A NOVEL OF PRIMARY DISTRIBUTION NETWORKS WITH MULTIPLE DISTRIBUTED GENERATOR PLACEMENT FOR LOSS REDUCTION

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1 A NOVEL OF PRIMARY DISTRIBUTION NETWORKS WITH MULTIPLE DISTRIBUTED GENERATOR PLACEMENT FOR LOSS REDUCTION TERLI JAHNAVI M.Tech(PE). CRR COLLEGE OF ENGINEERING, ELURU. N PAVAN KUMAR, Assistant Professor CRR COLLEGE OF ENGINEERING, ELURU. ABSTRACT - A methodology is proposed for multiple distributed generator (DG) placement in primary distribution network for loss reduction in this paper. In this paper an improved analytical (IA) method is proposed. This method is based on improved to calculate the optimal size of four different DG types and a methodology to identify the best location for DG allocation. Optimal location for distributed generator (DG) is selected by Analytical expressions and the optimal DG size calculated by IA method and ISF method. These two methods are tested on two test systems 33-bus, 69-bus & 84 bus radial distribution systems. To achieve a high loss reduction in largescale primary distribution networks investigates the problem of multiple distributed generators (DG units) placement is proposed in this paper. A technique to get the optimal power factor is presented for DG capable of delivering real and reactive power. Moreover, loss sensitivity factor (LSF) and exhaustive load flow (ELF) methods are also introduced. By using the numerical methods we can test the IA method and validated on three distribution test systems with varying sizes and complexity. By using simulation results we can analyze the IA method is effective as compared with LSF and ELF solutions. I. INTRODUCTION Nowadays power systems have to operate closer to their stability limits due to the growth in the load demand. Economic and environmental considerations are the reasons for few transmission networks lines to be constructed. The main advantages of using renewable energy based DG sources are the elimination of harmful emissions and inexhaustible resources of the primary energy. However, the main disadvantages are relative low efficiency, high costs, and intermittency. In recent years, the penetration of distributed generator (DG) into distribution systems has been increasing rapidly in many parts of the world. At present, there are several technologies used for DG applications that range from traditional to non-traditional technologies. The former is non-renewable technologies such as internal combustion engines, combined cycles, combustion turbines and microturbines. The latter is renewable technologies such as solar, photovoltaic, wind, geothermal, ocean, and fuel cell. As the penetration of DG units increase in distribution system, it is in the best interest of all players involved to allocate them in an optimal way such that it will increase reliability, reduce system losses and hence improve the voltage profile while serving the primary goal of energy injection. DG units are modeled as synchronous generators for small hydro, geothermal, and combined cycles; combustion turbines; and wind turbines with power electronics. Induction generators are used in wind and small hydro power generation. Improper allocation or sizing of (DG) unit may cause voltage rise due to increase the real power flow back to grid or causes voltage to fall due to increase the reactive power flow into feeder [5], so the problem of (DG) planning has recently received much attention by power system researchers so as to garner maximum benefit from (DG) allocation in (RDNs). DG units are considered as power electronics inverter generators or static generators for technologies such as photovoltaic (PV) plants and fuel cells. For instance, DG using a PV gridconnected converter is controlled on the basis of the droop-control technique presented. The converter is capable of providing active power to local loads and injecting reactive power to stabilize load voltages. Furthermore, the type of DG technology adopted will have a significant bearing on the solution approach. The genetic algorithm (GA) based method has been presented to determine the size and location of DG. GA is suitable for multiobjective problems and can lead to a near optimal solution, but demand higher computational time. An analytical approach based on an exact loss formula has been presented to find the optimal size and location of single DG. A probabilistic-based planning technique has been proposed for determining the optimal fuel mix of different types of renewable DG units (i.e., wind, solar, and biomass) in order to minimize the annual energy losses in the distribution system. A technique for DG placement using 2/3 rule which is traditionally applied to capacitor allocation in distribution systems with uniformly distributed loads has been presented. The genetic algorithm (GA) based method has been presented to determine the size and location of DG. In this method, a new

2 methodology has been proposed to quickly calculate approximate losses for identifying the best location; the load flow is required to be performed only twice. GA is suitable for multi-objective problems and can lead to a near optimal solution, but demand higher computational time. An analytical approach based on an exact loss formula has been presented to find the optimal size and location of single DG. A probabilistic-based planning technique has been proposed for determining the optimal fuel mix of different types of renewable DG units (i.e., wind, solar, and biomass) in order to minimize the annual energy losses in the distribution system. An effective method based on improved analytical (IA) expressions to place four different types of single DG for loss reduction. However, multiple DG unit placement has not been addressed in the paper. To overcome limitations in previous works, this paper proposes an IA method for allocating four types of multiple-dg units for loss reduction in primary distribution networks. The importance of DG operation (i.e., real and reactive power dispatch) for loss minimization along with a fast approach as a simple way to quickly select the power factor of DG units that is close to the optimal power factor is also presented. II. METHODOLOGY This section focuses on a detailed description of IA method. To check the effectiveness and applicability of the proposed method, LSF and exhaustive load flow (ELF) methods for allocating multiple DG units are used. LSF method has been employed to select the candidate locations for single DG placement to reduce the search space. A brief description of LSF algorithm for multiple DG units is given at the end of this section. ELF method known as a repeated load flow solution demands excessive computational time since all buses are considered in calculation; however, it can lead to a completely optimal solution. A. Power Losses The total real power loss in a power system is represented by an exact loss formula. P = α P P + Q Q + β Q P P Q (1) Where α = r cosδ V V δ ; β = r sinδ V V δ B. IA Method In this work, an effective methodology is proposed to find the optimal location, size, and power factor of multiple DG units in distribution networks. A brief description of the IA expressions and optimal power factors for single DG allocation is presented as follows : 1) IA Expressions: Type 1 DG (i.e., 0 < DG PF < 1): is capable of injecting both real and reactive power (e.g., synchronous generators). The optimal size of DG at each bus-i for minimizing losses can be given by (2) and (3): P = ( ) (2) Q = ap (3) In which, a = (sign)tancos (PF ) sign = +1:DG injecting reactive power X = α P β Q Y = α P β Q The aforementioned equations give the optimum size of DG for each bus i, for the loss to be minimum. Any size of DG other than DGi P placed at bus i, will lead to a higher loss. With this assumption, the optimum size of DG for each bus, given by the aforementioned relations, can be calculated from the base case load flow (i.e., without DG case). This methodology requires load flow to be carried out only two times for single DG allocation, one for the base case and another at the end with DG included to obtain the final solution. Type 2 DG (i.e., 0 < DG PF < 1): is capable of injecting real power but consuming reactive power at each bus i for the minimum loss is given by (2) and (3). Type 3 DG (i.e., DG PF = 1, a = 0): is capable of injecting real power only (e.g., PV, micro turbines and fuel cells which are integrated to the main grid with the help of converters/inverters). The optimal size of DG at each bus i for the minimum loss is given by reduced equation (4) P = P α P β Q (4) Type 4 DG (i.e., DG PF = 0, a = ): is capable of delivering reactive power only (e.g., synchronous compensators). The optimal size of DG at each bus i

3 for the minimum loss is given by reduced equation (5) Q = Q α Q + β P (5) The computational procedure to allocate multiple DG on the basis of the IA expressions is described in detail as follows. Step 1: Enter the number of DG units to be installed. Step 2: Run load flow for the base case and find losses using (1). Step 3: Calculate the power factor of DG using (8) or enter the power factor of DG Step 4: Find the optimal location of DG using the following steps. a) Calculate the optimal size of DG at each bus using (2) and (3). b) Place the DG with the optimal size as mentioned earlier at each bus, one at a time. Calculate the approximate loss for each case using (1) with the values and of the base case. c) Locate the optimal bus at which the loss is at minimum. Fig. 1. Flow chart of IA method to allocate multiple DG units Step 5: Find the optimal size of DG and calculate losses using the following steps. a) Place a DG at the optimal bus obtained in step 4, change this DG size in small step, update the values and, and calculate the loss for each case using (1) by running load flow. b) Select and store the optimal size of the DG that gives the minimum loss. Step 6: Update load data after placing the DG with the optimal size obtained in step 5 to allocate the next DG. Step 7: Stop if either the following occurs: a) the voltage at a particular bus is over the upper limit; b) the total size of DG units is over the total load plus loss; c) the maximum number of DG units is unavailable; d) the new iteration loss is greater than the previous iteration loss. The previous iteration loss is retained; otherwise, repeat steps 2 to 6. 2) Power Factor Selection: Consider a simple distribution system with two buses, a source, a load, and a DG connected through a transmission line as shown in Fig. 1 Fig. 2. A simple distribution system with single DG. The power factor of the single load ( D PF ) is given as PF = (6) The power factor of the single DG injected ( DG PF ) is given as PF = (7) It is obvious that the minimum loss occur when the power factor of the single DG as (6) is equal to that of the single load as (7). To find the optimal power factor of DG units for a radial complex distribution system, a fast approach is proposed. A repeated approach is also introduced to check the effectiveness of the fast approach. It is interesting to note that in all the three test systems used in this work, the optimal power factor of DG units placed for loss reduction is found to be closer to the power factor of the combined load of respective systems. a) Fast Approach: The power factor of the combined load of the system ( D PF ) can be expressed by (6). In which, the total active and reactive power of the load demand is expressed as P = P ; Q = Q The possible minimum total loss can be achieved if the power factor of DG ( DG PF ) is selected to be equal to that of the combined load ( D PF ). That can be expressed as PF = PF (8) b) Repeated Method: In this method, the optimal power factor is selected by calculating a few power factors of DG units (change in a small step of 0.01) that are near to

4 the power factor of the combined load. The losses are compared, and the optimal power factor of DG units at which the total loss is at minimum is determined. 3) Optimization Algorithm for Multiple DG Allocation: This algorithm is made on the basis of the improved analytical expressions [27] to find the optimal buses at which the losses are the lowest and where multiple DG units are best placed. The IA expressions help to reduce the solution space. C. LSF Method: In this work, the sensitivity factor of active power loss is employed to find the most sensitive buses to place DG units which are capable of injecting active power only (i.e., Type 3 DG). The LSF at the i th bus is derived from equation (1) with respect to active power injection at that bus, which is given as α = = 2 α P β Q (9) Fig. 3 shows the flow chart of LSF method for multiple DG placement. Similar to IA method, the procedure to find the optimal locations and sizes of multiple DG units using the loss sensitivity factor is described in detail as follows. Fig. 3. Flow chart of LSF method to allocate multiple DG units Step 1: Enter the number of DG units to be installed. Step 2: Run load flow for the base case and find losses using (1) Step 3: Find the optimal location of DG using the following steps. a) Find LSF using (9). Rank buses in descending order of the values of their LSFs to form a priority list. b) Locate the highest priority bus. Step 4: Find the optimal size of DG and calculate losses using the following steps: a) Place a DG at the bus with the highest priority obtained in step 3, change this DG size in small step, update the values and calculate the loss for each case using (1) by running load flow. b) Select and store the optimal size of the DG that gives the minimum loss. Step 5: Update load data after placing the DG with the optimal size obtained in step 4 to allocate the next DG. Step 6: Stop if either the following occurs: a) the voltage at a particular bus is over the upper limit; b) the total size of DG units is over the total load plus loss; c) the maximum number of DG units is unavailable; d) the new iteration loss is greater than the previous iteration loss. The previous iteration loss is retained; otherwise, repeat steps 2 to 5. III. NUMERICAL RESULTS A. Test Systems The proposed methodology is tested on three test systems with varying sizes and complexities. The first system used in this paper is 16-bus test radial distribution system with a total load of 28.7 MW and 5.9 MVAr. The second one is 33-bus test radial distribution system with a total load of 3.7 MW and 2.3 MVAr. The last one is 69-bus test radial distribution system with a total load of 3.8 MW and 2.69 MVAr. In this paper 84 bus test radial distribution system analyzed.. B. Assumptions and Constraints The following are the assumptions and constraints for this paper: 1) The lower and upper voltage thresholds are set at 0.90 pu and 1.05 pu, respectively. 2) The maximum number of DG units is three, with the size each from 250 kw to the total load plus loss a nd the maximum DG penetration is 100%. C. IA method 1) 16-Bus Test System: Table I presents the simulation results of placing DG units by various techniques. The results of the base case and three cases with DG numbers ranging from one to three are compared. The results include the optimal sizes and locations of DG units with respect to the total losses by each technique. The loss reduction, computational time, and schedule of installed DG units of each technique are also presented in the table. For all the cases, IA leads to a completely optimal solution as compared with ELF, i.e., the optimal locations and sizes of DG units by IA is the same as those by ELF. Among all the cases, LSF yields the lowest loss reduction due to poor choice of locations. For instance, placing single DG by IA, ELF, and LSF yields loss reductions of 67.06%, 67.06%, and 62.15%, respectively. TABLE I DG PLACEMENT BY VARIOUS TECHNIQUES FOR 16-BUS SYSTEM

5 2) 33-Bus Test System: Similar to 16-bus system, Table II presents the results of the optimal sizes and locations of DG units by various techniques. For single DG, the loss reduction by IA, at 47.39% is the same as that by ELF. TABLE II DG PLACEMENT USING IA METHOD FOR 33 BUS SYSTEM 4)84 -Bus Test System: Similar to 16-bus system, Table IV presents the results of the optimal sizes and locations of DG units by various techniques for 84- bus system. TABLE IV DG PLACEMENT BY VARIOUS TECHNIQUES FOR 84-BUS SYSTEM IA needs a short computational time. Particularly, for thr ee DG units, the time by IA is 0.40 s, nearly twice longer than that by LSF at 0.23 s. 3) 69-Bus Test System: Table III presents the results of optimal sizes and locations of DG units by IA method.for all the cases, IA leads to a globally optimal solution ascompared with ELF; particularly the results by IA are the same as those by ELF in terms of loss reduction, optimal locations, and sizes of DG units. However, it is better than ELF in terms of computational time. Particularly, for three DG units, the time by IA is 0.71 s, nearly thirty-three times shorter than that by ELF at s. TABLE III DG PLACEMENT BY VARIOUS TECHNIQUES FOR 69-BUS SYSTEM D. ISF method 1) 16-Bus Test System: The results of the base case and three cases with DG units at the optimal and combined load power factors are compared. Table V shows the simulation results of the optimal sizes, locations, and power factors of DG units by IA for this system. The power factor of the combined load is 0.98 lagging. The optimal power factor of DG units is identified at 0.99 lagging. In all the cases, the results of loss reduction at the optimal power factor are slightly higher compared to those at the combined load power factor. TABLE V DG PLACEMENT AT OPTIMAL AND COMBINED LOAD POWER FACTORS FOR 16-BUS SYSTEM

6 increases. Table VII shows the simulation results of the optimal sizes, locations, and power factors of DG units by IA. The results of the base case and three cases with DG units at the optimal power factor are compared. The optimal power factor of DG units is determined to be equal to the combined load power factor at 0.82 lagging 2) 33-Bus Test System: Table VI shows the simulation results of the optimal sizes, locations, and power factors of DG units by IA. The power factor of the combined load is 0.85 lagging. The optimal power factor of DG units is identified at 0.82 lagging. TABLE VI DG PLACEMENT AT OPTIMAL AND COMBINED LOAD POWER FACTORS FOR 33-BUS SYSTEM TABLE VII DG PLACEMENT AT OPTIMAL POWER FACTOR FOR 69-BUS SYSTEM Similar to 16-bus test system, three DG units at the optimal power factor produces a maximum loss reduction of 89.45%; while one DG at this factor obtains a minimum loss reduction of only 67.85%. The more the number of DG units is installed, the better the loss reduction increases. 4) 84-Bus Test System: Table V shows the simulation results of the optimal sizes, locations, and power factors of DG units by isf. The results of the base case and three cases with DG units at the optimal power factor are compared. 3) 69-Bus Test System:. As a result, selection of the power factor of DG units that is equal to that of the combined load can lead to an optimal solution for this system. The number of DG units become larger, the loss reduction TABLE VIII DG PLACEMENT AT OPTIMAL POWER FACTOR FOR 69-BUS SYSTEM

7 E. Results Of Voltages Tables IX to XI indicate the minimum and maximum voltages for all the cases of 16, 33, and 69- and 84 bus test systems, respectively. In all the cases, after DG units are added, the total losses can reduce significantly while satisfying all the power and voltage constraints. TABLE IX VOLTAGES OF CASES FOR 16-BUS SYSTEM TABLE X VOLTAGES OF CASES FOR 33-BUS SYSTEM TABLE XI VOLTAGES OF CASES FOR 69-BUS SYSTEM It is interesting to note that the voltage profile improves when the number of DG units installed in the system is increased. Power factors of DG units too have an influence on voltage profiles as expected. IV. CONCLUSION The paper presents an extended Methodology based on Improved Analytical Expression, for DG placement to secure minimum loss, Stability index indicator and cost benefit factor. The system with DG placed to minimize loss is subjected assessment of voltage stability. This paper has presented IA method for multiple DG allocation for loss reduction in large-scale distribution systems while fulfilling the main objective of energy injection. The proposed IA method is effective as corroborated by ELF and LSF solutions in terms of loss reduction and computational time. LSF method may not lead to the best choice for DG placement. The number of DG units with appropriate sizes and locations can reduce the losses to a considerable amount. Among different DG types, DG capable of delivering both real and reactive power reduce losses more than that of DG capable of delivering real power only in one or two or three DG cases. In this paper, we have to analyze the 84-bus system using IA method and ISF method. This could be a good guidance for operating DG units that have the capability to deliver both real and reactive power for minimizing losses. In all the test systems used in this work, the operating power factor of DG units for minimizing losses has been found to be closer to the power factor of the combined load of the respective systems. REFERENCES [1] D. Singh and R. K. Misra, Effect of load models in distributed generation planning, IEEE Trans. Power Syst., vol. 22, no. 4, pp , Nov [2] I. El-Samahy and E. El-Saadany, The effect of DG on power quality in a deregulated environment, in Proc. IEEE Power Eng. Soc. Gen. Meet., 2005, vol. 3, pp

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