A hybrid fuzzy neural network-expert system for a short term unit commitment problem

Microelectron. Reliab., Vol. 37, No. 5, pp. 733-737, 1997

~

Copyright © 1997 Elsevier Science Ltd

Pergamon

Printed in Great Britain.All rights reserved 0026-2714/97 $17.00+ ,00 PII:

S0026-2714(96)00095-9

A HYBRID FUZZY NEURAL NETWORK-EXPERT SYSTEM FOR A SHORT TERM UNIT COMMITMENT PROBLEM NARAYANA PRASAD PADHYt, S. R. PARANJOTHIt and V. RAMACHANDRAN~ t School of Electrical and Electronics Engineering, College of Engineering, Anna University, Madras-600025, India School of Computer Science and Engineering,College of Engineering, Anna University, Madras-600025, India

(Receivedfor publication 2 May 1996)

Abstract--Themajor aim of this paper is to develop a hybrid model to determine the unit commitment schedule of generating units. It is proposed to produce the unit commitment schedules in two steps. In the first step, the input layer of the neural network is designed to adopt nonlinear fuzzy membership values of the load demand profile. In the second step, an adaptive expert system is used to manipulate the schedules by using rule-base and inferencemechanism. To demonstrate the effectivenessof this model, extensive studies have been performed for different load profiles belonging to the power systems of Tamilnadu consisting of thermal, nuclear, hydro, wind and diesel power plants. Copyright © 1997 Elsevier Science Ltd.

INTRODUCTION It has been recognized that an accurate short-term unit commitment process presents a great saving potential for electric utility corporations. For nearly the past two decades, a dynamic programming approach [1] has been used to solve the power systems unit commitment problem. But the importance of a mathematical based dynamic programming approach has been reduced drastically due to its complexity and problem dimensions. Hence it is required to have a method which yields fast solutions for the unit commitment scheduling problems. In recent years, the planning and operation of complex systems such as power systems involved many uncertainties. The prediction of load demand is not precise. In the proposed method, the load demand profile has been considered as fuzzy in nature and the linguistic values are used to represent load demands [2]. More efforts have been taken already to develop AI based unit commitment problems, but completely successful results have remained uncertain due to certain limitations. It has been proven that the neural networks will provide better results for complex and uncertain data for which traditional approaches are not adequate [3]. With neural computing approaches to solving unit commitment problems, the majority of time is spent training the neurons off-line. This approach also does not guarantee the optimal solution. In the case of fuzzy systems it is not so easy to create the exact membership function for the load demand profile. The expert system alone may not be able to better serve complex problems. Both fuzzy and expert systems are well known for their ability to represent, manipulate and utilize the data and information that possess non-statistical uncertainty [4, 5]. It is proposed to 733

design a neural network with a fuzzy input layer for solving the unit commitment problem. The outputs of the neural network are analyzed through an adaptive expert system [6, 7] to have effective and fast decision making in scheduling the units. The generation scheduling is an enormously complicated problem due to the characteristics of

C demand R°al°ad profile Divide the load demandinto five differentlevels (precisely) definedfuzzy values

Initialize

andexecute neural network

1

Execute expert system )

. Fa-ff i

,f7:7

r

No

./-'i f ~

Yes

Fig. l. Flow chart for the hybrid model.

N.P. Padhy et al.

734

generating units and operational constraints. It is very difficult to modify the existing procedures as additional constraints are introduced in the system [8]. The proposed hybrid model yields a promising approach to solve a short-term unit commitment problem which will offer good performance and overcome the above said difficulties. The flow chart for the proposed hybrid model is shown in Fig. 1.

The schematic representation of a fuzzy neuron for the load demand profile is shown in Fig. 3.

Neural network configuration In the proposed method, a three layer ANN model is used. The input layer of the ANN is made to adopt a fuzzy classified load demand profile which consists of N neurons, where N represents the total hours in the schedule period. Since the load demand is forecast at one hour interval for the short term unit commitment problem, the value of N is considered as equal to 24. Similarly the neurons in the output layer from the output schedule are of size N ' M , where M represents the number of units to be scheduled. The output schedule has values of 1 and 0 to represent on and off conditions replacing for the units. The schematic representation of the neural network is shown in Fig. 4. For training purpose, a set of load profiles and their corresponding commitment schedules are prepared. The corresponding commitment schedules are obtained on and off line using dynamic programming techniques. The above model is trained with input [load variables] vs output [commitment schedule]. Presently the number of input neurons are considered to be 24

FUZZY-NEURAL NETWORK DESIGN

Input layer representation Linguistic values are used to represent the load demands. A triangular membership function shown in Fig. 2. has been used to assign values for the input load demand profile. It has been considered that the regions very low, low, medium, high and very high are overlapping. Let the input vector to the neural network be X

=

(x1, x 2 .....

xi).

The fuzzy neurons associated with X are defined by, X = (/1(xl),/*(x2) . . . . . U(xi)), where i,(xi) denotes the grade of membership of xi in this set.

Very

0

•

MW

.

Very .

( Demand )

Fig. 2. Triangular membership function for load demands.

VH H NI

FN

L VL Fig. 3. Schematic representation of fuzzy neurons.

-0

A hybrid fuzzy neural network-expert system

735

Fuzzy input layer [Load demand )

Hidden layer

Output layer (Pre - scheduling )

Fig. 4. Schematic representation of ANN. and output neurons to be 240 (24"10) using 50 hidden neurons. During training, the input parameters to back propagation neural networks comprise different features of demands. This input pattern is used for producing 24 h ahead unit commitment scheduling. The BPN algorithm has been used to train the neural network to obtain initial scheduling. An adaptive expert system has been designed to handle different constraints like minimum up/down time, spinning reserve etc. Supervised learning is used to assign membership function values to the input nodes and for each training vector. The error is computed with respect to each such desired output. After a number of iterations, the neural network converges to a minimum error solution. Finally the proposed model is capable of presenting an initial commitment schedule successfully. This trained fuzzy neural network will produce the initial schedule pattern for any input fuzzy load profile. AN ADAPTIVE EXPERT SYSTEM To implement the final scheduling process quickly and most efficiently, it is desirable to reduce the amount of calculations as required by traditional approaches by introducing an adaptive expert system. The schematic representation of an adaptive expert system is shown in Fig. 5. The minimum up/down time constraints impose the most challenging task for optimal scheduling. The units with larger minimum up/down time will have a greater impact over the

Pre scheduling O/P of fuzzy neuron

,----q I

"t ~

network

engine

base

inference

Wor io memory

4--t----'

Fig. 5. Schematic representation of an adaptive expert system. overall scheduling strategy. However, from a practical point of view, units that have larger minimum up/down times are the least probable to be committed and decommitted in the short term scheduling. Depending upon the demand, different types of units are brought to the system for scheduling. The rule base consists of a set of production rules. The production rules are expressed in terms of: IF (premise); THEN (conclusion). For example: IF the load demand p medium demand; THEN go for thermal units. The performance of the present model can be improved by simultaneously satisfying different constraints by implementing a number of production rules. In many emergency conditions, as in the case of a short term unit commitment problem, a suboptimal solution using an expert system can be obtained for a real time operation instead of a

736

N. P. Padhy et al.

Pre-scheduling

t ----~

Forecasted load profile

Fuzzy classifiedload profile

Input

Fuzzy neural [ network -/for pre] scheduling

t!0 l 0:0 0 .1.1.1.

1 1 0

Scheduling 1 1 1

oio o [

1 I 1 1 I 1

Manipulation [using _ Iexpert -[system

H

Inference engine

VL

I User inference

I/P

Oul A~ut

Knowledge base if and then rules Working ] memory I

l vsO

f-----'

T

Ii Networktraining l-

I[ New schedule

Fig. 6. Block diagram representation of the hybrid fuzzy neural network-expert system for a short term unit commitment problem. mathematical optimal solution that requires a longer time to take action. The structure of the proposed hybrid fuzzy neural network-expert system consists of the following parameters: (a) fuzzyfied input load profile; (b) pre-scheduling from fuzzy neural network; (c) final scheduling through an adaptive expert system and has been illustrated by the block diagram representation as shown in Fig. 6. Test results

To demonstrate the effectiveness of the proposed method, the load demand profile shown in Fig. 7 together with unit data shown in Table 1 of an electric

16oo[1500[~

14oollk"

~ o

1300 II 1200 1100

T

o

~800

~

70O '1

i,i, 4

6

M

8

H

I

I

I

I

I

10

12

14

16

18

Time (h)

Fig. 7. A load demand profile.

20

22

Generation (MW)

Cost coefficients

Unit number

(Min)

(Max)

A

1 2 3 4 5 6 7 8 9 10

20 20 30 30 50 0 250 50 120 80

60 80 100 120 150 80 520 150 520 200

15 25 40 32 29

B

C

1.40 0.00551 1.50 0.00396 1.35 0.00393 1.40 0.00382 1.54 0.00212 (wind plant) 105 1.39 0.00127 100 1.32 0.00135 (hydal plant) 82 1.21 0.00148

utility of Tamilnadu (INDIA) have been considered. Once the ANN is initiated, membership functions representing the load demand profile have been inserted into the input layer. The ANN will yield a preschedule at the output layer shown in Table 2. This result has been analyzed by again sending the output to the adaptive expert system to have a real time commitment schedule which is shown in Table 3. The comparison of the cost of generation due to the hybrid model and dynamic programming is given in Table 4.

/

-4 ½

Table 1. Generating unit data

CONCLUSION

24

A hybrid fuzzy neural network-expert system is developed for calculating a one day ahead schedule for short-term unit commitment problems. In this method the neural network is implemented in such a way that it can accept fuzzy values in the input layer. An

A hybrid fuzzy neural network-expert system

737

Table 2. Pre-scheduling after fuzzy neural network processing Hour Units

1

2

3

4

5

1 2 3 4 5 6 7 8 9 l0

1 0 0 1 1 1 l 0 l 1

0 0 0 0 1 1 l 0 l 1

1 1 1 0 0 1 l 0 l 0

1 1 1 0 0 1 l 0 l 0

1 1 1 0 0 1 l 0 l 0

6

7

1 0 0 0 0 1 l 0 l 1

8 0 0 0 0 1 1 l 0 l 1

9 1 0 0 0 0 1 l 0 l 1

10 1 1 1 0 0 1 l 0 l 0

1 1 1 0 0 1 l 0 l 0

11

12

0 0 0 0 1 1 l 0 l 0

0 0 0 0 1 1 l 0 l 0

13 0 0 0 0 1 1 l 0 l 0

14 0 0 0 0 1 1 l 0 l 0

0 0 0 0 1 1 l 0 l 0

15

16

0 0 0 0 1 1 l 0 l 0

0 0 0 0 1 1 l 0 l 0

17

18

19

20

21

22

23

24

22

23

24

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 l 0 0 0 0 0 1 1 1 1 1 1 1 1 1 l l l l l l l 0 0 0 0 0 0 0 l l l l l l l 0 0 0 0 0 0 1

Table 3. Final scheduling through an adaptive expert system Hour Units 1

2 3 4 5 6 7 8 9 10

1

2

3

4

5

6

7

8

9

10

11

12

1 0 0 1 1 l l 0 1 1

1 0 0 0 1 l l 0 1 1

1 1 I 0 0 l l 0 1 1

1 1 1 1 1 1 1 0 0 1 1 0 0 0 1 1 0 0 l l 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 l l l l l l l l l l l l l l l l l l 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0

13 0 0 0 0 1 l l 0 1 0

14 0 0 0 0 1 l l 0 1 0

0 0 0 0 1 l l 0 1 0

15

16

0 0 0 0 1 l l 0 1 0

0 0 0 0 1 l l 0 1 0

17 0 0 0 0 0 l l 0 1 0

18 0 0 0 0 0 l l 0 1 0

0 0 0 0 0 l l 0 1 0

19

20

0 0 0 0 0 l l 0 1 0

0 0 0 1 0 l l 0 1 0

21 0 1 0 0 0 0 1 1 1 1 l l l l 0 0 1 1 0 1

Table 4. Computation of total cost of generation Method

Total cost of generation Rs./day

1. Proposed (hybrid model) 2. Dynamic programming approach

1.008 p.u. 1.000 p.u.

expert system is used to m a n i p u l a t e the initial c o m m i t m e n t schedule for real-time operation. The rules are developed to meet constraints successfully. The results o b t a i n e d from the above model are highly impressive a n d e n c o u r a g i n g for real-time implem e n t a t i o n in any power systems.

4. 5. 6.

REFERENCES

1. Snyder, W. L., Powell, Jr. and Rayburn, J. C., Dynamic programming to unit commitment, IEEE Trans. PWRS, 1987, 2(2), 339-350. 2. Ouyang, Z. and Shahidehpour, S. M., Short term unit commitment expert system, Electric Power System Res., 1990, 18(1) 1-13. 3. Ouyang, Z. and Shahidehpour, S. M., A hybrid artificial neural network-dynamic programming approach

7.

8.

to unit commitment. IEEE Trans. PWRS, 1991, 438-442. Dash, P. K., Dash, S. and Rahman, S., A hybrid neural network-fuzzy expert system for short term load forecasting. ESAP, 1993, 175-181. Kim, K. H., Park, D. Y. and Park, J. K., A hybrid model of artificial neural network and fuzzy expert system for short term load forecast. ESAP, 1993, 164-169. Padhy, N, Paranjothi, S. R., Ramachandran, V. and Ramaraj, N., Application of expert system to a short term unit commitment problem. Proc. Nat. System Conf. 95, pp. 343-349, 1995. Padhy, N., Ramachandran, V. and Paranjothi, S. R., A fuzzy decision system for solving unit commitment problem. Proc Nat. Conf. on Neural Networks and Fuzz)' Systems, pp. 287-299, 1995. Sheble, G. B. and Fahd, G. N., Unit Commitment Literature Synopsis. IEEE Trans. on Power Systems, 1994, 9 (1) 128-135.