Application of genetic algorithm for optimal sizing and placement of distribution transformers in PT PLN East Medan Indonesia

Transcription

1 Application of genetic algorithm for optimal sizing and placement of distribution transformers in PT PLN East Medan Indonesia Sarjiya, Husni Rois Ali, and Reynold B. A. Pardede Citation: AIP Conference Proceedings 1755, (2016); View online: View Table of Contents: Published by the American Institute of Physics Articles you may be interested in Case studies on optimal location and sizing of renewable energy generators in distribution system Journal of Renewable and Sustainable Energy 8, (2016); / Optimal sizing and siting of DGs for loss reduction using an iterative-analytical method Journal of Renewable and Sustainable Energy 8, (2016); / Survey of brucellosis in goats using Rose Bengal Test (RBT) and Complement Fixation Test (CFT) methods in Gunungkidul district, special region of Yogyakarta, Indonesia AIP Conference Proceedings 1755, (2016); / Cascade neural network for predicting epitope on P24 HIV-1 AIP Conference Proceedings 1755, (2016); /

2 Application of Genetic Algorithm for Optimal Sizing and Placement of Distribution Transformers in PT PLN East Medan Indonesia Sarjiya 1, a), Husni Rois Ali 1, Reynold BA Pardede 2 1 Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 2 PT. PLN (Persero) Jakarta Indonesia a) Corresponding author: sarjiya@ugm.ac.id Abstract. The distribution system is the closest network to the customer. Therefore, the distribution network should be designed economically to obtain a particular level of quality. A good long-term planning needs to be carried out to get such a system. One such plan is to get the optimal sizing and placement of distribution transformers. The objective of the study is to obtain optimal location and capacity of the distribution transformer in PT PLN (Persero) East Medan, North Sumatra. The location and optimum capacity are obtained by minimizing the investment costs and operational costs of electric power distribution system without violating voltage constraint. Genetic Algorithm method is used to get the optimal costs. The results show that optimal sizing and placement distribution transformers problem in PT PLN (Persero) East Medan, Sumatera Utara can be solved efficiently using Genetic Algorithm in less than 200 generations. Sensitivity analysis on the utilization factor, demand factor and coincidence factor have been reported. The increase of the demand factor and coincidence factor are directly proportional to the increase in investment costs. Meanwhile, the utilization factor is inversely proportional to the value of the investment cost. INTRODUCTION Power system planning is one of the important things for obtaining the economic operation of power system. In the power distribution system, the minimum cost of operation can be obtained by properly planning and design of distribution network to reduce the losses. Electricity utility supply in Indonesia which owned by PT. PLN (PERSERO) composed of the 500kV transmission system in Jawa Madura Bali and 275 kv transmission system in Sumatera. The primary distribution system is 20 kv level and the second distribution is 220 V. In this case, to get the optimal investment cost, a study of optimal sizing and placement of distribution transformer need to be done. Currently, power system planning based on optimization process has done using a computer program like power flow calculation, short circuit, load forecasting, voltage regulation, capacitor bank planning, reliability calculation and locating and sizing distribution transformers. Optimization is a way to get the optimal value. In this case, the optimal value means that a way to get the coordinate/location of a distribution transformer that minimizes the total cost. The cost component in the optimization problem of sizing and location of power distribution system planning can be classified as investment cost and operational cost. The planning process can be divided into two steps i.e. the optimal location of the distribution transformer and the optimal routing feeder to supply the transformer [1]. Optimal sizing and placement of distribution transformer previously have been done by some other method such as Imperialist Competitive Algorithm (ICA) [2], Ant Colony Optimization (ACO) [3], Spatial Information-based Self-Adaptive Differential Evolution (ISADE) [4], Flower Pollination Algorithm [5] and other optimization methods. Imperialist Competitive Algorithm is an optimization method with the colonial system [2]. The differences of this method with other methods are the best location of transformer candidate can be outside of location candidate. A Spatial Information-based Self-Adaptive Differential Evolution (ISADE) is a method that gets the best value from differential equation [4]. Ant Colony Optimization (ACO) is a method that adopted from ant behavior. Advances of Science and Technology for Society AIP Conf. Proc. 1755, ; doi: / Published by AIP Publishing /$

3 This method can be used to determine the optimal capacity of distribution transformers [3]. Flower Pollination Algorithm (FPA) is an optimization method that adopted from the behavior of flowers which attract insects by color [5]. In this paper, optimal sizing and placement of distribution transformer in 20kV distribution network in PT. PLN (Persero) East Medan is obtained by applying Genetic Algorithms (GA) method. The results show that GA can efficiently solve the formulated optimization problem. The sensitivity analysis on utilization, demand, and coincidence factors has been reported. PROBLEM FORMULATION In this study, the expected goal is the minimization of the total cost of the investment and operating expenses of the distribution system [1]. The objective function of this optimization problem is expressed as: where C total : total cost (Rp) C sub,i : equipment and construction cost of i-th distribution transformer (Rp) C L : value of loss load (Rp/kWh) C FL : construction cost of low voltage line (Rp/Km) PWF : present worth factor L ij (1) : distance between i-th distribution transformer and j-th load point (Km) LSF : loss factor Infr : inflation rate Intr : interest rate ny : planning period (years) P cu,i : copper losses of i-th distribution transformer at rated power (kw) P d,i : dispatched power of i-th distribution transformer (kw) P r,i : capacity rating of i-th distribution transformer (kw) P iron,i : iron losses of i-th distribution transformer (kw) R : resistance of conductor in low voltage line (Ohm/Km) S ij : load of j-th load point connected to i-th distribution transformer (kva) ns : number of distribution transformer candidates nl : number of load points V : nominal voltage of low voltage line (Volt) : decision variable, 1 if distribution transformer associated with i-th location is built otherwise, otherwise : decision variable,1 if i-th distribution transformer feeds the j-th load point, otherwise In designing a power distribution system, it is necessary to consider some constraints. The limitations considered in this study are as follows [1]: 1. All of the load points must be supplied, and each load point can be provided only from one transformer. Mathematically, such constraint can be expressed as: (2) 2. Transformer loading must be in an acceptable margin. Mathematically this constraint can be expressed as:

4 (3) where, kmin and kmax are the minimum and maximum permissible percentage transformer loading, respectively. 3. The voltage at each load point does not exceed the specific allowable limit. Mathematically this constraint can be described as follows: (4) where, is the maximum acceptable voltage drop. DATA AND METHODOLOGY Data For conducting this research, data was given by PT. PLN (PERSERO) East Medan. Table 1 is an example of load location which latitude and longitude are obtained from google earth coordinate [6]. TABLE 1. Data of load location No Latitude Longitude Load (kw) The coordinate is taken from google earth, so it must be converted to Cartesian by multiplying each latitude and longitudes with (conversion google earth coordinate to Cartesian) [7]. Before choosing the capacity of the transformer, firstly the database of the transformer must be fixed. Table 2 shows the specification and price of distribution transformer [8]. TABLE 2. Specification and price of distribution transformer Open-Circuit Short-Circuit kva Cost (Rp) Losses (kw) Losses (kw) 25 39,143, ,428, ,000, Table 3 shows the example of the coordinate of transformer candidates. The distance between candidate and load point has to be determined because it would be needed to calculate the voltage drop, losses, and investment cost in that network. The Euclidean formula is chosen to calculate this distance [7]. The calculated distances are saved in a matrix as shown in Table 4. TABLE 3. Example of coordinate of transformer candidate No X Y , , , ,

5 Load TABLE 4. Transformer load distance matrix Trans D 11 D 12 D 13 2 D 21 D 22 D 23 3 D 31 D 32 D 33 Methodology Flowchart of the optimization process based on GA method is shown in Fig. 1(a). From Fig. 1(a), the simulation is started by varying the location candidate of the transformers. There are 13 location candidates with variations of the location candidates as below. Case 1: number of transformer candidate = 4 (No. 4, 6, 10, 12) Case 2: number of transformer candidate = 6 (No. 1, 4, 6, 7, 10, 13) After varying the location candidate, the next step is creating the individual which represents the solution candidate. From case 1, one example of the individuals is shown in Table 5. TABLE 5. Transformer-loads connection Load Trans Load Trans Load Trans Load Trans Load Trans All of those individuals create the number of populations. In this case, the total individuals are 20 because the populations are set to be 20. After creating all individuals, each must be evaluated its fitness score. The flowchart of evaluation is shown in Fig. 1(b)

6 (a) Overall Genetic Algorithm process (b) Fitness calculation process FIGURE 1. Flowchart of optimization process The further step is the calculation of transformers load. In case 1, the total loads connected to transformers No. 4, 6, 11, 12 are calculated. Based on the total loads of each transformer, the transformers capacity can be determined. From the database, the transformers losses cost and transformers installation cost then can also be calculated. The last step is fitness calculation as expressed by equation 5. All of the costs are combined to obtain the fitness which represents the total cost. 5) where F1 = Transformer installation costs, F2 = cable installation costs, F3 = cable losses costs, F4 = open circuit losses costs, F5 = short circuit losses costs. If the voltage drop exceeds the limits, penalties scores should be added to the fitness score. By adding the penalties, this individual is expected not to be the best individual. SIMULATION RESULTS Before running the simulation, all parameters must be set in advance. In this work, the parameters of the simulation are planning period = 20 years, inflation rate = [9], interest rate = [10], cable resistance = 1.54 x 10-3 ohm/m [11], energy cost = Rp 1,352/kWh [12], voltage drop limit = -10%, minimum transformers load = 40%, losses factor = 0.49, utilization factor (UF)=80%, demand factor (DF)=0.8 and coincidence factor (CF)=0.7, generations = 200 and populations=20. The simulation results of optimal location and sizing of distribution transformer are shown in Fig. 2. As depicted in Fig. 2, the small dots represent the location of the load customers. The adjacent customer loads are connected by low voltage networks which are represented by green line to each of the centered small dots. This centered dots represents the location of the distribution transformer. The capacity of each distribution transformer for supplying the connected customer load is shown by the number around that centered small dot. From this simulation result it can be seen that for supplying all of the customer loads in that area, it should be installed seven distribution transformers with the total capacity of the transformer is 200 kva

7 In the simulation process based on the flowchart, as shown in Fig. 2, the optimal result which is represented by the minimum of fitness score was obtained in less than 200 generations as shown in Fig. 3. At the beginning of generation, the fitness score is much higher because it just represents the fitness score of the initial condition of the population. With the increasing of generation, the location candidate and size of distribution transformers are varied to get the better fitness score. The fitness scores at the end of generation represent the best optimal result by ensuring that the fitness score remains constant with the increased number of generation. FIGURE 2. Simulation results Generations FIGURE 3. Fitness Plot In details, the information of each optimal location and size of distribution transformers are shown in Table 6. At transformer no.2, the connected load exceeds the capacity of the transformer. It is allowed because the customer is not always using the installed power at the maximum limit and not all customers experiencing peak loads at the same time

8 TABLE 6. Optimal size and location of distribution transformer Transformer Coordinate Transformer Capacity No Connected Load (kva) Peak Load (kva) (kva) X Y TOTAL Transformer loading has correlations with the transformer sizing. Load characteristic becomes one of the considerations in the transformer loading. There are three factors should be considered in determining the capacity of transformer i.e. utilization factor (UF), demand factor (DF) and coincidence factor (CF). For example, the characteristic of small residential loads differences with luxurious residential loads. Small residential loads likely have low installed power and electricity consumption also approached to the maximum usage. In this case, the DF of small residential loads can be said to be close to 1. Luxurious residential loads likely have a significant installed power, but the usage is not as big as the installed power. In this case, the DF will be small. In this study, a sensitivity analysis was conducted to show the effect of UF, DF, and CF towards the cost. 1. Sensitivity analysis on utilization factor (UF). The effect of utilization factor variation to the cost are shown in Table 7. It can be seen that if the transformers are a utilized approach to the rated which is represented by UF value toward 1, the smaller investment cost required. Effect of utilization factor to the cost can be approached with a linear function y = -108x as shown in Fig. 4. TABLE 7. Influence of utilization factor on cost UF DF CF Total Capacity (kva) Fitness (Rp) ,080,700, ,008,200, ,310, ,750,000 FIGURE 4. Linear relationship approximation between utilization factor and cost

9 2. Sensitivity analysis on demand factor (DF) Tabel 8 shows the sensitivity of cost on the variation of demand factor. The simulation indicates that the smaller value of demand factor, the less of the investment cost needed. In distribution system planning, the demand factor value should be adjusted based on the characteristics of the load. Effect of demand factor towards the cost can be approximated by the linear function y = 7.107x as shown in Fig. 5. TABLE 8. Influence of demand factor on cost UF DF CF Total Capacity (kva) Fitness (Rp) ,220, ,008,200, ,083,200, ,137,300,000 FIGURE 5. Linear relationship approximation between demand factor and cost 3. Sensitivity analysis on coincidence factor (CF) Table 9 shows the effect of simultaneity peak load each connected load to the cost. In this scenario, DF value was set 0,8 by the general condition. Table 9 implies that the greater value of simultaneity which is represented by the higher value of coincidence factor, the greater capacity of the transformer is needed. Thus, the cost of the required investment will be great. Coincidence factor influence towards the cost can be approached with a linear function y = x as shown in Fg. 6. TABLE 9. Influence of coincidence factor on cost UF DF CF Total Capacity (kva) Fitness (Rp) ,008,200, ,087,600, ,152,100, ,224,300,

10 FIGURE 6. Linear relationship approximation between coincidence factor and cost CONCLUSION The optimal sizing and placement of distribution transformer problem at PT PLN East Medan has been solved effectively to obtain the minimum of investment cost and operational cost using Genetic Algorithms in less than 200 generations. For supplying all of the customer loads in the location under study, it should be installed seven distribution transformers with the total capacity is 200 kva. From the sensitivity analysis, it also can be concluded that the increase of the demand factor and coincidence factor are directly proportional to the increase of investment cost. Meanwhile, the utilization factor is inversely proportional to the value of the investment cost. In other words, the greater of the utilization factor, the smaller investment cost. REFERENCES 1. M. Ramezani, H. Falaghi, M. P. Moghaddam, M. Haghifam, Genetic based Algorithm for Optimal Placement of Distribution Transformers, in IEEE Power Engineering Society General Meeting (IEEE, 2006). 2. S. Najafi, R. Gholizadeh, On Optimal Sizing, Siting, and Timing of Distribution Substations,, in the Proceeding of 18th IEEE Conference on Electrical Distribution Network (IEEE, 2013). 3. E.I, Amoiralis, M.A.Tsili, P.S. Georgilakis, A.G. Kladas, Ant Colony Solution to Optimal Transformer Sizing Problem, in the Proceeding of 9th IEEE International Conference on Electrical Power Quality and Utilization (IEEE, 2007). 4. L. Nian, J. Zhang, W. Liu, A Spatial Information-based Self-Adaptive Differential Evolution for Distribution Substations Location and Sizing, in the Proceeding of IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application (IEEE, 2008). 5. S.J. Huang, P.H. Gu; W.F. Su; X.L. Liu; T.Y. Tai, Application of Flower Pollination Algorithm for Placement of Distribution Transformers in A Low-Voltage Grid, in the Proceeding of IEEE International Conference on Industrial Technology (IEEE, 2015). 6. PLN Costumers RBM ATATDAD date 30 April 2015 at 12:58:29, PT. PLN (PERSERO) East Medan. 7. Bakhtiyar, Measure the distance on google map using Euclidean and haversine, available at 8. Price offer distribution transformers Starlite PT. AsataUtama Elect. to PT. PLN (PERSERO) East Medan, (PLN, 2015). 9. Inflation rate report of Bank Indonesia, available at Interest rate report of BI, available at PLN Book 3, Construction Standard of Low Voltage Distribution System, (PLN, Jakarta, Indonesia, 2010). 12. Electricity Tariff PLN, Electricity Tarif Adjustment in April 2015 (PLN, Jakarta, Indonesia, 2015)