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Outline

Outline. Terminology Typical Expert System Typical Decision Support System Techniques Taken From Management Science and Artificial Intelligence Overall Project Project Evolution -- Synthesis of Techniques System Diagram Ultimate Goal for System Outputs. Outline (continued).

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Outline

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  1. Outline • Terminology • Typical Expert System • Typical Decision Support System • Techniques Taken From Management Science and Artificial Intelligence • Overall Project • Project Evolution -- Synthesis of Techniques • System Diagram • Ultimate Goal for System Outputs

  2. Outline (continued) • Plan for First Prototype of Automated System • Immediate Objective for Prototype Automated System • System Flow Diagram • A Possible Starting Point • Basis for Storage Injection/Withdrawal Model Computation • Monthly Plan for Supply Selection • Rules for Monthly Injection Computation • Rules for Monthly Supply Selection • Rules for Monthly Withdrawal Computation • Discussion Agenda -- Input from Team of Experts • Next Steps

  3. Typical Expert System • Accumulates knowledge, including tricks • Codifies expert knowledge, often in the form of rules • Makes the expertise available, even when the expert is not, by emulating the decision-making ability of the human expert • Performs (at best) as well as the human expert that it emulates, but cannot go beyond the knowledge that was gathered

  4. Typical Decision Support System • Assists managers in their decision processes about semi-structured tasks • Supports managerial judgment by providing a smorgasbord of analytical tools and models • Seeks to improve the effectiveness of decision-making and to generate better solutions than are currently in use • Helps managers respond to novel or unanticipated situations

  5. Techniques Taken from Management Science and Artificial Intelligence • Linear Programming (LP) • Heuristic Search • Pattern Recognition • Machine Learning

  6. As our project evolves, we find that it needs a synthesis of techniques • Gathers knowledge from multiple experts • Uses rules to simulate the decisions made in managing gas sources for the pipeline • Tries more possible solutions than are possible to evaluate by hand • Not an exhaustive search • Guided by heuristics from human experts • Uses machine learning techniques to try to improve the rules

  7. Knowledge-Based Application Development Project UNCONTROLLED EVENTS SystemFailures IndustrialDemand Weather - Supply- Pipeline- Burnertip- Interconnect GAS SOURCES OBJECTIVE Storage Well & Pipeline Supply Transportation Imbalance Demand Curtailment High Reliability of Service Lower Gas Cost Contracts - Take-or-Pay - Recoup - Tests for Deliverability Regulations - Ratable Physical System Capacity - Maximum Limits (e.g., MAOP, Well Deliverability, Injection, Withdrawal) - Minimum Required - Transients CONSTRAINTS

  8. Ultimate Goal for System Outputs • Create monthly plans for selection of gas supply that will minimize WACOG, while maintaining a high reliability of service and meeting contractual and regulatory requirements • Consider the yearly cycle when developing the monthly plans • Support replanning on a real-time basis in response to changing circumstances during the month

  9. Immediate Objective for Prototype Automated System • Create monthly* plans for selection of gas supply that will result in a lower WACOG* Generate plans for winter heatingseason only • Use rules that assure meeting contractual and regulatory requirements • Present information that allows managers to appraise the level of risk associated with each plan

  10. First Prototype of Automated System -System Flow Diagram WACOGModel Multiple Budgeted Demand Monthly Plans Scenarios Rule-BasedExpertSystem WACOG/RiskProfile Weather-Driven WeatherProfiles Demand

  11. Basis for Storage Injection/Withdrawal Model Computation - A Starting Point • Weather profile for each calendar month • Need to add electric generation usage later • Sample table from academic paper • Shows only December • Based only on estimates, not analysis • Consider this a starting point

  12. Basis for Storage Injection/Withdrawal Model Computation - A Starting Point * Sample Taken From Academic Paper

  13. Rules for Monthly Supply Selection - A Starting Point

  14. Agenda • Consider the architecture of the proposed model • Granularity of models • Discuss temperature thresholds • Discuss translations of weather profiles to Bcf of gas • Incremental demand by residential and commercial customers • Storage injection and withdrawal

  15. Next Steps • Further analysis of weather data • Research historical transportation imbalances and use of storages • Implement a very simple version of this system in CLIPS • Compare the Possible Starting Point method to the current Operating Guidelines

  16. Decision Support Model for Gas Expert System Project Deliverability Max. & Contractual Min. Actual GasSupply PredictedResponse ofSystem PreliminaryCalculation Model Actual Weather ManualComparisonto Actual Rules&Contestants Validation

  17. Decision Support Model for Gas Expert System Project PredictedResponse ofSystem Deliverability Max. & Contractual Min. Gas SupplyForecast EvaluationFunctionor “Critic” PreliminaryCalculation Model Weather Scenarios WeatherHistory Risk Factors &Distribution ofProbable WACOG GenerateScenarios(Monte CarloMethod) Rules&Contestants Current Implementation

  18. Decision Support Model for Gas Expert System Project PredictedResponse ofSystem Deliverability Max. & Contractual Min. Gas SupplyForecast EvaluationFunctionor “Critic” PreliminaryCalculation Model Weather Rules &Contestants Scenarios WACOG& RiskFactors WeatherHistory GenerateScenarios ModifyRules Partially Automate the Search for BetterRules by Using A.I. Techniques

  19. GasXpert System Design Overview Genetic Programming • Selection • Crossover • Mutation • Create NewGasXpertPlans asCLIPS Rules • Fitness • Evaluate Perfor-mance ofAll Plansin Popu-lationAcrossAllScenarios Create Weather and Demand Scenarios Expert System Control, Constraint,and Input/Output Rules GasXpert Plan (Supply contracts and storage capacitiesare considered fixed in this model) EvaluatePerformance ofGiven Plan Across AllScenarios EvaluatePerformance ofPlan on GivenScenario

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