ANNBased Operational Planning of Power Systems M E

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

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.

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

Basic Neuron Model x 1 W 1 j W 2 j x 2 x 3 W 3 i Neuron i vi

Sigmoid Function • Use plain sigmoid formula Alternatively

Three Layer Back Propagation Network y 1 yn yi W 1 q q v 1 q xm x 1 xj

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.

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

HNN Formulae Energy Function Neuron Dynamics

General Idea • Take NLP problem

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.

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

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.

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

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

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

• Satisfy upper and lower limits on power generations

NN Aided Unit Commitment

Back Propagation Assisted Unit Commitment

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

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.

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

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

• • 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.

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.

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

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.

Mapping ED to HNN • Write the energy function as:

• Finds mappings as:

Improvements Choose large A Use momentum term

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

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