USWRP Value of Weather Information in Electric Power Workshop Tasks

Task I: Homework assigned on first day

Current practice in making weather-related decisions. Participants each fill in a provided matrix of decision-vs-weather information to prepare for breakout groups.

Task II: Breakout groups on the second day

 

Breakout groups Sessions 1 and 2,

“disciplinary”

Breakout groups Session 3,

“cross-disciplinary”

  1. Load and demand + meteorologists
  2. Transmission and distribution + meteorologists
  3. Generation and power markets + meteorologists
  4. Systems: reliability, environment, regulation + meteorologists

Groups are re-formed to integrate disciplines.

 

Each new group is composed of members from groups 1,2,3, and 4.

 

Session 1:  Current Weather-Related Decision Making

  1. What weather information is most important to your sector’s decisions?
  2. How important is it?
  3. Where does it come from?
  4. How does your sector acquire and use the newest kinds of weather information?
  5. What are the difficulties in using the newest weather information?

Session 2:  Usefulness of Improved Weather Information and Decision Tools

  1. Ideally, what weather information would be needed for your decisions?
  2. What improvements in weather information, that are feasible in 5-10 years, would be most useful?
  3. What changes to reporting and communicating of this information would be helpful?
  4. What changes to weather-related decision tools would be the most helpful?
  5. What improvements in the links between weather research and your sector would help you better use weather information?

Session 3:  Integration – Projects, Testbeds, Demonstrations

  1. What are some niche applications of tailored weather information in the electric power system?
  2. What tailored weather forecasts could help the operation of the electric power system?
  3. What new weather information will be (must be) available to meet these needs?
  4. What kind of collaboration is necessary/attractive/likely to research and develop these ideas?
  5. What tools and knowledge do decision makers need in order to make the most of weather information?
  6. What relationships are necessary to increase and maintain the value of weather information?

USWRP Workshop: “Increasing the Value of Weather Information in the Operation of the Electric Power System”

 


 

Decisions that Require a Load Forecast:

Sample table for illustrating the ideal weather information needed to make various kinds of load forecasts (across the top), which support different decisions

 

Decision or Research Question

Forecast Time Scale1

Forecast Spatial Scale2

Forecast Methodology3

High Impact Events4

Weather & Climate Information5

Supply Planning

Mid-term

Long-range

Regional

Climatology

Forecast

Climate Phenomena

Heat Wave

Synoptic weather

Supply Scheduling (Operations)

Short-term

Mid-term

Local

Regional

Mesoscale Model

Observations

Peak values

Heat Wave

Convective Ppt

Lightning

Temperature

Humidity/Heat Index

Wind/Wind Chill

Cloud Cover/Insolation

Risk Management

Short-term

Mid-term

Local

Regional

?

Peak Values

Heat Wave

Expected Value

Uncertainty

Modeling Energy Use (Efficiency Studies/

Base Load Estimates)

Short-term

Local

Regional

?

Peak values

Heat Wave

Convective Ppt

Lightning

Temperature

Humidity/Heat Index

Wind/Wind Chill

Cloud Cover/Insolation

Demand Side Management Tool: Anticipating Response

…etc.

…etc.

…etc.

…etc.

…etc.

Distributed Generation: Effect on Peak Shaving

…etc.

…etc.

…etc.

…etc.

…etc.

…etc.

…etc.

…etc.

…etc.

…etc.

…etc.

1Short-term=1-2 day, mid-term=2-10 day, long-range=>10 day

2Local=1-2km, regional=mesoscale

3Forecast Methodology is not important to users, as long as the forecast is provided with a measure of its quality (“skill”)

4Forecasts of High Impact Events incorporate time of event, location, extreme values of the weather, and duration

5All weather and climate reports should contain measures of accuracy and uncertainty for strategic planning. Users express strong preference for an expected value and a confidence interval or other specific description of the uncertainty distribution.