OPTIMIZATION OF MODE IN DISTRIBUTION ELECTRICAL GRID BY USING RENEWABLE ENERGY SOURCES FOR RURAL ENERGY SUPPLY

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 7, July 2018, pp , Article ID: IJMET_09_07_149 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed OPTIMIZATION OF MODE IN DISTRIBUTION ELECTRICAL GRID BY USING RENEWABLE ENERGY SOURCES FOR RURAL ENERGY SUPPLY Sh. Shokolakova, S.A. Keshuov Kazakh National Agricultural University Almaty, Kazakhstan A.A. Saukhimov, K.K. Tokhtibakiev Almaty University of Power Engineering and Telecommunications Almaty, Kazakhstan O. Ceylan Kadir Has University, Istanbul, Turkey E. Shuvalova Tallinn University of Technology, Tallinn, Estonia ABSTRACT Kazakhstan plans to support integration of renewable energy sources (RES). For instance, according to [1,2] until 2020, it is planned to connect 53 RES with a total value of 2000 MW. From those 53 RES, most of them will be located in rural areas and will be connected to electrical power distribution grid. Power losses is an important problem in Kazakhstan power systems, and largest share of power losses are related to distribution system losses and are approximately 65 %. Using RES as distributed generators (DGs) in near future, in order to reduce power losses may be one of the important tasks of Distribution System Operators (DSOs) of Kazakhstan. Approach of minimizing power losses may be applied by changing the injected/absorbed active and reactive powers at the points of DG connection [1,2]. This paper models the power losses optimization problem by using a recently developed heuristics based optimization model: Moth Flame Optimization (MFO). It solves the power loss minimization problem on a modified 33 bus electrical grid with DGS. Key words: Power Losses, Renewable Energy Sources, Distributed Generation, Optimization Algorithms editor@iaeme.com

2 Optimization of Mode In Distribution Electrical Grid by Using Renewable Energy Sources For Rural Energy Supply Cite this Article: Sh. Shokolakova, S.A. Keshuov, A.A. Saukhimov, K.K. Tokhtibakiev, O. Ceylan and E. Shuvalova, Optimization of Mode In Distribution Electrical Grid by Using Renewable Energy Sources For Rural Energy Supply, International Journal of Mechanical Engineering and Technology, 9(7), 2018, pp INTRODUCTION In the distribution power system of Kazakhstan, significant transmission tariff costs are related to the amount of energy losses that fluctuate in 5-17% of consumption [3]. Also in Kazakhstan the agricultural and civil electrical supply consumers use RES. This may be further used as DGs so that power flows may be managed and hence energy losses may be reduced and better voltage stability indexes may be obtained. Smart Grid technologies may help consumers to sell power for peak shaving in electrical grid as shown in Fig. 1. Figure 1 Structure of power flow in distribution electrical grid when DG connection exists. However, DSO companies may not be efficiently implementing these solutions since optimally locating DGs with optimal sizing is not applicable because of the deregulated structure of the power systems. Hence, this paper aims at minimizing the power losses with DGs and improving the voltage profiles. Decision tasks can be solved through the use optimization approaches included analytical and numerical methods [4]. Analytical methods may result in increased computational efforts with suboptimal solutions [5]. Numerical methods may be classified as gradient based ones and derivative free methods. Steepest descent method, conjugate gradient method, penalty method may be given as examples of gradient based methods [4-6]. Some examples to derivative free methods are genetic algorithms, particle swarm optimization, simulated annealing, harmony search etc. [4-6]. In this paper we use a recently developed nature inspired optimization method: Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation [7] editor@iaeme.com

3 Sh. Shokolakova, S.A. Keshuov, A.A. Saukhimov, K.K. Tokhtibakiev, O. Ceylan and E. Shuvalova The rest of the paper is organized as follows: The next section describes the optimization model. Then methodology of MFO algorithm will be given briefly and implementation details of it on electrical power distribution system will be explained. Test system will be given next, and before conclusions numerical results of the optimization model will be explained. 2. OPTIMIZATION MODEL We aim to minimize the losses and improve voltages in power distribution system. Assuming that there are n DGs and m capacitor banks connected to the system, the optimization model will be as follows: min (1). =1,2,,! " " # " $=1,2,,% 0.95 ) 1.05 *=1,2,,+,- Where,.,!*,+,-,),," # represent the control variables, number of lines, voltage magnitude node *, /0 bank capacitor s position and reactive power output of the $ /0 DG in the system respectively. It should be noted that the solution of the optimization model given below is for a single time step. This means that the solutions obtained determine the near optimal bank capacitor positions, reactive power outputs of DGs for a given time step. If simulation is performed on a daily perspective, at each time step solutions should be obtained. 3. MOTH FLAME OPTIMIZATION MODEL MFO is a recently developed nature inspired derivative free method [7]. It is a nature inspired method and is based on the movements of moths against the moon-light. Moths use moon-lights as their navigation systems, however artificial lights may trick them and hence they may fly around them in a spiral path which is used in MFO [7]. Like all other population based derivative free methods, first step of MFO is creating initial solution candidates randomly in the search space. These initial solution candidates are called moths. Similar to moths, flames should also be created initially in a similar manner. After creating initial moths and flames, objective function values are evaluated and corresponding objective function values are computed. Note that during the evaluation of the algorithm, moths search all the search space however flames represent the obtained best results. There are basically three main functions in MFO. These are 1, and 2 functions. The first function is 1 function and as specified above it consists of initialization of moths and flames, and computation of fitness values of them. The second function is used for changing the positions of moths in the search space. It is mathematically a logarithmic function representing the spiral movements and is given below: 34,5 =6 7,/ cos;2<=+5 (2) Where, 4,5,6,? and, represent /0 moth, /0 flame, the distance between them, a constant and a random number between -1 and 1 respectively. The 2 function is used for stopping the algorithm i.e. the algorithm stops if a predefined stopping criterion is met editor@iaeme.com

4 Optimization of Mode In Distribution Electrical Grid by Using Renewable Energy Sources For Rural Energy Supply 4. MFO ALGORITHM FOR DISTRIBUTION SYSTEM LOSS MINIMIZATION Steps for solving distribution system loss minimization using MFO are given as follows: The first step consists of initializing the parameters of MFO and then initializing moths and flames. The parameters to be decided are such as the size of the moths and flames, and the number of generations. For this specific problem, control variables are bank capacitor positions and the reactive power outputs of the DGs. Initial moths with a size of A are shown as follows: D,,E, " " FGH,H FGH,I " FGH,J O 4= C E, E,E E, " FGI,H " FGI,I " FGI,J N C N B K, K,E K, " FGL,H " FGL,I " FGL,J M Flames are initialized in a similar manner. The second step evaluates each solution candidate using the objective function given in (1). For this aim, power flow simulation is run. The next step uses the logarithmic spiral function given in (2) so that the moths fly around the solution space, and better solutions are obtained as the number of iterations increase. Stopping criterion check is the final step of the algorithm. 5. DATA PREPARATION AND TEST SYSTEM We use 33 bus distribution system [8] for simulation purposes. We modify the system as shown in Fig. 2. We assume that nodes, 7, 9, 10, 12, 13, 15, 19, 20, 23, 24, 27, 29, 30 are PV installed nodes. Nodes, 11, 21 and 28 are bank capacitor installed nodes. We assume that each PV station has a capability of maximum 500 kw active power output. We also assume that each PV station may inject/absorb reactive power between -200 and 200 kvar at all simulation times. This range is between 0 and 500 kvar for capacitors. (3) Figure 2 Modified 33 node test system. We used same daily load profiles for all nodes in the test system. For this aim we use yearly based 15 minute resolution electricity consumption data for Building 74 on the Lawrence Berkeley National Lab campus [9]. Then we selected a random day (17 th of August 2014 for our case) and scaled all 15 minute loads. Fig. 3 illustrates this situation. This daily data is used in the simulations for all nodes in the test system editor@iaeme.com

5 Sh. Shokolakova, S.A. Keshuov, A.A. Saukhimov, K.K. Tokhtibakiev, O. Ceylan and E. Shuvalova Figure 3 Scaled daily load profile, used in the simulation for all nodes in the test system We used the same PV output profiles for all PV installed nodes. By using renewables. energy ninja [10, 11, 12] website, we provided the latitude and longitude information for Almaty, and obtained yearly hour based PV outputs. Then we selected the same day, and assumed that for each hour the PV outputs don t change. We scaled the outputs and used these data as PV outputs in the simulations as shown in Figure [4]. Figure 4 Scaled PV outputs, used in the simulation for all PV installed nodes in the test system 6. TESTS AND RESULTS We performed all simulations on Matlab and used open source power systems package Matpower [13] for load flow calculations. We performed simulations on each 15 minute interval: at each time step loss minimization problem is solved then the results are saved. Fig. 5 illustrates active power losses in for 2 different simulation cases. The first case assumes that there is no control applied in the distribution system, and the bank capacitors are set to 0 kvar. The second case considers the reactive power capabilities of PVs and also adjusts the bank capacitors to their near optimal values. It is clear from the figure that nearly for all time intervals editor@iaeme.com

6 Optimization of Mode In Distribution Electrical Grid by Using Renewable Energy Sources For Rural Energy Supply better active power losses are obtained when controls are applied in the system. Also it is obvious from the figure that, the active power loss pattern is following the same pattern of the PV outputs. Capacitor reactive power values are shown in Fig. 6. The system has 3 capacitors installed on nodes 11, 21 and 28 respectively. From the figure it is observed that the reactive power of the capacitor on node 28 is not to set its maximum value during the simulations. However, capacitors on nodes 11 and 21 reach to their maximum values during the times that PVs provide highest active power outputs. Figure 5 Active power losses with two different cases: case without controls, case with controls. Figure 6 Capacitor reactive powers in kvar through the day We illustrate reactive power outputs of 4 representative PV outputs from each lateral in the system in Fig. 7. PVs on nodes 23 and 29 set their reactive power outputs to their maximum amounts nearly during all simulation intervals. This is not the case for the PVs on nodes 7 and editor@iaeme.com

7 Sh. Shokolakova, S.A. Keshuov, A.A. Saukhimov, K.K. Tokhtibakiev, O. Ceylan and E. Shuvalova We also illustrated the voltage magnitudes of all nodes in the system for all simulation time intervals. Fig. 8 shows the voltage magnitudes of all nodes when there is no control applied. Fig 9. Illustrates the voltage magnitudes of all nodes when capacitors and reactive power outputs of PVs are calculated using MFO based optimization algorithm. Figure 7 Reactive power outputs of 4 representative PV outputs Figure 8 Voltage magnitudes of nodes for all simulations when no control is applied From Fig. 8 and Fig 9., one may easily see that after the voltage control approach is applies voltage magnitudes are brought in between the allowed ranges: 0.95 pu and 1.05 pu. We also calculated the mean voltage magnitudes for all simulations for 2 different simulation cases. When no control is applied mean of the all voltage magnitudes are found as pu with a standard deviation of These magnitudes become better when optimization approach is applied: pu and editor@iaeme.com

8 Optimization of Mode In Distribution Electrical Grid by Using Renewable Energy Sources For Rural Energy Supply Figure 9 Voltage magnitudes of nodes for all simulations when optimization approach is applied 7. CONCLUSION AND FUTURE WORK This paper solves power loss problem in distribution systems with reactive power capabilities of PVs and capacitors by using a recently developed population based intelligent method MFO. Numerical tests are performed on a modified 33 bus distribution system, by using 15 minute resolution dataset. From the simulations it is observed that by using MFO based optimization approach, active power losses are decreased, and better voltage magnitudes are obtained. We observe that using capacitors coordinated with PV inverters is able to provide efficient results, hence this approach may be an alternative to conventional approach of using voltage regulators. REFERENCES [1] Law of Republic of Kazakhstan 165-IV. About support of integration renewable energy sources. July 4, [2] Saukhimov A. A., Tokhtibakiev, K. K., Bektimirov, A. T., Didorenko, E.V., Shuvalova, E., Shokolakkova Sh. Low frequency oscillations research in the National Electric Networks of Kazakhstan. World Science and Engineering congress WSEC, 1, 2017, ; [3] Tokhitibakiev, K., Shokolakova, Sh., Arystanov, N., Nurtaza, N., Shubekova, K., Saukhimov, A. Identification of Design Scheme Parameters of Electric Network Using WAMS Data. 10th Electric Power Quality and Supply Reliability Conference IEEE. Tallinn, 2016, [4] Tural, S., Ceylan, O., Paudyal, S. Optimal Voltage control in Distribution feeders with Large Penetration of Wind. University of Power engineering conference, [5] Hung, D. Q., Mithulananthan, N., Lee, K. Y. Optimal placement of dispatchable and Non dispatchable RDG units in distribution networks for minimizing energy loss. International Journal of Electrical Power & Energy Systems, 55, , 2014; [6] Kalkhambkar, V., Rawat, B., Kumar, R., Bhakar, R. Optimal Allocation of Renewable Energy Sources for Energy Loss Minimization. Journal of Electrical Systems, 113(1), 2017), [7] Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 2015, editor@iaeme.com

9 Sh. Shokolakova, S.A. Keshuov, A.A. Saukhimov, K.K. Tokhtibakiev, O. Ceylan and E. Shuvalova [8] Baran, M. E., Wu, F. F. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Transactions on Power Delivery, 4(2) 1989, doi: / [9] Long-term electricity and gas consumption for LBNL Building 74. U.S. Department of Energy. [10] Pfenninger, S., Staffell, I. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy, 114, 2016, doi: /j.energy [11] Pfenninger, S., Staffell, I. Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output. Energy, 114, 2016, doi: /j.energy [12] Renewables.ninja website. [13] Surekha S. Bhalshankar and Dr C.S. Thorat, Hybrid Renewable Energy AC Based Nano- Grid Distributed Generation System for Smart Home: International Journal of Electrical Engineering & Technology, 9(3), 2018, pp [14] S.Dileep Kumar Varma And Divya Dandu, Modelling And Simulation Of Hybrid Renewable Energy Sources Connected To Utility Grid, Volume 4, Issue 5, September October (2013), pp , International Journal of Electrical Engineering and Technology (IJEET). [15] Neeraj Sharma, Jimmy Kansal and Ashwagosha Ganju, Off-Grid Hybrid Renewable Energy System Sizing For High Altitude Cold Deserts, Volume 4, Issue 7, November - December 2013, pp , International Journal of Advanced Research in Engineering and Technology (IJARET). [16] Manvendra Verma and Garima Malik, Understanding the Indian Polices for Renewable Energy with Reference to Indian Rural Areas: A Study after Post Independence Era. International Journal of Civil Engineering and Technology, 8(11), 2017, pp [17] Zimmerman R. D., Murillo-Sánchez C. E., Thomas R. J. MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education. Power Systems, IEEE Transactions on, 26(1), 2011, editor@iaeme.com