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) 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

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

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

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

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

Knowledge-Based Application Development Project Weather Industrial Demand System Failures UNCONTROLLED EVENTS - Supply - Pipeline - Burnertip - Interconnect 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 Storage Well & Pipeline Supply Transportation Imbalance Demand Curtailment High Reliability of Service Lower Gas Cost GAS SOURCES OBJECTIVE

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

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 heating season 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

First Prototype of Automated System - System Flow Diagram Monthly Plans Rule-Based Expert System Budgeted Demand Weather Profiles Weather-Driven Demand WACOG Model Multiple Scenarios WACOG/Risk Profile

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

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

Rules for Monthly Supply Selection - A Starting Point

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

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

Decision Support Model for Gas Expert System Project Preliminary Calculation Model Rules & Contestants Predicted Response of System Manual Comparison to Actual Actual Gas Supply Deliverability Max. & Contractual Min. Actual Weather Validation

Decision Support Model for Gas Expert System Project Preliminary Calculation Model Rules & Contestants Predicted Response of System Gas Supply Forecast Deliverability Max. & Contractual Min. Current Implementation Evaluation Function or “Critic” Risk Factors & Distribution of Probable WACOG Weather History Generate Scenarios (Monte Carlo Method) Weather Scenarios

Decision Support Model for Gas Expert System Project Preliminary Calculation Model Predicted Response of System Gas Supply Forecast Deliverability Max. & Contractual Min. Partially Automate the Search for Better Rules by Using A.I. Techniques Evaluation Function or “Critic” Weather History Generate Scenarios Weather Scenarios Modify Rules Rules & Contestants WACOG & Risk Factors

GasXpert System Design Overview Genetic Programming Selection Crossover Mutation Create New GasXpert Plans as CLIPS Rules Create Weather and Demand Scenarios Expert System Control, Constraint, and Input/Output Rules GasXpert Plan (Supply contracts and storage capacities are considered fixed in this model) Evaluate Performance of Plan on Given Scenario Evaluate Performance of Given Plan Across All Scenarios Fitness Evaluate Perfor- mance of All Plans in Popu- lation Across All Scenarios