Selection of generating equipment for a solar-diesel complex

A methodology for selecting the composition of generating equipment included in a solar-diesel complex to optimize its operating mode by minimizing the specific fuel consumption of diesel generator units is described. Software developed according to the principle of real-time integer nonlinear programming is used to calculate the minimum operating costs during the operation of the complex. Various restrictions on the operating modes of electrical equipment were taken into account. For a diesel power plant having a minimum capacity not less than 30% of the nominal capacity, the distribution of capacity between diesel generator units takes into account individual consumption characteristics. For an electrical energy storage system whose permissible change in capacity varies from 50 to 100%, the charge/discharge rate is limited to “1C”. For the solar power plant, the change in inverter efficiency was taken into account depending on its load as predicted for a 24-hour period. The research used a model to simulate full-scale equipment of a solar-diesel complex comprising two diesel generator units of 12 and 30 kW, a solar power plant simulator having a capacity of 6.6 kW, an energy storage system, and an active load simulator with a capacity of up to 50 kW. An algorithmic description of the operational principles of an automated control system for ensuring the energy efficiency of solar-diesel complexes in operation is provided. The developed SCADA system is suitable for modeling the operating modes of a solar-diesel complex under conditions close to actual. Depending on the operating conditions of the solar-diesel complex and the equipment parameters (the number of diesel generator units and their nominal capacities, the capacity and power of the electrical energy storage system, as well as the installed capacity of the grid solar power plant), operating mode modeling accuracy can be increased by as much as 30%. Thus, the obtained results of modeling the operating mode of a solar-diesel complex demonstrate the possibility of significantly refining the estimates of the operating parameters of such energy facilities by taking into account the actual energy characteristics of diesel generator units.

1. Belila A., Benbouzid M., Berkouk E.-M., Amirat Y. On energy management control of a PV-diesel-ESS based microgrid in a stand-alone context. Energies. 2018;11(8):2164. https://doi.org/10.3390/en11082164.

2. Mohamed M.M., Zoghby H.M.El., Sharaf S.M., Mosa M.A. Optimal virtual synchronous generator control of battery/supercapacitor hybrid energy storage system for frequency response enhancement of photovoltaic/diesel microgrid. Journal of Energy Storage. 2022;51:104317. https://doi.org/10.1016/j.est.2022.104317.

3. Vinogradov A.A. Analysis of influence of non-proportional distribution of load between parallelly operating generators for the power factor of loading of each generator. Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S.O. Makarova. 2018;10(2):411-419. (In Russ.). https://doi.org/10.21821/2309-5180-2018-10-2-411-419. EDN: XOOCUH.

4. Djohra S., Aries N. Cost optimization of a wind-solar-diesel system with battery storage. Journal of Renewable Energies. 2022;25(1):127-151. https://doi.org/10.54966/jreen.v25i1.1076.

5. King D.L., Boyson W.E., Kratochvil J.A. Photovoltaic array performance model. Available from: https://energy.sandia.gov/wp-content/gallery/uploads/043535.pdf [Accessed 30th September 2024].

6. Soto W., Klein S., Beckman W. Improvement and validation of a model for photovoltaic array performance. Solar Energy. 2006;80:78-88. https://doi.org/10.1016/J.SOLENER.2005.06.010.

7. King D.L., Gonzalez S., Galbraith G.М., Boyson W.Е. Performance model for grid-connected photovoltaic inverters. 2007. https://doi.org/10.2172/920449.

8. Diaf S., Diaf D., Belhamel M., Haddadi M., Louche A. A methodology for optimal sizing of autonomous hybrid PV/ wind system. Energy Policy. 2007;35(11):5708-5718. https://doi.org/10.1016/j.enpol.2007.06.020.

9. Marenkov S.A. Applying of electrical energy storage technologies to increase the reliability of power system based on renewable energy sources. International Research Journal. 2016;6-2:103-107. (In Russ.). https://doi.org/10.18454/IRJ.2016.48.094.

10. Bulatov R.V., Nasyrov R.R., Burmeyster M.V. Application of energy storage systems for the purpose of improving the installed capacity utilization factor of power plants based on renewable energy sources within electric systems. Electric Power. Transmission and Distribution. 2021;6:74-80. (In Russ.). EDN: XKDFJJ.

11. Elistratov V.V., Konishchev M.A., Denisov R.S., Bogun I.V. An Arctic wind-diesel power plant with an intelligent automatic control system. Elektrichestvo. 2022;2:29-37. (In Russ.). https://doi.org/10.24160/0013-5380-2022-2-29-37. EDN ZWLNQR.

12. Karamov D.N. Mathematical modeling of an autonomous power supply system using renewable energy sources. Proceedings of Irkutsk State Technical University. 2015;9:133-140. (In Russ.). EDN: UJWFLH.

13. Rybakov V.V., Peshekhonov N.E., Voronin A.E. On the issue of improving the efficiency of diesel power plants. Proceedings of the Tula State University. 2020;9:488-494. (In Russ.). EDN: VLBIYU.

14. Narynbaev A.F., Kremer V.A., Vaskov A.G. Evaluating day-ahead solar radiation forecasts from ICON, GFS, and MeteoFrance global NWP models. Applied Solar Energy. 2024;60(3):491-500. https://doi.org/10.3103/S0003701X24600152.

15. Holmgren W.F., Hansen C.W., Mikofski M.A. Pvlib python: a python package for modeling solar energy systems. The Journal of Open Source Software. 2018;3:884. https://doi.org/10.21105/joss.00884.

16. Lavrik A.Y., Zhukovsky Y.L., Lavrik A.Y., Buldysko A.D. Fatures of the optimal composition of a wind-solar power plant with diesel generators. Power engineering: research, equipment, technology. 2020;22(1):10-17. (In Russ.) https://doi.org/10.30724/1998-9903-2020-22-1-10-17.

17. Kusakana K. Daily operation cost minimization of photovoltaic-diesel-battery hybrid systems using different control strategies. In: IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society. 9–12 November 2015, Yokohama. Yokohama: IEEE; 2015, р. 003609-003613. https://doi.org/10.1109/IECON.2015.7392661.

18. Orero S.O., Irving M.R. Large scale unit commitment using a hybrid genetic algorithm. International Journal of Electrical Power & Energy Systems. 1997;19(1):45-55. https://doi.org/10.1016/S0142-0615(96)00028-2.

19. Sidorov D., Muftahov I., Tomin N., Karamov D., Panasetsky D., Dreglea A. A dynamic analysis of energy storage with renewable and diesel generation using Volterra equations. IEEE Transactions on Industrial Informatics. 2020;16(5):3451-3459. https://doi.org/10.1109/TII.2019.2932453.

20. Cristóbal-Monreal I.R., Dufo-López R. Optimisation of photovoltaic–diesel–battery stand-alone systems minimising system weight. Energy Conversion and Management. 2016;119:279-288. https://doi.org/10.1016/j.enconman.2016.04.050.