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ANN-Based Operational Planning of Power Systems

Explore artificial neural network (ANN) basics and applications in power system planning. Learn about ANN models, back propagation networks, Hopfield networks, and more. Discover how ANNs are used in unit commitment, economic dispatch, maintenance scheduling, and expansion planning. Follow a multi-stage ANN-aided planning approach. Gain insights into training patterns, sub-optimal scheduling, and ANN schedule refinement. See how ANN can enhance operational planning efficiency and accuracy.

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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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