2. 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

3. 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.

4. 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

5. Basic Neuron Model W 1j W 2j W 3i Neuron i x1x1 x2x2 x3x3 vivi

6. Sigmoid Function Use plain sigmoid formula Alternatively

7. x1x1 xjxj xmxm y1y1 ynyn yiyi W 1q v 1q q Three Layer Back Propagation Network

8. 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.

9. Hopfield Network Each neuron contains two op amps. The output of neuron j is connected to input of neuron i through a conductance W ij

10. HNN Formulae Energy FunctionNeuron Dynamics

11. General Idea Take NLP problem

12. 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.

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

14. 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.

15. Constraints Minimum up and down times Ramping limits. Power balance

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

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

18. Satisfy upper and lower limits on power generations

19. NN Aided Unit Commitment

20. Back Propagation Assisted Unit Commitment

21. Approach A-1 Multi-stage Approach ANN-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

22. 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. Stage 1: ANN Prescheduler

23. 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

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

25. 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.

26. 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.

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

28. 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.

29. Mapping ED to HNN Write the energy function as:

30. Finds mappings as:

31. Improvements Choose large A Use momentum term

32. 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

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