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Dont Ask me what I want, ask me what I do: the key to valid requirements documentation Mary Altalo Ocean.US.

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Presentation on theme: "Dont Ask me what I want, ask me what I do: the key to valid requirements documentation Mary Altalo Ocean.US."— Presentation transcript:

1 Dont Ask me what I want, ask me what I do: the key to valid requirements documentation Mary Altalo Ocean.US

2 Understanding How Climate, Wx, Water Information is used- Getting to Requirements Information in Federal Resource Management Decisions Information in Federal Resource Management Decisions –Setting codes and standards- building safety –Management of natural resources-water, forests –Regulatory oversight of social systems: energy, transport, water, health Information in International Trade/Aid Decisions Information in International Trade/Aid Decisions –Treaty and Trade Agreements –Canada Northern Trade Route –Aid priorities and planning- Zimbabwe Power, resettlement issues in Industry Operations and Planning Decisions in Industry Operations and Planning Decisions –Tactical Operations –Strategic Planning From Mitigation to Adaptation From Mitigation to Adaptation –From Way of Life (practice) to Business Process Reengineering (new tools and practices)

3 Diagnostic Approach to Assessing Vulnerability and Risk

4 Driving Principles for Managing with Environmental Information Regulatory Regulatory Decision Accountability/Shareholder Value Decision Accountability/Shareholder Value Safety of Life and property Safety of Life and property Market Economics & Competitive Advantage Market Economics & Competitive Advantage Risk reduction Risk reduction Reliability, Efficiency, Sustainability Reliability, Efficiency, Sustainability Corporate Social Responsibility- Indices Corporate Social Responsibility- Indices

5 Critical forecast periods Sub day, 2-4 day, 90 day

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7 Weather Decision Support Analysis Tool Set for Power Management Market Demand Fuel Type Fuel Price Regulatory Constraints Fuel Availability MaintenancePower Pricing Tactical Planning Infrastructure Planning,Design &siting Generation Commitment Dispatch Management Strategic Planning Revenue Forecasting/ Stock Pricing Load Balancing Market Demand Regulatory Constraints Equipment Readiness Grid Availability Weather Impacts Emergency Response Market Demand Fuel Type Fuel Price Regulatory Constraints Fuel Availability Market Demand Transmission Constraints Maintenance Availability Congestion Infrastructure Tariff Calling Market Demand Price Options Environmental Constraints Available Generation Power Industry Decision Aid Architecture

8 Environmental Information Flow on the Operational Decision Process: Risk Reduction Areas scheduling and demand balancing scheduling and demand balancing asset management and replacement asset management and replacement enterprise wide contingency and financial planning enterprise wide contingency and financial planning demand reduction and price responsive loads demand reduction and price responsive loads environmental dispatch environmental dispatch congestion management congestion management NMHS Other Providers: GODAE team Met-ocean Data Collection Present Value-Added Service Providers Met-Ocean Data Formatting Met-ocean Data Analysis and IT Services: Quantify, source, cost and reduce weather data error Short Mid Long term Demand Forecasting Models Met-Ocean Pre- Processing DATA INFORMATIONKNOWLEDGEACTIONOUTCOMESIMPACTS Load Model Error Analysis: Improve Software and Support Economic/ Performance Valuation of Weather/ocean Error Impacts Decision Analysis, Dependencies and Support Tools Met-ocean Information Integration Met-ocean Information Evaluation Improved profit Increased Efficiency Improved reliability Increased safety Decreased Liability Decreased Risk Decreased Exposure Met-ocean Decision Support Operational Decisions Outcomes Situational Awareness----Decision Support----Optimal response

9 U.S. Coastal Utilities Trial Areas

10 Key Utility functions/decisions requiring coastal weather/climate/ocean data Load Balancing-single utility and grid Load Balancing-single utility and grid –Generation commitment- fuel mix choice (fossil fuel, hydro, wind) –Dispatch scheduling Power Marketing Power Marketing –Cash trading –Power pricing –Fuel pricing and procurement Tariff Scheduling Tariff Scheduling Natural Gas Storage Management Natural Gas Storage Management Revenue Projections Revenue Projections Infrastructure siting Infrastructure siting Management strategic planning Management strategic planning

11 Electricity Demand Model Error - Neural Net Diagnostics Skill of the Environmental Forecast Impacts the Skill of the Power demand forecast Power Demand Forecast Model- AANSLFF, RER Metrix, etc. or ensemble Weather Forecast Model- AVN, MRF, etc or ensemble

12 Urban Utility Case Study Findings 1: Significant load error due to weather Most utilities calculate weather error in MW as well as percentage of variance of the load. Analysis indicates that on some days, variance in the load forecast in MW may be solely due to weather error. This appears to be from events or unmodeled mesoscale features such as back door fronts, sea breeze and afternoon thunderstorms. The cost of such events can be up to $10M/day in wasted generation Most utilities calculate weather error in MW as well as percentage of variance of the load. Analysis indicates that on some days, variance in the load forecast in MW may be solely due to weather error. This appears to be from events or unmodeled mesoscale features such as back door fronts, sea breeze and afternoon thunderstorms. The cost of such events can be up to $10M/day in wasted generation

13 Wind Resource Map Produced by SiteWind Bring in new data and reconfigure and train load model to use it optimally if neededIT support services

14 Key Cost Findings Northeast Energy The project estimated that the benefits of improving day ahead weather forecast accuracy by one degree F or by reducing forecasting error by 50% for days 2-7 is: --$20-25 million per year for a regional transmission authority --$20-25 million per year for a regional transmission authority --$1-2 million/year for a major distribution utility. --$1-2 million/year for a major distribution utility. Optimal use of weather information could yield savings of $8–18 million/year for a major university system (electric and natural gas). If these savings were generalized to other regional transmission organizations, large statewide colleges and universities and regional transmission authorities the total savings would be for the Northeast Region: -- $ million/year for ISOs --$30-60 million for regional electric distribution companies. -- $38-67 million for Statewide university campuses Furthermore, capturing the events on top of this will yield significantly higher savings (millions/day).- seabreeze, backdoor fronts, afternoon showers

15 West Coast Power Load balancing Problems Coastal winds Coastal fog Microclimates El Nino

16 Consequences of Electrical Load Error Underforecast- May Underforecast- May –Generation shortfall of 5000MW ( load required by 500,000 homes )- near blackout –Buy on spot $200+ with caps up to 1000 w/o caps –One day 5-7% error costs of $4-7M/day –Cause- ocean-related weather forecast error of 4% Overforecast- September Overforecast- September –Generation overcommittment –Similar magnitude –Less costly (previously contracted generation) and less visible impact

17 Retrospective Analysis:Relationship of Coastal Weather Uncertainty and Cost

18 Cal ISO Mean Daily Forecast Error Delta breeze and weather/load forecast errors contribute to major errors in prediction of Delta Breeze effects. Delta breeze is defined as the conditions when the wind speed is > 12 knots and the direction is between 190 degrees and 280 degrees. Delta Breeze can change load by 500MW Direct Costs: 250k per breeze day; 40 events per year An overforecast problem Delta Breeze events

19 Principle Causes of Uncertainty on Energy Operations and Planning Uncaptured WIND Events Uncaptured WIND Events –Coastal Delta Breeze- Cal ISO –Lake effects- Salt Lake City- Pacificorp, Great Lakes- SUNY Buffalo –Seabreeze- NE ISO –Coastal Frontal passage- 2-4 day Uncaptured PRECIPITATION Events Uncaptured PRECIPITATION Events –Rain vs. snow/ice –Regional day ahead error in precipitation- Pacificorp –Afternoon thunderstorms –Marine Layer, fog- SDG&E –Drought and flood, flash flood Uncaptured CLIMATE Events Uncaptured CLIMATE Events –Climate outlooks –weather events frequency –El Nino and seasonal events –Decadal ocsillations- NAO RESOLUTION- Spatial, temporal RESOLUTION- Spatial, temporal –Sub grid level –Targeted watershed level, Nodal, congestion and population –Topographic Effects- microzones –Hourly changes during events Load Model Error Load Model Error –50% load error at certain event periods –Cant incorporate probabilities/ ensembles Sub-optimal Use Sub-optimal Use

20 Requirements

21 Activating the Information Link to an Engineering Requirement Codifying

22 Turning a Parameter into a Factor in an Engineering Equation The key to activating observing system Information Case study 1: Activating rain and wind data in reservoir management. Improved precipitation, and winds feed into actions for runoff conservation, reservoir management, water quality The Revised Universal Soil Loss Equation (RUSLE) due to rainfall The Revised Universal Soil Loss Equation (RUSLE) due to rainfall A=RKLSCP A=RKLSCP soil erosion the equation due to Wind soil erosion the equation due to Wind E = f (I K C L V) E = f (I K C L V) Climatic factor C determined by wind velocity and soil surface moisture R rainfall erosion index, which includes the amount as well as the force of the precipitation Better ocean observations and Ocean-atmospheric models lead to better precipitation forecasts Better ocean observations and models lead to better wind forecasts

23 Activating Inundation and Sea Level Information Case Study Two: Wave, sea level, storm surge, wave height and Beach Erosion and sediment transport 4 Affected Hydrodynamic Processes which are measures by GOOS Storm Surge Storm Surge Tidal Ranges and Currents Tidal Ranges and Currents Waves Waves With rising sea level, there is increased impact of coastal storms. Improved wave prediction as well as the inundation parameters are required in the mitigation strategies of beach nourishment and armament The relationship between sea level rise and waveheight (Dean,1986) Relationship between wave height and beach erosion Dean (1986)

24 Link to Decision Support Tools and Management Scenarios

25 Linking Forecast Simulation Tools with Emergency Response Simulation Tools can aid in Severe Weather Emergency Energy Management Storm Tracking with simulation tool- predict hurricane landfall Emergency preparedness with CATS (consequence assessment tool set) Locate critical energy assets, estimate damage and position for relief Expert Grid Management Situational Awareness and Power Restoration Management Tool Data-Information KnowledgeAction and Outcomes

26 Economic Valuations for Risk Reduction

27 The Value of a Seasonal Seabreeze Forecast to Energy Demand Forecasting 2. Major Sea Breeze Events Cause Significant Electricity Demand Error as power demand drops. Savings of up to 2 million per year for increased accuracy of forecast 1. Major sea breeze events (May-September) are not captured adequately in the day ahead temperature forecast 3. Bringing in more offshore observation data can enhance forecast accuracy 4. Using ensemble forecasting techniques can enhance information content of the forecast (probabilities) adding spread around forecast 5. Improving power management for national grid operators

28 Value of Improved Seasonal Precipitation Forecasts to Agriculture Irrigation Pump Load Forecasting and Power Grid Stability 1. Timing and total irrigation time varies from season to season depending on soil moisture causing unpredicted draw on electricity grid due to start of pumps Ocean Obs/ Models/Thorpex type experiments enter here T &Ppt forecast Soil moisture Crop type Planting time Prediction of Power and Water Demand from Irrigation Load Power Purchasing on contract vs spot Grid stability and Cost savings (.25M/yr for I utility)

29 Using ensembles to improve day ahead Weather forecasts for A Southern Power Utility with L. Smith of the LSE A 30% reduction ( real dollar savings of $300,000 ) over two very different days A 30% reduction ( real dollar savings of $300,000 ) over two very different days

30 Value of Ensemble Forecasting to Electricity Load Forecast Accuracy 1. Existing weather error is costly 2. Ensembles have more information- Precision & Probability 4. Saves Utility network operator $15M during summer peak period over present method 3. Works with asymmetric cost curve

31 The GOOS Challenge

32 Defining the Program Elements Based on Societal performance metricsTechnology performance metrics Know-how KnowledgeInformationDataObservations Alpha testBeta test -Maritime Safety -Homeland Security -Natural Hazards -Climate Change -Public Health -Ecosystem management -Marine Resources - Scientific Understanding GOOS IS ARRANGED AROUND ITS VALUE STREAM Feedback for EVOLUTION e.g. NDBC

33 Wx/Climate, Water Information Supply Chain Knowledge Management as well as Data Management Regional Alliance Approach

34 Activated Environmental Information The Management Decision Process

35 GOOS Information Products in the EU Market Value Chain GOOS/GEO

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