2014 · Volume 15 · Issue 2
International Journal of Emerging Electric Power Systems Editor-in-Chief
Tarlochan Sidhu, University of Ontario Institute of Technology, Canada Associate Editors
S.A. Khaparde, Indian Institute of Technology, India Eugeniusz Rosolowski, Wroclaw University of Technology, Poland Tapan Kumar Saha, University of Queensland, Australia Editorial Board
O.H. Abdalla, University of Helwan, Egypt M. El Hachemi Benbouzid, University of Western Brittany, France I. Dudurych, EirGrid plc – Transmission System Operator, Ireland L. Goel, Nanyang Technological University, Singapore G. Harrison, The University of Edinburgh, UK M. Kaiser, Louisiana State University, USA Y-L. Ke, Kun Shan University of Technology, Taiwan G. Ledwich, Queensland University of Technology, Australia P. Lopes, INESC Porto, Portugal
N. Mithulanatha, Asian Institute of Technology, Thailand H. Nouri, University of the West of England, UK R. Mota Palomino, National Polytechnic Institute, Mexiko A. Pahwa, Kansas State University, USA A.G. Phadke, Virginia Tech, USA O.R. Saavedra, Federal University of Maranhão, Brazil M. Shahidehpour, Illinois Institute of Technology, USA C. Singh, Texas A&M University, USA Y.H. Song, University of Liverpool, UK S.C. Srivastava, Indian Institute of Technology, India E. Tag-Eldin, Cairo University, Egypt D. Thukaram, Indian Institute of Science, India D. Tziouvaras, Schweitzer Engineering Labs, USA F. Wen, Zhejiang University, China I. Zamora, ETSI de Bilbao – University of the Basque Country, Spain
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International Journal of Emerging Electric Power Systems (IJEEPS) publishes significant research and scholarship related to latest and up-and-coming developments in power systems. The mandate of the journal is to assemble high quality papers from the recent research and development efforts in new technologies and techniques for generation, transmission, distribution and utilization of electric power. The range of topics includes electric power generation sources; integration of unconventional sources into existing power systems; generation planning and control; new technologies and techniques for power transmission, distribution, protection, control and measurement; power system analysis, economics, operation and stability; deregulated power systems; power system communication; metering technologies; demand-side management; industrial electric power distribution and utilization systems. ISSN 2194-5756 ∙ e-ISSN 1553-779X All information regarding notes for contributors, subscriptions, Open access, back volumes and orders is available online at www.degruyter.com/ijeeps. RESPONSIBLE EDITOR Tarlochan Sidhu, Institute of Technology, Faculty of Engineering and Applied Science, University of Ontario,
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International Journal of Emerging Electric Power Systems
2014 | Volume 15 | Issue 2
Contents Mengting Yu, Jingang Wang, Jun Ma, Hu Peng, and Lan Xiong Research on Non-contact Voltage Transducer for High-Voltage Transmission Lines Based on Inverse Problem of Electric Field 101 I. Archundia-Aranda and R. O. Mota-Palomino Harmonic Penetration Method for Radial Distribution Networks 111 I. Archundia-Aranda and R. O. Mota-Palomino Erratum to Harmonic Penetration Method for Radial Distribution Networks [Int. J. Emerg. Electr. Power Syst. DOI 10.1515/ijeeps-2013-0093] 119 Jorge Hans Alayo A Least Cost Transmission Planning Model Considering Operation Cost 121 Abdullahi Lanre Amoo, Usman O. Aliyu, Dalila Mat Said, Abdullah Asuhaimi Mohd Zin, and Abubakar Sadiq Bappah Monte Carlo Simulation of a Microgrid Harmonic Power Flow 129
M. Basu Multi-objective Differential Evolution for Dynamic Economic Emission Dispatch 141 Anna Rita Di Fazio, Giuseppe Fusco, and Mario Russo Testing New Reactive Power Control of DERs by Real-Time Simulation 151 D. Hsu and L. Kang Dispatch Analysis of Off-Grid Diesel Generator-Battery Power Systems 161 Yuan Liao Identification of Faulted Feeder Section in Distribution Systems Using Overcurrent Information from Switching Devices 171 Anup Kumar Panda and Ranjeeta Patel PI and Fuzzy-Controlled 3-Phase 4-Wire Interleaved Buck Active Power Filter with Shoot-Through Elimination for Power Quality Improvement using RTDS Hardware 177
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doi 10.1515/ijeeps-2013-0041
International Journal of Emerging Electric Power Systems 2014; 15(2): 129–140
Research Article Abdullahi Lanre Amoo*, Usman O. Aliyu, Dalila Mat Said, Abdullah Asuhaimi Mohd Zin, and Abubakar Sadiq Bappah
Monte Carlo Simulation of a Microgrid Harmonic Power Flow Abstract: With the transformation of power utility companies from vertical structure to full deregulated entities, the need for the integration of distributed generation (DG) resource in the form of Microgrid (MG) system would soon become indispensable in most deregulated power systems. This is due to renewability of such generation systems. The power quality performance in terms of intermittent energy of these DG systems supply is the major limitation to their full integration as the sole generation entities that can propel rapid decentralization of electric power systems operation. Nonetheless, the acceptable standard is to operate them in an islanding mode or as a MG optimally dispatch among generation mix. This paper developed a total harmonic distortion models for a Microgrid bus in a Nigerian grid system and applied Monte Carlo technique to reliably predict the level of harmonic power flow in the system. The result shows that the distortion increases as the demand factor of the station decreases. Keywords: Monte Carlo, Fluke VR1710, Nigerian power system *Corresponding author: Abdullahi Lanre Amoo, Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria, E-mail:
[email protected],
[email protected] Usman O. Aliyu, School of Engineering and Engineering Technology, Abubakar Tafawa Balewa University, Bauchi, Nigeria, E-mail:
[email protected] Dalila Mat Said, Centre of Electrical Energy Systems (CEES), Faculty of Electrical Engineering, Universiti Teknologi Malaysia. 81310 Johor Bahru, Johor, Malaysia, E-mail:
[email protected] Abdullah Asuhaimi Mohd Zin, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia, E-mail:
[email protected] Abubakar Sadiq Bappah, Department of Vocational and Technology Education, Abubakar Tafawa Balewa University, Bauchi, Nigeria, E-mail:
[email protected]
1 Introduction Monte Carlo Simulation (MCS) entails solving harmonic power flow equations at numerous times and according
to Caramia et al. [1] each time in the iterative process an input variable vector element assigned with probability density functions (pdfs) is generated. Most often randomness is imposed on the net harmonic currents produced in electric power systems and the solution invoked using stochastic processes [2, 3]. A Monte Carlo procedure was also implemented using Electromagnetic Transient Programme (EMTP/ ATP) platform for evaluation of lightning overvoltage in transmission lines. Besides its application in power system performance evaluation, it is more useful in optimal design of the system [4]. A Microgrid can be defined as a cluster of loads and distributed generation capable of operating as a single controllable unit [5]. According to reference [6], distributed energy resources (DR) include combustion engines, microand mini-gas turbines, wind turbines, fuel cells, solarthermal systems and photovoltaic systems, low-head hydro units, geothermal systems, battery storage, capacitor storage, low-and high-speed flywheel systems and superconducting magnetic energy storage (SMES) which may be integrated in varying proportion with the conventional grid systems or operating in islanding modes of different types. The search for alternative energy resources that could wholly or partly replace the use of fossil fuel energy resource known for their negative environmental impact has continued to receive considerable attention. Ref. [7] has shown that as the penetration of Microgrid improves, the system losses will reduce and consequently provide improvement in the voltage profile for rural feeders as well as reduction in congestion at urban feeders. Except for few distributed-energy resources like microand mini-hydro units and others working with rotating AC machines, the rest require the use of power electronics converters to provide compatibility with conventional AC grid. These power electronics converters have poor fault tolerance which can hardly go beyond twice the rated steady-state current [7] and as such existing switchgear and protective relays would not provide reliable protection for the security of power supply in the framework of
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emerging Microgrid systems. Hence, they are mostly used in to avert system stability and safety issues that are common with renewable energy sources typical in islanding model [8]. Similarly, there may be problem of harmonic disturbances due to the use of fast switching and nonlinear characteristics associated with power electronics converters deployed for control of output frequency, voltage and real and reactive power in the Microgrid systems. The penetration of DG may not too serious a problem once the standard for integrating and interphasing equipment is strictly adhered to. In addition to minimization of the impact of harmonic distortion with a well-designed PV inverter [9], a recent paper has looked into the level of penetration of this DG resource and concluded that harmonious operation can still be established in the system once the deadly harmonic current flowing through ground is properly managed [10]. Loss of confidence often set into any power utility companies which cannot guarantee reliability power supply for critical loads, i.e. loads, according to reference [11] that must necessary be available 99.9% of time. There is need to have compromise between inevitability of Microgrid systems and impact of such emerging technologies yet to be fully integrated into energy-mix scenario. The full operational experiences have not yet been articulated in the recent time except only by way of computer simulation because it involves solving multi-criteria decisions such as planning and economics arbitration among the stakeholders which could be dispersed within the grid systems. The significance of Microgrid systems cannot be overemphasized especially in areas where the cost of extending grid systems is high and distributed-energy resources could be viable alternatives operating as weak system. In this paper, the Monte Carlo simulation engine is applied to the formulation of real and reactive power model considering variation in the net harmonic current injection at a Microgrid bus of Power Holding Company of Nigeria (PHCN) network.
1.
Perform the fundamental load flow analyses (treating all nonlinear devices as linear loads). Make initial guess for the harmonic bus voltage magnitudes and phase angles. 2. Determine the nonlinear device currents either by calculation or by measurements. 3. Develop the mismatch equations to include real and reactive power mismatches as well as current vector mismatches for all the harmonic and fundamental, if it is small enough then stop otherwise proceed. 4. Evaluate the product of the inverse Jacobian matrix and mismatch vectors. 5. Update the total power throughout the network and go to step 3.
2.2 Monte Carlo simulation As previously mentioned, the Monte Carlo algorithm has been developed herein to characterize harmonic distortion in large power systems due to integration of distributed Microgrid systems. The flow chart of the Monte Carlo simulation algorithmic framework adopted in this paper from ref. [13] is depicted in Figure 1.
i=1 Random Input Data
DETERMINISTIC MODELLING Supply Modelling
Network Modelling Non-linear loads Modelling
Linear Loads Modelling
Control System Conversion System
2 Theoretical considerations
Distortion Indexes Evaluation i = i+1 Output statistics adjournment
2.1 Harmonic power flow algorithm A recent paper [12] presented the harmonic power flow algorithms as well as MATLAB code; with the intent of introducing the analytical tool to technical and engineering students in Nigeria. Consequently, the algorithm is summarized in abridged form as follows:
no
i = imax? yes stop
Figure 1 Flow Chart of Monte Carlo Algorithm
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2.3 The Study systems
The deterministic model for only the linear loads in the networks was developed from computer software (PCFLO). The distortion index (THDv/i) for the candidate bus installed with PV inverters was extracted from the software and regressed with power at the stated buses. The regressed models were then subjected to Monte Carlo model using the pdf of uniformly distributed power as the inputs. This
The harmonic power flow algorithm was used to determine probabilistic behavior of Power Holding Company of Nigeria (PHCN) network (Figure 2) modified with a Photovoltaic (PV) distributed generation (Figure 3) and validated by the 5-Bus test Network (Figure 4) in the PCFLO package developed by Grady.
B20 B30 B18 B6 B9
B7
B12
B31
B13
B19
G2 B29
B5
B11
B8
G4
B15 B17 B24 B26
B22
B10
B14 B2 B27 B16
G6
B25
B28 B3
B23
B1
B4
G5
G3
Figure 2 31-Bus Nigerian Grid System (330 kV Network only as at 2002)
NIGER CHAD
LEGEND Proposed PV Micro-grid Unit 330 kV line
Rest of the System
BAUCHI
BENIN
132 kV line 330/132 kV Transformer Equivalent PV Source at Bauchi bus
Gombe
Jos
CAMEROON B19
BAUCHI
GOMBE
B13 JOS
B20 B30
B18
B6 B7
B9
B12
B31
G2 B29 B11
B5 G4
B8 B15
B17
B24 B22
B10
B26
B14
B2 B25 B1 G3
B23
B16
131
B27 B3 G5
B28 B4
Figure 3 The Modified Nigerian Grid System with a MG Bus
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Rest of system
Swing bus
Converter load
Sub LV
Sub HV
Linear load
DG
Figure 4 One-line diagram of a 5-Bus network
randomness is repeated for a theoretical network at bus 5 (Figure 4). The MC operation is stopped after 1,000 runs and few samples are extracted to characterization of distortion level in the networks. The standard index used is THD of voltage and current as in eqs (1) and (2). sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n P Vrrms ðhÞ ð1Þ
Vrrmsð1Þ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n P Irrms ðhÞ
THDi ¼
This section presents the result of the harmonic voltage quality of loads placed close to the Microgrid in the large power system networks used as case studies.
3.1 5-Bus network
h¼2
THDv ¼
3 Results and discussions
h¼2
ð2Þ
Irrmsð1Þ
The 5-Bus network MC simulation results for probabilistic harmonic power flow with the nonlinear load placed bus 4 while the linear load at bus 5 is subjected to stochastic variation as in Figures 5 and 6. Table 1 gives the sample variation to predict the trend of harmonic flow in the network.
70 68
Linear load (%)
66 64 62 60 58 56 54 52 50 0
100
200
300
400
500
600
700
800
900
1,000
Random sample Figure 5 Plot of random sample of real load power for the 5-Bus model
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48 46
Reactive load (%)
44 42 40 38 36 34 32 30
0
100
200
300
400 500 600 Random sample
700
800
900 1,000
Figure 6 Plot of random sample of reactive load power for the 5-Bus model
Table 1
THDv ¼ 0:0058QD þ 0:12QD þ 9:2
Sample variation in HPF in 30-Bus IEEE network
Real power PD (%)
Reactive power QD (%)
THDi (%)
THDv (%)
31 26 21 16
5.8 6.7 7.8 9.5
7.31 8.09 9.30 9.50
50 40 30 20
With this simulated data points in PCFLO, the model equations that represent deterministic models for THDi and THDv are given in eqs (3) and (4) respectively.
THDi ¼ 0:002PD 2 0:26PD þ 14
Table 2
where THDi represents total harmonic distortion of line current and THDv stands for total harmonic distortion of bus voltage. PD and QD are the linear load real power and reactive power respectively. Monte Carlo simulation of the HPF indexes for the network is tabulated in Table 2. The summary of Monte Carlo process is tabulated in Table 2.
3.2 31-Bus PHCN network The 31-Bus PHCN network MC simulation results for probabilistic harmonic power flow with the DG placed at a bus (Figure 3) while the linear load at bus B19 is subjected to stochastic variation. Table 3 gives the sample variation to predict the trend of harmonic flow in the network.
Simulation details for Monte Carlo of 5-Bus network
Summary of Monte Carlo model THDi THDv
ð3Þ
ð4Þ
Simulation time (s)
Min. value
Max. value
Mean value
0.017 0.011
5.55 2.03
6.00 7.34
5.67 4.92
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Table 3
Sample variation in HPF in 31-Bus PHCN network
Real power PD (%)
The random sample of linear load for the model is plotted as shown in Figure 6.
Reactive power QD (%)
THDi (%)
THDv (%)
17.5 12.5 7.5 2.0
0.20 0.20 0.30 0.30
0.32 0.32 0.32 0.33
102.9 92.9 82.9 72.9
With this simulated data points in PCFLO, the model equation for the deterministic model is given in eqs (5) and (6).
THDi ¼ 1:1 1018 PD 2 0:004PD þ 0:6 THDv ¼ 9:1 105 QD 2 0:0024PD þ 0:33
3.3 Experimental results The experimental results were acquired in the 132 kV/ 33 kV subtransmission substation designated as Bauchi (Figure 3) to shows the probabilistic behaviour of THDv and the frequency of interruption of power supply in the area for 1 week. The monitoring device was the Fluke VR1710 voltage quality recorder that logs important power data at the low voltage side of the station transformer. The results are presented as in Figures 13–19.
ð5Þ
ð6Þ
where THDi represents total harmonic distortion of line current, THDv for total harmonic distortion of bus voltage. PD and QD are the linear load real power and reactive power respectively.
4 Summary of results and discussions With the random variation of the linear load shown in Figures 5, 6, 9 and 11, the sensitivity harmonic models of eqs (1–6) were subjected to MC model which resulted into Figures 7, 8, 10 and 12. The simulation time, minima,
250
Freq. of occurence
200
150
100
50
0
5.6
5.65
5.7
5.75
5.8
5.85
5.9
5.95
6
6.05
THDi (%) Figure 7 Frequency distribution of THDi for 5-Bus network
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35 30
Freq. of occurence
25 20 15 10 5 0
2
3
4
5 THDv (%)
6
7
8
Figure 8 Frequency distribution of THDv for 5-Bus network
120
Linear load (%)
100
80
60
40
20
0
0
100
200
300
400 500 600 Random sample
700
800
900
1,000
Figure 9 Plot of random sample of real power for the 31-Bus model
maxima and means of each network which are summarized in Tables 2 and 4. In the cases considered, it was observed that as the linear load decreases, there is a system wide increase in total harmonic indexes which tend to corroborate the phasor representation of the sum harmonics þ V fundamentals of Iharmonics þ Ifundamentals and V synonymous to load power triangle vectors of real and reactive power. But in this case as the fundamental current and/or voltage decrease(s), the denominator of the
total harmonic distortion indexes (eqs 1 and 2) decrease and therefore leading to effective increase in THD of voltage and current. That is, Irms(1) and Vrms(1) are the fundamental current and voltage respectively, which are decreasing and effectively increasing the THDi and THDv as a consequence. In the experimental results, the THDv measured in the Bauchi bus exhibited maximum value of 7% and in other days it was in the neighbourhood of 4%. These
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30
25
Freq. of occurence
20
15
10
5
0 0.1
0.15
0.2
0.25
0.3
0.35 THDi
0.4
0.45
0.5
700
800
0.55
0.6
Figure 10 Frequency distribution of THDi for 31-Bus network
19.5
Reactive load (%)
19
18.5
18
17.5
0
100
200
300
400 500 600 Random sample
900
1,000
Figure 11 Plot of random sample of reactive power for the model
values are apparently less than that obtained during the simulation because the THDv are reflected to transmission and hence the value is less than 1%. Also revealed from Figures 13 to 19 was the prolonged outages which has been observed to promote the propagation of harmonics.
5 Conclusion This paper has presented probabilistic modeling of power systems with embedded considering stochastic changes in static loads system buses. More specifically a total
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harmonic Microgrid at various harmonic
A. L. Amoo et al.: Monte Carlo Simulation
35
30
Freq. of occurence
25
20
15
10
5
0 0.3155
0.316
0.3165
0.317 THDv
0.3175
0.318
0.3185
Figure 12 Frequency distribution of THDv for 31-Bus network
Table 4
Simulation details for Monte Carlo of 31-Bus network
Summary of Monte Carlo model THDi THDv
Simulation time (s)
Min value
Max. value
Mean value
0.019 0.018
0.148 0.149
0.5511 0.5517
0.3419 0.3550
5 4.5
Acquired on 27th October, 2013
THDv (%)
4 3.5 3 2.5 2 1.5 1 18
19
20
21
22
23
24
Time (24 hrs) Figure 13 Day 1 Voltage distortion of 132 kV/33 kV Bauchi transmission station service
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3.5 Acquired on 28th October, 2013 3
THDv (%)
2.5 2 1.5 1 Interruption
Interruption 0.5 0
0
2
4
6
8 10 Time (24 hrs)
12
14
16
18
Figure 14 Day 2 Voltage distortion of 132 kV/33 kV Bauchi transmission station service
4.5
Acquired on 29th October, 2013
4 3.5 THDv (%)
3 2.5 2 1.5 Interruption
1
Interruption
Interruption
0.5 0
0
5
10
15
20
25
Time (24 hrs) Figure 15 Day 3 Voltage distortion of 132 kV/33 kV Bauchi transmission station service
7 Acquired on 30th October, 2013
6
THDv (%)
5 4 3 2 Interruption
Interruption
1 0
0
5
10
15
20
25
Time (24 hrs) Figure 16 Day 4 Voltage distortion of 132 kV/33 kV Bauchi transmission station service
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5 4.5 Acquired on 31st October, 2013 4
THDv (%)
3.5 3 2.5 2 1.5 1 Interruption
0.5 0
0
5
10
15
20
25
Time (24 hrs) Figure 17 Day 5 Voltage distortion of 132 kV/33 kV Bauchi transmission station service
3.5 Acquired on 1st November, 2013 3
THDv (%)
2.5 2 1.5 1 Interruption
0.5 0
0
5
10
15
20
25
Time (24 hrs) Figure 18 Day 6 Voltage distortion of 132 kV/33 kV Bauchi transmission station service
distortion models for a Microgrid bus in Nigerian grid system have been developed and subsequently applied to predict the level of harmonic power distortion in the system. This paper has also demonstrated the adverse effect of frequent load shedding common in the Nigerian case study system. This has shown to promote and escalate the harmonic power flow in the system. Finally, it is recommended that there is need for proactive
actions particularly the enactment of power quality standards by the Nigerian Electricity Regulatory Commission (NERC) with respect to integration of Microgrid infrastructure. This will drive the Nigerian Distribution and Transmission Companies (DISCO and TRANSICO) to bring about improvement in quality of power delivery to various electricity consumers in Nigeria.
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3.5 Acquired on 2nd November, 2013
THDv (%)
3
2.5
2
1.5
1
0
5
10
15
20
25
Time (24 hrs) Figure 19 Day 7 Voltage distortion of 132 kV/33 kV Bauchi transmission station service
References 1. Caramia P, Carpinelli G, Esposito T, Varilone P, Chiumeo R, Mastandrea L, et al. Probabilistic harmonic power flow for assessing waveform distortions in distribution systems with wind embedded generation. Paper presented at the power electronics, electrical drives, automation and motion. International Symposium on SPEEDAM 2006, 2006. 2. Cavallini A, Montanari GC, Cacciari M. Stochastic evaluation of harmonics at network buses. IEEE Trans Power Deliv 1995;10:1606–13. 3. Staats PT, Grady WM, Arapostathis A, Thallam RS. A statistical method for predicting the net harmonic currents generated by a concentration of electric vehicle battery chargers. IEEE Trans Power Deliv 1997;12:1258–66. 4. Szolusha KD. Power electronics models for integrated design of high speed electric motor drives. M.Eng Thesis, Massachusetts Institute of Technology, Cambridge, 1998. 5. Lasseter RH. Microgrids. Paper presented at the power engineering society winter meeting, 2002. IEEE. 6. Katiraei F, Iravani MR, Lehn PW. Micro-grid autonomous operation during and subsequent to islanding process. IEEE Trans Power Deliv 2005;20:248–57. DOI:10.1109/ TPWRD.2004.835051. 7. Vasiljevska J, Lopes J, Matos MA. Multi-microgrid impact assessment using multi criteria decision aid methods.
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Paper presented at the PowerTech, 2009 IEEE Bucharest, 2009. Merino J, Veganzones C, Sanchez JA, Martinez S, Platero CA. Power system stability of a small sized isolated network supported by a combined wind-pumped storage generation system: a case study in Canary Island. Energies 2012;5:2351–69. Newmiller J, Whitaker C, Ropp M, Norris B. Renewable systems interconnection: distributed PV systems design and technology requirements. In: October 30, 2007 – Draft B, 2007 Retrieved from https://www1.eere.energy.gov/solar/.../ distributed_pv_system_design.pdf. Amoo AL, Bappah AS, Aliyu UO. Virtual platforms for teaching harmonic load flow to technical and engineering students in Nigeria. Proceeding IEEE Nigercon 2013;2:98–107. Islam MS. Modeling critical load in Abubakar Tafawa Balewa University, Bauchi. Unpublished, 2008. Amoo AL, Said DM, Yusuf A, Mohd Zin AA. Harmonic power flow in Nigerian power system with PV site. Paper presented at the Power Engineering and Optimization Conference (PEOCO), 2013 IEEE 7th International, 2013. Testa A, Langella R. Considerations on probabilistic harmonic voltages. Paper presented at the Power Engineering Society General Meeting, 2003 IEEE, 2003.
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