Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System

Ogbonna Chima Otumdi; Constance Kalu; Idorenyin Markson

doi:doi:10.11648/j.ep.20170101.15

| Peer-Reviewed

Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System

Ogbonna Chima Otumdi, Constance Kalu, Idorenyin Markson

Published in Engineering Physics (Volume 1, Issue 1)

Received: 25 October 2016 Accepted: 3 January 2017 Published: 30 January 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In this paper, the loss of load probability for stand-alone photovoltaic (SAPV) power system was determined for an ICT Center with total daily energy demand of 346480 Wh/day. However, the different electrical appliances are classified into four (4) different load priority levels depending on the acceptable loss of load probability of the appliance in the data center. The ICT Center has annual averaged daily solar radiation of 4.7kWh/m². day and minimum (worst case) daily solar radiation of 0.574 kWh/m² day which occurred on 17^th of June. The SAPV system is expected to satisfy with zero loss of load probability the critical load (server, switches, routers, Vsat) estimated at about 81210 Wh/day. In this wise, dynamic load shading approach can be employed to switch off certain loads based on their priority level and available solar irradiation. A cubic regression model is derived to enable the load scheduler to determine the possible LLOP for any give load level. The approach presented in this paper provides the relevant mechanism to determine at what point the dynamic load shading unit can turn off or turn on appliances in any load priority level in response to the temporal variation in solar radiation at the Data Center.

Published in	Engineering Physics (Volume 1, Issue 1)
DOI	10.11648/j.ep.20170101.15
Page(s)	27-32
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Stand-Alone Photovoltaic, Loss of Load, Cubic Regression Model, Loss of Load Probability, Prioritized Load

References

[1]	Tsao, J., Lewis, N., & Crabtree, G. (2006). Solar FAQs. US department of Energy.
[2]	Hoffert, M. I., Caldeira, K., Benford, G., Criswell, D. R., Green, C., Herzog, H.,... & Lightfoot, H. D. (2002). Advanced technology paths to global climate stability: energy for a greenhouse planet. science, 298 (5595), 981-987.
[3]	de Winter, F., & Swenson, R. B. (2006). A wake-up call. Solar Today, 15-19.
[4]	Kellogg, W. D., Nehrir, M. H., Venkataramanan, G., & Gerez, V. (1998). Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems. IEEE Transactions on energy conversion, 13 (1), 70-75.
[5]	Zhou, W., Lou, C., Li, Z., Lu, L., & Yang, H. (2010). Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems. Applied Energy, 87 (2), 380-389.
[6]	Kaundinya, D. P., Balachandra, P., & Ravindranath, N. H. (2009). Grid-connected versus stand-alone energy systems for decentralized power—a review of literature. Renewable and Sustainable Energy Reviews, 13 (8), 2041-2050.
[7]	Salas, V., Olias, E., Barrado, A., & Lazaro, A. (2006). Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Solar energy materials and solar cells, 90 (11), 1555-1578.
[8]	Ulleberg, O. (1998). Stand-alone power systems for the future: optimal design, operation and control of solar-hydrogen energy systems. NTNU, Trondheim, Norvège.
[9]	Simões, M. G., & Farret, F. A. (2014). Modeling and Analysis with Induction Generators. CRC Press.
[10]	Camacho, E. F., Samad, T., Garcia-Sanz, M., & Hiskens, I. (2011). Control for renewable energy and smart grids. The Impact of Control Technology, Control Systems Society, 69-88.
[11]	Hadjipaschalis, I., Poullikkas, A., & Efthimiou, V. (2009). Overview of current and future energy storage technologies for electric power applications. Renewable and sustainable energy reviews, 13 (6), 1513-1522.
[12]	Gordo, E., Khalaf, N., Strangeowl, T., Dolino, R., & Bennett, N. (2015). FACTORS AFFECTING SOLAR POWER PRODUCTION EFFICIENCY.
[13]	Tobnaghi, D. M., & Naderi, D. (2014). The Effect of Solar Radiation and Temperature on Solar cells Performance.
[14]	Fesharaki, V. J., Dehghani, M., Fesharaki, J. J., & Tavasoli, H. (2011, November). The effect of temperature on photovoltaic cell efficiency. InProceedings of the 1stInternational Conference on Emerging Trends in Energy Conservation–ETEC, Tehran, Iran (pp. 20-21).
[15]	Chander, S., Purohit, A., Sharma, A., Nehra, S. P., & Dhaka, M. S. (2015). A study on photovoltaic parameters of mono-crystalline silicon solar cell with cell temperature. Energy Reports, 1, 104-109.
[16]	Arab, A. H., Chenlo, F., & Benghanem, M. (2004). Loss-of-load probability of photovoltaic water pumping systems. Solar Energy, 76 (6), 713-723.
[17]	Celik, A. N. (2007). Effect of different load profiles on the loss-of-load probability of stand-alone photovoltaic systems. Renewable Energy, 32 (12), 2096-2115.
[18]	Tsalides, P. H., & Thanailakis, A. (1986). Loss-of-load probability and related parameters in optimum computer-aided design of stand-alone photovoltaic systems. Solar cells, 18 (2), 115-127.
[19]	Kazem, H. A., & Khatib, T. (2013). A novel numerical algorithm for optimal sizing of a photovoltaic/wind/diesel generator/battery microgrid using loss of load probability index. International Journal of Photoenergy, 2013.

Cite This Article

Plain Text BibTeX RIS

APA Style

Ogbonna Chima Otumdi, Constance Kalu, Idorenyin Markson. (2017). Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System. Engineering Physics, 1(1), 27-32. https://doi.org/10.11648/j.ep.20170101.15

Copy | Download

ACS Style

Ogbonna Chima Otumdi; Constance Kalu; Idorenyin Markson. Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System. Eng. Phys. 2017, 1(1), 27-32. doi: 10.11648/j.ep.20170101.15

Copy | Download

AMA Style

Ogbonna Chima Otumdi, Constance Kalu, Idorenyin Markson. Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System. Eng Phys. 2017;1(1):27-32. doi: 10.11648/j.ep.20170101.15

Copy | Download

@article{10.11648/j.ep.20170101.15,
  author = {Ogbonna Chima Otumdi and Constance Kalu and Idorenyin Markson},
  title = {Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System},
  journal = {Engineering Physics},
  volume = {1},
  number = {1},
  pages = {27-32},
  doi = {10.11648/j.ep.20170101.15},
  url = {https://doi.org/10.11648/j.ep.20170101.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ep.20170101.15},
  abstract = {In this paper, the loss of load probability for stand-alone photovoltaic (SAPV) power system was determined for an ICT Center with total daily energy demand of 346480 Wh/day. However, the different electrical appliances are classified into four (4) different load priority levels depending on the acceptable loss of load probability of the appliance in the data center. The ICT Center has annual averaged daily solar radiation of 4.7kWh/m2. day and minimum (worst case) daily solar radiation of 0.574 kWh/m2 day which occurred on 17th  of June. The SAPV system is expected to satisfy with zero loss of load probability the critical load (server, switches, routers, Vsat) estimated at about 81210 Wh/day. In this wise, dynamic load shading approach can be employed to switch off certain loads based on their priority level and available solar irradiation. A cubic regression model is derived to enable the load scheduler to determine the possible LLOP for any give load level. The approach presented in this paper provides the relevant mechanism to determine at what point the dynamic load shading unit can turn off or turn on appliances in any load priority level in response to the temporal variation in solar radiation at the Data Center.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System
AU  - Ogbonna Chima Otumdi
AU  - Constance Kalu
AU  - Idorenyin Markson
Y1  - 2017/01/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ep.20170101.15
DO  - 10.11648/j.ep.20170101.15
T2  - Engineering Physics
JF  - Engineering Physics
JO  - Engineering Physics
SP  - 27
EP  - 32
PB  - Science Publishing Group
SN  - 2640-1029
UR  - https://doi.org/10.11648/j.ep.20170101.15
AB  - In this paper, the loss of load probability for stand-alone photovoltaic (SAPV) power system was determined for an ICT Center with total daily energy demand of 346480 Wh/day. However, the different electrical appliances are classified into four (4) different load priority levels depending on the acceptable loss of load probability of the appliance in the data center. The ICT Center has annual averaged daily solar radiation of 4.7kWh/m2. day and minimum (worst case) daily solar radiation of 0.574 kWh/m2 day which occurred on 17th  of June. The SAPV system is expected to satisfy with zero loss of load probability the critical load (server, switches, routers, Vsat) estimated at about 81210 Wh/day. In this wise, dynamic load shading approach can be employed to switch off certain loads based on their priority level and available solar irradiation. A cubic regression model is derived to enable the load scheduler to determine the possible LLOP for any give load level. The approach presented in this paper provides the relevant mechanism to determine at what point the dynamic load shading unit can turn off or turn on appliances in any load priority level in response to the temporal variation in solar radiation at the Data Center.
VL  - 1
IS  - 1
ER  -

Copy | Download

Author Information

Ogbonna Chima Otumdi

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
Constance Kalu

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
Idorenyin Markson

Department of Mechanical Engineering, University of Uyo, Uyo, Nigeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Ogbonna Chima Otumdi, Constance Kalu, Idorenyin Markson. (2017). Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System. Engineering Physics, 1(1), 27-32. https://doi.org/10.11648/j.ep.20170101.15

Copy | Download

ACS Style

Ogbonna Chima Otumdi; Constance Kalu; Idorenyin Markson. Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System. Eng. Phys. 2017, 1(1), 27-32. doi: 10.11648/j.ep.20170101.15

Copy | Download

AMA Style

Ogbonna Chima Otumdi, Constance Kalu, Idorenyin Markson. Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System. Eng Phys. 2017;1(1):27-32. doi: 10.11648/j.ep.20170101.15

Copy | Download

@article{10.11648/j.ep.20170101.15,
  author = {Ogbonna Chima Otumdi and Constance Kalu and Idorenyin Markson},
  title = {Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System},
  journal = {Engineering Physics},
  volume = {1},
  number = {1},
  pages = {27-32},
  doi = {10.11648/j.ep.20170101.15},
  url = {https://doi.org/10.11648/j.ep.20170101.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ep.20170101.15},
  abstract = {In this paper, the loss of load probability for stand-alone photovoltaic (SAPV) power system was determined for an ICT Center with total daily energy demand of 346480 Wh/day. However, the different electrical appliances are classified into four (4) different load priority levels depending on the acceptable loss of load probability of the appliance in the data center. The ICT Center has annual averaged daily solar radiation of 4.7kWh/m2. day and minimum (worst case) daily solar radiation of 0.574 kWh/m2 day which occurred on 17th  of June. The SAPV system is expected to satisfy with zero loss of load probability the critical load (server, switches, routers, Vsat) estimated at about 81210 Wh/day. In this wise, dynamic load shading approach can be employed to switch off certain loads based on their priority level and available solar irradiation. A cubic regression model is derived to enable the load scheduler to determine the possible LLOP for any give load level. The approach presented in this paper provides the relevant mechanism to determine at what point the dynamic load shading unit can turn off or turn on appliances in any load priority level in response to the temporal variation in solar radiation at the Data Center.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Determination of Loss of Load Probability for Stand-Alone Photovoltaic Power System
AU  - Ogbonna Chima Otumdi
AU  - Constance Kalu
AU  - Idorenyin Markson
Y1  - 2017/01/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ep.20170101.15
DO  - 10.11648/j.ep.20170101.15
T2  - Engineering Physics
JF  - Engineering Physics
JO  - Engineering Physics
SP  - 27
EP  - 32
PB  - Science Publishing Group
SN  - 2640-1029
UR  - https://doi.org/10.11648/j.ep.20170101.15
AB  - In this paper, the loss of load probability for stand-alone photovoltaic (SAPV) power system was determined for an ICT Center with total daily energy demand of 346480 Wh/day. However, the different electrical appliances are classified into four (4) different load priority levels depending on the acceptable loss of load probability of the appliance in the data center. The ICT Center has annual averaged daily solar radiation of 4.7kWh/m2. day and minimum (worst case) daily solar radiation of 0.574 kWh/m2 day which occurred on 17th  of June. The SAPV system is expected to satisfy with zero loss of load probability the critical load (server, switches, routers, Vsat) estimated at about 81210 Wh/day. In this wise, dynamic load shading approach can be employed to switch off certain loads based on their priority level and available solar irradiation. A cubic regression model is derived to enable the load scheduler to determine the possible LLOP for any give load level. The approach presented in this paper provides the relevant mechanism to determine at what point the dynamic load shading unit can turn off or turn on appliances in any load priority level in response to the temporal variation in solar radiation at the Data Center.
VL  - 1
IS  - 1
ER  -

Copy | Download