Optimal Placement and Sizing of STATCOM to Improve Power Quality Considering Wind Generation E. Ma’ali Amiri*1, B. Zaker*2, H. Rahbarimagham*3, M. Abedi*4, G.B. Gharehpetian*5 *
Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran E-mails:
[email protected],
[email protected],
[email protected],
[email protected],
[email protected] 1
Abstract— utilizing wind energy as distributed generation resources cause voltage drops in grids because of the nature of induction machine which used in it. Therefore, voltage profile had been always a concern in grids which have large wind farms. Nowadays, Flexible AC Transmission System (FACTS) devices become a strong tool to improve dynamic voltage profile and control the power flow in power system lines. One of the most important components of these devices is static synchronous compensator (STATCOM) that has the capability of dynamic tracking of the voltage magnitude at the installation point. However, because of some reasons such as high price of the FACTS devices in addition to installation and maintenance costs it is very important to find optimal location and size of these devices for utility grids. This paper investigates the application of the Genetic Optimization Algorithm (GA) to find the optimal place and capacity of the STATCOMs in order to improve the voltage profile of a network that have wind farms as distributed generation. Also the stochastic behavior of the wind speed has been considered to approach the actual condition of the wind generation. Therefore, the bus voltages in the network have fluctuations corresponding to the changes of the reactive power that absorbed by the wind turbines in the farms. For validation purposes this method has been applied to IEEE 14-bus test system including two wind farms at different places. Keywords- wind generation; voltage profile; FACTS; statcom; genetic algorithm.
I.
INTRODUCTION
Renewable energies have more attraction in recent few years for human being because of the less environmental pollution and less expense than the natural fossil fuels. Besides the advantages of these kinds of energies, their impacts on the system planning and operation are also important. Many of these energies such as wind generation and photovoltaic are usually far from load centers. Thus a lot of new constructions like transmission buildings and reliability, quality, and stability additive equipment are needed. In recent years wind generation has a global growth and became an important source to replace with fossil fuels. The wind turbines with 2-5 MW capacity had already been available in past years and their cost of energy is very adequate against the other conventional clean fuels. However wind energy is much dependent on geographical installation location of the wind turbines. Also because of the uncertainty of output active power and absorbing reactive power which depends on the wind speed in different hours of a
day, it is expected that voltage variation of each node of the grid becomes a significant problem. This voltage variation can damage the equipment and devices of the grid and consumers. To solve these problems some of the methods have been always used by utilities. The transformer tap changers (TCs) and mechanical switched capacitor (MSC) banks are utilized to fix these problems in [1-4]. But other issues such as power fluctuations and harmonics cannot be solved quite by these devices because of their slow dynamic response. Therefore a fast shunt reactive power compensator is needed to overcome these problems more effectively that has been pointed in many literatures [5-9]. FACTS devices are key components of the utilities to solve the power system problems such as controlling power flow in the distribution and transmission lines and improving voltage magnitude and stability of the critical points in the grid [10-11]. Static synchronous compensator (STATCOM) is a powerful FACTS device that considered to use in this application because its excellences and advantages than other equipment. It has very fast response time (less than 2 cycles) to compensate the voltage magnitude of the installation point by injecting reactive power to it [12]. According to recent progresses in power electronic devices, high power semiconductor switches and digital control technologies STATCOM devices with faster dynamic response (almost a quarter of a cycle) and lower costs are emerged [13]. These devices can significantly improve and fix the distributed generation problems in power system. Therefore one could have more reliable and efficient renewable energy resources. In this paper the optimal location and capacity of the STATCOM devices to improve the voltage profiles of network buses by adopting an intelligent algorithm has been investigated. The network which has been considered for this study contains two wind farms as distributed generation. The stochastic behavior of the wind speed also has been considered and modeled by a Probability Distribution Function (PDF). Therefore the wind turbine’s exchanged active and reactive power is changes by wind speed variations. Three cases have been investigated to achieve the acceptable voltage profile which is matches with the standards. The location and capacity of the FACTS devices has been obtained and depicted in each case. The paper has been organized as follows: STATCOM characteristic and operation has been described in Section 2.
Stochastic behavior of the wind and wind turbines has been discussed in Section 3. Genetic algorithm basis has been outlined in Section 4. Simulation results have been presented in Section 5. And finally the paper is concluded is Section 6. II.
STATCOM DESCRIPTION
One of the most important components of FACTS devices is STATCOM that have the capability of dynamic tracking of the voltage magnitude at the installation point. This great capability imposes utilizing this device in the situation like severe voltage drops or especially in fault condition. There is also the ability of the exchanging active power if being use a battery source instead of a capacitor in its structure. The control of the installation point voltage magnitude is done by adjusting the output reactive power. The STATCOM structure is comprises a DC/AC voltage source converter and a reactance X L which is model of a transformer between STATCOM and the system. The exchanged reactive power between the STATCOM and the grid is considered as Eq. (1). This is the main equation of the STATCOM operation.
Q=3
VS (VC − VS ) XL
(1)
where Q is the exchanged reactive power between STATCOM and the grid, V S is the installation point voltage, V C is the converter output voltage and X L is the transformer reactance between STATCOM and the grid. If the V C be greater than the V S , the reactive power is injected to the system by the converter. This operation is called capacitive mode. Conversely reactive power is absorb from the system if the V C be less than the V S . So it is called inductive operation mode. If the V C and V S be equals no reactive power will be exchanged. There are two direct and indirect controlling methods to adjust the voltage of a bus which is coupled by the STATCOM. In direct method the converter DC side voltage is kept constant and adjusting the output voltage is done by changing switching intervals of the converter. In indirect method the magnitude of the output voltage is altered by change of the DC voltage magnitude through temporary phase relocation between V C and V S. According to standards in a power system usually it is desirable to keep the bus voltage deviation within ±5% pu around the 1 pu. Therefore a voltage stabilizer device is needed to compensate bus voltage fluctuations within the determined range. In this paper the STATCOM is used for this purpose. III.
WIND TURBINE
In recent years because of environmental concerns, exploitation of renewable energy sources such as wind power has been increased. Using wind energy cause a considerable reduction in pollutant gases which are emitted from fossil fuel burning. Because of stochastic nature of wind speed, it is modeled with a Probability Distribution Function (PDF). One of the most common PDFs which are used for this purpose is weibull distribution. Weibull PDF has been shown in Fig. 1 and its density function is as below:
Fig. 1. Weibull PDF α
α −1 − v β
αv fv ( v ) = β β
e
(2)
where α and β are the shape and scale parameter respectively. Many equations are suggested in literature to convert wind speed to electrical power [14], but among all of them the following formula is accurate and simple enough [5]:
= Ρ 0.5 ρν 3 Α (W )
(3)
where P is converted power, A is the cross-section swept by the wind turbine blades in m2 (A = πR2) which R is the radius of the rotor, ρ is the air density and v indicates the wind speed. In this paper weibull distribution with corresponding parameters β=11 and α=12 is used for wind speed modeling. Air density (ρ) considered to be 1.2235 (kg/m3) and wind turbine radius (R) is 45m. A wind generator absorbs reactive power from AC network as like as an asynchronous generator. According to the literatures, it is common to consider cosϕ=0.8. Therefore, Q of the wind generator is obtained from Eq. (3). IV.
GENETIC ALGORITHM
Genetic Algorithm is one of the optimization methods inspired by evolution. Actually GA is an iterative procedure which leads to find global minimum point. It begins with a set of random solutions called initial population which constitute first generation [15]. Some basic operations of GA are selection, mutation and crossover. These operations are presented in Fig. 2 as flowchart. In each generation, individuals which are near to the minimum point are selected for the next generation. Other ones undergo mutation, crossover, and finally evaluation. A brief explanation of each step is outlined as below: Initialization: Initial population produces first generation including N individuals. Reproduction: According to the fitness values, each individual will be reproduced. Individuals with higher fitness values have more chance to survive and constitute next generation. Crossover: Two individuals are selected and then crossover is applied to them as a mathematic function.
Mutation: Mutation introduces random changes in instructions in the population and new individuals are generated.
WF 14
13 12
Termination: If the objective function is minimized in range of desired accuracy the procedure will finished, otherwise return to Reproduction step.
10
11
9
6
G1
7
G4
8
1 5
G5
WF
4
2 G2 3 G3
Fig. 3. Single line diagram of the IEEE 14 Bus test system
Fig. 2. Genetic algorithm operation chart
V.
SIMULATION RESULTS
Because of some reasons such as high price of the FACTS devices, installation and maintenance costs, it is very important to find optimal location and size of these devices in grids. This paper investigates the application of the Genetic Optimization Algorithm to find the optimal place and capacity of the STATCOM in order to improve voltage profile within the specified range. To validate the represented method, it has been applied to the IEEE 14-bus case study as shown in Fig. 3. The network data have been given in TABLE I. Considering the constraints and conditions of different spots in the grid, it has been decided to set up two wind farms as the distributed generation in transmission voltage level at bus 5 and 14. The average output power for each one assumed to be 45 MW with power factor 0.8. It has been considered that the wind speed is varying based on the weibull distribution function described in section III. Therefore the reactive power which is absorbed by the turbines is changes corresponding to the wind speed. Load flow problem has been solved for the case study considering the wind farms and the bus voltages have been obtained. Fig. 4 shows the voltage profile of the network buses without utilizing any compensators. According to the Fig. 4 it is obvious that the bus voltages deviation which has been mentioned in Section 2 is more than the standard range for many of the buses.
Bus no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14
TABLE I. Network Loads and Generator Data Bus Load Generation type Mw Mvar Mw Mvar Q min Slack 0 0 PV 29.7 12.7 40 42.4 -40 PV 94.2 29 0 23.2 0 PQ 47.8 17.9 0 0 0 PQ 17.6 7.6 0 0 0 PV 17.2 7.5 0 12.2 -6 PQ 0 0 0 0 0 PV 0 0 0 17.4 -6 PQ 29.5 11.6 0 0 0 PQ 9 4.8 0 0 0 PQ 3.5 1.8 0 0 0 PQ 8.1 3.6 0 0 0 PQ 13.5 5.8 0 0 0 PQ 14.9 5 0 0 0
Q max 50 40 0 0 24 0 24 0 0 0 0 0 0
To prevent any drop in magnitudes of the bus voltages, it has been decided to use some FACTS devices (STATCOM) to improve the voltage profile of the grid. In first step one STATCOM is used as Case 1. A Root Mean Square objective function is utilized to determine the optimal location and MVAr size of the STATCOMs. The optimization problem is solving when the following objective function is minimized. 14
∑(V − V ) i=2
i
ref
2
(4)
where V i and V ref is the bus voltage and reference voltage respectively. It could be inferred from Fig. 4 that the obtained results are not acceptable. Thus it has been decided to increase the number of the FACTS devices in the network. Therefore an additional STATCOM is added to the network in Case 2 and Case 3. The GA parameters and optimization results of the place and size of the STATCOMs in each case have been presented in TABLE II and TABLE III respectively. Figs. 5-7 illustrate the Comparison between voltage profiles in sequential cases.
1.2
No STATCOM
2 STATCOM 3 STATCOM
1
1
Voltage(PU)
Voltage(PU)
0.8 0.6 0.4 0.2 0 0
0.6
0.4
2
4
6
8
Bus Number
10
12
14
1.2
2
0.8 0.6 0.4
2
4
6
8
Bus Number
10
12
14
Fig. 5. Comparison the voltage profile of the buses between case 1 and no STATCOM
1.2
1 STATCOM 2 STATCOM
1
Voltage(PU)
6
8
10
Bus Number
12
14
TABLE II. Genetic Algorithm Parameters
0.2 0 0
4
By comparing these Figs, it can be conclude that the voltage magnitudes of the network buses in each case are better than the previous case. But from Case 2 to Case 3 there is no significantly changes and the voltage magnitudes are acceptable in Case 2. So it has been concluded that the reactive power compensation has been completely done with two STATCOMs in order to obtained good results matching the standards.
1 STATCOM NO STATCOM
1
0
Fig. 7. Comparison the voltage profile of the buses between case 2 and 3
Fig. 4. The voltage profile of the buses with no STATCOM.
Voltage(PU)
0.8
Generation
500
Population
50
Selection
Stochastic Uniform
Crossover
Scattered
Mutation
Adaptive feasible
Case 1 STATCOM 2 STATCOMs 3 STATCOMs
0.8
TABLE III. Results of Optimization Bus Size Bus Size No. (MVAr) No. (MVAr) 13 16.44 7 4.98 13 57.41 7 7.94 4 32.28
Bus No. 14
Size (MVAr) 37.98
0.6
VI. 0.4 0.2 0
2
4
6
8
Bus Number
10
12
14
Fig. 6. Comparison the voltage profile of the buses between case 1 and 2
CONCLUSION
In this paper the optimization of installation location and size of the exchanged reactive power between the STATCOM and grid in order to obtain admissible range of voltage deviation has been done. The proposed method has been applied to a transmission system in presence of two wind farms as the distributed generations. The stochastic behavior of the wind speed has been considered to approach the actual condition of the wind generation, so it has been modeled by a wiebull PDF with specified parameters according to the literatures. Therefore, the bus voltages in the network have fluctuations corresponding to the changes of the reactive power that absorbed by the wind turbines in the farms. Controllable parameters have been applied to the genetic
algorithm to reach the acceptable voltage profile of the network buses. Three different cases have been investigated. Simulation results confirm that using one STATCOM can improve the voltage profile but it is not enough. Therefore, it has been decided to add one more FACTS device in two next cases. Results have been shown that voltage profiles in the last two cases have not significantly difference. Therefore, it is acceptable to use two STATCOMs to keep the voltage deviations in the standard range. REFERENCES [1] [2]
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