Performance Evaluation in Photovoltaic Power Generation Plants
Abstract
In photovoltaic (PV) power systems, performance is a critical matter. In general, it is revealed after constructing and monitoring the real system, which fails to provide in advance estimations that could benefit planning, dimensioning, and predictive maintenance. This paper proposes a Petri Net model that can quickly anticipate, with reasonable accuracy, the performance levels for PV systems with different sizes, architectures, generation profiles, and stages of construction. By receiving a set of offline specifications, the model canbe exploited in advance, before actually constructing the real infrastructure tobe measured. The proposed model is tested using a real PV plant and resultsshow an accuracy of 93.4% in comparison with the real system.
Keywords:
Petri nets, Power Generation, Performance
References
Cassandras, C. G. and Lafortune, S. (2008). Introduction to Discrete Event Systems. Springer Science
Cassettari, L., Bendato, I., Mosca, M., and Mosca, R. (2017). Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing. Applied Energy, 190:841–851.
da R. Maschio, D. M., Mumbelli, J. D. C., Bonafin, A. C. T., and Teixeira, M. (2020). Supervisory Control of Distributed Power Generation Systems with Petri Net-based Customization. In International Workshop on Discrete Event Systems, pages 1–6, Riode Janeiro, Brazil.
Denholm, P., Ela, E., Kirby, B., and Milligan, M. (2010). Role of Energy Storagewith Renewable Electricity Generation. Technical report, National Renewable EnergyLab. (NREL), Golden, CO (United States).
Desrochers, A. A. (1994). Applications of Petri Nets in Manufacturing Systems: Modeling, Control and Performance Analysis. IEEE Press.
Ekyalimpa, R., Werner, M., Hague, S., AbouRizk, S., and Porter, N. (2016). A combined discrete-continuous simulation model for analyzing train-pedestrian interactions. In IEEE Winter Simulation Conference (WSC), pages 1583–1594
IEA, I. E. A. (2018). World energy balances: Overview. https://shorturl.at/fuL46.
Jana, D. and Chakraborty, N. (2020). Generalized stochastic Petri nets for uncertain renewable-based hybrid generation and load in a microgrid system. International Transactions on Electrical Energy Systems, 30(4):e12195.
Kartson, D., Balbo, G., Donatelli, S., Franceschinis, G., and Conte, G. (1995).Modelling with Generalized Stochastic Petri Nets. John Wiley & Sons, Inc.
King, D. L., Kratochvil, J. A., and Boyson, W. E. (1997). Measuring solar spectral andangle-of-incidence effects on photovoltaic modules and solar irradiance sensors. In IEEE Conference on Photovoltaic Specialists, pages 1113–1116.
Murata, T. (1989). Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE, 77:541–580.
Nehrir, M., Wang, C., Strunz, K., Aki, H., Ramakumar, R., Bing, J., Miao, Z., and Sala-meh, Z. (2011). A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control, and applications. IEEE Transactions on Sustainable Energy, 2(4):392–403
Olivares, D. E., Cañizares, C. A., and Kazerani, M. (2014). A centralized energy management system for isolated microgrids. IEEE Transactions on Smart Grid, 5(4):1864–1875.
Oyn Naversen, C., Farahmand, H., Helseth, A., and Catalão, J. (2019). Hydrothermal scheduling in the continuous-time framework. ArXiv, pages arXiv–1912.
Parvania, M. and Scaglione, A. (2015). Unit commitment withcontinuous-time generation and ramping trajectory models. IEEE Transactions on Power Systems, 31(4):3169–3178.
Paulista, C. R., Peixoto, T. A., and de Assis Rangel, J. J. (2019). Modeling and discrete event simulation in industrial systems considering consumption and electrical energy generation. Journal of Cleaner Production, 224:864–880.
Simon, D. F., Teixeira, M., and da Costa, J. P. (2021). Availability estimation in photovoltaic generation systems using Timed Petri Net simulation models.InternationalJournal of Electrical Power and Energy Systems. Accepted for publication.
SuperGrid (2021). Supergrid institute: shaping power transmission. www.supergrid-institute.com.
Villalva, M. G., Gazoli, J. R., and Ruppert Filho, E. (2009). Comprehensive approach tomodeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 24(5):1198–1208.
Yang, H. E., French, R., and Bruckman, L., editors (2019).Durability and Reliability of Polymers and Other Materials in Photovoltaic Modules. William Andrew, 1 edition.
Zimmermann, A. and Knoke, M. (2007). TimeNET 4.0 - A Software Tool for the Performability Evaluation with Stochastic and Colored Petri Nets
Cassettari, L., Bendato, I., Mosca, M., and Mosca, R. (2017). Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing. Applied Energy, 190:841–851.
da R. Maschio, D. M., Mumbelli, J. D. C., Bonafin, A. C. T., and Teixeira, M. (2020). Supervisory Control of Distributed Power Generation Systems with Petri Net-based Customization. In International Workshop on Discrete Event Systems, pages 1–6, Riode Janeiro, Brazil.
Denholm, P., Ela, E., Kirby, B., and Milligan, M. (2010). Role of Energy Storagewith Renewable Electricity Generation. Technical report, National Renewable EnergyLab. (NREL), Golden, CO (United States).
Desrochers, A. A. (1994). Applications of Petri Nets in Manufacturing Systems: Modeling, Control and Performance Analysis. IEEE Press.
Ekyalimpa, R., Werner, M., Hague, S., AbouRizk, S., and Porter, N. (2016). A combined discrete-continuous simulation model for analyzing train-pedestrian interactions. In IEEE Winter Simulation Conference (WSC), pages 1583–1594
IEA, I. E. A. (2018). World energy balances: Overview. https://shorturl.at/fuL46.
Jana, D. and Chakraborty, N. (2020). Generalized stochastic Petri nets for uncertain renewable-based hybrid generation and load in a microgrid system. International Transactions on Electrical Energy Systems, 30(4):e12195.
Kartson, D., Balbo, G., Donatelli, S., Franceschinis, G., and Conte, G. (1995).Modelling with Generalized Stochastic Petri Nets. John Wiley & Sons, Inc.
King, D. L., Kratochvil, J. A., and Boyson, W. E. (1997). Measuring solar spectral andangle-of-incidence effects on photovoltaic modules and solar irradiance sensors. In IEEE Conference on Photovoltaic Specialists, pages 1113–1116.
Murata, T. (1989). Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE, 77:541–580.
Nehrir, M., Wang, C., Strunz, K., Aki, H., Ramakumar, R., Bing, J., Miao, Z., and Sala-meh, Z. (2011). A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control, and applications. IEEE Transactions on Sustainable Energy, 2(4):392–403
Olivares, D. E., Cañizares, C. A., and Kazerani, M. (2014). A centralized energy management system for isolated microgrids. IEEE Transactions on Smart Grid, 5(4):1864–1875.
Oyn Naversen, C., Farahmand, H., Helseth, A., and Catalão, J. (2019). Hydrothermal scheduling in the continuous-time framework. ArXiv, pages arXiv–1912.
Parvania, M. and Scaglione, A. (2015). Unit commitment withcontinuous-time generation and ramping trajectory models. IEEE Transactions on Power Systems, 31(4):3169–3178.
Paulista, C. R., Peixoto, T. A., and de Assis Rangel, J. J. (2019). Modeling and discrete event simulation in industrial systems considering consumption and electrical energy generation. Journal of Cleaner Production, 224:864–880.
Simon, D. F., Teixeira, M., and da Costa, J. P. (2021). Availability estimation in photovoltaic generation systems using Timed Petri Net simulation models.InternationalJournal of Electrical Power and Energy Systems. Accepted for publication.
SuperGrid (2021). Supergrid institute: shaping power transmission. www.supergrid-institute.com.
Villalva, M. G., Gazoli, J. R., and Ruppert Filho, E. (2009). Comprehensive approach tomodeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 24(5):1198–1208.
Yang, H. E., French, R., and Bruckman, L., editors (2019).Durability and Reliability of Polymers and Other Materials in Photovoltaic Modules. William Andrew, 1 edition.
Zimmermann, A. and Knoke, M. (2007). TimeNET 4.0 - A Software Tool for the Performability Evaluation with Stochastic and Colored Petri Nets
Published
2021-07-18
How to Cite
MASCHIO, Dierli M. da R.; DUARTE, Bruno; COSTA, Jean Patric da; TEIXEIRA, Marcelo.
Performance Evaluation in Photovoltaic Power Generation Plants. In: WORKSHOP ON PERFORMANCE OF COMPUTER AND COMMUNICATION SYSTEMS (WPERFORMANCE), 20. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 108-118.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2021.15727.
