Dispatchable Resources

This table shows modeled “dispatchable” capacity for California. Dispatchable plants can generate electricity according to grid operators' commands (or “dispatch”). Most dispatchable plants use heat to generate electricity, such as plants that burn fossil fuels. In our modeling methodology, the Expected Capacity for dispatchable resources is constant over all hours of the 365-day modeling period. We estimate 40,709.1 MW of total Expected Capacity for California.

Historical experience is indicated by Capacity Factors and Availability Factors. Capacity Factors are calculated by dividing observed generation in the indicated year by Installed Capacity. Capacity Factors are not used to model Expected Capacity but are presented only as reference values. Availability Factors are based on historical data for scheduled outages and mechanical breakdowns; these factors are used to compute Expected Capacity. For example, in California, the largest dispatchable resource is Natural Gas, with 41,011 MW of Installed Capacity. Because this resource is not always dispatched by grid operators (or available for dispatch), the Capacity Factor is 25.2%. The Availability Factor is 80%—multiplying this factor by the Installed Capacity we get the Expected Capacity of 32,808.8 MW for Natural Gas plants.

For modeling purposes, users can add or subtract Installed Capacity to account for new generation plants or the retirement of existing plants. Likewise, Availability Factors can be changed to reflect experience within the Region. Changes to Installed Capacity and Availability Factors are reflected in the Expected Capacity column.

Solar Resources   ( January February March April May June July August September October November December )

This candlestick graph shows modeled solar capacity in California over the hours of an average day in the selected month. Solar generation contains two highly predictable components—season and time of day—and a cloud cover component that varies within a statistical range.

The black line shows Installed Capacity for solar resources. The yellow bars show historical solar generation plus and minus one standard deviation. The narrow yellow sticks show maximum and minimum observed values. We model Expected Capacity for solar based on the mean of hourly observations for each month and hour of the day, resulting in a “50/50” expectation of capacity—i.e., the expectation will be above the actually available capacity half of the time and below capacity half of the time. Expected Capacity, the red line, is usually less than Installed Capacity because of nighttime periods and lower sunlight during some seasons and hours. Changing the month selection will show different Expected Capacities based on historical observations for that month.

For modeling purposes, users can add or subtract Installed Capacity for solar resources. The New Expected Capacity for each hour of the day is shown by the green line. Adding or subtracting Installed Capacity changes Expected Capacity proportionally based on the time of day. New Installed Capacity is shown by the brown line.

Installed Capacity: 21,552 MW
Generation Profile Source Region: California
Change Capacity (+/-):

Hydroelectric Resources

This candlestick graph shows modeled hydroelectric capacity in California. Hydroelectric generation depends on water in rivers and reservoirs which in turn depends on rainfall or, in some areas, the melting of snowpacks. As a result, hydroelectric capacity partially depends on the season (or month) of the year.

The black line shows Installed Capacity for hydroelectric resources. The blue bars show historical hydroelectric generation plus and minus one standard deviation. The narrow blue sticks show maximum and minimum observed values. We model Expected Capacity based on the maximum observation for each month. Expected Capacity, the red line, is usually less than Installed Capacity because of lower river flows and reservoir levels in some seasons. When dam operators order the turbine gates to open fully, the maximum generation capacity is limited by seasonal conditions .

For modeling purposes, users can add or subtract Installed Capacity for hydroelectric resources. The New Expected Capacity for each month is shown by the green line. Adding or subtracting Installed Capacity changes Expected Capacity proportionally based on the month. New Installed Capacity is shown by the brown line.

Installed Capacity: 13,600 MW
Generation Profile Source Region: California
Change Capacity (+/-):

Wind Resources — Onshore Wind

This candlestick graph shows modeled wind capacity in California. In most geographies, wind generation is correlated to the season of the year. In coastal regions, wind generation can also correlate with the time of day, but we do not model this effect because most wind generation in the U.S. is in inland areas.

The black line shows Installed Capacity for wind resources. The green bars show historical wind generation plus and minus one standard deviation. The narrow green sticks show maximum and minimum observed values. We model Expected Capacity for wind based on the mean of hourly observations for each month, resulting in a “50/50” expectation of capacity—i.e., the expectation will be above the actually available capacity half of the time and below capacity half of the time. Expected Capacity, the red line, is usually less than Installed Capacity because wind conditions are inconsistent and unreliable.

For modeling purposes, users can add or subtract Installed Capacity for wind resources. The New Expected Capacity for each month is shown by the green line. Adding or subtracting Installed Capacity changes Expected Capacity proportionally based on the month. New Installed Capacity is shown by the brown line.

Installed Capacity: 6,655 MW
Generation Profile Source Region: California
Change Capacity (+/-):

Wind Resources — Offshore Wind
( January February March April May June July August September October November December )

These two graphs display modeled offshore wind capacity for California during the month of August. Due to stronger and more consistent wind offshore, turbines in coastal waters provide often power with higher reliability and efficiency than turbines on land. GridClue models offshore wind capacity for every month and hour of the year.

The top graph shows August wind speeds in coastal waters near California as published by the National Renewable Energy Laboratory. The lower graph estimates megawatts of capacity over the hours of the day for the selected month. Because wind power generated varies with the cube of the wind speed, the 24-hour variation for megawatt capacities in the lower graph is more extreme than wind speeds displayed in the upper graph.

Users can adjust the default scenario for capacity, wind speed, and capacity factor using the controls below. The Wind Profile control populates the modeling value for Average Wind Speed. To aid in determining New Capacity and other modeling values, the Department of Energy has a list of current and planned wind projects that can be accessed at this link: Offshore Wind Market Report. The DOE project list is a good starting point for modeling capacity additions for offshore wind power. NREL publishes an average capacity factor for offshore wind.

Select an Offshore Wind Project from the drop down list or enter a custom scenario. Click 'Reset Defaults' to select the default wind speed and capacity values.

Battery Resources

This bar graph shows modeled battery storage in California. Default modeling assumptions include charge and discharge cycles of four hours each and a “round-trip efficiency” of 80%. To optimize solar resources, default charging is at midday. Default discharging is modeled at the evening load peak.

The controls on the left show modeling assumptions. Modeled megawatts of electricity demand consumed by charging—and capacity available by discharging—are shown by the negative and positive bars, respectively. Usable energy stored is determined by megawatt capacity multiplied by charging hours; this result is discounted by the round-trip efficiency before being displayed. Megawatt demand from charging—and capacity from discharging—are reflected in “Expected Capacity” at the Summary of Modeling Results at the bottom of this page.

Imported Power  ( January February March April May June July August September October November December )

This candlestick graph shows modeled import capacity for California over the hours of the days in the selected month. Interchange depends on transmission capacity between regions, contractual agreements to transfer power, and emergency transfers to avoid blackouts.

The gray bars show historical net interchange plus and minus one standard deviation. Net exports are displayed as negative imports. The narrow grey sticks show maximum and minimum observed values.

Expected Capacity of imports is the red line. When Expected Capacity is negative, this signifies exports. We model imports and exports based on the average observation for each month and hour of the day. Changing the month selection will show different Expected Capacities based on historical observations for that month.

GridClue conservatively uses the average import/export value for modeling because extreme weather events often extend beyond a single region. During times of grid stress, imports and exports persist because of pre-existing commitments that are typically kept even if load sheds will result for the exporter. For emergency planning, GridClue does not allow double-counting of generation reserves supplied through imports.

GridClue modeling does not allow users to add imports. However, generation capacity outside of the modeled area with firm transmission can be added as dispatchable capacity, "Pseudo-Tied Capacity." A decade or more can be required to construct new transmission lines to support imports. Also, adding imports implicitly assumes that additional dispatchable generation would be available in other regions. Within the GridClue modeling paradigm, adding generation capacity within a region but not increasing imports prevents arbitrary double-counting of import capacity among modeled regions.

Demand Scenarios

To estimate electricity demand scenarios, we use observed hourly data from the U.S. Energy Information Administration (EIA). The user-selected date range determines the Demand Profile. Users can model increases and decreases in base case demand to reflect scenarios for electrification (such as daily charging of EVs), energy conservation, demand response, and other changes. Users can also adjust default values for operating reserves and contingency reserves.

Demand increases for passenger EV adoption are estimated using households' average daily miles driven for California, paired with the selected car's efficiency rating as reported by the EPA. The estimated increase in electricity consumed is then distributed across the day based on the selected EV charging profile: Home, Work/Public, or Universal.

Credit for demand response is estimated using the most recently EIA-reported "Potential Peak Demand Savings (MW)" for each utility in California. This value is applied to the hourly demand scenario with the assumption that demand reduction could be implemented at any time.

Demand From Electric Vehicles

Components of Demand Day