Advanced
Coordinated Voltage and Reactive Power Control Strategy with Distributed Generator for Improving the Operational Efficiency
Coordinated Voltage and Reactive Power Control Strategy with Distributed Generator for Improving the Operational Efficiency
Journal of Electrical Engineering and Technology. 2013. Nov, 8(6): 1261-1268
Copyright © 2013, The Korean Institute of Electrical Engineers
  • Received : April 29, 2013
  • Accepted : August 07, 2013
  • Published : November 01, 2013
Download
PDF
e-PUB
PubReader
PPT
Export by style
Article
Author
Metrics
Cited by
TagCloud
About the Authors
Ki-Seok Jeong
Dept. of Electrical Engineering, Kyungpook National University, Korea. (jksowl@knu.ac.kr,oneye@knu.ac.kr)
Hyun-Chul Lee
Dept. of Electrical Engineering, Kyungpook National University, Korea. (jksowl@knu.ac.kr,oneye@knu.ac.kr)
Young-Sik Baek
Corresponding Author: Dept. of Electrical Engineering, Kyungpook National University, Korea. (ysbaek@knu.ac.kr)
Ji-Ho Park
Dept. of Electrical Engineering, Koje College, Korea. (ec21319@koje.ac.kr)

Abstract
This study proposes a voltage and reactive coordinative control strategy with distributed generator (DG) in a distribution power system. The aim is to determine the optimum dispatch schedules for an on-load tap changer (OLTC), distributed generator settings and all shunt capacitor switching on the load and DG generation profile in a day. The proposed method minimizes the real power losses and improves the voltage profile using squared deviations of bus voltages. The results indicate that the proposed method reduces the real losses and voltage fluctuations and improve receiving power factor. This paper proposes coordinated voltage and reactive power control methods that adjust optimal control values of capacitor banks, OLTC, and the AVR of DGs by using a voltage sensitivity factor (VSF) and dynamic programming (DP) with branch-and-bound (B&B) method. To avoid the computational burden, we try to limit the possible states to 24 stages by using a flexible searching space at each stage. Finally, we will show the effectiveness of the proposed method by using operational cost of real power losses and voltage deviation factor as evaluation index for a whole day in a power system with distributed generators.
Keywords
1. Introduction
Modem power systems are often affected by an inadequate reactive power supply and a reduction of voltage stability margin. Those could be negative effects on voltage profiles, active and reactive power losses [1] . The operator can improve this situation by reallocating reactive power generations in the system, by adjusting transformer taps, by changing generator voltages, and by switching VAR sources. The system losses can also possibly be minimized [2] .
A voltage and reactive power control strategy in conventional distribution systems is normally achieved by incorporating on-load tap changers (OLTC) and shunt capacitors [3] . The transformer with OLTC changes its tap position to control the lower side voltage magnitude directly, whereas the capacitor banks affect the higher side voltage magnitude indirectly by changing the amount of reactive power demand at the bus [4] .
The introduction of distributed generation (DG) affects the power flows, which in turn alters some bus voltage profiles and influences the control strategy with voltage and reactive power controllers [5] . As the penetration of DG units increases in the distribution system, all players would be best served by allocating them in an optimal way so as to increase reliability, reduce system losses, and improve the voltage profile while serving the primary goal of energy injection. [6] . In order to meet the objective function such as active power losses, voltage regulation, and receiving power factor, there has to be coordination control between DG and conventional voltage and reactive power control equipment.
Many researchers have addressed the problem of voltage and reactive power control in distribution systems. Recently, most of them have focused on an automated remote dispatch either by using one-day-ahead daily dispatch schedules or real-time control [7] . The two method mentioned above can be applied to a smart grid which is a future power system combined with IT technology. A future distribution network is a smart grid that will be integrated with advance communication facilities, linking all voltage and reactive power controllers such as shunt capacitors, OLTC and DG to sensors and actuators with the network control center [8] .
When dynamic programming is employed to determine the optimal dispatch of some controllers in the distribution system, the computational burden is acceptable because of the relatively small searching space. However, to find the optimal schedule of more controllers requires a very large search space that is computationally time-consuming [7] . To reduce the computational burden, some techniques should be applied to the algorithm.
In this paper, coordinated voltage and reactive power control methods are proposed that adjust the optimal control values of capacitor banks, OLTC and the AVR of DGs by using voltage sensitivity factor (VSF) and dynamic programming (DP) with the branch-and-bound (B&B) method.
The coordination control algorithm is implemented by a Python script and the result is used to apply for PSS/E. The PSS/E allows the user to access the system by using a Python script [9] .
2. Problem Formulation
A voltage and reactive power control strategy involves coordination among all shunt capacitors, the OLTC, and the DG in the distribution system to minimize active power losses and to improve the voltage profile according to the load demand and DG output power based on a time series. Conventionally, voltages at the primary bus of a substation change slightly over a day and are therefore assumed to have a constant value. The OLTC setting is based on the change of load [7] . With the hourly load and generation data for the next day advancing, the determination of an optimal dispatch is desirable in the next 24 hours [10] . In other words, the on/off status of shunt capacitors, the tap position of OLTC, and the voltage set point of the distributed generator must be determined for each hour of the following so that total system loss can be minimized.
The optimal dispatch schedules for the settings of OLTC, DG and all shunt capacitor switching can be formulated mathematically as an optimization problem.
The following objective function is set to minimize the real power losses and keep all the voltages within the limits as much as possible:
Min
PPT Slide
Lager Image
where
x controllable variables
N number of stages in a day, which is 24 for a 1 hour interval between i and i +1
Nl number of branches including transformer
PLoss,i, j real power loss of each branch j at time i
R a penalty factor
Nb number of buses
Vi,k voltage at bus- k at time i
PPT Slide
Lager Image
reference bus voltage at bus- k at time i
The objective function is subject to standard power balancing equality constraints as well as the following additional inequality constraints including control variables limits and state variables limits.
PPT Slide
Lager Image
PPT Slide
Lager Image
PPT Slide
Lager Image
PPT Slide
Lager Image
where
VG,k voltage set point of distributed generators at bus- k
PPT Slide
Lager Image
minimum allowed voltage of DGs (i.e., 0.95 p.u.)
PPT Slide
Lager Image
maximum allowed voltage of DGs (i.e., 1.05p.u.)
NDG number of distributed generators
Tm OLTC tap position at mth transformers
PPT Slide
Lager Image
lower limit of tap position (i.e., -8)
PPT Slide
Lager Image
upper limit of tap position (i.e., +8)
NT number of transformers with OLTC
Cn status of nth shunt capacitors including fixed capacitor and switched capacitor.
PPT Slide
Lager Image
lower limit of status of capacitor (i.e., 0)
PPT Slide
Lager Image
upper limit of status of capacitor
NC number of shunt capacitors
Vi,k bus voltage at bus-k at time i
PPT Slide
Lager Image
minimum allowed voltage (i.e., 0.99 p.u.)
PPT Slide
Lager Image
maximum allowed voltage (i.e., 1.04 p.u.)
3. Solution Algorithm
Using the mathematical model for the optimal dispatch problem described in the last section, we will proceed to solve this problem and determine an optimal dispatch by using voltage sensitivity factor and dynamic programming with the branch-and-bound (B&B) method. Finally, we will show the effectiveness of the proposed method by using system operation costs as an evaluation index.
Based on the proposed method, an analytical software tool has been developed in a PSS/E environment with Python to run the load flow, calculate voltage sensitivity factor, power losses, and eventually to trace the optimal dispatch schedules.
- 3.1 Voltage sensitivity factor
Using VSF can reduce the computational effort. This process is performed each bus in control area which is described in next section.
The linearized steady state system power voltage equations are the following
PPT Slide
Lager Image
At each operation point, we keep P constant and evaluate voltage sensitivity by considering the incremental relationship between Q and V. To reduce (6), letting ΔP = 0 ,
PPT Slide
Lager Image
where
PPT Slide
Lager Image
JR is called the reduced Jacobian matrix of the system. This is the matrix which directly relates the bus voltage magnitude and bus reactive power injection [11] .
The diagonal elements of are the voltage sensitivity factors, ΔV/ΔQ, at each bus k [12] . That is,
PPT Slide
Lager Image
From (7), we can obtain the matrix of the voltage sensitivity factors each time i .
- 3.2 Dynamic programming with the b&B method
The optimal dispatch of OLTC tap position, voltage set point of DG and status of shunt capacitors can be determined by using dynamic programming. However, to find the optimal dispatch schedule for control variables across the whole distribution system one day in advance by dynamic programming requires a very large search space that is computationally time-consuming [7] . Therefore, we need to calculate the computational burden which means the total length of possible states or search paths through the system state at 24 stages. In this study, since there are two or more possible values for each capacitor and there are 17 possible positions for OLTC and 11 possible voltage set point of DG are described in Table 1 , the maximum length of each stage is following
PPT Slide
Lager Image
From (9) and Table 1 in next section, we can obtain (144×17×11) 24 states at 24 stages as the size of the whole searching space. If we try to solve the problem using total length described in (9), it will be entailed with a heavy computational burden.
Therefore, we propose several techniques to reduce the size of the searching space. First, possible states of shunt capacitors at each stage can be reduced by checking the initial status of shunt banks and the initial voltage profiles. That is, if the initial voltage is lower than the reference voltage, the states which have a status lower than the initial values could be excluded from counting the possible states.
Second, the branch-and-bound method is applied [10] . Branch and Bound is a general purpose method to solve discrete optimization problems, and is useful to solve small instances of hard problems. However, using the B&B may take exponential time in the worst-case. In this study, we try to find out some ranges based on saved control values at the previous stage by using B&B with specific upper and lower bounds.
This will be limited to the OLTC tap position and the voltage set point within ±2 steps based on their setting values at i -1 stage. In this way, the computational burden can be greatly reduced. However, the searching space at each stage will be created with flexible length L ( i ). If the initial values of each shunt capacitor in this study have zero, the size of the searching space at initial stage would be L (0) = (144×5×5) as a length of feasible states.
- 3.3 Selection of evaluation index
The evaluation index includes costs of system losses and receiving power factor [13] at the point of common coupling (PCC) as a means of improving economic efficiency. We also consider voltage deviation factor (VDF) as an indication for improving voltage profile across whole network.
To perform this process, we assume that the following parameters with the electric service agreement including electric charges. Electricity price per unit is 65 KRW/kWh. And the standing charge per unit is 7.111 KRW/kWh [14] .
The system operational costs are determined by considering two parts is described in Fig. 1 . First, we use the cost of loss penalty per hour which is calculated from active power losses (kW) and electricity price. Second, the cost of receiving power factor penalty or incentive which is calculated from the standing charge and power factor rate can be considered. The operating time of one day is 24 hours based on load demand and DG output power profiles, which are introduced in next section.
PPT Slide
Lager Image
Illustration of the system operational costs including losses and receiving power factor
- 3.4 Computational procedure
The flowchart of the solution algorithm is represented in Fig. 2 . The VSF and dynamic programming with some techniques, including the B&B method are applied to an optimal dispatch strategy. In computational procedure of the outer loop, we create the VDF matrix based on VSF to minimize the computational effort during the whole proposed control using initial load flow results each time i . This block also defines the feasible states with length L ( i ) at each time i . This process is repeated until the final stage is reached.
PPT Slide
Lager Image
Flowchart of optimal dispatch schedule algorithm using VSF and DP with B&B method
The inner loop is employed to execute the load flow, compute J ( i, r ) and save the feasible states with the minimum objective value at stage i .
Finally, we can find the optimal dispatch schedule backtracking saved data.
In the next section, the case study with modified IEEE 14 bus test system will be indicated the simulation results using this computational procedure.
4. Case Study
- 4.1 Test system
The modified IEEE 14bus test system in Fig. 3 is used to demonstrate the effectiveness of the proposed method. For research purposes, the connection between node 7 and 9 is closed and load is added at node 7. Five fixed or switched capacitors are added as shown in Fig. 3 . This paper assumes that the transformer between node 5 and 6 is changed from a fixed tap OLTC and distributed generator is connected to node 10. Table 1 presents the detailed data of the OLTC and DG. Table 2 shows a summary of the control variables including five shunt capacitors, OLTC and DG for proposed method. In this study, the load model for load flow studies is adopted constant MVA which means bus loads are not a function of voltage magnitude.
PPT Slide
Lager Image
Modified IEEE 14bus system
The detailed data of the OLTC and DG
PPT Slide
Lager Image
The detailed data of the OLTC and DG
Ranges of control variables in proposed method
PPT Slide
Lager Image
Ranges of control variables in proposed method
The total load of control area which includes node 6, 9, 10, 11, 12, 13, 14 is 77.7 MW and 38.5 MVAr for normal load condition. The control area of test system is based on the 154 kV distribution systems.
- 4.2 Load and generation profile based on time series
In a day, assuming the network is scanned every hour, for twenty-four hours operation, the network is scanned for 24 times. We assume the following as shown in Fig. 4 : First, load level varies from 40% to 100% for heavy load condition which is 86.33MW. Second DG output power varies from 45% to 80% for the rated capacity which is 50MW.
PPT Slide
Lager Image
Typical load demand and DG power output curve
- 4.3 Simulation results
Fig. 5 shows the voltage profiles of before control and the proposed method. The results of bus voltage of before control as shown in Fig. 5(a) tend to depend on the load demand and DG power output are plotted shown in Fig. 4 . All of the customer’s voltages should be kept within the permissible voltage limits (1.015±0.025 p.u.) [15] . However, in Fig. 5(a) , bus voltages at some nodes from 5:00 to 24:00 exceed the minimum allowed voltage. The bus voltage at node 14 is the lowest in the control area as shown in Fig. 5(a) . On the other hand, all of the bus voltages are kept within the permissible voltage limits. The voltage profile at node 14 is greatly improved.
PPT Slide
Lager Image
Voltage profiles of control area for the whole day : (a) before control, (b) proposed method
Fig. 6 shows the comparison of voltage deviation factors from the voltage profile results as shown in Fig. 5 . The VDF is considered as an evaluation index to valid the effectiveness of proposed method. In Fig. 6 , one can see that proposed method at each hour is more effective with regard to improving the voltage profiles.
System active power losses (MW) in the control area at each hour with and without control are represented in Fig. 7 .
At each stage i , we can compute objective and save the feasible states with the lowest objective value which is system losses. These saved values at each stage i are less than the simulation results from before control, respectively.
Receiving power factors (%) at the PCC at each hour with and without control are shown in Fig. 8 . as a means of improving economic efficiency. In this study, the receiving power factor is considered as evaluation index for calculating system operational costs. The proposed method can be seen to have contributed to improving the receiving power factor.
PPT Slide
Lager Image
Comparison of voltage deviation factors
PPT Slide
Lager Image
System loss without and with control
PPT Slide
Lager Image
Receiving power factor without and with control
Table 3 shows the optimal dispatch schedule of OLTC, DG and five shunt capacitor switching based on the time series data as shown in Fig. 4 . The initial tap position of OLTC is -2, the initial set point of DG is 1 p.u., and the initial status of the two fixed capacitor ( C 2 and C 4 ) is on (1). And the initial status of three fixed or switched capacitor ( C 1 , C 3 and C 5 ) is off (0). To keep the voltage fluctuation in the the permissible voltage limit at node 14 in the whole day, the status of the shunt capacitor C 5 is on (1) at all stages.
Optimal dispatch schedules from proposed method.
PPT Slide
Lager Image
Optimal dispatch schedules from proposed method.
Comparison of the results from several cases concerning the reduced computational burden
PPT Slide
Lager Image
Comparison of the results from several cases concerning the reduced computational burden
The summary of simulation results
PPT Slide
Lager Image
VDFimp%=[VDF(before control)- VDF(proposed method)]/ VDF(before control) Cost of Losses=Losses (kW)·electricity price(KRW/kWh)
The proposed method has been implemented using the Python in PSS/E environment. The total objective values and execution time of the optimal dispatch schedules by several techniques with and without flexible searching space at each stage are summarized in Table 4 .
The total real power losses, power factor and their cost based values (KRW/day) are summarized in Table 5 .
Based on the simulation results summarized in Table 5 and assumed conditions described in 3.3, total operating cost (TOC) of one year can be obtained the following results.
PPT Slide
Lager Image
Scatter plots between VDF and Total operation cost: (a) only losses, (b) only PF, and (c) both losses and PF
Operating expenses and Annual Saving (in million won)
PPT Slide
Lager Image
Annual Saving = [TOC(before control)- TOC(proposed method)] ·100/TOC(before control)
Fig. 9 shows the correlation between two variables which are VDF and total operational cost using scatter plots. From three sub plots in Fig. 9 , we have the following observations. First, total system costs and VDF are shows relatively reduced. Second, considering both losses and VDF is greatly reduced after control as shown in Fig. 9(c) .
Table 5 shows a summary of the total annual operating cost and benefit of proposed method according to three cases. Cleary the proposed method reduces the system operational costs and improve the voltage profiles from Fig. 9 and Table 6 .
5. Conclusion
A new coordinated voltage and reactive power control strategy is proposed in this paper. The control variables include switched or fixed shunt capacitor bank, tap position of on-load tap changer and voltage set point of distributed generator. The proposed method is implemented by a Python script and the result is used to apply for PSS/E. Optimal dispatch schedules are obtained by using voltage sensitivity factor and dynamic programming with branch-and-bound method. To avoid the computational burden, voltage deviation matrix based sensitivity factor are applied and the searching space is reduced with flexible size. The evaluation index includes costs of system losses and receiving power factor at the point of common coupling as a means of improving economic efficiency. Simulation results indicate that the proposed method is effective in handing the system operational cost saving and voltage regulation. This control strategy makes it possible to minimize the total operational costs with system losses and receiving power factor and improve the voltage profile for a whole day in power system with distributed generators. The combined objective function including receiving power factor and loss minimization needs to be studied in the future.
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: 2013006460). This research was supported by Kyungpook National University Research Fund, 2013.
BIO
Ki-Seok Jeong received the B.S. and M.S. degrees from Kyungpook National University, Korea in 2008 and 2010, respectively. He is currently working toward the Ph.D. degree at Kyungpook National University graduate school. His current interests include reactive power compensation and planing, distributed generations, voltage stability, and optimization.
Hyun Chul Lee received the B.S. and M.S. degree from electrical engineeering of Wonkwang University, Korea in 2002 and 2004, respectively. He worked as senior researcher of Green NetPower Company, from 2007 to 2010. He is currently working toward the Ph.D. degree at Kyungpook National University graduate school. His special fields of interest are Superconductor application, power system control, power quality, and FACTS in Power system.
Ji-Ho Park received the B.S., M.S., and Ph.D. degrees in electrical engineering from Kyungpook National University, Korea in 1991, 1996, and 2001, respectively. He is currently an Invitation Professor in the Department of Electrical Engineering at Koje College. His research interests are power system stability analysis and voltage control.
Young-Sik Baek received the B.S., M.S., and Ph.D. degrees in electrical engineering from Seoul National University, Korea in 1974, 1979, and 1984. He was a Lecturer at Myungji University from 1979 to 1985. He is currently a Professor in the Department of Electrical Engineering at Kyung-pook National University. His areas of research are power system operation and control.
References
Zjang Wen , Liu Yutian 2002 “Optimal Var Planning in Area Power system,” International Conference on Power system Technology 4 2072 - 2075
Mamandur K. R. C. , Chenoweth R. D. 1981 “Optimal Control of Reactive Power Flow for Improvements in Voltage Profiles and for Real Power Loss Minimization,” IEEE Transactions on Power Apparatus and Systems PAS-100 (7) 3185 - 3194    DOI : 10.1109/TPAS.1981.316646
Viawan F.A. , Karlsson D. 2008 “Voltage and Reactive Power Control in Systems with Synchronous Machine-Based Distributed Generation,” IEEE Transactions on Power Delivery 23 (2) 1079 - 1087
Kim Gwang Won , Lee Kwang Y. 2008 “Coordination Control of ULTC Transformer and STATCOM Based on an Artificial Neural Network,” IEEE Transactions on Power Systems 20 (2) 580 - 586
Jenkins N. , Allan R. , Crossley P. , Kirschen D. , Strbac G. 2000 Embedded Generation Inst. Elect. Eng. London, U.K.
Hung Duong Quoc , Nadarajah Mithulananthan 2013 “Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction,” IEEE Transactions on Industrial Eletronics 60 (4) 1700 - 1708    DOI : 10.1109/TIE.2011.2112316
Hu Z. , Wang X. , Chen H. , Taylor G. A. 2003 “Volt/VAr control in distribution systems using a time-interval based approached,” IEE Proceedings-Generation, Transmission and Distribution 150 (5) 548 - 554    DOI : 10.1049/ip-gtd:20030562
Che Wanik Mohd Zamri , Erlich Istvan , Mohamed Azah 2010 “Intelligent Management of Distributed Generators Reactive Power for Loss Minimization and Voltage Control,” IEEE MELECON 685 - 690
PSS/E-32, Program Application Guide Shaw Power Technologies Inc.
Liang Ruey-Hsun , Cheng Chen-Kuo 2001 “Disptch of Main Transformer ULTC and Capacitors in a Distribution Systeml,” IEEE Transactions on Power Delivery 16 (5) 625 - 630    DOI : 10.1109/61.956748
Kundur P. 1994 Power System Stability and control McGraw-Hill, Inc. 991 - 992
Seshadri P. S , Patton A, D. 1999 “Bus Voltage Sensitivity : An Instrument for Pricing Voltage Control Service,” IEEE Power Engineering Society Summer Metting 2 703 - 707
hu Jinlei , Guo Yao Zhang , Xie Huifan 2006 “Optimization of Power Factor for Operation of Small Hydro Stations,” IEEE International Conference on Power system Technology 1 - 5
Vovos P.N. , Kiprakis A.E. , Wallace A.R. , Harrison G.P. 2007 “Centralized and Distributed Voltage Control: Impact on Distributed Generation Penetration,” IEEE Transactions on Power Systems 22 (1) 476 - 483    DOI : 10.1109/TPWRS.2006.888982
Lee Ik-jong 2008 An examination on proper voltage operation standard for 154kV power system through demand analysis, Master thesis Department of Electrical Engineering, Seoul National University of Technology Korea 9 - 12