, Battery, operation pattern, etc.)

YES

NO

10:00-15:00?

Making hot water

SOH<100%?

Excess?

SOC<60%?YES

NO

YESNO

YES NO

Charge

Excess?

NO

NO

Excess Electricity Statistic

YES

NO

Peak Supply is enough?

YES

NO

SOC<60%&&Supply Excess?

Charge

YES

NO

+20% peak supply

SOC=95%

Charge

Excess? NO

YES

NO YES

SOH>20%? Making hot water to SOH=20%, updating load

NO

YES

Page 20

Optimal Combination of Power Sources to Correspond to Demands

Page 21

Charging EVDriving HP

PV and Wind

Example of Co-Exist of Nuclear and Renewable Energy in Low-Carbon Smart Electricity System

Page 22

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

106

111

116

121

126

131

136

141

146

151

156

161

166

171

176

181

186

191

196

201

206

211

216

Hours

PV power Gas Nuclear Original load Load with off‐peak charge Load with smart charge

Technology:Obtained best capacity mixObtained operation patternHourly electricity loadSolar radiation/wind speedTemperature, hot water load

EconomyFuel price, capital cost, etc

TechnologyBlackout is allowed or notGeneration priorityMax. fuel consumptionMax./Min. capacity factor Max. excess electricity

EnvironmentAcceptable CO2 emission

Economy Acceptable generation cost

TechnologyMix of Electricity productionMix of installed capacityFuel consumptions Excess electricityCapacity factorsShare of renewables

EnvironmentCO2 emission

EconomyPower generation cost

Hour-by-HourSimulation

Smart Control StrategiesOperation patterns, G2V, V2G charge/discharge, etc.

Rule Input

Operation patterns

Environment: CO2 factor of fossil fuel

New Electric Devices:Battery, EV, HP, Fuel cell, washing machine, lighting, etc.

Data Flow

Integration

Data Input

Output

2: Hour-by-Hour Simulation Model

Page 23

Software Interface of the Hour-by-Hour Simulation

Framework

Data Input

Rule Input

OutputAnnual

Monthly

Page 24

Daily

Outline of Presentation

• Background

• Modelling

• Case StudyTEPCO area, Japan to 2030

• Summary

• Future work

Page 25

Service Area of TEPCO

Page 26

Service areas of 10 Electric Power Companies

Service area of TEPCO

0

2

4

6

8

10

12

14

16

18

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030

Inst

alle

d C

apac

ity (G

We)

Year

S1S2S3History

Nuclear Power Scenarios in Tokyo Area

Page 27

Fukushima Daiichi 1-4

Page 28

Least CO2 Emission Least NPV

S1

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030

Pow

er G

ener

atio

n (G

Wh)

PVWindBiomassOilGasCoalNuclearHydro

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030

Pow

er G

ener

atio

n (G

Wh)

PVWindBiomassOilGasCoalNuclearHydro

S2

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030

Pow

er G

ener

atio

n (G

Wh)

PVWindBiomassOilGasCoalNuclearHydro

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030

Pow

er G

ener

atio

n (G

Wh)

PVWindBiomassOilGasCoalNuclearHydro

S3

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030

Pow

er G

ener

atio

n (G

Wh)

PVWindBiomassOilGasCoalNuclearHydro

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030

Pow

er G

ener

atio

n (G

Wh)

PVWindBiomassOilGasCoalNuclearHydro

Obtained Optimized Electricity Mixes

Obtained Optimized Capacity Mixes

Page 29

Least CO2 Emission Least NPV

S1

0

20

40

60

80

100

120

2010 2015 2020 2025 2030

Inst

alle

d C

apac

ity (

GW

) PHydroPVWindBiomassOilGasCoalNuclearHydro

0

20

40

60

80

100

120

2010 2015 2020 2025 2030

Inst

alle

d C

apac

ity (

GW

) PHydroPVWindBiomassOilGasCoalNuclearHydro

S2

0

20

40

60

80

100

120

2010 2015 2020 2025 2030

Inst

alle

d C

apac

ity (

GW

) PHydroPVWindBiomassOilGasCoalNuclearHydro

0

20

40

60

80

100

120

2010 2015 2020 2025 2030

Inst

alle

d C

apac

ity (

GW

) PHydroPVWindBiomassOilGasCoalNuclearHydro

S3

0

20

40

60

80

100

120

2010 2015 2020 2025 2030

Inst

alle

d C

apac

ity (

GW

) PHydroPVWindBiomassOilGasCoalNuclearHydro

0

20

40

60

80

100

120

2010 2015 2020 2025 2030

Inst

alle

d C

apac

ity (

GW

) PHydroPVWindBiomassOilGasCoalNuclearHydro

Annual Simulation Result

Page 30

Bon FestivalNew Year

Strong solar irradiation and low demand in Spring

Hot SummerHigh COP

Cold WinterLow COP

Cold WinterLow COP

Page 31

Monthly Simulation Result

Page 32

Daily Simulation Result

Charging EV using excess electricity Making hot-water using

excess electricity

Excess electricity

Excess electricity

Electricity Mixes with the Penetrations of EV and HP

Page 33

050

100150200250300350400

0 EV 0HP

(Opti)

0 EV 0HP

2 EV 2HP

5 EV 5HP

0 EV 0HP

(Opti)

0 EV 0HP

2 EV 2HP

5 EV 5HP

Least CO2 Least NPV

Pow

er G

ener

atio

n (G

Wh) Oil

LNGBiomassWindPVCoalNuclearHydro

Excess Electricity Reductions in Different Scenarios with EV and HP

Page 34

0100020003000400050006000700080009000

10000

0 EV0 HP

2EV0HP

2 EV2 HP

5 EV0 HP

5 EV5 HP

0 EV0 HP

2EV0HP

2 EV2 HP

5 EV0 HP

5 EV5 HP

Least CO2 Least NPV

Exce

ss E

lectri

city

(GW

h)

CO2 Reductions in Different Scenarios with EV and HP

Page 35

-40-20

020406080

100120140160180

0 EV0 HP

2 EV0 HP

2 EV2 HP

5 EV0 HP

5 EV5 HP

0 EV0 HP

2 EV0 HP

2 EV2 HP

5 EV0 HP

5 EV5 HP

Least CO2 Least NPV

CO

2Em

issio

n (M

illio

n To

nnes

) Reduction by HP

Reduction by EV

Emission of Power Generation

Summary

• Power generation was planned toward future low-carbon smartelectricity systems using an integrated model.

• Nuclear power, renewable energy and clean thermal power need tobe considered together in future low-carbon smart electricity systems.

• New electric devices and smart control strategies can help the systemto integrate more nuclear and renewable energy.

Page 36

The integrations of new electric devices (EV,HP) will not need additionalcapacity, and their electricity demands are met by increased gas powergeneration and excess electricity.

One million EV and HP can reduce:- 1.2-1.5 and 0.1-0.4 TWh excess electricity respectively;- 2 million and 0.6 million tonnes net CO2 emission respectively.