Increasing the Value of Weather Information
in the Operation of the Electric Power System

Workshop Report
6-7 November 2002
Boulder, Colorado

Jeremy Hackney, Workshop Organizer

Environmental and Societal Impacts Group
National Center for Atmospheric Research

Sponsored by the United States Weather Research Program (USWRP, uswrp.org)

*The National Center for Atmospheric Research is sponsored by the National Science Foundation.


Click here for PDF version of report and speaker presentations in PDF.

Please cite this publication as follows:

Hackney, J., 2003: Increasing the Value of Weather Information in the Operation of the Electric Power System. Report of Workshop held 6-7 November 2002. Boulder, CO: Environmental and Societal Impacts Group, National Center for Atmospheric Research.

Copies of this report can be obtained upon request from:

Environmental and Societal Impacts Group
National Center for Atmospheric Research
PO Box 3000
Boulder, CO 80307-3000
Email: esiginfo@ucar.edu


Table of Contents

Workshop Summary
Context of the Workshop within USWRP
Background
Presentation Summaries
Discussion Group Activities
Group Reports I
Group Reports II
Potential Near-Term Projects
Next Steps
Appendi
x: Workshop Handout
Appendix: Speakers
Appendix: Participants
Appendix: References
Appendix: Pre-breakout Questions
Appendix: Glossary and Acronyms


Workshop Summary

Overview, Structure, and Goals

The vulnerability of the electric power system to routine weather, as well as to weather extremes, modifies and amplifies the direct impacts of weather on society. These direct impacts, such as winds, precipitation, and temperature, are magnified in social and economic importance when they degrade the quality of electricity, including: reduced voltage, unstable frequency, interruptions in service, downed power lines, and abrupt price fluctuations. These secondary impacts can concentrate, extend, or shift the spatial and temporal extent of the weather event. Reliable and high quality electric service is central to public welfare and economic productivity, yet much of the electric power infrastructure, decision making, and consumption, remains highly exposed to the weather. Precise advance knowledge of the weather's influence on the electric power system may substantially reduce some of the most common and expensive societal impacts of weather.

It is a rare occasion when weather researchers and information providers sit down with the users of weather information in the electric power sector. Yet, it is clear that improved weather information is critical to mitigating physical risks in this sector, and it also became clear during this workshop that the industry would like to interact more often and more closely with the providers of weather information.

Many decision makers are involved in producing and delivering electricity, with different decision protocols, different time and space horizons, and different areas of concern. However, they all share the goal of maintaining service reliability while meeting necessary revenue or cost goals. Utilities, regulators, electricity consumers, and researchers need to know how to better use weather information in order to reduce the amplification of weather impacts while ensuring a healthy business. Policy makers who seek tools to align these goals also need access to weather information in order to recognize the limits and opportunities presented by weather's impact on the system. New weather information might also be needed, challenging weather researchers.

This workshop on improving the value of weather information for electric power operations outlined and prioritized research themes in meteorology that would be the most important in the electric power system. The theme encompassed reliability, information, and decision-making, in view of challenges and opportunities in the electric power industry's emerging deregulated environment. Restructuring of the industry may provide a sufficiently competitive climate for decision makers to accept new concepts and tools that enable compensated risks related to weather and communications, such as a forward market for reliability, forecasts of equipment operating capacity, and decentralized system optimization under uncertainty.

Thirty-five representatives from the electric power industry, electric power research, academia, and research meteorology were invited to participate in the two-day workshop. The first day's presentations were divided into four areas of business in the electric power sector: Demand, Supply, Delivery, and Trans-utility systems issues. The next day's discussions began with four groups, and then the groups mingled to exchange ideas.

The goals of the workshop were to:

  • Identify and describe research projects that will demonstrate and test how specific changes in forecast quality, communication, and use can add value to electric power operations; and
  • Recommend consortia and collaborations to carry out the projects.

Workshop results

The workshop was held on 6-7 November 2002, at the National Center for Atmospheric Research (NCAR) campus in Boulder, Colorado. Thirty-five experts from the electric power industry and the meteorological sciences attended to discuss the weather information needs of the electric power sector and the collaborations necessary to improve the value of this information.

The presentations emphasized five major aspects of meteorological information in electric power: its value; which weather information is used and how it is procured; how this information is summarized and passed on to decision makers; internal and external barriers to better use of the information; and, finally, desired improvements in meteorological information and meteorologically based decision tools:

  • Keynote: Reliability and Risk, Dr. Maria Ilic, EIC/CMU
  • Visualizing the Transmission System, Dr. James Weber, PowerWorld
  • Transmission, Complexity, and Probabilistic Modeling, Dr. Stephen Lee, EPRI
  • Recovery and Restoration of an Electric Power Distribution System, Jane Preuss, GeoEngineers
  • Demand Modeling, Dr. Frank Monforte, RER/Itron
  • Load as a Resource, Dr. Michael Kintner-Meyer, PNNL
  • Distributed Generation, Dr. Scott Samuelsen, APEP
  • Operational Meteorology in a Utility, Patrick Walshe, TVA
  • Commercial Decision Tools, Rich Wilson, Meteorlogix
  • Research Decision Tools, Bill Mahoney, NCAR

The discussions were lively and productive. Several suggested action items require a partnership to enhance decision support systems. These included, in the near term,

  • the development of a common data format in archives and forecasts,
  • coupling of forecasts to GIS-based frameworks,
  • detailed analysis of the causes for failure in different weather events,
  • demonstrations of new weather products and their value in the context of industry decision making, and
  • education programs to inform the industry of cutting-edge capabilities in meteorology.

Meanwhile, the industry needs to cultivate a culture that can utilize probability in decision-making, and in the slightly longer term, in collaboration with the government sector, it needs to develop business tools to use probabilistic forecast information.

The US Weather Research Program (USWRP), which sponsored the workshop, provides critical research underpinnings needed to improve forecasts required by this sector. However, this research must be focused in appropriate areas: improving forecasts of temperature and wind in the atmospheric surface layer; urban weather; cloud cover and quantitative precipitation; precise timing of fronts; seasonal deviation from climatology; and extreme events that could alter normal operations or threaten power outages.

Accomplishments of the Workshop:

1. Identification of weather research needs for specific industry decisions

a. Improved temperature and wind forecasts
b. Urban weather (heat island effect, etc.)
c. Cloud cover and quantitative precipitation forecasts (QPF)
d. Precise timing of the arrival of fronts
e. Deviation from climatology (seasonal forecasts)
f. Forecasts of extreme events (events capable of causing disruption)

2. Identification of patterns of the use of weather information in industry

a. Perception of poor forecast reliability, non-applicable weather variables
b. Inability to use detailed weather information (time constraints, data format, quality control)
c. Culture which prefers deterministic forecasts rather than probability

3. Identification of enthusiastic extramural collaborators in industry and government

a. Advanced Power and Energy Program (APEP), University of California Irvine
b. Tennessee Valley Authority (TVA)
c. National Renewable Energy Laboratory (NREL)
d. Electric Power Research Institute (EPRI)
e. Colorado Public Utilities Commission (CPUC)
f. Electricity Industry Center (EIC), Carnegie Mellon University (CMU)
g. PowerWorld, Inc.
h. Western Area Power Association (WAPA)
i. Consortium for Electric Reliability Solutions (CERTS)

4. Identification of near-term collaborative research goals

a. Common weather data format
b. Increased compatibility of weather data with GIS
c. Continuing education in industry about meteorological capabilities
d. Development and value demonstration of probabilistic decision making and tools
e. Study of relevance of weather forecasts in consumer decisions under deregulation
f. Urban weather
g. Denser observation network with quality control and online access or delivery of data

Next Steps

The next steps following this workshop involve:

  1. Gathering more information in small workshops on specific topics, e.g., the spatial optimization of information needs, the discovery of aligned programs in the topic, and focused stakeholder exchanges;
  2. Assembling educational materials for the industry about the modern capabilities of weather forecasting;
  3. A demonstration project in the potential value of probabilistic forecasts with application to a particular business or operational decision.

Context of the Workshop within USWRP

USWRP and the Societal Impacts of Weather

The USWRP aims to bring improved, more precise forecasts to vulnerable regions and weather-sensitive economic sectors. This process involves fundamental research, public-private cooperation and collaboration, and the development and evaluation of prototype forecast products and decision support tools. In the planning process, researchers, forecasters, and the users of forecast information identify the most relevant issues with high potential for scientific progress. Through collaborative workshops, priorities, milestones, and resource needs are developed that initiate a fast track for scientific progress and transition into operational forecasting and commercially available value-added products. Seven U.S. agencies currently support the US Weather Research Program: NOAA (National Oceanic and Atmospheric Administration), NSF (National Science Foundation), NASA (National Aeronautics and Space Administration), and the Departments of Defense, Transportation, Energy, and Agriculture.

The Environmental and Societal Impacts Group (ESIG) is taking a central role in establishing the research agenda for the "societal impacts" context of USWRP research. ESIG is suggesting an approach that provides useful scientific guidance to weather researchers by examining the decision making of prepared, sophisticated, formal decision makers who rely on weather information, whose actions affect cross-sections of society in a significant way, and who can adapt their decisions according to information about expected weather conditions.

A series of meetings or less formal discussions are envisioned with decision makers in different commercial and government sectors to analyze weather information needs, the technical capabilities of decision makers, barriers to the use of weather information, and improved meteorological research or services to increase the value of weather information. Follow-up on these discussions includes grant proposals to USWRP and other sources from discussion participants and other interested stakeholders to carry out collaborative research and demonstration projects.

USWRP Sector Decision-Maker Template

This workshop establishes the following template for societal impacts projects by sector:

  • Perform background research to identify main industry decisions, value of weather information and mitigation strategies, climate decision-making, and progressive individuals to discuss these issues.
  • Identify priority research that will have a high potential to enhance the efficiency and reliability of the electric power system.
  • Guide future meteorological research in societally beneficial directions by establishing a template for collaborative public-private partnerships.
  • Form durable partnerships between researchers and managers on the formulation of strategic research plans.

The electric power sector is the first set of decision makers to participate in this process.

Background

Identifying Weather Information Needs in the Electric Power Sector

Routine and extreme weather affect electricity consumption and the performance of equipment, both of which affect the cost and quality of electricity (frequency, voltage, reliability). Utilities use weather forecasts to interact with customers regarding impacts on service, to hedge risk in financial markets, and to plan maintenance, operations, infrastructure expansion, and recovery from damage. As deregulation and restructuring proceed, the industry will need to establish new responsibilities and relationships that require new tools and information for decision-making. This workshop explored how weather forecasts and improved weather information can provide resilience in the dynamic technological and institutional environment that characterizes the current electric power sector.

Previous workshops and studies have surveyed and documented the need for improved weather information in the electric power industry (see references). Tailored weather information and decision support tools are currently available for some applications, but weather research and information is not being used systematically in the electric power sector. Besides opportunities to improve tailored weather information, barriers in technology, economics, culture, and policy could be removed to improve the value and effectiveness of new weather information.

Recommendations from previous studies included changes to weather forecast variables, forecast communication methods, use of forecasts in decision support systems, observations and databases, data formats, data distribution systems, quality control procedures, interactions between researchers and users of weather data, and more effective use of the Internet.

Trends and Issues in the Electric Power Industry

Deregulation and Related Institutional Issues

Deregulation in the electric power industry is changing the allocation of responsibilities and risk in electric power system operations and management, as well as increasing the utility of enhanced weather forecasts. Incentives in the traditional regulated industry encourage utilities to investment in physical infrastructure to maintain reliability. The deregulated industry involves decentralized decision-making. There has been difficulty in assigning responsibility for maintaining reliable service, profitability for stakeholders, and the moderation of price fluctuations. In the deregulated environment, incentives tend to encourage making profit on transactions of the lowest-cost electricity, rather than investing in software or hardware to maintain reliability. Power transfers can place a burden the grid and degrade reliability, with the consumer potentially paying the biggest penalty. At the same time, reliable electric power of high quality is ever more important in the expanding digital economy. Increased reliability is in high demand. Capturing the value of reliability will require a new market strategy and technology to coordinate new consumer relationships centered on telecommunications and control engineering. Decentralized stakeholders, probably communicating online, will need reliable, customized weather forecasts to manage both infrastructure operations and financial risks.

Grid Management

Grid management involves many entities, and stakeholders with differing expertise all need the high quality information in forecasts and effective tools and decision support systems to integrate data, plan, and communicate their actions. New tools for representing and communicating grid conditions must emphasize speed, simplicity, and accuracy. Many tools in the industry are heading in the direction of visualization, as they are in meteorology. Visualization tools in the electric power industry will combine the physical world of meteorology and electricity infrastructure with abstract notions of system performance. Emerging tools like probabilistic grid flow models seem to be well suited to integrating certain kinds of weather forecasts. Ensuring the highest value application requires ongoing collaborations between meteorologists in both the public and private sector and experts in the management of electric power system operations.

Demand Modeling

Demand, or load, modeling is a key planning tool that is strongly weather-dependent, and has become increasingly crucial to operating efficiencies in the restructured industry. Collaborative research can help determine the most valuable representation of weather variables and their uncertainty for assimilation into demand modeling tools. Under competition, and with increased consumer involvement in the market, demand side management (DSM) alternatives will also alter demand models as prices change with load and as consumers plan their own consumption more consciously, with their own weather information and demand models. This complex interaction of social and physical variables has the potential to produce significant nonlinear dynamics in local and regional demand for electricity.

Distributed Generation

Some energy customers meet portions of electricity demand with local generation in order to more precisely anticipate energy costs and to maintain power quality and reliability. Military bases, hospitals, and companies in the credit-card industry, telecommunications, banking, and computer-chip manufacturing are most active in early adoption of distributed generation. Knowledge of accurate, location-specific weather, climate, and electricity market information is necessary to model the value of this investment over the long term. If retail electricity prices vary with load, and therefore weather, this calculation can be very complex.

Optimal operation of the distributed generation depends, at a minimum, on accurate 1- to 2-day weather forecasts. When co-generation of heat (cold) is combined with distributed generation installation, the importance of the weather becomes crucial to economic performance. These relationships must be studied and new tools for decision-making refined and developed. As fuel cells for distributed generation reach market maturity, effective weather forecasts will be part of shaping a transition to a more sustainable energy economy. The economic and reliable operation of the leading renewable resource, wind power generation, is especially demanding of accurate weather forecasts and tools for planning and operations.

Presentation Summaries

Overview

The presentations took place after an overview of the USWRP from Lead Scientist Bob Gall and introductory remarks from Robert Harriss, which took note of the broad set of opportunities before the participants. Many speakers echoed the theme that better weather information's highest value is enabling more efficient and coordinated use of resources, which in turn increases the reliability and productivity of the electric power system. Long-range planning is governed by national and local economics and policy and by technologies used in the local service area; medium-range decisions are dominated by maintenance planning, which is affected by exceptional (not usually expected) weather events; and roughly 20% of the value of industry decisions in the short term is influenced directly by weather. Better weather information cannot guarantee total weather resilience of the power system, however, because there is an economic limit to the practical defense against weather extremes.

Value of an Accurate and Timely Weather Forecast

The financial stakes of an accurate weather forecast can be astronomical, even if reliability is not affected. The impact depends on the decision made, as well as the type of weather phenomenon. For example, the release of water from a dam in response to a forecast of 2 centimeters of rainfall in the drainage above the dam can cause a 7-figure loss if that rain doesn't fall. But the amount of precipitation has little effect on planning for electricity demand unless it is tied to strong changes in temperature or cloud cover. So, advance knowledge of the rainfall is of much less value for the demand-driven decisions than it is for the supply problem of running the hydro dam.

Use of Weather Information

Speakers reinforced previous findings that weather information is difficult to use operationally in this industry, and that utilities face high costs (learning, hiring, computing) to make productive use of weather information. The expense is usually prohibitive for small utilities to use formalized weather information at all. Weather information is used across utilities for similar decisions. In different utilities, this is often done with different decision tools and different information sources. This is because of the differing influence of local weather patterns, terrain, local electrical technology (consumers and infrastructure), demand patterns, the electric market structure, the size of the utility, and its connections with other utilities. However, load planning, using demand (load) forecasts and models, are particular exceptions in which common products and common sources of weather data are used across utilities. To a first-order, the day-ahead peak of the dry bulb temperature is the most important weather variable in this forecast.

Characterizing the Decision Makers

In general, industry decision makers using weather information are decentralized, have little meteorological training, occupy various positions in the company, are responsible for different regions, and come from various backgrounds. This situation will likely continue under deregulation. If consumers influence the electricity market in the future via innovations like demand management contracts, reliability service contracts, and distributed generation, accurate and symmetric weather information will be critical for efficient markets.

Internal Barriers to New Weather Tools and Information

Culturally, the industry is conservative, partly because it has only been rewarded for "keeping the lights on," but not for innovative or operational efficiency, as was pointed out by more than one participant. Decisions are made in a deterministic framework, and there are long-standing practices to employ deterministic (expected value) forecasts. Operational decisions are proprietary secrets, and security of information is a concern. New concepts such as probabilistic forecasting, web-based communications, and increased automation will require cultural change before they are adopted.

External Barriers to New Weather Tools and Information

Economically and politically, deregulation of the industry may force separate generation, transmission, and distribution companies to market themselves to customers by combining cost, reliability, and innovative services. Under pressure to compete, the decision-making culture may be flexible enough to adopt new tools and new modes of thought more rapidly than the vertical monopolies, in an effort to optimize operations and utilize infrastructure instead of investing in new infrastructure. Markets are incomplete, and technical and economic tools must continue to be developed.

Improvements Required to Weather Information

Meteorologically, the needed improvements to routine weather forecasts focus on the accuracy of a few parameters (dry bulb temperature, dew point, wind speed and direction, cloud cover, and quantitative precipitation), and a few issues of time and spatial scale (urban and point locations, spatial grids under 1 km, hourly to 3-hourly time series forecasts, 1- to 5-day horizon, and correct timing of fronts). Forecasts of extreme events could help in recovery planning from damage, but may not prevent outage.

Summaries of the Presentations

Keynote: Reliability and Risk

Maria Ilic delivered the keynote address, which set the context of weather discussions within the framework of reliability and risk under the differing incentives of the regulated and restructured industry. Technological and organizational developments in the industry may be leading it toward decentralized management of physical and financial risks, as well as adding to the difficulty in defining quantities like reliability and optimized operations. Technological advancement will enable, and industry restructuring will force, the development and merging of regional electricity markets with the physical electric power system. Integrating these two systems will require new technology, new definitions, and transparent information about the state of the electric power grid.

Competition will rely on decentralized stochastic optimization and feature separate functional and/or corporate entities for power supply, delivery, and consumption; decision making under uncertainty will be decentralized and iterative; temporally and spatially active price signals will close the loop to engage demand with supply; a potential will exist for valuing the right technologies for operations and control; and there may be issues with reliability and long-term system evolution. The role of information technology and its research is hypothesized to be critical in this communication between these actors and in controlling the operations of the system.

Open research problems include developing tools for moving from sequential operations/planning to interactive iterative decision making over various time horizons; the type of and rate at which information is provided; the realization that electric power reliability is a dynamic, not static, problem, and illustrates that market failure is related to a lack of adequate information. Reliability may become a private property, and reliability insurance may be a component of the new market. Weather information and weather tools overlap industry needs in developing smart components and smart controls in the distributed stochastic optimization of operations and planning.

Visualizing the Transmission System

James Weber emphasized the need for graphical depiction of grid loads and demonstrated the utility of graphical user interfaces in case applications by illustrating contours of local marginal price and line power transfer distribution functions (PTDF, the measure of where a "shipment" of power from point A to point B actually ends up on the grid). The systems previously built to serve local areas are now carrying long-distance shipments that could affect operations far from the origin and consumption of the electricity. The stakeholders interested in power issues are now therefore much broader, and a graphical interface showing derived parameters helps non-specialists to understand the system's performance. The tool could be useful to identify areas for optimal placement of new generation capacity, faulty transmission equipment, abuses of market power, and operator training.

Transmission, Complexity, and Probabilistic Modeling

Stephen Lee discussed the complexity of transmission grid operations and presented a probabilistic model of power transfers. Stakeholders would benefit from knowing the effect of transmission limitations on their activities in that community. The Community Activity Room (CAR) is a metaphorical concept for treating these limits as walls of a room within which market activities can freely move around in order to reach equilibrium. The ability to analytically describe and visualize the CAR in the state space of net interregional power exports will enable greater understanding of the need for more interstate transmission superhighways. This metaphor will also enable transmission planning and operation for a large interconnected power system to evolve to the next generation of probabilistic power system reliability assessment and integration of reliability and market efficiency. The weather also influences transmission capacities, outage probabilities, and scheduling of wholesale power transfers. Better forecasts of wholesale power transactions can lead to greater reliability and cheaper electricity prices. When an online probabilistic CAR is displayed for the entire community to see, it is quite conceivable that reliability can be turned into an equivalent energy product that has monetary value. It is the clear path to reliability insurance or reliability contracts.

Recovery and Restoration of an Electric Power Distribution System

Jane Preuss illustrated the close interaction between urban tree-trimming and land-reclamation practices with failures in distribution lines under natural conditions such as extreme weather and earthquakes. Urban and suburban policies to manage vegetation around distribution lines may have more effect on reliability than the weather. This project analyzed the potential role of external variables influencing recovery and restoration rates for an urban electric power utility, with a focus on recovery rates for the distribution system. Outage data have been obtained for five events in the area serviced by an urban utility that agreed to provide the principal investigators with basic data obtained from field logs that identifies outages by location (feeder) and cause. Data pertaining to feeders could be correlated with neighborhoods. Outages due to tree failures during both windstorms and the earthquake have been correlated with municipal boundaries, which are more restricted than the entire service district. Tree-related failure of power lines, poles and other distribution equipment occurs more often outside of the Seattle City limits. The City of Seattle Ordinance 90047 prohibits specific tree species with invasive root systems and species that are brittle and break up in wind. It also has adopted guidelines for how far trees must be planted and trimmed back when located in public areas where lines will be posted. Service restoration rates should be based upon empirical data rather than expert opinion to be realistic in planning for disaster recovery efforts.

Demand Modeling

Frank Monforte presented a cascading regression of load data versus weather variables to specify a load model, in which a base model independent of weather explains the day's load with a minimum absolute percent error (MAPE) of roughly 3%, which can be improved to 1.33% using recorded average dry bulb temperature. Additional improvement can be achieved by using time- and location-specific temperature. Other variables, such as dew point, cloud cover, and wind speed provided less predictable improvements to the models. Most of the demand can be forecast by knowing past demand and cultural and climatic indicators, such as the day of the week and date. Knowledge of the evolution of the day is more important than knowing just the coincident temperature.

Using forecast temperature as input to the model, which comes at 3-hour averages at airport locations, requires interpolation to hourly intervals and to local grid scales, and results in MAPE of 5% rather than the 1.33% obtained with historical hourly data. Better demand models will require more accurate forecasts of dry bulb temperature at the micro-spatial scale where the electric demand actually takes place, not at airports that may be miles away, and on hourly time intervals, though higher frequencies would be better. The secondary weather variables needed depend on the region in which demand is to be forecast (local technology, local weather). The critical forecast period is from 1 day to 10 days. Beyond 10 days a forecast of the weather patterns is necessary. Beyond two years, a weather (climate) forecast is not useful for demand modeling, as the state of the economy and adoption of technology by consumers takes over in significance. Specific changes that could be made focus on technology, data quality, and service. Doppler radar images are often in utility control rooms, and operators are able to convert this heuristically into an effect on their system load. How can we put this skill into an artificial neural network (ANN) or a load model statistic?

Load as a Resource

Michael Kintner-Meyer of PNNL explained the concept of load-as-a-reliability-resource (Consortium for Electric Reliability Technology Solutions [CERTS] research project), in which commercial customers are compensated for forgoing electric service. The peak load hours are brief, but the bulk of the infrastructure investment goes into maintaining reliability during these peaks. Load relief is an alternative to this investment, but it is hard to sell facilities managers on reducing consumption. Motivation for participation in compensated emergency demand reduction programs include the fact that it provides the image of good corporate citizenship, and it is clear how it saves money.

In determining the capacity to reduce demand, the solar radiance and a temperature record (dry, wet bulb) are the most important variables, in combination with the thermal response of the building (system inertia). Weather and thermal data are needed for at least 3 seasons in order to plan the demand reduction strategy. About 1 GW of the 820 GW in the US (0.1%) consists of bid load reductions that are used and compensated. A thermal load forecast would be part of a portfolio of tools and technologies to reduce system load, including: on-site generation, thermal/electrical storage, pre-cooling/pre-heating to shift power demand from peaks, shift of manufacturing away from a plant with high electric cost, and financial risk management like weather derivatives.

The loads (consumers) of tomorrow will be actively engaged in electricity and derivative markets in a pervasive information technology (IT) control and communications environment with central control of load and distributed intelligence. Weather adjustments are currently made after the fact. They need to be included in the system in time for the market decisions. The practical time horizon for weather information to be useful is half a day to tens of days.

Distributed Generation

Scott Samuelsen presented details about distributed generation (DG) of electricity and heat with gas micro-turbine generators and fuel cells, with scenarios for widespread use of DG in grid-connected office "power" parks and hybrid distributed generation systems which might include personal transportation, micro-turbine/fuel cell combinations, or PV/fuel-cell combinations. DG set close to a load can increase the reliability of the electric supply by eliminating the delivery system that is subject to overload and failure, by providing a local source of heat, and recently, as a source of income in demand response or grid voltage support strategies. Cleaner DG technology and falling grid reliability have made DG a more popular alternative. Quiet, clean DG presents a reversal of the trend of giant power generators away from urban regions and a return to generation in cities, potentially changing the utilization of transmission lines. Cogeneration of heat might also alter the system electric loads. Power parks are one strategy to propagate DG. They would be office parks along highways with good natural gas infrastructure and fuel cells to generate electricity and heat, nearing 80% efficiency and with low emissions.

IT is at the heart of the DG system for local/remote dispatch control and local measurement of dry bulb temperature and relative humidity. Forecasts of these variables are a critical input to load profile control to plan the use of electricity generated at the site versus the amount exported, and the use of the waste heat recovery (heat, cooling, processing). Solar insolation is important for renewable DG that uses PV and hydrogen storage, and forecasts would help manage the stored hydrogen.

Operational Meteorology in a Utility

Patrick Walshe is the staff meteorologist at TVA. He described temperature and demand forecasting in a large utility. TVA has the additional responsibility for maintaining acceptable water flows for recreation and natural preservation of the Tennessee River drainage.

TVA has one person making weather forecasts, which are converted to an energy plan, essentially for today and the next day, with a 10-day outlook. TVA purchases weather information from a vendor with whom Walsh is in frequent contact. With Walsh's corrections the ANNSTLF load model (TESLA model is in testing) is used to make the day's forecast for energy demand. This demand amount (MW) is passed on to energy schedulers who make generation commitments, after which the power system operator, the river system operator, and marketers plan efforts to accommodate this generation commitment.

TVA's weather information consists of a rolling hourly forecast of temperature and relative humidity for 5 cities in its service area, out to 240 hours from the present hour's forecast (10 days). The value of a correct or incorrect load forecast, especially combined with potential savings for efficiently scheduled maintenance and river operations, can be very high (6-7 figures) for precipitation and 5-6 figures for maintenance. The significance of errors in forecast temperature are less when the temperature is 65 degrees Fahrenheit, and increase if it is warmer or colder, up to 350 MW/degree. Better load forecasts would result from increased frequency of reporting current data, and improved accuracy of short-range meteorological models: MOS, local mesoscale effects, cloud cover, and precipitation forecasts (especially in summer convection, which affects peak load forecasts).

Commercial Decision Tools

Rich Wilson presented the weather information and decision tools commonly offered to the industry by private vendors. Meteorlogix, WeatherBank, and WSI are the main providers. Typical products include automated load-forecast systems, integrated OMS/EMS technologies, enterprise GIS, improved local forecasts (2-5 day), improved prediction of high impact weather events, and long range seasonal and climate forecasts. The responsible individuals who purchase and maintain weather information tools are typically the vice president or manager of operations, dispatch manager, IT manager/analyst. Almost 95% of their business is with regulated utilities, where the trend is to use more GIS-integrated tools, especially for transmission/distribution applications.

Co-ops, municipalities, and other smaller gas and electric utilities are a potentially large market in need of weather information and decision tools, but they are generally priced out of the integrated systems that are developed for large utilities. Meteorlogix has not emphasized Internet services, though this seems to be an easy and promising medium to use, and it would be the method of access to smaller companies. Meteorlogix has a continued interest in open-architecture structures and web-based solutions. Decision tools are designed to suit market conditions above all.

Most Meteorlogix energy clients do not employ meteorologists directly, and consider Meteorlogix meteorologists as part of their company's team. Meteorologists in the private sector are trained with models and they often rely on them more than their judgment. The 2-5-day forecast horizon is becoming more important as forecasts improve. No significant improvement in 1-day forecasts has been seen recently.

The variables in which Meteorlogix customers demand improvement are: temperature, precipitation, dewpoint, and other specific and individual result from a consensus forecast. Standard products that nearly all utilities use are degree-day forecasts, historical data, real-time weather, short-term and long-term (climate) forecasts, and model data. To improve weather forecasts for utility decision making, Meteorlogix needs denser networks of weather stations, NWS preferred, and the frequency of reporting (hourly data) needs to be increased. Direct integration with user's GIS platforms would be valued by customers.

Decision Tools: Research and Technology Transfer

Bill Mahoney presented decision support systems (DSS) from a general research perspective, based on experience in aviation, range testing, water resource management, emergency management, and road maintenance decision tools produced at RAP/NCAR. RAP specializes in developing solutions to specific problems and transferring the technology from research to US government agencies, private sector, and foreign governments. Classifying decision tools by purpose can be helpful: strategic, tactical, operations management, incident management, "what if" scenario analysis, or training tool. A tailored solution is necessary and a bottom-up approach works better than top-down, beginning with stakeholder needs and available data and technological capabilities in the industry, and iterating the system with testing and customer feedback.

The weather information needs of the electric power community are highly specialized and not traditionally recognized in weather research. Probabilistic forecasts could help: what is the chance that temperature could exceed a certain threshold? The scientific challenges appear to be in boundary layer meteorology, thermodynamics, new probabilistic and statistics metrics, numerical modeling, and verification with limited data. Untapped IT and weather forecast capabilities offer significant opportunities to help. DICAST is a 6-model mesoscale forecast ensemble (probabilistic forecast) with fused data inputs developed and used at RAP. GIS systems used in road safety foreasts are integrated with RAP decision tools.

Wind Energy Potential and Coupling with Hydropower

Bob Gough presented the potential for wind electricity generation in the vast Midwest wind resource, and suggested weather information that could help advance this technology. 75%-100% of the US electricity demand in the lower 48 states is available in the wind of the Midwest alone. South and North Dakota are downwind of much of the US electric demand. If wind power were used to its potential, Kyoto targets could be met (200-250 GW). "Basin Electric" provides 25% of the electricity in the Great Plains from 6 WAPA dams on the Missouri River. There has been a precipitation shift: old data may not be helpful for projections of future hydro production. WAPA has a 20-year planning horizon. Wind power could be used to complement H2O power. A participant suggested working with meteorologists to get economic value of wind and understand the system to get congressional support. Policy issues might be barriers.

Oklahoma's Wind Power Initiative

Tim Hughes presented Oklahoma's progress in promoting wind generation, Oklahoma Wind Power Initiative (OWPI) (www.seic.okstate.edu/owpi/). He announced the Oklahoma State Energy Plan Meeting, which showed a big change in policy attitudes: earlier the State was only concerned with its gas and oil economy and did not want to make renewable energy plans.

The Oklahoma MesoNet has 114 stations with wind data at 10 meters elevation. Unfortunately not 100m. OWPI has made maps of Oklahoma's wind resource, based on MesoNet records and extrapolated to 50 m height, overlaid with transmissin lines. The next step is to work with people who have 100 m towers to verify the extrapolation to 50 m that is presented on the maps. NREL has worked on the low-level jet stream, and blades of wind turbines today reach over 100 m high. A participant asked what the cost of a MesoNet station was and if utilities could buy them? There were really good economies of scale for the 114 stations, about $7000 per station 10 years ago. Today they're over $10,000 apiece. OU/OSU did its own installation and instrumentation to save money. Law enforcement cooperation saves $1 million a year in communications costs by permitting the use of its radio frequency and repeater links to communicate automated measurements. The system has a 15-minute update including 5 min & 15 minute soil moisture averages.

Discussion Group Activities

The participants had challenging instructions for the discussions. First, they were to outline the current practices for using weather information in their area of expertise. Then they had to anticipate improvements that their colleagues could make, given improved weather information and/or decision support systems. And finally, they were to suggest improvements to weather information or its use and the means for achieving these improvements. Meteorologists made sure that the wished-for improvements remained realistic within bounds of scientific capability.

Participants were asked to develop well-defined recommendations, to think about specific issues, and to speak in a vocabulary that could be understood by all disciplines. They were to avoid issuing a "laundry list" of dreams and were asked to attempt to place a value on improved weather information. They were also asked to estimate necessary scales in space and time that would be needed in new weather information. Their thoughts were to be framed in terms of the USWRP research and technology transfer foci: operational test beds, fundamental research, demonstration projects, and case studies. They were asked to outline long-term issues, such as how to keep people involved in joint research, as well as short-term needs; to consider scenarios and to keep a holistic viewpoint, including policy variables. They were to consider their discussion as a contribution toward writing an RFP. The instructions are in an appendix to this document.

The four breakout groups

Participants were divided into four groups, representing the four distinct operating and decision-making groups in the industry today:

  • Generation/Supply
  • Demand Forecasting
  • Delivery (Transmission/Distribution)
  • Regulatory Oversight & Markets for issues that reach over the boundaries of a single utility.

Pre-breakout preparation matrix

Before breaking out into groups, participants were asked to outline the top decisions and associated weather concerns in their area of expertise, in preparation for the breakout group sessions. On the whole, the needs written down by the participants before the group discussions agree with the needs brought out in the presentations.

As in the presentations, there is considerable variation between participants' opinions with respect to what weather data would be helpful for making what decisions. This is consistent with the different operating environments within the utilities and the responsibilities held by the participants. There is also no specific mention of the need for probabilistic forecasts, though the concept of a "distribution" of forecasts is mentioned several times. The value of combined physical weather variables arises repeatedly: wind/temperature, humidity/temperature, wind speed/direction, temperature/duration, reflecting the need for indices that contain more information than single-variable reporting. The matrices, as they were filled out by participants, are in an appendix to this document.

Breakout group questions

Each group was given the same questions. Questions 1-5 focused on bringing out the weather-related decisions and their value in each area of decision-making, and were to be answered in the first breakout session. Questions 6-11 for the second session demanded consideration of what changes in weather information would be most useful, how useful, and why. Questions 12-17 were intended for the third breakout session and focused on how to go about making improvements in weather knowledge, reporting, strategies, and decision tools:

  1. What weather and weather information is important to your sector's decisions? List top decisions and the weather/weather information.
  2. How important is the weather information [can you react to it; what is its value in making the decision]?
  3. Where does the weather information come from for these decisions?
  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 [institutional barriers, data formats, timeliness, accuracy, etc.]?
  6. If ideal weather information were available, what weather information would be needed for your important decisions [List decisions. At what planning horizon would perfect information be useful, what variables would be of interest, what value would ideal weather information have, etc.]?
  7. Of the improvements in weather information that are feasible in 5-10 years, which would be most useful? [lead time vs. accuracy, spatial density, ensembles, etc.]
  8. What changes to reporting, archiving, and communicating of this information would be helpful? [frequency of reports, on-line updates, interactive forecast products, report confidence, etc.]
  9. Would better and more useful weather information change the importance of decisions you make?
  10. What changes to weather-related decision tools would be the most helpful?
  11. What improvements in the links between weather research and your sector would help you better use weather information? [keeping informed about new research, web info resources, tech transfer programs, etc.]
  12. What are important niche applications of tailored weather information in the electric power system? [summary of discussions in Sessions 1 and 2]
  13. What decisions in the overall operation of the electric power system could be improved with tailored weather forecasts? [How do you integrate the summary above]
  14. Outline what new weather information is necessary to meet these needs? [tie if possible to USWRP research]
  15. What kind of collaboration is necessary/attractive/likely to be successful in researching and developing these ideas?
  16. What new tools and knowledge do decision makers need in order to make the most of weather information?
  17. What relationships are necessary to increase and maintain the value of weather information?

Group Reports I

Overview

The groups were eager to address the issues as a big picture rather than in individual questions, so they tended to answer multiple questions in a single answer. As a result of this rapid progress, the final (third) group session was replaced by a plenary discussion of "Next Steps" to pursue this research and development topic.

This first group report focuses on questions 1-5. The reports emphasize that weather forecasts vary in value according to the weather phenomenon, the decision, the timing of the forecast, and the ease (cost) of using the forecast in a decision protocol. In general, weather forecasts are used to improve productivity by avoiding failure or recovering more quickly from it, to avoid planning maintenance that would compromise the system's ability to cope with unexpected demand spikes, and to schedule the lowest-cost service at a particular time (grid management and scheduling generation). The latter application is recognized as having the highest value and most common application of weather information, because the short-term nature of these decisions means they are well supported by accurate short-term weather forecasts. Weather events that can cause grid failures such as icing, highly localized winds, or lightning, are more difficult to predict, and their forecasts are useful but less valuable because of their lack of specificity. Longer-term forecasts that might be useful for planning maintenance are also less accurate and therefore less valuable than day-ahead forecasts.

The industry's decisions are characterized by conservatism and the desire to minimize loss because of the high social consequences of failure. The weather forecasts used are based on NWS forecasts, either directly or as they are presented on television or in local papers. This information is often supplemented with local observations and corrected according to experience. None of the groups reported widespread use of automated decision support systems. The group participants did not identify any method for regular knowledge or technology transfer from research meteorology to operational forecasting at a utility, regulatory bureau, or large electricity consumer.

Demand Group Report I

The demand group considered the problem of predicting electricity demand (load) from the point of view of a system operator and also from the point of view of a building energy manager, giving two interesting and distinct sets of weather needs and values of this information. System-wide load estimates are made on the time scale of one-half to 10 days, with hourly updates. Building (facilities) controls need more frequent updates in order for the heating and cooling system, as well as service curtailment contracts, to be used efficiently. Weather information for system-wide load estimates are NWS forecasts, often supplemented with local measurements, and/or purchased at higher spatial resolution from private vendors. Facilities need to supplement local forecasts with site-specific data that they measure themselves. The two kinds of load estimates use the same driving variables, however: typically the day's average dry-bulb temperature and relative humidity. Corrections are sometimes made for wind. In each case, the load model must have better than 5% accuracy to have value, but every 1% improvement in accuracy below 3% is extremely valuable, particularly in temperature extremes. One participant remarked that, for demand forecasts up to 5% accuracy, weather information is of practically no value, whereas every % improvement below 2% accuracy is worth millions of dollars. Accurate forecasts where the temperature is more than 10°F above or below 65°F are far more valuable than forecasts when temperatures are near 65°F. System-wide and facility-based load models would benefit from the same kinds of improvements in weather forecasts: microclimate adjustments; common data format (XML was suggested); more and better representation of urban weather; and improved forecast of precipitation.

Discussion of the Demand Group Presentation

Discussion focused on how this presentation emphasizes that weather information needs are user-specific. In addition to the needs of utilities and the facility managers outlined here, ISOs and regulators (e.g. NARUC) could also use demand models to monitor or influence electric markets and reliability. It is important that the ISO understand the market and what the motivations of the ISO and other members of the electricity community are for using this information. Dynamic game theory with weather information may help model load curtailment bids for advance knowledge of the markets, for instance.

Another line of comments and questions were aimed at the demand forecasting activities at a utility: "How good can the demand forecast get with perfect weather information? How much would it be worth? Who makes the adjustment to demand forecasts: meteorologists, the financial sector decision maker, or the power scheduler?" The value of the forecast is hard to estimate and it depends on the market and the action that the utility takes in response to the model. The ANNSTLF model, which is not a regression but a neural network, has been tested at TVA. Its accuracy depends on the system in which it is used. 1.5%-2% accuracy is typical with perfect temperature information. But people's habits change in a power crisis. It's therefore hard to get a regression to work well in such cases because of the limited data available for regression. Finally, the adjustments to demand forecasts are made by the staff meteorologist at TVA up to 2pm for the following day, but this specific activity varies by utility.

To reach new audiences, or to introduce new weather forecast tools to demand forecasters, participants suggested that research meteorologists attend facility managers' association meetings. The importance of nice graphics should not be underestimated in this endeavor. It was also recommended that research meteorologists come watch operational decision making in a utility and see the difficulty with using weather forecasts in practice.

Transmission and Distribution Group Report I

Transmission and distribution both involve wires, and both kinds of wires are threatened directly by very high winds and ice storms. But each network is threatened by different secondary effects from weather, has different consequences if it fails, and their managers have different responses.

Transmission networks are subject to failure when elements are overheated by large transactions of power. This practice of sending electricity over long distances is increasing at an alarming rate as deregulated systems enable energy traders to buy the lowest-cost power and sell it for more, regardless of its origin and destination. Overheated transformers fail structurally, while wires can expand and sag into obstacles, causing short circuits of the very high voltage. Otherwise, transmission wires are high enough that they are not normally endangered by ground-based obstacles such as trees. In addition, transmission towers are often remote and difficult to monitor and repair. They can be subject to secondary weather-related hazards like wildfires, flash floods, and landslides (Hawkins, CAISO). Failures of transmission grids result in power outages for thousands to millions of consumers. On the positive side, the power-carrying capacity of a transmission line or transformer is increased by wind, as this cools the wire.

Distribution wires are lower voltage and lower to the ground. They are threatened by weather primarily through the action of falling or breaking trees. They affect hundreds to tens of thousands of consumers from the neighborhoods level to portions of cities.

The transmission and distribution group noted that weather-related decisions in this area currently focus on avoiding blackouts and restoring service after an interruption. One use of weather forecasts is mobilizing crews ahead of time to perform repairs in the event of a major weather disturbance, such as a hurricane or a storm that could produce icing (which can pull down both

kinds of lines). Regular maintenance must also be planned by taking unusual weather into account, so that the system is not undergoing major maintenance during a time of high electric demand. A scale called a "line rating" relates the maximum amount of power a line can carry at a given air temperature, and forecasts are sometimes used to curtail power shipments before they can exceed these ratings. This practice reflects the risk-averse decision making in this industry.

The group looked to the future to dynamic line ratings, based on weather forecasts and local temperature and wind observations, in order to make the most of the existing infrastructure. Accepting probabilistic forecasts of loads means that lines may be permitted to operate for short times at capacities over their fixed rating. Better weather information in the hands of transmission and distribution operators would also let them better plan the use of curtailment contracts, giving more advance notice and choosing the most advantageous contracts to activate. In this respect, distribution and transmission decision makers had needs in common with the demand forecasting group.

Currently, weather forecasts come from the mass media. There is a culture of conservatism because the positive rewards for innovation are outweighed by the negative impacts if the system fails. Therefore, acceptance of new forecast tools will be slow because of a perceived risk with no apparent benefit. Research money is also unlikely to be allocated to this kind of research within utilities unless managers can be convinced of the value of forecasts. If accepted, the useful weather information would include local temperature and wind forecasts, and forecasts of icing. Weather conditions across the grid might also be of interest, because this would tell operators where the demand and supply, as well as transmission bottlenecks, could be in the next day or two. Also, outlooks up to 2 weeks ahead would help re-schedule maintenance to avoid weather disturbances or weather that would drive high electric demand during the maintenance window. Long-term wind information might help municipalities with tree planting and pruning guidelines to protect distribution networks.

Discussion of the Transmission and Distribution Group Presentation

Discussion reiterated the importance of a consistent data format in accomplishing any goal that would integrate weather information with decision making. The fact that many transmission decisions are fundamentally probabilistic (likelihood of outage), and that future weather information may also be available in a probabilistic format, was also a common theme in the discussion.

Probabilistic information could be very valuable for planning the state of the system and for forecasting its reliability. Estimating the probability of a storm outage allows an ISO or utility to plan for and warn about the likelihood of curtailments, which are inconvenient, in order to prevent widespread blackouts, which are disastrous. Probabilistic models would help in siting transmission with respect to cooling winds, or with respect to distributed generation that is influence by weather (e.g. wind turbines), and to anticipate the distributed generation that will be running in certain weather conditions, since these large consumers will buy from the grid if that is more expedient. Expressions of both spatial and temporal overlap of weather and infrastructure would be needed. A "risk portfolio" based on demand, generation, and transmission would result.

The adoption of probabilistic forecasting faces barriers. Forward-looking individuals must be identified and convinced of the value added by such forecasts. One participant noted that there is inertia to overcome, and that transmission operators don't use weather information at all today except for dispatching crews after a failure. The need to see a link between a weather forecast and an application that they make regular use of. At the root of it, a participant remarked, any forecast is essentially a probability of an outage. A participant suggested that using probabilistic forecasts, but expressing only what is highly certain about the forecast to a decision maker, could diminish the cultural resistance to using forecasts of reliability, say the dynamic thermal rating. However, it was pointed out that, ultimately, someone would have to take the responsibility for making a decision based on the forecast instead of on the old static limit, which would hinder innovation.

Participants noted that EPRI already has technology for real-time dynamic line ratings, and that this investment is relatively inexpensive compared to replacing wires with high-tech alternative materials that sag less when heated. A 10% increase in rating that may be possible with dynamic line ratings could mean a very large financial return. Such a demonstration may be what is needed to usher in probability and forecasting into this sector of the industry.

Generation and Supply Group Report I

The most important application of weather forecasts in electricity supply decisions is estimating the price of electricity ahead of time to gain a market advantage. This estimate is strongly based on the estimate of the demand for electricity, since supply availability and costs seem to be well known within a utility. A participant noted that forecasting is the principal element of business economics, and the demand estimate is the "lifeblood" of such a forecast. Suppliers must balance air quality, fuel consumption, electricity price, operating cost, maintenance, and the management of hydrological resources, to make profit. Failing to correctly anticipate the necessary supply forces a supplier to buy or sell on the volatile spot market, which greatly reduces the predictable revenues. Miscalculating supply is considered to be less a problem for reliability, and more a problem of controlling operating costs or losing advantage in the power market. Occasionally however, badly timed generator maintenance can result in blackouts.

The weather forecasts are the official local NWS forecasts for the nearest locations, and forecasts from private vendors. At TVA, they are modified by local temperature measurements according to a regression model. When hydrological forecasts are valuable, these forecasts are usually supplemented with a system of rain and flow gauges maintained by the utility. Precipitation forecasts are notably unreliable, however. 4-10 day forecasts are used to decide whether or not to defer planned maintenance, while seasonal climatologies are used to plan hydro resources. The highest value weather information for supply is next-day to 10 days.

Suppliers use commercial demand models and supplement them with their own information to make a marketing plan one day ahead of when the power will be needed: dry bulb temperature (the key index), dewpoint, wet-bulb temperature, winds, cloud cover, and precipitation are important variables. TVA uses a deterministic value for the forecast, and does not employ distributions. To learn about new meteorological knowledge and tools, industry meteorologists take advantage of invitations to demonstrate and evaluate new decision tools or new forecasts. This system relies on the staff meteorologist "keeping an eye" on the AMS newsletter, for example. Weather information could be more useful if it were not necessary to first have a middleman render NWS forecasts into a useful decision-supporting format. Automation would be helpful, for which a consistent reporting format would be necessary. Accurate precipitation forecasts would be extremely valuable for managers of hydroelectric dams.

Discussion of the Generation and Supply Group Presentation

Some follow-up discussion focused on the types of generation and its weather-dependence. Hydrological generators and wind turbines are not the only weather-exposed costs in generating electricity. The efficiency of thermal generators is also affected by weather. Gas turbines are not monitored and rated in real-time. The industry is very resistant to this idea. Ramping rates and other secret information must be protected. A participant suggested that this idea could be turned around however. The performance that could be expected under certain weather conditions could be calculated, illustrating the value of a weather forecast to the generating companies with respect to the efficiency (fuel efficiency) of their generators. A compenent of fuel price could also be included, since the price of natural gas will also vary with the weather.

Systems Group Report I

Value of Weather Information: Variables, Timing, Decisions

The systems group presented their discussion in a table format. The table is in an appendix on the website. The systems group has a longer time horizon in their point of view:

For the demand forecast decision, 1 day (operations) and 1-20 year (planning) forecasts are valuable. Temperature is the most important variable, along with cloud cover, at the sub-utility/utility/regional scale. The highest value improvements in weather forecasting in the electric industry would accrue here, over the 1-hour to 10-day range.

For wholesale purchases and trading decisions, 1 hour information is useful in the spot market, 1 month-1 year outlooks are useful for planning, and 1 day to 2-10 day forecasts are needed for most market decisions. The latter are of very high value. Weather forecasts have somewhat less value in the shorter and longer time frames. Temperature and duration of temperature is important, at the regional scale.

Precipitation forecasts are useful in the long-term > 1 year (for capital investment), and in operations (at outlooks of < 1 month). The shorter term forecasts carry high value, but the longer-term capital decisions are worth even more.

For planning recovery from severe weather, 1 hour to 1 month forecasts (ice storms/hurricanes) that help schedule and place crews have high value. Knowing the probability of extreme events over the long-term (years) has about the same value.

For managing fuel resources (e.g. gas storage), forecasts of temperature for > 1 day-1 month are most useful, and this decision has a high value.

This group sees weather-related decisions in transmission and distribution planning as less valuable, and the need of forecasts is long-term (> 1 year). The resolution ranges from the sub-utility (distribution), through utility, to the regional scale.

Discussion of the Systems Group Presentation

Discussion of the systems presentation focused on engaging utilities and decision makers in improvements to tools and information. Utilities are diverse across service areas and in the equipment that they and their customers use. GIS representations are very important. How utilities make decisions must be taken into account when changing weather data/information. With regard to setting up meetings with utilitites, since many were invited to this workshop and they showed less interest in coming than expected, attending the meetings of the associations might be a place to start. Finding interested individuals, perhaps starting with NARUC and EPRI as a conduits of communication, is important.

Group Reports II

Overview

Instead of answering the remaining questions in order, groups chose to report weather information needs and decision support systems according to the highest need and best chance of successful improvement. Refer to the questions to understand the context of these reports. Finally, each group recommended research themes and even specific projects. Additional comments were made by participants in the plenary session. The results are recorded here.

Demand Group Report II

The demand group provided answers to the questions for Session II. Their concerns focus on the need for a common data format with cleaner data, and tailored products geared toward urban locations and fine spatial scale (microclimate). Products with quality graphical representations would be valuable. Maintaining contact with the weather research sector should take place via education, collaboration, and demonstration projects.

Ideal Weather Information and Decision Improvements

  • Microclimate adjustments (facility-specific forecasts)
  • Data presented in common format (XML standard)
  • More and better urban representation
  • Precipitation forecast improvement

Most Useful Feasible Weather Improvements in 5-10 Years

  • Long-term regional trends (linkage to capital improvements & facilities)
  • Improved spatial and density characteristics of forecast
  • Weather products tailored to user
  • THORPEX experiment

Changes to Reporting, Archiving, Communicating

  • XML standards for data protocols
  • Internet portals
  • Common data acquisition point

Changed Importance Of Decisions

  • Building facility manager software
  • Collaborations with DOD/DOE, co-funding
  • ASHRAE standards
  • Better integration with observed data (i.e. from schools) yields better coverage & microclimate forecasts which increases usability

Most Helpful Changes To Weather-Related Decision Tools

  • Less data cleaning
  • Common data format
  • Better graphics
  • Animation

Improved Links between Weather Research and Your Sector

  • Outreach and education
  • Utiliity associations
  • Regulatory associations
  • Utiltiy operations
  • Commercial news organizations

Transmission and Distribution Group Report II

The transmission and distribution group summarized the new decisions and decision tools that could be possible with improved weather information. They persistently emphasized that a standard data format for weather forecasts and other weather information would be very useful to their decision tools. This branch of the industry may also have to make more in-depth study of the impact of weather on its operations in order to make use of improved weather information, because the exact mechanisms of some impacts are unknown. There may be cultural barriers that have hindered more widespread study and use of weather information. A demonstration of the value of weather information for decision-making might be needed.

Adopting New Kinds of Weather Information

  • Dynamic line rating could be helped by localized forecasts. Temperature and wind data measured on transmission infrastructure could be shared with NWS or private weather information provider to improve forecasts at line's location. This is 2-way communication with sophisticated weather models.

Project #1 Dynamic Line Rating

  • Real time: weather measurements are adequate at the site of the line.
    • No Project in Real-Time
  • Week-month ahead
    • Micro-climate
    • A line limit really corresponds to a few particular spans (a span is at most ~200m)
    • Forecasting temperature and wind conditions would allow for a variable line rating with time.
  • Standardized data format

Project #2 Tracking Weather Events

  • Creating an animation of the weather forecast superimposed on top of the transmission system
  • Create an animation of the weather forecast superimposed on top of the distribution system/ where the customers (demand) are.
    • Emergency preparation for electric operators
    • Short-term feasible project
  • Remark: Meteorlogix has a popular product like this.
  • Standardize data format

Project #3 Modeling the Effect of Weather on Equipment Outage Rates

  • How does a "thunderstorm" increase the likelihood of an outage? What circumstances and characteristics make a "thunderstorm" threatening?
  • Without these quantitative measures, quantitative weather forecasts are of limited use
  • Standardize data format

Themes also Mentioned

  • WS 2-way communication service could use users' input observations to provide automated, tailored weather forecast for region (e.g. dynamic line loading).
  • Link weather forecast to an application that decision makers need
  • Area fertile for research is wind generation and overheated transmission lines
    • Geographical spatial correlation is critical for wind turbine spacing

Generation and Supply Group Report II

The generation and supply group looked at information needs over the short, medium, and long-terms, and brainstormed a series of 2-day workshops to delve deeper into the needs for new weather information and decision support tools. Information needs center on ways to manage uncertainty by understanding climate variability, and by using probabilistic forecasts. Value demonstrations are needed for weather forecasts.

Adopting New Kinds of Weather Information

1. When available, by invitation to demonstrate and evaluate
2. Availability of DICAST, for example, is not widely known
3. Watching AMS newsletters

Projects

Short-Term Forecast

  1. Dry bulb temperature modeling (0-84 hours, 0-384 hours)
  2. Boundary layer modeling improvements
  3. Strengthening observational networks
    • Explore available resources
    • Extend as needed
  4. Probabilistic range specification in dry bulb temperature
  5. Climate variability

Mid Term Forecast

  1. Precipitation estimation
  2. Climate variability
  3. Seasonal interannual snow and stream flow

Long-Term Forecast

  1. Climate variability
  2. Seasonal interannual snow and stream flow

Brainstorming

2-day workshops:

  1. Spatial optimization of information needs
  2. Discovery of aligned programs, areas of intersection and coordination
  3. Focused stakeholder exchanges

Interagency Collaborations: Aligned Interests

  1. EPA
  2. DOD
  3. SCAQMD
  4. POWER (Plains Organization for Wind Energy Resources)
  5. EPRI
  6. UWIG (Utility Wind Power Interest Group)
  7. DOE
  8. CEC
  9. AMS

Themes Also Mentioned

Sell managers of generation infrastructure the idea that we can predict generator performance with weather information.

Systems Group Report II

The systems group emphasized engaging utilities and decision makers in making improvements to tools and information, because of the diversity across utilities. All utilities have different needs based on their geographic situation and the technology used by their customers. Case studies of the value of weather forecasts, and demonstrations of probabilistic forecasts, would be a way to get the attention of decision makers. They recommended focusing on the RTOs and the standards and regulatory agencies, NARUC and NERC.

Tool #1 Probabilistic Forecasts

  • Probabilistic forecasting
    • What's the value of probabilistic forecasts to electric industry participants?
      • Case studies
      • Work with company financial people (e.g., experience with Union Pacific)
  • Focus on RTOs to improve forecasting
    • New institutions may be more amenable to new approaches

Tool #2 Consensus Forecasts

  • Is there added value from consensus forecasting?
    • What's optimal size of a consensus set?

Themes Also Mentioned

  • Value of increased frequency/density of information sources: means from a 20 yr database.
  • Engage utilities and decision makers in improvements to tools and information
    • Diversity across utilities:
      • Vary by service area
      • Vary by equipment they use (infrastructure)
  • Remark: Marketers are a better audience than RTO. RTO is just same old utility people.
  • Emphasize NARUC, NERC, utility commission involvement
  • Importance of GIS representations and the type of utility: how they make decisions must be taken into account in changes to weather data/information.
  • Consensus forecasting: 2 or 40? All utilities are different.

Potential Near-Term Projects

The group gathered in a final plenary discussion of the ideas from the presentations and the breakout groups. Some brainstorms and many well-developed projects emerged which emphasize understanding, research, and technology transfer to enhance reliability tools in the electric power industry and illustrate the high value of improved weather forecasting for USWRP.

Fundamental knowledge and societal impacts case studies will be useful to help the industry realize the mechanisms of weather impacts on their operations, and to help identify which weather forecasts are most useful: when, where, and for what decisions. Products (tools) and demonstrations of leading-edge meteorological capabilities should have the context of an industry decision in order to show relevance and value. Given the variable needs of utilities and the different decision making entities involved in the industry, choosing a test bed for demonstrations or technology transfer is an important first step. The location of the weather-related problem, the decisions being made, and the weather forecast tools must be chosen in a complementary fashion. Continued outreach to this industry will be necessary to sustain the relationships begun in workshops, case studies, and technology demonstrations. Trade meetings, academic research, and regulatory commissions all offer avenues for continued contact.

Fundamental Knowledge, Case Studies, and Demonstrations of Value

  1. Detailed end-to-end assessments of outages and service impacts.
    • Case studies and scenarios
    • NERC does not have much depth as to why weather was a factor in outages. More comprehensive case studies needed: what was forecast, weather impact, reason for impact, length & impact of outage, recovery
    • People in operations have an idea of causes but these are based on instinct
      • Need to identify threshold points
      • Need to quantify losses of an unnecessary spilloff on a hydropower dam. E.g. in anticipation of a hurricane whose impact didn't materialize as large as expected (value study, see below).

       

  2. Are there best practices in using forecasts? Are people making studies of value?
    • EPRI doesn't track this. It is a sensitive issue. Comparison is difficult, too.
  3.  

  4. Improved indices of weather impacts might lead to new understanding of how people demand power based on the weather and on weather impacts on electric power service and productivity. Combined indices like heat and humidity (heat index), successive days of heat (heat wave index), heat and calm (no wind), urban heat island effects, etc. could be more useful than one measure alone. Fundamental research is needed to discover how people react and how this is related to their lifestyles, household electrical technology, etc., and how compound indices relate to business decisions at a utility.
    • For utility load forecasting - while customer-weighted hourly temperatures from airport sites are fairly uniformly used in load forecasting [Sailor (Portland State U.) has] found significant value in optimized degree day parameters (e.g.optimizing the choice of threshold temperature rather than just using 18C), and also using other parameters such as latent enthalpy days (a better parameter than dew point or relative humidity). While wind itself is typically not a very powerful predictor of loads, [Sailor is] starting to explore degree-day style versions of a wind parameter that may be useful in some applications.
    • There are a number of other issues related to how weather data are "averaged" for a utility service area. Smarter aggregation of weather data may also lead to more robust load forecasting capabilities.
    • For transmission distribution threats: temperature + wind for dynamic line loading. Temperature + humidity for icing. Add wind for tree fall index maps.
    • For generation and air quality forecasts: temperature, wind, and insolation.
  5.  

  6. The spatial density of observations that would be helpful to utilities' forecasting efforts, especially in urban regions, is not known. Nor is the requisite quality/precision (e.g. are high school measurements good enough?). Improvements on synoptic forecasts are currently made in a statistical regression after the forecast, instead of re-running weather models with the higher spatial input. What density is most useful, and how should it be used? (see also Products)


  7. Value studies of weather tools to demonstrate to agencies (see also Outreach). Either case studies or models of potential value. The value of a forecast is often not known to much precision, and there is little reproduceable basis on which to make the value assertion. Well-documented, up-to-date value studies can be convincing arguments for weather forecasts, as well as illuminating discrepancies in assessing "value" by different stakeholders in society, industry, and politics.
    • Illustration of the value of a temperature forecast for estimating future generating capacity (illustrate value)
    • Wind power production: wind power is literally turning weather into money. Need to analyze old wind data and connect to wind power production. Back cast to figure it out. Long-term analysis. Bring in climate change. Present the high value of accurate same-day wind forecasts.
    • We (RAP) can demonstrate now our capability to forecast temperature more accurately. We just didn't know how important it was. Combine such a microclimate analysis with a decision and calculate the value to industry (1 degree improvement in RMS relative to NWS). 2 parallel streams: what would we have done if we hadn't done that? With a control.
    • Just a sample demonstration of the value of probabilistic tools could be effective in convincing decision makers (in-house RAP DICAST, etc. RMS improvement over NWS)
    • Get it in front of people. Demonstrate its utility. Attract Meteorlogix, RER/Itron to take it. Demonstrates national value of weather forecast.
    • You're right they won't tell you the value even if they know. Getting information in front of certain individuals is paramount. Their acceptance of it alone would show its value to others in the industry without requiring publication of sensitive proprietary data.
    • Estimate the value of research. Raising value = reducing risk of bad decisions in industry. Ultimate value = cost of service to decision makers vs. cost to society of blackout. [EPRI asks] the host utility to document a dollar value in demonstration projects.

Products/Tools

Improved Decision Support Tools

  1. Weather information integrated into decision support systems.
    • Graphical depiction of weather forecast vs. infrastructure or service area.
    • Integrated qantitative weather variables & quantitative business operations/planning.
    • Study the update frequency vs. the type of weather threats depicted.

  2. Development of an Urbanized Mesoscale Model - load forecasters complain about using weather forecasts for local airport sites that are not representative of the weather affecting their customer base. Developing a model capability that can provide urban-specific weather forecasts would benefit the power utilities, but would also find significant application in air quality and heat-related health arenas. This project would need to include better land cover representation, anthropogenic heating, thermal storage in urban environments, and complex radiative exchange (diurnal albedo) of dense urban areas. Dave Sailor at Tulane University (Portland State University after 2003).

  3. Load forecasting improvements
    • Various levels of sophistication and quality of demand forecasts
    • Load forecasts near front of many processes. Chains of models not developed for probabilistic inputs. Need application designed for probabilistic input AND output.

Improved Weather Information

  1. PP or MOS-based forecasting for a dense network of urban meteorological stations for the purpose of improved load model training and forecasting - Just as the NWS provides a MOS forecast product at 1st order airport stations the same techniques can be applied to a dense network of urban weather stations (for example, U. GA has a nice network in Atlanta, and the Automated Weather Source (AWS) AirWatch maintains on the order of 5 to 20 standardized high school sites in most major US cities, there are many other possible sources). Rather than having a utility use an average of airport forecasts it could use the corresponding average of "urbanized" forecasts for cities in its service area. This project would demonstrate the value of such forecasts through a proof-of-concept demonstration applied to a city for which load data are available (and publishable).
    • Consider use of NCDC climate reference network. 6000 stations nationwide. Well-maintained, long-term, not high-end stations. Interested in temperature. Planned but not well-funded.
    • Consider studying the value of increased density of observations for downscaling and for forecast intialization.
  2. Standard weather data format. Stakeholders get together for standard. Georeferenced XML or other that is easily incorporated into existing software. $1-200k for 1.5 years might be a reasonable budget for this.
    • ESRI buy-in OPEN GIS challenging secrecy
    • Common Information Model CIM at EPRI expanding definition of scope. Must first determine what is included in the weather data. IEC international standard.

Choosing a development/demonstration partner or testbed

  1. Utility selection is case-specific. Need particular application
  2. Capture a bit of all these things in one project
  3. Computer common format is not fundable by USWRP
  4. ASHRAE good organization for collaboration. Research funds & demonstrations all the time. Co-funding, strategizing. [Facilities management, demand]
  5. TVA seems open to demonstrations on-site, and is running a demonstration of probabilistic transmission forecasting.
    • Diverse areas at TVA. Depends on impact. Meteorology driven by COO. While meteorology is well-supported, it's a good idea to do it soon. Data formats are less important. Data cleaning is more important. But other opportunities to do that, not need to be USWRP electric program. Air/water quality group is completely separate.
    • Air quality & USWRP + NOAA possible direction
  6. WAPA: drought, line sag & capacity. Time is right for WAPA partnership
  7. TVA-CAISO-WAPA good sign. All are EPRI members. Easy to fit to TVA probabilistic transmission forecasting.
  8. Longer term: attend annual meetings of load forecasters, operations trainers

Outreach

  1. Demonstration of value of probability to utilities (see also Fundamental Knowledge)
    • Temperature, precipitation. Forecasts can do well on distributions but needs to be sold to utilities. How to use distributions. Point out that weather forecasts are not deterministic. GIS & other representations.
    • Selling probability to other industries, too. Need to because they become more confident slowly. Work at it continuously. Evolve iteratively how it might be used.
    • Utilities used to a stochastically developed load forecast. That's the "in". May need more direct way to introduce this. Seminars at NARUC (regulators & utilities) for example.
    • Truth in advertising by giving probabilities. EPRI bi-annual forecasting conference.
    • There are absolute limits to predictability in complex systems, even with perfect models and observations. The use of probability measures is helpful to keep in mind.
    • Cannot optimize a system based on probabilistic models, e.g. unit commitment. That is the elegance and difficulty of probability vs. the error and apparent utility of deterministic forecasts
  2. (Continuing) education for industrial decision makers
    • Videotape for education about value of weather forecasts, probabilistic forecasts
    • NOAA could make it to illustrate probabilistic forecasts
    • What about using COMET for this? Is online instruction a feasible alternative? But then, there would be no feedback from participants
    • Scheduling a program at the Training Center of the Western Power Administration in Lakewood would guarantee the right audience, and feedback would be available.
    • Availability of DICAST, for example, is not widely known
    • Top-down convincing of utilities (decision makers) with quantified value of probabilistic vs. deterministic forecasts. Requires an (hypothetical, tested) implementation of a probabilistic forecast in a decision tool.
  3. Establish relationships between weather research community and utility decision making community. Mutual understanding of cultures. Attend meetings and present meteorological research/trends in research. Private: time is worth more than money. Broad involvement of private/government; phased projects; clear budget and benefits; timeline; shared funding; network with other agency activities.
    • NARUC
    • NERC
    • Academia (New Mexico State U., U. Illinois)
    • CPUC
    • EPRI
    • AMS: trend toward using probabilistic forecasts. Actual number isn't as important as getting probability right. Market needs to adjust to this.
    • Facilities managers association
    • 2-day workshops:
      • Spatial optimization of information needs
      • Discovery of aligned programs, areas of intersection and coordination
      • Focused shareholder exchanges
  4. Sustaining relationships
    • Publicly available data sources for business groups. Bring to group meetings and get feedback on it. At EPRI, for example.

Next Steps

The next steps following this workshop will focus on maintaining the momentum of the workshop participants, following up on their direct interest, and seeking out specific opportunities they mentioned.

  1. Gathering more information in small workshops on specific topics like the spatial optimization of information needs, the discovery of aligned programs in the topic, and focused stakeholder exchanges;
  2. Assembling educational materials for the industry about the modern capabilities of weather forecasting;
  3. Creating a demonstration project in the potential value of probabilistic forecasts with application to a particular decision.
Appendix: Workshop Handout

National Center for Atmospheric Research
Foothills Laboratory, Building 2, in Boulder, Colorado
3450 Mitchell Lane

November 6 & 7, 2002.

Sponsored by the United States Weather Research Program (USWRP) http://uswrp.org

Overview

The electric power industry has long applied weather information to reduce risk and increase reliability. Under deregulation, decision makers in electric power will become more diverse and decentralized. Reliability may require new tools in policy, technology, and information. In this workshop, participants from the electric power industry and meteorology will build on known needs for improved weather information to advise improvements and demonstration projects in weather forecasting, communications, and decision support tools. Collaborative research projects will be outlined which integrate the needs for weather information with the capabilities of meteorology and information systems. Discussions will focus on projects with a 5-10 year horizon.

Goals

  • Study and improve relationships for transferring meteorological knowledge to decision makers
  • Guide future meteorological research in societally beneficial directions by establishing a template for collaborative studies in industry sectors
  • Form durable partnerships between researchers and industry
  • Increase efficiency and reliability of the electric power system

Workshop Organization

Content

Jeremy Hackney
hackney@ucar.edu
(303) 497-8111


Robert Harriss
harriss@ucar.edu
(303) 497-8106
Administrative

Carey Kerschner
kersch@ucar.edu
(303) 497-8197

Anne Oman
anneoman@ucar.edu
(303) 497-8117

 

 


 


 

Meeting Agenda

Presentations take place in room FL2-1001
Breakout groups are in rooms:
Blue : FL1-2011; Yellow : FL2-3107
Green : FL3-2062; Red : FL3-3073

November 6, 2002

800-830 Breakfast
830-900 USWRP: Goals, Projects, and Progress, Robert Gall, NCAR
900-930 Keynote: Reliability and Risk, Maria Ilic, Carnegie Mellon University
930-1000 Transmission Visualization Tool, Jamie Weber, PowerWorld, Inc.
1000-1030 Break
1030-1100 Transmission, Complexity, Probabilistic Modeling, Stephen Lee, EPRI
1100-1130 Recovery and Restoration of Electric Power Distribution System, Jane Preuss, GeoEngineers
1130-1200 Lunch
1200-1230 Lunch
1230-100 Lunch
100-130 Demand Modeling, Frank Monforte, RER Inc.
130-200 Load as a Resource, Michael Kintner-Meyer, PNNL
200-230 Distributed Generation, Scott Samuelsen, NFCRC
230-300 Break
300-330 Operational Meteorology in a Utility, Patrick Walshe, TVA
330-400 Decision Tools: Commercial, Rich Wilson, Meteorlogix
400-430 Decision Tools: Research, Brant Foote, RAP
430-500 Conclusion and Tasks for Day 2, Robert Harriss, NCAR
500-600  
600 Social Dinner

November 7, 2002

800-830 Breakfast
830-900 Logistics, Work Groups Overview, Bob Harriss
900-930 Session 1: Current Weather-Related Decision Making
930-1000 Session 1: Current Weather-Related Decision Making
1000-1030 Break
1030-1100 Present Results of Session 1 Work Groups
1100-1130 Session 2: Improved Weather Information & Tools for Decisions
1130-1200 Session 2: Improved Weather Information & Tools for Decisions
1200-1230 Lunch
1230-100 Lunch
100-130 Present Results of Session 2 Work Groups
130-200 Session 3: Integrated Projects
200-230 Session 3: Integrated Projects
230-300 Session 3: Integrated Projects
300-330 Break
330-400 Present Results of Session 3 Work Groups
400-430 Discussion and Meeting Close, Bob Harriss

Breakout Groups for Day 2

Brown Barbara Load and Demand Red
Burg Geoffrey Transmission & Distribution Yellow
Carman Leslie Transmission & Distribution Yellow
Davis Chris -
Davis Todd -
Fellows Jack Systems Oversight Blue
Foote Brant Systems Oversight Blue
Gall Robert Transmission & Distribution Yellow
Gaynor John Load and Demand Red
Gough Robert Generation & Supply

Green

Hackney Jeremy Generation & Supply Green
Harriss Robert Systems Oversight Blue
Hawkins David -
Hughes Tim Generation & Supply Green
Ilic Marija Systems Oversight Blue
Jain Shaleen Load and Demand Red
Jaramillo Luke Transmission & Distribution Yellow
Johnson Edward Transmission & Distribution Yellow
Kintner-Meyer Michael Load and Demand Red
Larson Doug Systems Oversight Blue (Leader)
Lee Stephen Transmission & Distribution Yellow (Leader)
Lyke Toni M. Load and Demand Red
Mahoney William Generation & Supply Green
McElvain Frank Systems Oversight Blue
McGinnis Seth Generation & Supply Green
Monforte Frank -
Moore Michal Load and Demand Red (Leader)
Moore Wayne -
Morss Rebecca Generation & Supply Green
Morza Monirul Systems Oversight Blue
Myers Bill Load and Demand Red
Niemeyer Victor Systems Oversight Blue
Parrish Patrick Transmission & Distribution Yellow
Pavlovic Lucy Load and Demand Red
Samuelsen Scott Generation & Supply Green (Leader)
Schwartz Marc Generation & Supply Green
Stortz Michael Load and Demand Red
Tebaldi Claudia Load and Demand Red
Thresher Robert Generation & Supply Green
Wagoner Rich Transmission & Distribution Yellow
Walshe Patrick Generation & Supply Green
Weber James Transmission & Distribution Yellow
Wilson Richard Systems Oversight Blue
Winger Wendell Systems Oversight Blue
Worrell Lynn Transmission & Distribution Yellow

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, Test beds, 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?


Task I: Homework

Please fill out a matrix of the decisions in your field, and the weather information that is used. This is for preparation for the breakout groups. A sample matrix is included below.

Task I Sample Weather-Related Problems in Wind Power Generation:

Description of Decision or Problem
Decision or Problem Summary Timing and Frequency of Decision Value of Decision
Dispatch Wind Power Contracts 24-48h ahead, Daily decision 3-5 ¢/kWh
Estimate the Wind Resource Value Project planning; repeat every few years Entire Project Value

Weather Phenomenon Affecting the Decision
Type of Weather Details of Concern Frequency of Weather Event
Wind Gust;Calm;Average;DirectionTiming Nighttime; All the time
Wind Gust;Calm;Avg Spd;Hrly Spd;Duration Climatology
Freezing precip.    

Weather Forecast, Information, or Model Used
Variables Lead Time Resolution Source/ Decision Tool
Benefit vs. no Forecast
Wind Max;Wind Avg 24h 1 km2 area50m alt.Hourly avg FTP, WasP 2¢/kWh
WindCold/Precip Data Archive Point location at 50m and 100m Own obs;Wind Inter-polator Entire Project Value

Please define terms like "short-term", "local" and "severe", etc. in quantitative terms so that we all understand the needs of the different users of weather information.

Sample weather-dependent problem areas for each group
How is weather information important?

Systems Oversight Transmission and Distribution

Enforcing air quality regulations
Monitor peak load
Monitor peak price
Track power quality, e.g.:
-
Loss of load probability
- Average interruption index
- Average interruption frequency index
Weather-dependent seams issues

Dynamic thermal limits
Planned maintenance
Outage recovery (crew positioning)
System expansion
Automation
Applying a load forecast
Uncertainty in capacity, load
Generation and Supply Load and Demand
Estimate wind or solar resource value
Design wind turbine layout
Grid integration (wind, DG)
Supply contracts (wind, DG)
Daily operations (wind, DG)
Cogeneration of heat/cold:
- Planning
- Operations
Generator maintenance
Competitor's weather impacts

Demand contracts
Demand side management
Consumer price response
Distributed generation
Derivative markets
Competition for load segments
Variable weather across load area
Urbanizing effects
Model of baseline electricity consumption

Please pick up blank matrices on the first day of the workshop, and fill them out before meeting in the breakout groups

Appendix: Speakers

ROBERT L. GALL
Interim Deputy Director of NCAR
Senior Scientist, Division Director of MMM
Lead Scientist, USWRP
NCAR
P. O. Box 3000
Boulder, Co 80307-3000

Tel: (303) 497-8160
Fax: (303) 497-8181
gall@ucar.edu

Dr. Robert Gall was recently appointed Interim Deputy Director of NCAR. He has been the Lead Scientist of USWRP since 1999, and a Senior Scientist and Director of the Mesoscale and Microscale Meteorology Division (MMM) at NCAR since 1991. In 1993, he became an AMS fellow. Before joining NCAR, Dr. Gall was a professor at the University of Arizona from 1984-1991. He holds a BS in atmospheric science from Pennsylvania State University, and MS and PhD degrees from the University of Wisconsin.


MARIJA D. ILIC
Professor of ECE and EPP
Carnegie Mellon University
Porter Hall B25
Pittsburgh, PA 15213-3890

Phone:412-268-9520
Fax: 412-268-5787
milic@ece.cmu.edu
www.ece.cmu.edu/~milic

Dr. Marija Ilic has recently been appointed full professor at the Carnegie Mellon Electricity Industry Center. She has been a Senior Research Scientist in the Department of Electrical Engineering and Computer Science at MIT since 1987. Her interest is in control and design of large-scale systems. She conducts research and teaches graduate courses in the area of electric power systems. Dr. Ilic is a consultant for Alfatech, Inc. and the California ISO. From September1999 until March 2001, Dr. Ilic was a Program Director for Control, Networks and Computational Intelligence at the National Science Foundation.

Prior to her years at MIT, Dr. Ilic was a tenured faculty member at the University of Illinois, Urbana-Champaign. She is a recipient of the First Presidential Young Investigator Award for Power Systems. Dr. Ilic is also an IEEE Fellow and an IEEE Distinguished Lecturer.
Dr. Ilic received her M.Sc. and D.Sc. degrees in Systems Science and Mathematics from Washington University in St. Louis.

Dr. Ilic has co-authored several books on the subject of large-scale electric power systems: Ilic and Zaborsky, Dynamics and Control of Large Electric Power Systems, John Wiley & Sons, Inc., 2000; Ilic, Galiana and Fink (eds.), Power Systems Restructuring: Engineering and Economics, Kluwer Academic Publishers, 2nd printing 2000; Allen and Ilic, Price-Based Commitment Decisions in the Electricity Markets, Springer-Verlag London Limited, 1999; Ilic and Liu, Hierarchical Power Systems Control: Its Value in a Changing Industry, Springer-Verlag London Limited, 1996. Dr. Ilic is also a contributing editor for Unlocking the Benefits of Restructuring: A Blueprint for Transmission (PU Reports, 2000).


JAMES WEBER
PowerWorld Corporation
1816 South Oak Street
Champaign, IL 61820

Tel: (217) 384-6330
Fax: (217) 384-6329
weber@powerworld.com

James D. Weber is Director of Operations at PowerWorld Corporation in Champaign, IL. His primary duties involve the development and maintenance of the PowerWorld Simulator software product as well as working on new products that will move Simulator's graphic capabilities to real-time information systems such as trading floors and energy management systems. He received his B.S. degree in Electrical Engineering from the University of Wisconsin - Platteville in 1995, and his M.S. and Ph.D. degrees from the University of Illinois in 1997 and 1999.


STEPHEN T. LEE
Area Manager, Grid Operations and Planning
Electric Power Research Institute

Tel: (650) 855-2486
Fax: (650) 855-2511
slee@epri.com

Dr. Stephen Lee has extensive experience in power system planning and operation for electric utilities, developing and applying new techniques of simulation and optimization for combined generation and transmission systems, interchange distribution, congestion management, transactions and energy scheduling, generation system production simulation, maintenance scheduling, unit commitment, generation and transmission planning, system security and adequacy, and system stability.

With EPRI since May 1998, Stephen Lee was the Area Manager of Grid Operations & Planning until June 2002, responsible for EPRI research projects and cooperative projects with the North American Electric Reliability Council (NERC) for supporting security processes. He is currently the GOP Senior Technical Leader and the project manager for the Transmission Program of the EPRI Power Delivery Reliability Initiative. He was the project manager of the NERC Interchange Distribution Calculator which is used to implement the NERC guidelines for Transmission Loading Relief for curtailing transactions in the Eastern Interconnection. He is also active in NERC committees and a representative for EPRI on the NERC Market Interface Committee. He is closely involved in the latest developments of transactions information system, transactions management, and congestion management.

Stephen Lee received his B.S., M.S., Engineer, and Ph.D. degrees from M.I.T. in Electrical Engineering, specializing in Power System Engineering and Control Theory. Dr. Lee is a registered Professional Engineer in California and Massachusetts, and a Senior Member of IEEE.


JANE PREUSS
A.I.C.P., Principal
GeoEngineers, Inc.

jpreuss@geoengineers.com
http://www.geoengineers.com/people.asp


Jane Preuss has 24 years of experience in planning and provides expertise in two interrelated areas. One is land use and environmental planning. Theother pertains to mitigation and preparedness against the effects of natural hazards such as floods, landslides, earthquakes, tsunamis, and high winds. Jane also has considerable experience in preparing community plans. She often works with community groups to elicit their comments and suggestions as well as to prioritize issues and implement strategies. Jane has worked on international projects in locations such as Peru, Dominican Republic, Jamaica, Nicaragua, Mexico, Japan, Korea, Taiwan, Nepal, and Sri Lanka. In the United States Jane has completed projects in Washington, Oregon, Idaho, Alaska, California, and Hawaii.


FRANK MONFORTE

Vice President, Forecasting
RER/Itron

Tel: (858) 481-0081
frank@rer.com
http://www.rer.com/About/index.htm

Dr. Frank A. Monforte is Vice President of Forecasting at RER, where he specializes in the areas of energy and price forecasting, end-use forecasting, and statistical and mathematical modeling. He is a leading authority in the areas of short-term control area forecasting, load profiling, and retail scheduling. Dr. Monforte directs the development, support, and implementation of RER's forecasting and load profiling tools, including MetrixND, NDauto, and ProForm. Dr. Monforte's short-term load forecasting expertise includes models for the New York ISO, California ISO, PJM, NEPOOL, and the Australian System Operator, NEMMCO. Dr. Monforte directs the implementation of RER's load profiling systems, including systems in California, Illinois, and Texas. As the lead econometrician, Dr. Monforte was part of the team that developed the ERCOT Load Profiling System. Examples of retail scheduling system implementations he has directed include PPL, SCE, SDG&E, LCRA, ENRON, AMEREN, Illinois Power, and Alliant. In addition to these systems, Dr. Monforte directs the implementation of RER's Web-based forecasting tools and services, including eMetrix and eShapes. Dr. Monforte is the lead architect of Gaz de France's Web-based forecasting system, which is the first of its kind in the industry. He also directed the implementation of AMEREN's Web-based Load Profile Forecasting application that provides online load profile forecasts that retailers use in support of their market operations.

In addition to his forecasting responsibilities, he is a nationally recognized authority in the area of industrial end-use analysis. He has directed numerous commercial and industrial on-site survey projects. He has developed market potential estimates and forecasts for industrial technologies for both utilities and EPRI. He is the principal investigator for EPRI's industrial end-use forecasting model, INFORM. He is a principal investigator for an EPRI-sponsored study on the environmental benefits of increased electrification in the residential, commercial, industrial, and transportation sectors.

Dr. Monforte has co-authored publications on a range of problems including the use of neural networks for short-term load forecasting, long-term end-use forecasting, and the use of nonlinear programming techniques for development of a least cost gas supply planning tool.

Dr. Monforte received his B.A. in Economics from the University of California, Berkeley and his Ph.D. in Economics from the University of California, San Diego.


MICHAEL KINTNER-MEYER
Energy Science and Technology Directorate
Pacific Northwest National Laboratory
P.O. Box 999, K5-16
Richland, WA 99352

Tel: (509) 375-4306
Fax: (509)375-3614
Michael.Kintner-Meyer@pnl.gov

Michael Kintner-Meyer is Senior Research Scientist at the Pacific Northwest National Laboratory (PNNL) in Richland, WA. He is leading PNNL's Load-As-A-Resource program and actively involved in the assessment of demand response programs.
He has a Master Degree in Mechanical Engineering from the Technical University of Aachen, Germany and a Ph.D. in Mechanical Engineering from the University of Washington. He is member of ASHRAE's technical committee "Smart Building Systems". He chairs the Communications and Integration Subcommittee.



SCOTT SAMUELSEN
Director, Advanced Power and Energy Program
University of California at Irvine
Irvine, CA 92697-3550

Tel: (949) 824-5468
Fax: (949) 824-7423
gss@uci.edu
www.apep.uci.edu/samuelsen

Dr. Samuelsen is interested in energy conversion, fuel cells, combustion, fuel sprays, laser diagnostics, air quality, turbulent transport, alternative fuels, the modeling of reacting flows, practical energy systems, and the conflict between energy and the environment.

Dr. Samuelsen's current research activity focuses on energy generation, distribution and utilization, and includes the production of electricity, motive power and propulsive power from both fuel cells, gas turbines and hybrids of both. His work also explores the environmental impact of these energy systems, the dynamic between energy generation and atmospheric quality, and the development of environmentally preferred, high-efficiency energy generation integrated into buildings and building complexes.

Dr. Samuelsen directs the Advanced Power and Energy Program (APEP), which encompasses the National Fuel Cell Research Center (NFCRC), the UCI Combustion Lab (UCICL) and the Pacific Consortium on Energy and the Environment (PARCON).

His work at the UCICL is directed toward the development of advanced stationary gas turbine power systems. Research at the NFCRC is leading the evolution of power generation fuel cells, and the PARCON accelerates the development and deployment of advance energy systems around the world.


PATRICK WALSHE
Meteorologist, Electrical System Operations
Tennessee Valley Authority

Tel: (423) 751-7858
tpwalshe@tva.gov

Walshe started his career as an operational meteorologist at The Weather Channel in 1989 in Atlanta, GA. He was involved in all aspects of forecasting until he accepted the position of Assignment Manager at TWC in 1997. As a meteorologist in a news oriented field, Walshe was responsible for coverage of the most significant weather events of the last five years. The combined experience at TWC made him uniquely qualified to accept a position as meteorologist at the Tennessee Valley Authority in late 2001. Currently, Walshe is working on decreasing short and mid term forecast errors for temperature and demand for the nation's largest public utility.

Walshe earned a bachelors degree in Meteorology at Florida State University in 1988. He helped pioneer IP delivery of broadcast quality severe weather video in near real time as well as change the way television covers weather events and stories. During the most recent summer, Walshe helped TVA attain its lowest forecast temperature and demand error of record.


RICHARD WILSON
Director of Operations and Energy Services
Meteorlogix
420 Bedford Street, Suite 320
Lexington, MA 02420-1506

Tel: (781) 676-1095
Fax: (781) 676-1001Director of Energy
Email: Richard.Wilson@meteorlogix.com

With Meteorlogix for nearly 10 years, Richard P. Wilson serves as Director of Energy Services for Meteorlogix. In this position he is responsible for successfully establishing and expanding business opportunities within the energy and utility industries. He also provides expertise in the conceptualization of value-added weather for those markets.

Wilson previously managed and directed the company's MxInsight product line, including energy and long range forecast products. He has held other key positions at Meteorlogix, including Vice President of Meteorological Operations for the company's Minneapolis and Boston offices, and Director of Operations in Boston.

Wilson earned a bachelor's degree in physics from Boston College and a degree in Meteorology from Plymouth State College (the University System of New Hampshire) in Plymouth, NH. Wilson is a member of the American Meteorological Society.


G. BRANT FOOTE
Director, Research Applications Program
National Center for Atmospheric Research
PO Box 3000
Boulder, CO 80307-3000

Tel: (303) 497-8458
foote@rap.ucar.edu
http://www.rap.ucar.edu/staff/foote-staff.html

A specialist in mesoscale meteorology, Foote came to NCAR as a postdoctoral fellow in 1970. He was a project leader with the National Hail Research Experiment during the 1970s and became a senior scientist in 1982, serving in the Field Observing Facility, the Mesoscale and Microscale Meteorology Division, and, most recently, in RAP.

Foote earned his bachelor's degree in mathematics and his master's and doctoral degrees in atmospheric sciences at the University of Arizona. He recently ended an eight-year tenure as editor of the Journal of the Atmospheric Sciences, has served on several national and international committees, and led a number of large field programs, including most recently an experiment to study terrain-induced wind shear and turbulence in Hong Kong.


WILLIAM MAHONEY
Program Manager
Research Applications Program
National Center for Atmospheric Research
PO Box 3000
Boulder, CO 80301

Tel: (303) 497-8426
Email: mahoney@ucar.edu

Mr. Mahoney began working with NCAR as an associate scientist in 1981 as part of the Joint Airport Weather Studies Project (JAWS), where he flew onboard the University of Wyoming's Super King Air research aircraft studying windshear associated with microbursts and gust fronts. He participated in the development of the Low Level Windshear Alert System (LLWAS) and eventually led NCAR's team in the development and demonstration of the Terminal Doppler Weather Radar (TDWR). Mr. Mahoney is a well-known expert in windshear and windshear detection systems.

As an NCAR Program Manager, Mr. Mahoney has led several FAA-sponsored aviation weather projects with a particular emphasis on interactive processing systems. He led a research and system development program in Hong Kong, which resulted in the implementation of the Windshear and Turbulence Warning System (WTWS) at Hong Kong International Airport. More recently, Mr. Mahoney managed an aviation weather system modernization program for the Taiwan Civil Aeronautics Administration. This project involved the development and tailoring of high-resolution numerical weather forecast models, advanced, interactive processing systems, and implementation of windshear detection systems at Taiwan's major airports.

Mr. Mahoney is also involved in the Federal Highway Administration's (FHWA) Weather Information for Surface Transportation (WIST) initiative and is leading a team of five national laboratories on the development of a Maintenance Decision Support System (MDSS) for winter road maintenance managers.

In addition to his program management duties, Mr. Mahoney is involved in program development and commercialization activities at NCAR.

Mr. Mahoney received his M.S. degree from the University of Wyoming in 1983, specializing in windshear and cloud microphysics. His B.S. degree was in Aeronautics from Miami University of Ohio.



ROBERT C. HARRISS
Senior Scientist, ESIG Director
Environmental and Societal Impacts Group
National Center for Atmospheric Research
PO Box 3000
Boulder, CO. 80301

Tel: (303) 497-8106
Fax: (303)497-8125
http://www.esig.ucar.edu/HP_harriss.html
harriss@ucar.edu

Dr. Robert Harriss is a Senior Scientist and of the Director of the Environmental and Societal Impacts Group (ESIG) of the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. Prior to joining NCAR in 1999, Harriss was a Professor and holder of the Wiley Chair in Civil Engineering at Texas A&M University. He also initiated and directed the Sustainable Enterprise Institute aimed at finding systemic solutions to natural resource and organizational management problems.

Harriss served 13 years as a NASA Senior Scientist and Science Division Director for the Earth Sciences at the Langley Research Center and NASA Headquarters, respectively. He has also held faculty and research positions with the University of New Hampshire, Florida State University, Harvard University, and the United Nations Environment Program. Honors and awards received include the NASA Exceptional Scientific Achievement Award (1985), Election as a Fellow of the AAAS (1988), U.S. Senior Executive Service (1994-1997), and the NASA Outstanding Leadership Medal (1997). Harriss was born in Brownsville, Texas, and received his Ph.D. from Rice University in Houston, Texas.


Appendix: Participants

Brown Barbara bgb@ucar.edu 303-497-8468 RAP/NCAR
Burg Geoffrey Geoffrey.Burg@xcelenergy.com Distribution Planning and Operations, Xcel Energy
Carman Leslie Leslie.Carman@xcelenergy.com 303-571-3550 Distribution and Capacity Planning, Xcel Energy
Davis Chris cdavis@ucar.edu 303-497-8990 MMM/NCAR
Davis Todd todd.d.davis@saic.com 610-827-7564 SAIC
Fellows Jack jfellows@ucar.edu 303-497-1668 UCAR
Foote Brant foote@ucar.edu 303-497-8458 RAP/NCAR
Gall Robert gall@ucar.edu 303-497-1108 MMM/NCAR
Gaynor John John.Gaynor@noaa.gov 301-713-0460
X117
NOAA
Gough Robert rpwgough@aol.com 303-543-1017 Intertribal Council on Utility Policy
Hackney Jeremy hackney@ucar.edu 303-497-8111 ESIG/NCAR
Harriss Robert harriss@ucar.edu 303-497-8106 ESIG/NCAR
Hawkins David dhawkins@caiso.com 916-351-4465 California Independent System Operator
Hughes Tim thughes@ou.edu 405-447-8412 Oklahoma Wind Power Initiative

Ilic
Marija milic@ece.cmu.edu Electricity Industry Center, Carnegie Mellon University
Jain Shaleen sjain@cdc.noaa.gov 303-497-6295 CDC/NOAA
Jaramillo Luke luke.jaramillo@xcelenergy.com 303-294-2139 Forecast Analysis, Xcel Energy
Johnson Edward edward.johnson@noaa.gov 301-713-3028 x149 NOAA
Kintner-Meyer Michael michael.kintner-meyer@pnl.gov 509.375.4306 Pacific Northwest National Lab
Larson Doug dlarson@westgov.org 303-573-8910 Western Interstate Energy Board
Lee Stephen slee@epri.com 650-855-2486 Electric Power Research Institute
Lyke Toni M. ToniM.Lyke@xcelenergy.com Economics and Energy Forecasting, Xcel Energy
Mahoney William mahoney@ucar.edu 303-497-8426 RAP/NCAR
McElvain Frank fmcelvain@tristategt.org 303-452-6111 System Planning, Tri-States G & T
McGinnis Seth sethmc@turcotte.colorado.edu Colorado Chaos and Complexity Center, CU Boulder
Monforte Frank frank@rer.com 858 481 0081 RER/Itron
Moore Michal michal_moore@nrel.gov 303-275-3090 Economist, NREL
Moore Wayne wmoore@ucar.edu 303-497-8563 UCAR/NCAR
Morss Rebecca morss@ucar.edu 303-497-8172 ESIG-MMM/NCAR
Morza Monirul monirul.mirza@utoronto.ca 416-978 6201 Adaptation and Impacts Group, University of Toronto
Myers Bill bill@rap.ucar.edu 303-497-8412 RAP/NCAR
Niemeyer Victor niemeyer@epri.com Electric Power Research Institute
Parrish Patrick pparrish@comet.ucar.edu 303-437-8366 COMET/NCAR
Pavlovic Lucy Lucy.Pavlovic@xcelenergy.com 303-294-2482 Economics and Energy Forecasting, Xcel Energy
Preuss Jane jpreuss@geoengineers.com 425-861-6000 GeoEngineers
Sailor David sailor@tulane.edu 504-865-5308 Tulane University
Samuelsen Scott gss@uci.edu 949-824-5468 Advanced Power and Energy Program, UC Irvine
Schwartz Marc marc_schwartz@nrel.gov 303-384-6936 National Wind Technology Center/NREL
Stortz Michael mstortz@tristategt.org 303-452-6111 Load Forecast Manager, Tri-States G & T
Tebaldi Claudia tebaldi@ucar.edu 303-497-2830 ESIG-CGD/NCAR
Thresher Robert robert_thresher@nrel.gov 303-384-6922 National Wind Technology Center/NREL

Wagoner
Rich wagoner@ucar.edu 303-497-8404 RAP/NCAR
Walshe Patrick tpwalshe@tva.gov 423-751-7858 Meteorologist, TVA
Weber James weber@powerworld.com 217-384-6330 PowerWorld
Wilson Richard Richard.wilson@meteorlogix.com 781-676-1095 Energy Division, Meteorologix
Winger Wendell wendell.winger@dora.state.co.us 303-894-2874 Colorado Public Utilities Commission
Worrell Lynn lynn.worrell@xcelenergy.com 303-571-3542 Distribution and Planning, Xcel Energy

Appendix: References

The discussion above has used ideas and materials from the following publications:

Altalo, Mary G., M. Mondshine, J. Findsen, C. Mahoney, W. Keene, J. Doherty, Defining the Requirements of the U.S. Energy Industry for Climate, Weather, and Ocean Information, Science Applications International Corporation, 7/2000.

Barker, Brent, Technology and the Transformation of the Electric Industry. EPRI Journal 11-12/1996. p. 23-30.

Ilic, Marija, "The Future Power Grid", Power Quality, 1 June, 2002.

Lee, Steven, Community Activity Room (CAR) Metaphor for Interstate Transmission Highways, EPRI Presentation, 4/9/2002

Murnane, Richard, M. Crowe, A. Eustis, S. Howard, J. Koepsell, R. Leffler, and R. Livezey, "The Weather Risk Management Industry's Climate Forecast and Data Needs", Bulletin of the American Meteorological Society, August 2002.

Overbye, T. J. and J. D. Weber, "Visualizing the Electric Grid", IEEE Spectrum, February 2001, p. 52-58.

Pielke, Roger, Report of the U.S. Weather Research Program Workshop on the Weather Research Needs of the Private Sector, Palm Springs Workshop Report, Discussion Draft of 2 April 2001, 4/2/2001.

Samuelsen, Scott, Personal communication, National Fuel Cell Research Center, Personal Communication, 2002.

Societal Aspects of Weather: Report of the Sixth Prospectus Development Team of the USWRP to NOAA and NSF, Bull. Amer. Meteoro. Soc., 78, 867-875, 1997.

U.S. Environmental Protection Agency, Distributed Generation: The Hot New Way to Generate Power (Part 1), Inside the Greenhouse, Summer 2002 EPA-430-N-02-004.

University Corporation for Atmospheric Research, "The U.S. Weather Research Program. Saving Lives, Money, and Time: Better Weather Forecasts for the Nation," Brochure, 2002.


Appendix: Pre-breakout Questions

Description of Decision
Decision Summary Timing and Frequencey of Decision Value of Decision
Hydro Scheduling
Pump Storage Hydro
Hydropower Generation Loss of revenue due to less power production
Resource planning and maintenence 1 monty - 1 year $0 (not used yet)
River Forecast Every 8 hours Varies, but high
How to operate Columbia River Hydro System (which determines operation of rest of western electricity system) Annual Hundreds of million $
Snow Pack
Unit Commitment
Fuel Contracting mid term
Dispatch Renewables day ahead 3-5 cents
Vulnerabilty of Generation cost of importation of power
Wind Generation
Purchase/Sale Hourly/daily/mid-term Reliability & economics
Demand-Side management, e.g., interruptible load
Whether to announce emergency & trigger demand response Unknown Tens of millions $
Load Forecast Every 2 hours Varies, but high
Daily Load Forecast 1-2 days ahead $100k per degree
System forecasting peak demand Annually
Wholesale purcahses Daily
Transmission rating
G/Trans maintenance & scheduling Daily/weekly Reliability & economics
Vulnerability of transmission & distribution Power outage
Location of fires
Conductor capacity Could be as frequent as hourly in real time Unleash latent capacity
Planning decision 1-10 year horizon
Distribution Planning Annually
Transmission Planning Annually

Weather Phenomenon Affecting the Decision
Decision Summary Type of weather Details of concern Frequency of weather event
Hydro Scheduling
Pump Storage Hydro
Hydropower Generation Precipitation on Temperature May result low lake levels Seasonal
Resource planning and maintenence Temperature Precipitation Trends as well as
River Forecast Rainfall by basins Amounts over 6 hour period ?
How to operate Columbia River Hydro System (which determines operation of rest of western electricity system) Precipitation Total precip, particularly in upper Columbia River Basin Annual
Snow Pack
Unit Commitment
Fuel Contracting
Dispatch Renewables Sun/Wind Derate virtual firming 24h
Vulnerabilty of Generation Heat wave High demand of power hourly
Wind Generation
Purchase/Sale short-term & mid-term
Demand-Side management, e.g., interruptible load
Whether to announce emergency & trigger demand response Temperature Sustain region-wide hot/cold spell every year or two
Load Forecast Temp, RH Hourly forecast Temperature profile Hourly
Daily Load Forecast
System forecasting peak demand Temperature Summer patterns in parts of utility service area One time per summer
Wholesale purcahses Temp & day before temp Temp & day before temp 1 min
Transmission rating
G/Trans maintenance & scheduling mid-term F/C Temperature affecting load
Vulnerability of transmission & distribution Winter ice storm Breakdown of power transmission system Hourly
Location of fires
Conductor capacity Dry bulb temp, wind speed, solar radiation Accurate low wind speeds and dense coverage Al least hourly observations
Planning decision
Distribution Planning Temp Wind Speed Icing Wind speed, temp x precip (snow), icing potential 15 minutes
Transmission Planning Icing, temp, wind speed

Temp day before
Distribution wind speed
Snow
Icing potential

15 minutes

Weather Forecast Used
Decision Summary Variables Lead Time Resolution Source/Decision tool Benefit vs. no Forecast
Hydro Scheduling
Pump Storage Hydro
Hydropower Generation

Precipitation Temperature
Humidity
cloud cover

Seasonal Low Weather model Can put alternative power generation option in place
Resource planning and maintenence Tem/ % of norm 1 month to years Synoptic scale
Mesoscale
Vendor None yet, not used
River Forecast QPF average for basin w/ hi spots Up to 36 hours Mesoscale Weather Vendor
How to operate Columbia River Hydro System (which determines operation of rest of western electricity system) Unknown Unknown Unknown Unknown Unknown
Snow Pack
Unit Commitment
Fuel Contracting
Dispatch Renewables Wind speed insolation 24 hr 1 km2 ~3 cents
Vulnerabilty of Generation Dry Bulb temp Humidity 24 hr High resolution Weather model Saving resources in terms of power outages
Wind Generation
Purchase/Sale
Demand-Side management, e.g., interruptible load
Whether to announce emergency & trigger demand response Temperature 2-3 days Multiple states ?
Load Forecast Max
Min
Average
Up to 36 hours Fine grid resolution Computer models/weather vendor
Daily Load Forecast Temperature per each 1/2 hour for 24 hours 1/2 hour Years
System forecasting peak demand Max temp petterns and distribution Years Utility service territory and/or parts of service territory Huge $ value short term
Wholesale purcahses Wind Temp Precip x Temp Hourly Parts of Utility service area Value quicker & smarter purcases
Transmission rating
G/Trans maintenance & scheduling
Vulnerability of transmission & distribution Direction of movement of air mass 7 days high weather model Avoiding damage to many sectors dependent on power supply
Location of fires
Conductor capacity
Planning decision
Distribution Planning Temp & Wind speed distributions Year Parts of utility service area Avoided outages
Transmission Planning Temp
Wind Speed
Precip x temp Distributions
Year Parts of utility service area Avoided outages


Appendix: Glossary and Acronyms

AMS American Meteorological Society
ANN
Artificial neural network
ANNSTLF model A Neural Network Based Electric Load Forecasting System at EPRI
APEP Advanced Power and Energy Program, University of California, Irvine
ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers
AWS Automated Weather Source
CAISO
California Independent System Operator
CEC California Energy Commission
CERTS Consortium for Electric Reliability Solutions
CIM An EPRI model of markets
Climatology Long-term average and extreme values of weather phenomena
CMU Carnegie Mellon University
Co-generation Generation of heat and/or cold from the waste heat of electricity generation
COMET NCAR
COO Chief Operating Officer
CPUC Colorado Public Utilities Commission
Curtailment contract Turning off electric service to a customer for a short time during a critical period to reduce system load, in return for monetary compensation
Day-Ahead Refers to a market for electricity in which purchases are made one day in advance of their delivery
Dew Point Temperature at which water vapor precipitates, indicating the water content of the air
DG Distributed generation. Generating electricity near the place it is demanded, or away from the central concentration of generating plants that the system was designed around.
DICAST Dynamic, Integrated Forecast. A mesoscale model ensemble developed and maintained at RAP/NCAR
DOD Department of Defense (U.S.)
DOE Department of Energy (U.S.)
Dry Bulb Temperature The dry-bulb temperature of air is measured by a thermometer which is freely exposed to the air but is shielded from radiation and moisture.
EIC
Electricity Industry Center, Carnegie Mellon University

Electric Power System
Refers here to the physical and business sides of the industry: Fuel supply, generation, transmission, distribution, and consumption infrastructure. Producers, marketers, consumers, and regulators.
EPA Environmental Protection Agency (U.S.)
EPRI Electric Power Research Institute
ESIG Environmental and Societal Impacts Group (NCAR)
Forward Market Market for commodities that are deliverable in the future, a hedge on volatility
GIS Geographic Information Systems
IEC International body…
IT Information technology
MesoNet The Mesonet is a world-class network of environmental monitoring stations in Oklahoma.
MW MegaWatt = one million Watts (Joule/s)
NARUC National Association of Regulatory Utility Commissioners
NASA National Aeronautics and Space Administration
NCAR National Center for Atmospheric Research
NCDC National Climatic Data Center
NERC National Electric Reliability Council
NOAA
National Oceanographic and Atmospheric Administration
NREL National Renewable Energy Laboratory
NSF National Science Foundation
NWS National Weather Service
OWPI Oklahoma Wind Power Initiative
PNNL Pacific Northwest National Laboratory
POWER Plains Organization for Wind Energy Resources
QPF Quantitative Precipitation Forecast
RAP Research Applications Program, NCAR
RFP Request for proposal to fund research
RH Relative Humidity
RMS Root mean square error
RTO Regional Transmission Operator
SCAQMD Southern California Air Quality Management District
TESLA model A load forecast model
TVA Tennessee Valley Authority
Urban Weather Local weather over an urban area
USWRP Unites States Weather Research Program
UWIG Utility Wind Power Interest Group
WAPA Western Area Power Association
Weather Information Weather forecasts, climatological databases, measurements, publicly or privately procured, qualitative or quantitative.
Wet Bulb Temperature

The Wet-bulb Temperature of air is measured by a thermometer whose bulb is covered by a muslin sleeve which is kept moist with distilled andclean water, freely exposed to the air and free from radiation.