Abstract— The efficient and optimum economic operationand planning of electric power generation systems have always occupied an important position in the electric power industry. Main aim of electric power utilities is to provide high quality, reliable power supply to the consumers at the lowest possible cost while operating to meet the limits and constraints imposed on the generating units. This formulates the optimal power flow problem for finding the optimal combination of the output power of all the online generating units that minimizes the total production cost, while satisfying all the constraint. In this paper we use the Power System Analysis tool box in MATLAB to minimize the cost of electricity with optimal power flow for the southern grid of Kerala State Electricity Board. The paper concentrates on the savings with the incorporation of a wind farm in the system. The losses are also reduced with the application.

Keywords—Production cost, optimal power flow, unit commitment, PSAT, IEEE bus system

I. INTRODUCTION

he major considerations for any utility is to run at minimum cost, make maximum profit and to meet the

customer demands all the time. For this most efficient, reliable and economic operation is required and so we go for optimal power flow (OPF). As the power industrial companies have been moving into a more competitive environment, OPF has been used as a tool to define the level of the inter utility power exchange. The OPF has been widely used for both the operation and planning of a power system. The main objective of OPF is to minimize the operating cost, while satisfying all the equality and inequality constraints of the power system. The equality constraints include the bus real and reactive power balance, the generator voltage set points and the area megawatt interchange, whereas the inequality constraints consists of transmission line / transformer/ interface flow limits, generator active and reactive power limits and the bus voltage magnitudes.

In literature, the problem is solved by the optimization methods like Linear and Non-linear programming, Quadratic programming, Interior point method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Fuzzy control, Artificial Neural Network (ANN) etc. Some of these techniques which are based on classical optimization methods, such as linear programming or quadratic programming are highly sensitive to starting points and often converge to local optimum or

diverge altogether. Linear programming methods are fast and reliable but have a disadvantage associated with the piecewise linear cost approximation. Non linear programming methods have known problems of convergence and algorithm complexity. Newton based algorithms have difficulty with handling a large number of inequality constraints. Methods based on artificial intelligence techniques, such as artificial neural networks, can also be applied successfully. Recently many heuristic search techniques such as Particle Swarm Optimization (PSO), have been considered in the context of optimal power flow with cost considerations.

In this paper we use the Power System Analysis tool box in MATLAB to minimize the cost of electricity with optimal power flow for the southern grid of Kerala State Electricity Board. The Kerala grid includes hydro projects along with few number of thermal, diesel and wind projects. The Kerala power system is a hydro dominated one which mostly depends on the monsoon, hence during off-season and occasional failure of monsoon there is always power shortage and hence the board has to depend on other energy source for meeting the required demand. The power is imported from the central sector projects and also purchased from the neighboring states to meet the demand.

The economic considerations reveal that the fixed charges are very high for the hydro systems whereas the running costs are less, for the thermal and diesel plants the running costs are large compared to the fixed charges. The wind farms have low running costs but their operations are mainly dependant on the availability of wind energy, hence the optimally placed wind farms are great sources for economic power flows. Also the wind farm has significance as a renewable energy source in the present situation.

This paper is organized as follows: proposed methodology and modeling of the optimal power flow problem is described in section II. System modeling and the brief description of the grid considered is presented in section III. The results and discussions are described in section IV. Finally a brief conclusion is deduced in section V.

II. PROPOSED METHODOLOGY AND MODELING

In the solution of OPF, the main objective is to minimize total operating costs of the system. In OPF, when the load is light, the cheapest generators are always the ones chosen to

OPTIMAL POWER FLOW ANALYSIS OF KERALA GRID SYSTEM

WITH DISTRIBUTED RESOURCES Sreerenjini K1 , Thomas P C2, Anju G Pillai3, V I Cherian4 ,Tibin Joseph5 and Sasidharan Sreedharan6

1,2,3 PG Scholar, Amal Jyothi College of Engineering, INDIA. 4 Professor, Amal Jyothi College of Engineering, INDIA.

5Assistant Professor, Saintgits College of Engineering, INDIA. 6Professor, Vidya Academy of Science & Technology, INDIA

T

978-1-4673-2636-0/12/$31.00 ©2012 IEEE 160

run first. As the load increases, more and more expensive generators will then be brought in. Thus, the operating cost plays a very important role in the solution of OPF .In all practical cases, we assume that the variation of fuel cost of each generator (Fi) with the active power output (Pi) is given by a quadratic polynomial and the problem is formulated as Minimize:

Fi (Pi) = (1) Subject to

= PD+PL (2) Pi(min) ≤ Pi ≤ Pi(max) (3)

Where Fi = fuel cost of generator i; ai, bi, ci: cost coefficients; PD : load demand; Pi : real power generation; PL : power transmission loss; NG : number of generation busses The general form of the loss formula using B-coefficient is

PL= (4) where, Pi, Pj : real power injection at the ith, jth buses; Bij : loss coefficients which are constant under certain assumed conditions.

The method is applicable mainly for thermal plants, in this paper we consider hydro, diesel and the wind plants too. The significance of the paper is in considering the southern grid of Kerala for the power flow and optimal power flow anion of analysis when the wind farm Ramakkalmedu is incorporated. The economic consideration explains that the cost of production is decreased with use of wind energy. The other major plants that are included are the hydro power projects in southern Kerala (mainly Idukki hydro project, Sabarigiri), NTPC (Kayamkulam thermal power plant), Bhramapuram Diesel power plant.

III. SYSTEM MODELLING

A. Test Systems. The test systems for the problem are modeled using the

software according to the standards and the power flow and optimal power flow analysis were performed for results. The models used are static market models. The Fig.1 shows the 9 bus market model test system. The system consists of three generators and three loads. The system was simulated for the power flow analysis and the simulation results were found to be accurate. Then the optimal power flow solutions were obtained.

Figure1. WSCC 9 bus test system

Figure 2. Voltage magnitude profile of the 9 bus test system

Figure 3. Voltage phase profile of the 9 bus test system

TABLE 1 SOLUTION STATISTICS OF 9 BUS TEST SYSTEM

TOTAL LOSSES [MW]: 17.015 BID LOSSES [MW] 12.374 TOTAL DEMAND [MW]: 287.6257 TOTAL TRANSACTION LEVEL [MW]: 602.6257 IMO PAY [$/h]: 161.2942

B. Real System The real system is a part of the southern grid of Kerala State Electricity Board. The system was modeled to find optimal power flow with and without the wind farm operations. At first a grid without the wind project was modeled and this system included fifteen buses with three generators and eleven loads. The integrated system consisted of distributed resources with hydro, thermal and diesel plants. The Fig.2 shows the model for the real system without wind plant, which was simulated for the power flow analysis and optimal power flow.

TABLE 2. SOLUTION STATISTICS OF REAL SYSTEM WITHOUT WIND FARM.

TOTAL LOSSES [MW]: 13.493

BID LOSSES [MW] 10.278

TOTAL DEMAND [MW]: 257.098

TOTAL TRANSACTION LEVEL [MW]: 542.168

IMO PAY [$/h]: 275.2782

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Figure 4.Real system without wind farm.

Figure5. Voltage magnitude profile of real system without wind farm

Figure6. Voltage phase profile of real system without wind farm

Since there has been a growing interest in distributed generation by renewable resources, wind energy has been considered here for cost effective solutions in optimal power flow. For this the modeled real system was incorporated with a wind farm at Ramakkalmedu with a generation level of 4 MW.

Figure7. Real system incorporated with wind farm

Figure8.Voltage magnitude profile of real system with wind farm

Figure9.Voltage phase profile of real system with wind farm

TABLE3. SOLUTION STATISTICS OF REAL SYSTEM WITH WIND FARM.

TOTAL LOSSES [MW]: 11.874

BID LOSSES [MW] 9.69

TOTAL DEMAND [MW]: 281.458

TOTAL TRANSACTION LEVEL [MW]: 566.528

IMO PAY [$/h]: 251.0307

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IV. RESULTS AND DISCUSSIONSSimulations were performed for the IEEE 9 bus test

system, the power flow results were found to be within the limits for voltage magnitude and angles. The system stability was also analyzed and was found to be stable. The simulation results for optimal power flow showed total loss of 17.015 MW and the bid losses of 12.374 MW to meet the load of 287.6257 MW. The total cost for the optimal power flow was obtained as 161.2942 $/h.

The results consist of two steps. The first step is to perform the optimal power flow analysis on a test system and to extend it to a real system , the second part was to find the savings in the production cost when a wind farm was incorporated to the real system.

The real system without wind farm when simulated for power flow analysis showed the voltage magnitude and angles to be within limits, the system stability was maintained while the optimal power flow was carried out. The simulation results gave total cost of 275.2782$/h for meeting a load of 257.098 MW. The total losses were 13.493 MW and the bid losses were found to be 10.278 MW. Whereas the optimal power flow analysis for the system with wind farm showed a total cost of 251.0307$/h to meet the demand of 281.458 MW. The losses were 11.874 MW for the transaction.

The simulation results show that the incorporation of the wind farm resulted in savings in the production cost along with a decrease in the system losses.

V.CONCLUSION

In this paper, a part of the real system was modeled using the MATLAB toolbox PSAT. The power flow and optimal power flow were simulated with accuracy, the time required to perform optimal power flow was less compared to other methods. A better method of load dispatch will actually give a lot of savings to any utility particularly if unscheduled interchanges are effectively utilized during the availability based tariff regime. The optimal power flow analysis for the real system was performed considering the incorporation of a wind farm, which showed savings in the production cost with the utilization of wind farms. The work performed was for a part of a large utility which can be extended to complete large grids. This will result in simplified and accurate method to find the optimal and economic power flow for the system within less time domain.

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