MINIMISATION OF LOSSES AND COST IN A DEREGULATED POWER SYSTEM USING PARTICLE SWARM OPTIMIZATION

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1 International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN X Vol. 3, Issue 4, Oct 2013, TJPRC Pvt. Ltd. MINIMISATION OF LOSSES AND COST IN A DEREGULATED POWER SYSTEM USING PARTICLE SWARM OPTIMIZATION G KALIDAS BABU 1, A SANTOSH KUMAR 2 & S RAJESH KUMAR 3 1,3 Assistant Professor, Department of Electrical and Electronics Engineering, Nalla Narishma Reddy Group of Institute, Hyderabad, Andhra Pradesh, India 2 M. Tech Scholar, Department of Electrical Engineering, Sreenidhi Institute of Science & Technology, Hyderabad, Andhra Pradesh, India ABSTRACT Voltage control is accomplished by managing reactive power on an alternating current power system. Reactive power can be produced and absorb by both generation and transmission equipments. The transmission towers, customers, power marketers and government regulators need to pay very close attention to voltage control as the reactive power devices varies substantially in the magnitude and speed of response and in their capital costs. The amount of reactive power requirements are calculated using particle swarm optimization (PSO) and for optimizing system loss taking the objective function as minimization of network loss and total reactive power capital cost with the help of IEEE 30 bus system and verify the solution with optimal power flow and genetic algorithm techniques. KEYWORDS: Genetic Algorithm, Optimal Power Flow, Particle Swarm Optimization, Reactive Power, Reactive Power Costs and Voltage Control INTRODUCTION The Modern day power systems all over the world is stepping towards deregulated electricity markets, For smooth operation of power systems especially in generation and transmission services ancillary services are necessary. Reactive power is one of the important ancillary services which is necessary to maintain the voltage profile of the total power system. Usage of reactive power as one of the ancillary service has some applications which are useful for power system maintains and are presented below Meet the reactive power load requirement To control the bus voltage in a given system Decrease the network loss Relive the transmission block To maintain sufficient reserve to secure the security of system in emergency. Issues with Usage of Reactive Power Even though the total cost for reactive power support provided by the ancillary services in the power system accounts for only 1% of the total cost of power industry, but still it is necessary to make it clear how much is the actual investment made for reactive power support. The total cost incurred by the ancillary services for reactive power support is categorised in to two one is fixed

2 108 G Kalidas Babu, A Santosh Kumar & S Rajesh Kumar cost component and a second is variable cost component. Fixed cost is mainly the capital cost incurred by the ancillary services for setting up the devices and necessary components for the required reactive power support. Variable cost is related to the economically operation of reactive power devices which concentrates on the reduction of maintains and operation cost which helps in reduction of money spent by the ancillary services. Minimization of losses in reactive power is based on various factors like placement of reactive power devices and the rating of the devices to be placed such that minimum wastage of reactive power takes place which in turn helps in cost reduction for the ancillary services. Importance of Controlling Voltage Level Using Reactive Power Voltage control and reactive power management are two aspects of a single activity that both supports reliability and facilitates commercial transactions across transmission networks. On an alternating current (AC) power system, voltage is controlled by managing production and absorption of reactive power. The reasons why it is necessary to manage reactive power and control voltage are as follows: Both customers and power system equipment are designed to operate within a range of voltages, usually within±5% of the rated nominal voltage. Example: low voltages of equipment perform poorly, light bulbs provide less illumination, and induction motors can overheat and get damaged. High voltages can again damage the equipments and shorten their lifetimes. Reactive power consumes transmission and generation resources, reactive power production can limit a generator s real power capability. Reactive power on the transmission system incurs real power losses; both capacity and energy must be supplied to replace these losses. The system should withstand the loss of any power system equipment and to continue operating without having any impact on customers, in order to balance the system it should consume additional reactive power. Required reactive-power reserves in electrical system is necessary to maintain the system stability when any outages takes places. ALGORITHMS AND THE VARIABLES USED BY THE ALGORITHMS The main theme of this paper is minimization of total power loss in the power system considered. The objective function considered in the algorithm are active and reactive power demands which we assume as are known already and kept constant to obtain the solution. The two objective functions used are: Minimization of Cost of Real Power Generation (1) and Minimization of system transmission losses (2) The constraints used here are equality constraints which are load flow equations: (3) (4) The inequality constraints are

3 Minimisation of Losses and Cost in a Deregulated Power System Using Particle Swarm Optimization 109 Generation Limits (5) (6) Voltage Limits (7) Transmission Limits (8) Reactive Power Output Limit of Capacitors (9) where Active power output at bus i;, Active power limits of the unit at bus i; Reactive power output at bus i; Reactive power output at bus Active power load at bus i; Voltage phase angle at bus i; Voltage magnitude at bus i; Voltage magnitude limits; Transmission line load of line i-j; Transmission line limits; Reactive power output of capacitor at bus i; Reactive power output limits; Conductance of line i-j Total transmission loss of real power; NG NC Set of all generators; Set of all capacitors; Real power cost of the unit at bus i; Reactive power cost of the unit at bus i; Reactive power cost of capacitor at bus i;

4 110 G Kalidas Babu, A Santosh Kumar & S Rajesh Kumar Genetic Algorithm Introduction John Holland was the person who used Genetic algorithm (GA) in early seventies, this is the most convenient technique than other techniques like Calculus-based, Enumerative, where these solution methods have efficiency in delivering solutions is drastically low because of their search space is of big size and large variation from point to point in their process, and they are not robust for solving complex problems. The search is carried out randomly and information gained from a search is utilized in guiding the next search. The Algorithms of GA starts with random solution of initial search point from the total search space. The chromosome is formed by a sequence of all parameters of gene, the individual bit is called gene. Chromosome exists for each point in the search space. Their set of search points selected forming a population or in other words we call a set of chromosomes is called population and the number of chromosomes in a population is called population size and the total number of genes in a string is called string length. The different operators are processed and evaluated to generate a new population is processed and evaluated through various operators of GA to generate a new population and the process is carried out till the optimum point is reached. Phases of GA Genetic Algorithm depends upon the following phases: Initialization Initialization is the process of selecting size of the population which depends upon the size of search space. The performance and efficiency depends upon the size of population. Evaluation Evaluation is the phase that evaluates the stamina (fitness) of each chromosome i.e, gives the solution of minimisation and maximisation type problems which in voles taking the exact fitness function helps in flexibility for the given system. Genetic Operation Genetic Operation has some genetic operators for creating different population from the elder population with respect to the fitness function of chromosomes. Reproduction, crossovers, and mutation are the operators in GA. Control Variables Here the control variables are the real and reactive power output of generators, except for the swing bus. The reactive power output of shunt capacitors is denoted by Qc. Fitness Function Fitness function in GA is needed to evaluate the individuals. If the constraint does not meet the fitness function then a penalty is imposed. The various constraints are the boundary of voltage on all the buses, power output limit of the swing bus generator and transmission limits are also added as penalty terms to the objective function. To make the algorithm simply for the convenience of genetic operation, we assign negative sign. Hence minimization of objective function is maximization of the fitness function as follows:

5 Minimisation of Losses and Cost in a Deregulated Power System Using Particle Swarm Optimization 111 (10) (11) where are large numerical values, called the penalty factors. When constraints are violated, the fitness function is remarkable smaller than normal. Then the individual has less chance to evolve to the next generation. Algorithm Implementation of genetic algorithm for minimization of objective function is done in the following steps as mentioned below: Step1 : Read system data Step 2: Form Y bus for the given system Step 3: Random generation of chromosomes within the limits. Step 4: Start the generation loop Gen = 1 to max generations Step 5: Start the chromosome loop Chr = 1 to population size Step 6: Decode the chromosomes and assign to the respective P G, Q G and Q C Step 7: Run power flow by taking the above values If Newton rap son load flow is converged, compute fitness using one of the following functions Fit = f1= ai+bi pi+ci pi2 or f2= loss function Else Fit=0 Step 8: If fit for each chromosome is finished end chromosome loop. Else go to step 5 Step 9: Put all the chromosomes in descending order according to their fitness values, and perform elitism operation. Step 10: Select parents using roulette wheel technique and perform cross over and mutation. Step 11: If bit of (chromo1)-bit of chromo 30) <= Stop gen loop, Go to step 12 Else

6 112 G Kalidas Babu, A Santosh Kumar & S Rajesh Kumar Go to step 4 Step 12: Display results. Particle Swarm Optimization Introduction Dr. Eberhart and Dr.kennedy developed the concept of particle swarm optimization (PSO) by the inspiration of bird flocking in nature. The function of optimization technique is also based upon the population consideration only like in Genetic algorithm, but no consideration of operators like crossovers and mutation. This technique simulates the bird flocking behaviour i.e. if a number of birds formed a group for searching food in a particular area randomly, where there is only one piece of food which they don t know where the food is. Because they know the food how far they are in each iteration so the best strategy is the effective one to the bird following which is nearest to the food. Here the bird is considered as a each single solution in the search space call it as particle and in every iteration each particle is updated by best solution which can be stored than global best obtained at a distance by other particles in the population and local best particle which takes part of the population. Algorithm Implementation of PSO algorithm for minimization of objective function is done in the following steps as mentioned below Step 1: Read system data Step 2: Generate randomly all the particles within the respective ranges. Step 3: Generate randomly velocities of the particles in the range Step4: Run the generation loop Gen=1 to n Step 5: Run the particle loop Particle=1 to no of particles Step 6: For each particle run power flow and compute fitness Step 7: Find the p best of p best particle and g best of g best particle among the particles Step 8: Update velocity using formula V [] = w* v[] + c1*rand[] * (p best[] - present []) +c2 * rand[]* (g Best [] - present []) Step 9: Update particles Present [] = present [] + V Check limits and impose if violate Step 10: If gen<gen max Go to step 4 Else Step 11: Display results.

7 Minimisation of Losses and Cost in a Deregulated Power System Using Particle Swarm Optimization 113 Start Generation of particles Calculating fitness and fixing of P best and G best Updating velocity and direction of particles CASE STUDY AND RESULTS Calculating fitness for the new search points and velocities Fixing the best P best and G best for all particle. All the G best are stored If iteration Maximum? stop Figure 1: Flow Chart for PSO figure 2 To stduy the different alogrithms results an IEEE 30-bus power system is used in this work, which is shown in Figure 2: IEEE 30-Bus Power System The IEEE 30-bus system considered for study consists of 41 branches, six generator-buses and 20 load-buses. The possible reactive power source installation buses are 10 and 24. PV-buses are bus 2, 5, 8, 11, 13, and Vθ bus is bus 1. and the others are PQ-buses.. The various parameters used in the GA and PSO program are:

8 114 G Kalidas Babu, A Santosh Kumar & S Rajesh Kumar Table 1 No of Chromosomes 30 For each PG 12 bits For each QG 12 bits For each QC 10 bits Total no of generators 5 No of bits per chromosome =140 Probability of elitism (Pe) 0.15 Probability of crossover (Pc) 0.90 Probability of mutation (Pm) The particles taken in PSO 30 Table 2: Output of GA and PSO with Objective Function as Loss Function Objective Function as Loss Function PG (MW) (real power output of generator buses) QG (MVar) (reactive power output of generator buses) Power Loss in GA Power Loss in PSO QC (MVar) (reactive power output of shunt capacitor) Total Generation : PG(MW) QG(MVar) Cost($/hr) P loss (MW) Table 3: Output of GA and PSO with Objective Function as Cost Function Objective Function as Cost Funtion PG (MW) (real power output of generator buses) QG (MVar) (reactive power output of generator buses) Cost in GA Cost in PSO QC (MVar) (reactive power output of shunt capacitor) Total Generation : PG(MW) QG(MVar) Cost($/hr) P loss (MW)

9 Minimisation of Losses and Cost in a Deregulated Power System Using Particle Swarm Optimization 115 Comparsion of Objective Function Results with Different Methods By the application of different alogithms to the given IEEE bus system their is a varation in the results obtained based up on the objective function considered. The results obtained are tabulted, Table 2 shows a coparsion between the GA and PSO alogithm where the objective function is taken as loss function, so from the table we know that even though the cost is incresed but the total loss is decreased that is the ojective is meet similarly in table 3 again a coparsion between the GA and PSO alogithm is made where the objective function is taken as cost function, here the losses are incresed but the overall cost is decreasd. Comparing PSO with GA the following Advantages are It has one way sharing information with the help of global best and local best where as GA are two way changing information. PSO has a memory and also have no operators like crossover and mutation. PSO converges best solution in a short period of time than GA. Comparison of OPF, GA and PSO The objective is the minimization of total power loss and from the results in table 4 we see that though OPF and GA gives better results, the loss minimization is quite good in PSO compared to other methods. Table 4: Comparision of Power Loss in OPF,GA and PSO Objective Function OPF GA PSO PG (MW) (real power output of generator buses) QG (MVar) (reactive power output of generator buses) QC (MVar) (reactive power output of shunt capacitor) Total Generation : PG(MW) QG(MVar) P loss(mw) CONCLUSIONS This work makes a comprehensive survey of the important issues about reactive power in deregulated environment. Establishing that reactive power market is not only possible but also profitable.the total cost and the total emission by GA is less than the conventional methodthe usageof PSO method can still obtain lower fuel cost than the GA method, resulting in the higher quality solution. The results shows that the GA based approaches provide a global optimal solution than the conventional method which cab be more accurte by PSO approach within reasonable computation time.although the results are close, it can be inferred that when the costs of generation is minimized, the loss on the transmission network are not minimized.

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