 Open Access
 Total Downloads : 484
 Authors : Raghuram Pydisetti, A.Tejasri, M. Sridhar
 Paper ID : IJERTV1IS8534
 Volume & Issue : Volume 01, Issue 08 (October 2012)
 Published (First Online): 29102012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Solution Of Unit Commitment Problem Both In Traditional And Deregulated Environment
Raghuram Pydisetti 
A.Tejasri, Asst. Professor 
M. Sridhar, 
PG Scholar 
Guide 
Professor & HOD 
Department of Electrical Engineering 
Department of Electrical Engineering 
Department of Electrical Engineering 
Godavari Institute of Engineering&Technology 
Godavari Institute of Engineering&Technology 
Godavari Institute of Engineering&Technolog 
Rajahmundry 
Rajahmundry 
Rajahmundry 

Abstract
This project applies modified GA to the UC problem and illustrates details of the performance of Genetic Algorithm. The aim of this work is to propose the suitability of a new approach to the solution of the UC problem in both traditional and deregulated environments. In this approach, the GA maintains a population of highly fit chromosomes or strings and probabilistically modifies the population seeking a near optimal solution to the given task. A program is developed in MATLAB 6.1 for the proposed method for solving the UC problem. MATLAB
6.1 is a highperformance language for technical computing [32]. The name MATLAB stands for matrix laboratory. It integrates computation, visualization, and programming in an easytouse
environment where problems and solutions are expressed in familiar mathematical notation.

Introduction
The optimal fuel expense in power system generation is one of the prime research fields as fuel expenses constitute a significant part of the overall generation cost. Finding optimality in respect of fuel cost requires exhaustive search as a number of thermal units are generally involved having different characteristics and different types of fuels with distinct production cost .The scheduling problem in a power system involves the startup and shutdown schedules of the generating units to be
used to meet forecasted demand over a future short term (24168 hours) period. The objective is to minimize the total production cost while observing a large set of operational constraints.
The resultant schedule should minimize the operating cost during the study period, while satisfying the forecasted load demands and various constraints of the system and the individual units. Mathematically the UC problem is defined as a nonlinear, large scale, mixedinteger combinatorial optimization problem, involving thousands of 01 as well as continuous decision variables, and a wide spectrum of equality and inequality constraints. The exact solution of such a combinatorial optimization problem can be obtained only by complete enumeration such as in the dynamic programming or integer programming methods. However these methods are impractical in terms of computational time and memory size of computers, when the system involves more units or long study periods. Various approaches, such as priority listing, modification of dynamic programming and expert systems have thus been employed to
solve the UC problem. These methods suffer from the problems of suboptimal solutions.

Profit Based Unit Commitment
In the deregulated environment the generation companies (GENCOs), Transmission companies (TRANSCOs) and Distribution companies (DISCOs) interact via contracts. The contract prices are determined through auction. Electricity traders make bids and offers that are matched subject to the approval of an ISO who ensures that the system is operating safely within limits. These bidding strategies might be designed to limit the traders risk, to maximize profit, or some combination of both.
Strategies for selling power and reserve
In a restructured system, GENCO sells power in energy market and sells reserve in the reserve (ancillary) market. The amount of power and reserve sold depends on the way reserve payments are made. Given below
are two examples of reserve payment method [23].
Payment for Power Delivered
In this method, reserve is paid only when reserve is actually used. Therefore,
used, GENCO receives the spot price for the reserve that is generated. In this method, reserve price is much lower than the spot price. Revenue and costs in
(11) can be calculated from
the reserve price is higher than the
RV
P

SP X
(1 r)RP r SP )R X
power (spot) price, Revenue and costs in
can be calculated from
it t
N
T
i1 t 1
N T
it t
N
T
i1 t 1
N T
t it it
N
RV
P SP X
N T

r RP R

TC 1 r FPit X it r FPit Rit X it ST X it
T
it t
i1 t 1
it
i1 t 1
t it
X it
i1 t 1
i1 t 1
N T N T
TC 1 rFPit X it rFPit Rit X it ST X it
In this work results are simulated for
method A only i.e., payment for power
Where,
i1 t1
i1 t1
delivered.


Solution methodology to the uc
SPt forecasted spot price at hour t; RPt forecasted reserve price at hour t; Fi fuel cost of generator i;
ST startup cost
R probability that reserve is called and generated.
Payment for Reserve Allocated
In this method GENCO receives the reserve price per unit of the reserve for every time period that the reserve is allocated and not used. When reserve is
problem
Genetic algorithm is a multiple point probabilistic search technique and is characterized by the mechanism of natural selection and natural genetics. Genetic Algorithm consists of three basic operators, namely, reproduction, crossover, and mutation. The search is started from a randomly selected population of points. A genetic string called chromosome represents each of the points. The length of a genetic
algorithm string is measured by a genetic string called its fitness value. Based on the fitness values of the population strings, two parent strings are selected probabilistically in the process of
reproduction. Two child strings are then
Generate two child strings using crossover and mutation ;}
M=m+1}
Generation count = generation count+1;
generated from the parent string by using }
the mechanism of crossover, where one half of the first parent string is combined with the other half of the second parent. Mutation is then applied on the child string by complementing the child string
at selected bit positions, thus introducing
N
Penalty
=0
variety in the child population. The algorithm consists of the following steps:
Generate a population of solution strings:
Set generation count=0;
Repeat, {while the number of generation maximum generation
Set m=0;
Repeat, {while m number of population/2
Repeat, {select two parent strings;
Sta
Initialization
Generation=1
Check constraints
Are constraints
Y
Calculate penalty for each violation
Generation=generatio n+1
Run ELD and calculate the fitness function
Elitism, Reproduction, Crossover, Mutation
Apply Repair operator
N
Generations >
Genmax
Y
Stop
 <2>Results and Discussions
Simulations for the proposed method are carried out on a computer with following specifications:
Pentium4 Processor, 1.8GHz
The test system is taken from [lrep] consisting of 3 coalfired units and Table
e, tHhouer
1
2
3
4
5
6
7
8
9
11
12
Demand(MW)
170
250
400
520
700
1050
1100
800
650
400
550
meRsesetrvhee(MW)
20
25
40
55
70
95
100
80
65
40
55
Spot
wPrihcei(c$/hMWh)
10.55
10.35
9.00
9.45
10.00
11.25
11.30
10.65
10.35
10.75
10.60
5.1 shows the ranges of the unit data, minimum up time, minimum down time, startup cost, and initial status of the units. The total installed capacity is 1200 MW. The system hourly mean load varies between 170 MW to 1100 MW. Table 5.2 gives the forecasted demand, reserve and spot prices for 3 units, 12 period system. Simulation results are shown in Table 5.3 for method A (payment for power delivered as explained in section 3.2). Her reserve price is fixed at triple ti
spot price and the probability with
the reserve is being called and generated r is taken as 0.005.From table 5.3, it can be seen that GENCO chooses to off unit 1 in all scheduling periods and to sell power and reserve below the forecasted level in some periods. It is
because without regard that all demand and reserve have been met or not, running only two units (units numbers two and three in this case), provides higher profits than running all units as shown in Figs. 5.2 and 5.3
Table 5.1: Data for generating units (3 unit, 12 hr system)
Unit 1
Unit 2
Unit 3
Pmax (MW)
600
400
200
Pmin (MW)
100
100
50
A($/h)
500
300
100
B($/MWh)
10
8
6
C($/MWh)
0.002
0.0025
0.005
tup (h)
3
3
3
t(down) (h)
3
3
3
St($)(StartUp)
450
400
300
Initial state (h)
3
3
3
Table 5.2: Forecasted demand,reserve and spot prices(3 unit,12 hr system)
Table 5.3: Solution for profit based UC, r=0.005
14000
12000
Revenue & Fuel cost ($)
10000
8000
6000
Revenue
Fuel cost
Power(MW)
Reserve(MW)
Profit($)
Unit
1
Unit 2
Unit
3
Unit
1
Unit
2
Unit
3
0
0
170
0
0
20
531.4
0
0
200
0
0
0
570.0
0
0
200
0
0
0
300.0
0
0
200
0
0
0
390.0
0
385
200
0
15
0
210.0
0
400
200
0
0
0
1350.0
0
400
200
0
0
0
1380.0
0
400
200
0
0
0
990.0
0
400
200
0
0
0
810.0
0
129.99
200
0
35
0
818.1
0
199.99
200
0
40
0
804.6
0
349.99
200
0
50
0
929.2
Total
9173.3
4000
2000
RevenueFuel cost
0
400 500 600 700 800 900 1000 1100 1200
Power(MW)
Fig. 5.2 Revenue and Fuel cost at hourseven when all units are on
7000
6000
Revenue & fuel costs($)
5000
4000
Revenue
Fuel Cost
The results given in Table 5.3 show that the profit obtained by the proposed GA
method is higher when compared to
3000
2000
1000
0
Revenue – Fuel Cost
other methods [23].
150 200 250 300 350 400 450 500 550 600
Power (MW)
Fig. 5.3 Revenue and Fuel cost at hour seven when only units two and three are on
Figs. 5.2 and 5.3 shows the revenue received from power selling and total fuel cost at seventh hour when three units and two units are on respectively. According to Fig 5.2, the maximum profit (revenuecost) can be received when power is served between 850 to 950 MW. In profitbased UC, GENCO can now select to sell power and reserve below the forecasted level if it gives higher profit. Fig. 5.3 shows that by running two units and selling power at 600 MW, GENCO can get maximum profit.
The marginal cost for the first hour (Third unit generating 170 MW) is 7.7$/MWh. The MinPrice calculated as per eq. 3.22 for first hour is 7.4382$/MWh. Since MinPrice is less than the marginal cost (explained in section 3.2), the bid will be placed at marginal cost i.e. 7.7$/MWh.

Conclusion
The Unit commitment problem in the competitive environment has been solved by using the modified Genetic Algorithm. This helps the GENCO to decide how much power it should sell. The solution of the new UC problem by modified GA has given better results when compared to other methods. As the main objective of GENCO is to maximize profit, the proposed method effectively helps the GENCO to select the most profitable schedule of generating units.

References

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Saadat, Hadi, Power System Analysis, New Delhi, Tata McGraw Hill Publishing Company Limited, 2002.

Wadhwa, C.L., Electrical Power Systems, New Delhi, New Age International publishers, 2005.

Yo, Yaonan, Electrical Power Systems Dynamics, Academic Press, New York, 1983. [5] Anderson P.M., Analysis of Faulted Power Systems, IEEE Press, New York,1973.


] Kundur Prabha, Power System Stability an Control, Tata McGraw Hill, 2007.

W.D. Stevenson, Elements of Power System Analysis, 3rd Edition, McGrawHill,2008.