Costcompetitive Reduction of Carbon Emissions of up to 80% from the - - PowerPoint PPT Presentation

Cost–competitive Reduction of Carbon Emissions of up to 80% from the US Electric Sector by 2030

Alexander E. MacDonald Christopher Clack* Anneliese Alexander* Adam Dunbar Yuanfu Xie James Wilczak

NOAA Earth System Research Laboratory *Cooperative Institute for Research in Environmental Sciences

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Wind capacity factor: Power costs 3 to 4 cents in red areas.

Spectrum of atmospheric kinetic energy density. Weather energy is concentrated at large scales.

US 48 states Balancing Area

Though wind power may be missing in a small area, it is likely to be available in a larger area.

Solar PV Capacity Factor Map

% % % %

Step 1. We collected an extraordinarily detailed and accurate weather data set. Step 2. We collected electric load data concurrent in time with the weather data. Step 3. We developed a power system simulator that used all power sources and associated infrastructure (transmission and storage). Step 4. The simulator finds the least expensive configuration of the entire power system using hourly wind, solar and load concurrently. Step 5. The weather and economic simulator was used to study the geographic domain size effects of wind and solar energy generation systems.

US Study: National Energy System Designer

Rapid Update Cycle (RUC) Hourly Assimilation

11 12 13

Ti Tim e (UTC TC)

1-hr fcst

Background Fields Analysis Fields

1-hr fcst

3dvar

Obs 1-hr fcst

3dvar

Obs

Cycle hydrometeor, soil temp/moisture/snow plus atmosphere state variables

Hourly obs Data Type ~ Number

Rawinsonde (12h) 150 NOAA profilers 35 VAD winds 120-140 PBL – prof/ RASS ~ 25 Aircraft (V,temp) 3500-10000 TAMDAR (V,T,RH) * 200-3000 Surface/ METAR 2000-2500 Buoy/ ship 200-400 GOES cloud winds 4000-8000 GOES cloud-top pres 10 km res GPS precip water ~ 300 Mesonet (temp, dpt) ~ 8000 Mesonet (wind) ~ 4000 METAR-cloud-vis-wx ~ 1800 AMSU-A/ B/ GOES radiances – RR

RR only

Radar reflectivity/ lightning 1km

Wind Speed Video (m/s)

Solar Irradiance Video (W/m2)

Step 1. We collected an extraordinarily detailed and accurate weather data set. Step 2. We collected electric load data concurrent in time with the weather data. Step 3. We developed a power system simulator that used all power sources and associated infrastructure (transmission and storage). Step 4. The simulator finds the least expensive configuration of the entire power system using hourly wind, solar and load concurrently. Step 5. The weather and economic simulator was used to study the geographic domain size effects of wind and solar energy generation systems.

US Study: National Energy System Designer

100 200 300 400 500 600 700 800

Electrical Demand (GW)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

300 400 500 600 700 300 400 500 600 700

Electric Demand/Load

Step 1. We collected an extraordinarily detailed and accurate weather data set. Step 2. We collected electric load data concurrent in time with the weather data. Step 3. We developed a power system simulator that used all power sources and associated infrastructure (transmission and storage). Step 4. The simulator finds the least expensive configuration of the entire power system using hourly wind, solar and load concurrently. Step 5. The weather and economic simulator was used to study the geographic domain size effects of wind and solar energy generation systems.

US Study: National Energy System Designer

Land Use Constraints

• The type and amount of electricity generation installed in each RUC cell is

constrained by: – Spacing between facilities – Topography of the land – Land Use (residential, commercial, protected lands, etc…)

Cost Data/Values

Natural gas has a heat rate of 6,430 Btu / kWh. Variable O&M is $3.11 / MWh 2030 Estimates

HVDC Transmission Parameterization

Mathematical Optimization

= + + + +

Minimize:

+

• ≥

Subject to: ALL OTHER EQUATIONS CONSTRAIN THE MAGNITUDE OF ANY OF THE TERMS For details of the NEWS optimization see Clack et al., IJEPES 2015.

Present Paper Optimization Procedure

Yearly cost of variable generation ($/MW)

Installed capacity of variable generation (MW)

Yearly cost of conventional generation ($/MW)

Installed capacity of conventional generation (MW) Cost of conventional fuels ($/MWh) Natural gas generation (MWh) Installed capacity

• f transmission

(MW) Yearly cost of transmission stations ($/MW)

Yearly cost of transmission lines ($/MW-mile)

Length of transmission lines (mile)

Variable generator Filter Installed capacity

• f variable

generation (MW)

Weather Component (h)

Conventional generator filter Natural gas generation (MWh)

Electric demand (MWh) Nuclear generation (MWh) Excess generation (MWh)

Transmission power flux (MWh) Hydroelectric generation (MWh)

Subject to:

VARIABLE GENERATION

CONVENTIONAL GENERATION NET ELECTRIC DEMAND

Load constraint

Present Paper Optimization Procedure

Electric loss factor (%/mile) Transmission power flow (MWh) Transmission power flux (MWh) Transmission power flow (MWh) Length of transmission lines (mile)

VERY IMPORTANT CONSTRAINT AND EXTREMELY COMPUTATIONALLY EXPENSIVE HVDC transmission flux constraint

Present Paper Optimization Procedure

Peer reviewed description

• f the linear programming
• ptimization techniques

used.

• Optimization has O(106) equations, O(107) variables and O(108-

9) non zeroes

• Solves in O(106) iterations or O(105) seconds.
• We solve on a dedicated Server with 1 TB of RAM and 32

processors Present Paper Optimization Procedure

Step 1. We collected an extraordinarily detailed and accurate weather data set. Step 2. We collected electric load data concurrent in time with the weather data. Step 3. We developed a power system simulator that used all power sources and associated infrastructure (transmission and storage). Step 4. The simulator finds the least expensive configuration of the entire power system using hourly wind, solar and load concurrently. Step 5. The weather and economic simulator was used to study the geographic domain size effects of wind and solar energy generation systems.

US Study: National Energy System Designer

Cost optimized US Electric Power System for 2030

Dispatch of wind and solar PV within the simulation

Cost and Carbon Emission Analysis

2030

Conclusions

• Since weather is variable over large geographic

scales, wind and solar generation and use must also encompass large geographic areas to be reliable and cost effective.

• HVDC transmission grids would enable a large

domains big enough to make wind and solar work.

• The US could reduce CO2 emissions up to 80%

with comparable electric costs to recent decades.

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Questions . . . .

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