Research Interests. Power Generation Planning Toward Future Smart Electricity Systems. Social Revolution, Technology Selection and Energy Consumption

Transcription

1 Research Interests Power Generation Planning Toward Future Smart Electricity Systems Electricity demand estimation based on bottom-up technology optimization selection Multi-objective optimization of power generation planning Hour-by-hour real-time simulation for future smart electricity system design Zhang Qi, Ph.D. GCOE Assistant Professor Graduate School of Energy Science Kyoto University, Japan Best mix and optimized operation in deregulated electricity markets Energy environment policy and strategy Page 2 Outline of Presentation Social Revolution, Technology Selection and Energy Consumption Background Modelling Social System and Social Value + Energy System Technology Paradigm Shift in Infrastructure Industrial Revolution, The Depression, WWII 1 onward Decentralized, Centralized Decentralized local, small Renewable & Smart Grid Energy Case Study + Transportation Train Automobile EV Summary Power Stream Engine ICI, Motors Electricity Future work More electricity at end use side Communication Telegraph Radio, TV, Wireless Phone Internet, Smart Phone (Web 2. Technology) Page 3 Page 4

2 Power Generation Composition by Source in Major Countries Generating Capacity of Power Plants in Major Countries Source:IEA ENERGY BALANCES OF OECD COUNTRIES (211 Edition) / ENERGY BALANCES OF N-OECD COUNTRIES (211 Edition) Page 5 Source: JAIF, World Power Plants 211 Page 6 Fukushima Power Accident Blackouts Fukushima Accident(March.211) Renewable Energy Blackout in Korea (Sept,211) Blackout in India (July,212) Advanced Reactors Source: The Wall Street Journal, 212 Source: Photo-bolg, NBCNews, 212 Page 7 Page 8

3 Basic Concept of Smart Grid in Japan Keynote Speech at WNA Meeting about Power for Smart Grids power's development for future low carbon smart electricity systems ZHANG Qi, PhD, Assistant Professor, Kyoto University Source: The Federation of Electric Power Companies "Environmental Action Plan by the Japanese Electric Utility Industry" Page 9 Page 1 Some Papers about Power Generation Toward Low-Carbon Smart Electricity Systems Outline of Presentation Qi ZHANG, et al, Integration of power into Future Low-Carbon Smart Electricity Systems in Kansai Area, Japan, Renewable Energy, Vol.44, pp , 212. (SCI: , IF=3.2) Qi ZHANG, et. al, Economic and Environmental Analysis of Power Generation Expansion in Japan Considering Fukushima Accident using a Multi-Objective Optimization Model, Energy, Vol.44, pp , 212. (SCI: ; IF=3.9) Qi ZHANG, et. al, Scenario Analysis on Electricity Supply and Demand in Future Electricity System in Japan, Energy, Vol. 38, pp , 212. (SCI: ; IF=3.9) Qi ZHANG, et. al, A Methodology of Integrating Renewable and Energy into Future Smart Electricity System, International Journal of Energy Research, DOI: 1.12/er.2948, 212. (SCI, IF=2.2) Qi ZHANG, et. al. Long-term Planning for Power s Development in Japan for a Zero- Carbon Electricity Generation System by 21, Fusion Science and Technology, Vol.61, pp , 212. (SCI: ; IF=1.12) Background Modelling Case Study Summary Future work Page 11 Page 12

4 Multi-Objective Multi-Period Generation Planning 1: Optimization Model Planning & Back-casting Preconditions Users Results CO 2 Emission CO 2 Emission CO 2 Emission CO 2 Emission Cost 25 Cost Cost 21 Cost Input Data Execute Program GDX: GAMS Data Exchange Database 2 Optimization GDX file GAMS: General Algebraic Modeling System Interface Data Model Solver Result Interface Page 13 Page 14 Future Smart Electricity Systems Future Smart Electricity Systems Current Electricity System : stabilization : fluctuation /thermal/ generation units Future Smart Electricity Systems /thermal/ generation units windmill Supply-demand balance, more renewable and nuclear energy, less excess electricity, lower cost EV/battery Air-conditioner,, Others activated DR Source: CERIP Decentralized Energy Management Page 15 <Weather Bureau> HEMS or BEMS Smart Meter and Smart Control Devices EV/PHEV ahead weather and irradiation forecast Air Conditioner Specific Location Photovoltaics Other Appliances Heat Pump Water Heater [House or Building] Dispatch Area Grid Power Battery Matrix at Sub-stations Smart Accumulation Hot Water Tank Battery Smart Distribution Household number Electric Power Company 1: Centralized Management 2: Ahead forecast <Region/Nation> Penetration Level Page 16

5 Heat Pump System Electric Vehicle Coefficient of Performance (COP) >3 The role of EV in the Smart Grid System Example of a Weekday Moving Pattern 1% 8% 6% 4% 2% % -2% -4% Hotwater Consumption (%) Hotwater Production (%) Hotwater Storage (%) Hours SOC (State of Charge) Full Discharge Depth % Driving at morning Discharge for daily peak Driving to home 8: 19: Charge at night Charge at night Time Source: IBEC, Energy consumption calculation, 29 Page 17 Page 18 EV in Kyoto Control Strategy Module for Battery 21: EV 19, Charging Stations: 2 Max. SOC=95% Min. SOC=1% Charge<3% SOC/h Discharge<5% SOC/h Discharge actively (SOC>6%) Panel Start Read Data/rules(electricity mix, solar irradiation, Battery capacity, control strategy, etc.) Read hourly demand data and calculate hourly supply from renewable and nuclear energy SOC<95%? Charge (SOC<95%) (Max 3% SOC/hour) Excess? Generation>Load? SOC>6% Discharge (SOC>1%) (Max 5% SOC/hour) Enough? Peak Supply Peak Supply Enough? Discharge (SOC>1%) (Max 5% SOC/hour) Battery Enough? 214: EV 5, Charging Stations: 7 Grid Last Record? Error Alarm Charging Points Load Excess Electricity Statistic Feasible? /Thermal/ End Source: Environment policy department, Kyoto Government, 211 Page 19 Page 2

6 Control Strategy Module of EV and Priority of charging battery and making hot-water Charge actively or passively Discharge actively or passively Start Read Data/rules(electricity mix, solar irradiation, EV and, Battery, operation pattern, etc.) Read hourly demand data and calculate hourly supply data SOH>2%? generation>load? Making hot water to SOH=2%, updating load Example of Co-Exist of and Renewable Energy in Low-Carbon Smart Electricity System power Original load Load with off peak charge Load with smart charge EV Panel Hot Water Tank 1:-15:? Peak Supply is enough? SOH<1%? SOC<6%? SOC> 3%? SOC<6%&& +2% peak supply Supply Excess? Making hot water Discharge Charge Excess? Charge Excess? Enough? SOC=95% Charge Error Alarm Excess? Last Data Record? Load HW Excess Electricity Statistic Feasible? End Hours /Thermal/ Page 21 Page 22 Technology: Obtained best capacity mix Obtained operation pattern Hourly electricity load Solar radiation/wind speed Temperature, hot water load Economy Fuel price, capital cost, etc Environment: CO 2 factor of fossil fuel 2: Hour-by-Hour Simulation Model Data Input Technology Mix of Electricity production Mix of installed capacity Fuel consumptions Excess electricity Capacity factors Share of renewables New Electric Devices: Battery, EV,, Fuel cell, washing machine, lighting, etc. Smart Control Strategies Operation patterns, G2V, V2G charge/discharge, etc. Hour-by-Hour Simulation Output Operation patterns Economy Power generation cost Environment CO 2 emission Rule Input Technology Blackout is allowed or not Generation priority Max. fuel consumption Max./Min. capacity factor Max. excess electricity Economy Acceptable generation cost Environment Acceptable CO 2 emission Data Flow Integration Page 23 Data Input Framework Software Interface of the Hour-by-Hour Simulation Rule Input Annual Daily Output Monthly Page 24

7 Outline of Presentation Background Service Area of TEPCO Service areas of 1 Electric Power Companies Modelling Service area of TEPCO Case Study TEPCO area, Japan to 23 Summary Future work Page 25 Page 26 Power Scenarios in Tokyo Area Obtained Optimized Electricity Mixes Least CO 2 Emission Least N Installed Capacity (GWe) S1 S2 S3 History Fukushima Daiichi 1-4 S1 S Year S Page Page 28

8 Obtained Optimized Capacity Mixes Least CO 2 Emission Least N Annual Simulation Result S1 1 P P New Year Strong solar irradiation and low demand in Spring Bon Festival S P P S P P Cold Winter Low COP Hot Summer High COP Cold Winter Low COP Page 29 Page 3 Monthly Simulation Result Daily Simulation Result Excess electricity Excess electricity Charging EV using excess electricity Making hot-water using excess electricity Page 31 Page 32

9 Electricity Mixes with the Penetrations of EV and Excess Electricity Reductions in Different Scenarios with EV and EV (Opti) EV 2 5 EV (Opti) EV 2 5 LNG Excess Electricity (GWh) EV 2EV 2 5 EV 2EV 2 5 Least CO2 Least N Least CO2 Least N Page 33 Page 34 CO 2 Reductions in Different Scenarios with EV and Summary CO 2 Emission (Million Tonnes) Reduction by Reduction by EV Emission of Power Generation EV 2 Least CO2 5 EV 2 Least N 5 Power generation was planned toward future low-carbon smart electricity systems using an integrated model. power, renewable energy and clean thermal power need to be considered together in future low-carbon smart electricity systems. New electric devices and smart control strategies can help the system to integrate more nuclear and renewable energy. The integrations of new electric devices (EV,) will not need additional capacity, and their electricity demands are met by increased gas power generation and excess electricity. One million EV and can reduce: and.1-.4 TWh excess electricity respectively; - 2 million and.6 million tonnes net CO 2 emission respectively. Page 35 Page 36