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ANN-Based Operational Planning of Power Systems

ANN-Based Operational Planning of Power Systems. M. E. El-Hawary Dalhousie University Halifax, Nova Scotia, Canada. 7th Annual IEEE Technical Exchange Meeting, April 18-19, 2000 Saudi Arabia Section, and KFUPM. What am I to do?.

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ANN-Based Operational Planning of Power Systems

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  1. ANN-Based Operational Planning of Power Systems M. E. El-Hawary Dalhousie University Halifax, Nova Scotia, Canada 7th Annual IEEE Technical Exchange Meeting, April 18-19, 2000 Saudi Arabia Section, and KFUPM

  2. What am I to do? • I suspect that the audience includes people who are not power-oriented. • Offer a generic presentation. • Power examples are easily related to other areas.

  3. ANN Basics • Emulate behavior of systems of neurons. • A neuron nudges its neighbor in proportion to its stimulus. • The strength of the nudge is a weight. • Sum the weighted stimuli. • Scale using sigmoidal function

  4. Basic Neuron Model x1 W1j Neuron i W2j vi x2 W3i x3

  5. Sigmoid Function • Use plain sigmoid formula Alternatively

  6. Three Layer Back Propagation Network y1 yn yi W1q q v1q xm x1 xj

  7. The Process • Learning based on training patterns. • Initialize weights. • Present training patterns and successively update weights. • Updates initially based on steepest decscent. • Current trend is to use an appropriate NL descent method. • Iterate on weights until no further improvements.

  8. Hopfield Network • Each neuron contains two op amps. • The output of neuron j is connected to input of neuron i through a conductance Wij

  9. HNN Formulae Energy Function Neuron Dynamics

  10. General Idea • Take NLP problem

  11. Mapping Ignore inequality constraints Relate variable X to neuron output V The energy function will contain the m equality constraint terms in addition to the objective.

  12. Sample Operational Planning Problems • Unit Commitment • Economic Dispatch • Environmental Dispatch • Dynamic Dispatch • Maintenance Scheduling • Expansion Planning

  13. Unit Commitment • Given a set of available generating units and a load profile over an optimization horizon. • Find the on/off sequence for all units for optimal economy. • Recognize start up and running costs.

  14. Constraints • Minimum up and down times • Ramping limits. • Power balance

  15. Economic Dispatch • Find optimal combination of power generation to minimize total fuel cost. • We know the cost model parameters:

  16. Constraints • Meet power balance equation including losses. • L represents the losses and D is the demand • Losses are assumed constant

  17. Satisfy upper and lower limits on power generations

  18. NN Aided Unit Commitment

  19. Back Propagation Assisted Unit Commitment

  20. Approach A-1Multi-stage ApproachANN-Priority List-ANN Refined • Ouyang and Shahidehpour (May 1992) • Three stage process • Stage 1: ANN Prescheduling • Stage 2: Priority based heuristics. • Stage 3: ANN Refinement

  21. Stage 1:ANN Prescheduler • Obtain a set of load profiles & corresponding commitment schedules. • Cover basic categories of days. • Train ANN. • Feed forecast load to trained ANN. • Output of ANN is a preschedule.

  22. Pre-scheduling (cont.) • Input is 24 x N matrix. • N is load demand segments. • Each matrix element is related to a neuron in the input layer. • Each training load pattern corresponds to an index number in the output layer

  23. Pre-scheduling (cont.) • Recommends 50 to 100 training patterns. • NN prescheduling saves time and offers better matching.

  24. Stage 2:Sub-optimal Schedule • Consider outcome of prescheduling. • Use priority list. • Check minimum up and down times. • Examine on/off status of units and modify.

  25. Stage 3:ANN Schedule Refiner • Trained using pairs of sub-optimal solutions as input and optimal solution as output. • NN generalizes the refinement rule. • Used three different techniques.

  26. Training Pattern Generation(Cont.) • Operator generated better unit commitment solutions. • Base units are not involved in the refinement process.

  27. Hopfield Implementaions • Usually BP Nets are good at pattern recognition. • For optimization problems, the Hopfield network has been shown to be more effective. • By way of example, we show the application to economic dispatch.

  28. Mapping ED to HNN • Write the energy function as:

  29. Finds mappings as:

  30. Improvements Choose large A Use momentum term

  31. What Else? • Virtually every area involving prediction or optimization has been treated using ANN. • Examples include hand movement animation. • Computer communication network congestion management. • Computer communication network routing

  32. Thanks • I hope that we learned something together. • Thanks to all of you, and specially Dr. Samir Al-Baiyat and the Organizing Committee

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