Procjena mogućnosti priključenja fotonaponskog sistema na srednjenaponsku distributivnu mrežu primjenom sekvencijalne Monte Carlo simulacije / Assessing the Possibility of PV Integration to the Medium Voltage Distribution Network by Using Sequential Monte Carlo Simulation

Energija, ekonomija, ekologija, 2, XXVI (2024) (стр 15-24)
 

АУТОР(И) / AUTHOR(S): Predrag MRŠIĆ, Čedomir ZELJKOVIĆ, Predrag STEFANOV

Download Full Pdf    

DOI: 10.46793/EEE24-2.15M

САЖЕТАК / ABSTRACT:

U radu je razvijen simulator za procjenu mogućnosti priključenja fotonaponskog sistema date snage na unaprijed definisanoj lokaciji srednjenaponske distributivne mreže. Analiza je izvršena korišćenjem sekvencijalne Monte Carlo simulacije kojom se omogućava uvažavanje podataka sa velikim stepenom nesigurnosti, kao što su proizvodnja fotonaponskog sistema i potrošnja. Pored ovoga, sekvencijalna simulacija omogućava da se odrede periodi kada se narušavaju pogonska ograničenja. Za potrebe ove analize u radu je dat prijedlog probabilističkog modela vremenskih serija proizvodnje sistema i dijagrama potrošnje. Na bazi rezultata različitih radnih režima određene su raspodjele vjerovatnoće pojave vrijednosti napona u pojedinim tačkama mreže, opterećenja vodova, sa vjerovatno­ćama prekoračenja pogonskih ograničenja. Baziran na ovim pokazateljima definisan je prijedlog postupka za procjenu prihvatljivosti priključenja željenog fotonaponskog sistema na posmatranu mrežu. Proračun tokova snaga je izvršen u OpenDSS simulatoru, a priprema vremenskih serija ulaznih podataka za simulaciju izvršena je u programskom paketu Matlab. Razvijeni metod je testiran na primjeru IEEE mreže sa 33 čvora.

КЉУЧНЕ РЕЧИ / KEYWORDS:

Distributivna mreža, PV sistem, procjena mogućnosti priključenja, sekvencijalna Monte Carlo simulacija

ЛИТЕРАТУРА / REFERENCES:

  • Ismael, S.M., Abdel Aleem, S.H.E., Abdelaziz, A.Y., Zobaa A.F. State-of-the-art of hosting capacity in modern power systems with distributed generation, Renewable Energy, Vol. 130, pp. 1002-1020, 2019.
    https://doi.org/10.1016/j.renene.2018.07.008
  • Adefarati, , Bansal, R.C., Integration of renewable distributed generators into the distribution system: a review, IET Renewable Power Generation, Vol. 10, No. 7, pp. 873–884, 2016. https://doi.org/10.1049/iet-rpg.2015.0378
  • Schavemaker, P., van der Sluis, L. Electrical Power System Essentials, John Wiley, 2008.
  • Ramadhani, U.H., Shepero, M., Munkhammar, J., Widén, J., Etherden, N. Review of probabilistic load flow approaches for power distribution systems with photovoltaic generation and electric vehicle charging, International Journal of Electrical Power & Energy Systems, Vol. 120, No. 106003, 2020. https://doi.org/10.1016/j.ijepes.2020.106003
  • Borkowska, B. Probabilistic Load Flow, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-93, No. 3, pp. 752-759, 1974.
    https://doi.org/10.1109/TPAS.1974.293973
  • Allan, R. N., Grigg, C. H., Al-Shakarchi, M. R. G. Numerical techniques in probabilistic load flow problems, International Journal for Numerical Methods in Engineering, Vol. 10, No. 4, pp. 853-860, 1976.
    https://doi.org/10.1002/nme.1620100412
  • Allan, R.N., Da Silva, A.M.L., Burchett, R.C. Evaluation methods and accuracy in probabilistic load flow solutions, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-100, No. 5, pp. 2539-2546, 1981. https://doi.org/10.1109/TPAS.1981.316721
  • Hu, Z., Wang, X. A probabilistic load flow method considering branch outages, IEEE Transactions on Power Systems, Vol. 21, No. 2, pp. 507-514, 2006. https://doi.org/10.1109/TPWRS.2006.873118 Rubinstein, R.Y., Kroese, D.P. Simulation and the Monte Carlo Method, John Wiley & Sons, 2017. https://doi.org/10.1002/9781118631980
  • Bollen, M., Hassan, F. Integration of Distributed Generation in the Power System, Wiley – IEEE Press, Hoboken, USA, 2011.
  • Conti, S., Raiti, S. Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators, Solar Energy, Vol. 81, No 12, pp. 1473-1481, 2007.https://doi.org/10.1016/j.solener.2007.02.007
  • Baut, J.L., Zehetbauer, P., Bletterie, B., Kadam, S., Hatziargyriou, N., Smith, J., Rylander, M. Probabilistic evaluation of the hosting capacity in distribution networks, in Proc. Proceedings IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Ljubljana, Slovenia, pp. 1-6, 9-12 October 2016. https://doi.org/10.1109/ISGTEurope.2016.7856213
  • Püvi, V., Lehtonen, M. Evaluating distribution network optimal structure with respect to solar hosting capacity, Electric Power Systems Research, Vol. 216, 109019, 2023.https://doi.org/10.1016/j.epsr.2022.109019
  • Zhao, Z., Xue, Y., Liu, Z., Zheng, W., Duan, S., Yu, L. A novel estimation method for maximum PV hosting capacity in radial distribution networks using bus voltage and electrical distance, Electric Power Systems Research, Vol. 224, 109791, 2023. https://doi.org/10.1016/j.epsr.2023.109791.
  • Rabiee, A., Mohseni-Bonab, S.M. Maximizing hosting capacity of renewable energy sources in distribution networks: A multi-objective and scenario-based approach, Energy, Vol. 120, pp. 417-430, 2017. https://doi.org/10.1016/j.energy.2016.11.095
  • Schuol, J., Abbaspour, K.C. Using monthly weather statistics to generate daily data in a SWAT model application to West Africa, Ecological Modelling, Vol. 201, No. 3-4, pp. 301-311, 2007. https://doi.org/10.1016/j.ecolmodel.2006.09.028
  • Pickering, N.B., Hansen. J.W., Jones. J.W., Wells. C.M., Chan. V.K., Godwin, D.C. WeatherMan: a utility for managing and generating daily weather data, Agronomy Journal, Vol. 86, No. 2, pp. 332-337, 1994.
    https://doi.org/10.2134/agronj1994.00021962008600020023x
  • Zeljković, Č., Mršić, P., Erceg, B. Simulation-based energy assessment of PV systems installed in an urban environment, in Proc. 20th International Symposium Power Electronics Ee2019, Novi Sad, Serbia, October 23-26, 2019. http://dx.doi.org/10.1109/PEE.2019.8923517
  • European Commission Joint Research Centre, PVGIS Solar radiation tool, https://ec.europa.eu/jrc/en/pvgis [preuzeto 03.02.2024]
  • Aguiar, R.J., Collares-Pereira, M., Conde, J.P. Simple procedure for generating sequences of daily radiation values using a library of Markov transition matrices, Solar Energy, Vol. 40, No. 3, pp.269-279, 1988.
    https://doi.org/10.1016/0038-092X(88)90049-7
  • Aguiar, R.J., Collares-Pereira, M. TAG: A time-dependent, autoregressive, Gaussian model for generating synthetic hourly radiation, Solar Energy, Vol. 49, No. 3, pp.167-174, 1992.https://doi.org/10.1016/0038-092X(92)90068-L
  • Ridley, B., Boland, J., Lauret, P. Modelling of diffuse solar fraction with multiple predictors, Renewable Energy, Vol. 35, No. 2, pp. 478-483, 2010. https://doi.org/10.1016/j.renene.2009.07.018
  • Masters, G.M. Renewable and efficient electric power systems, Wiley Interscience, New York, 2004.
  • Perez, R., Ineichen, P., Seals, R., Michalsky, J., Stewart, R. Modeling daylight availability and irradiance components from direct and global irradiance, Solar Energy, Vol. 44. No. 5, pp. 271-289, 1990.
    https://doi.org/10.1016/0038-092X(90)90055-H
  • Dobos, A. P. PVWatts version 5 manual, Technical report, National Renewable Energy Laboratory, Denver, USA, 2014.
  • Soltani, A., Hoogenboom, G. A statistical comparison of the stochastic weather generators WGEN and SIMMETEO, Climate Research, Vol. 24, No. 3, pp. 215-230, 2003.https://doi.org/10.3354/cr024215
  • Reicosky, D.C., Winkelman, L.J., Baker, J.M., Baker, D.G. Accuracy of hourly air temperatures calculated from daily minima and maxima, Agricultural and Forest Meteorology, Vol. 46, No. 3, pp. 193-209, 1989. https://doi.org/10.1016/0168-1923(89)90064-6
  • Wang, P., Billinton, R. Time sequential distribution system reliability worth analysis considering time varying load and cost models, IEEE Transactions on Power Delivery, Vol. 14, No. 3, pp. 1046-1051, 1999. https://doi.org/10.1109/61.772352
  • Bae, I.S., Kim, J.O., Kim, J.C., Singh, C. Optimal operating strategy for distributed generation considering hourly reliability worth, IEEE Transactions on Power Systems, Vol. 19, No. 1, pp. 287-292, 2004. https://doi.org/10.1109/TPWRS.2003.818738
  • Starčević, V., Zeljković, Č., Kitić, N., Mršić, P., Erceg, B., Jovanović, V. PV System Integration Assessment by Automated Monte Carlo Simulation in DIgSILENT PowerFactory, in Proc. 20th International Symposium INFOTEH-JAHORINA, East Sarajevo, Bosnia and Herzegovina, 17-19 March 2021. https://doi.org/10.1109/INFOTEH51037.2021.9400525
  • Mahdavi, M., Sabillón, C., Ajalli, M., Monsef, H., Romero, R. A real test system for power system planning, operation, and reliability, Journal of control, automation and electrical systems, Vol. 29, pp.192-208, 2018.
    https://doi.org/10.1007/s40313-017-0361-8
  • Yaprakdal, , Baysal, M., Anvari-Moghaddam, A. Optimal operational scheduling of reconfigurable microgrids in presence of renewable energy sources,  Energies, Vol. 12, No. 10, pp. 1858, 2019. https://doi.org/10.3390/en12101858
  • Dugan, R.C., Montenegro, D. Reference guide, the open distribution system simulator, Electric Power Research Institute, Inc., Washington, 2022.
  • Weather Atlas, https://www.weather-atlas.com/ [pristupljeno 20.02.2024]
  • European Commission Joint Research Centre, PVGIS Solar radiation tool,
    https://ec.europa.eu/jrc/en/pvgis [pristupljeno 20.2.2024]
  • Cruz, M. R. M., Fitiwi, D. Z., Santos, S. F., Catalão, J. P. S. Influence of distributed storage systems and network switching/reinforcement on RES-based DG integration level, in Proc. 13th International Conference on the European Energy Market (EEM), Porto, Portugal, pp. 1-5, 6-9 June 2016. https://doi.org/10.1109/EEM.2016.7521337