PROBABILISTIC FLOW DISTRIBUTION AS A REACTION TO THE STOCHASTICITY OF THE LOAD IN THE POWER SYSTEM

For the analysis and control of power systems deterministic approaches that are implemented in the form of well-known methods and models of calculation of steady-state and transient modes are mostly use in current practice. With the use of these methods it is possible to obtain solutions only for fixed circuit parameters of the system scheme and assuming that active and reactive powers as well as generation in nodal points of the network remain the same. In reality the stochastic character of power consumption cause the casual fluctuations of voltages at the nodes and power flows in electric power lines of the power system. Such casual fluctuations of operation can be estimated with the use of probabilistic simulation of the power flows. In the article the results of research of the influence of depth of casual fluctuations of the load power of the system on the probability distribution of voltage at nodes as well as on the flows of active and reactive power in the lines are presented. Probabilistic modeling of flow under stochastic load change is performed for different levels of fluctuations and under loading of the mode of the system up to peak load power. Test study to quantify the effect of stochastic variability of loads on the probabilistic distribution parameters of the modes was carried out on behalf of the electrical network of the real power system. The results of the simulation of the probability flow distribution for these fluctuations of the load, represented in the form of discrete sample values of the active power obtained with the use of the analytical Monte-Carlo method, and real data measurements of their values in the network under examination were compared.

1. Anders G. J. (1990) Probability Concepts in Electric Power Systems. N.Y., Wiley. 682.

2. Voropai N. I., Ershevich V. V., Luginskii Ia. N., Mamikoniants L. G., Semenov V. A., Sovalov S. A. (1984) Powerful Power Systems Management. ?oscow, Energoatomizdat Publ. 256 (in Russian).

3. Rahman, S., Rinaldy, R. (1993) An Efficient Load Model for Analyzing Demand Side Management Impacts. IEEE Transactions on Power Systems, 8 (3), 1219–1226. DOI:10.1109/59.260874

4. Hashimov A. M., Rahmanov N. R., Ahmedova S. T., Mustafayev A. A. (2015) Voltage Stability Problem for GRID with Distribution Generation and Renewable Sources. Proceedings of the 11th International Conference on Technical and Physical Problems of Electrical Engineering, ICTPE 2015 (Bucharest, Romania). International Organization on “Technical and Physical Problems of Engineering” (IOTPE), 159–165.

5. Alvarado F., Hu Y., Adapa R. (1992) Uncertainty in Power System Modeling and Computation. IEEE International Conference on Systems, Man and Cybernetics, 1, 754–760, DOI: 10.1109/ICSMC.1992.271535.

6. Zhang P., Lee S. T. (2004) Probabilistic Load Flow Computation Using the Method of Combined Cumulants and Gram-Charlier Expansion. IEEE Transactions on Power Systems, 19 (1), 676-682. DOI: 10.1109/TPWRS.2003.818743

7. Chen P., Chen Z., Bak-Jensen B. (2008) Probabilistic Load Flow: A Review. 3rd International Conference on Deregulation and Restructuring and Power Technologies, DRPT 2008. China Computer Society, 1586–1591. DOI:10.1109/DRPT.2008.4523658.

8. Su C. L. (2005) Probabilistic Load Flow Computation Using Point Estimate Method, IEEE Transactions on Power Systems, 20 (4), 1843–1851. DOI: 10.1109/TPWRS.2005.857921

9. Papaefthymiou G., Schavemaker P. H., Vander Sluis L., Kling W. L., Kurowicka D., Cooke R. M. (2006) Integration of Stochastic Generation in Power Systems. International Journal of Electrical Power & Energy Systems, 28 (9), 655–667. DOI:10.1016/j.ijepes.2006.03.004

10. Chen P., Chen Z., Bak-Jensen B., Villafafila R., Sorensen S. (2007) Study of Power Fluctuation from Dispersed Generations and Loads and its Impact on a Distribution Network through a Probabilistic Approach. 9th International Conference on Electrical Power Quality and Utilisation. Barselona, Article number 4424113. DOI: 10.1109/EPQU.2007.4424113.

11. Gentle J. E. (2003) Random Number Generation and Monte Carlo methods. USA: Springer. 381

12. Guliyev H. B., Rakhmanov N. R. (2015) Probabilistic Modeling of Flow Distribution in the Electric Network with Distributed Generation Capacities Taking into Account the Uncertainty of Development and Consumption. Methodical Problems of Research the Reliability of Large-Scale Power Systems. Issue 65: Reliability Liberalized Energy Systems. Sankt-Petersburg, Power Systems Inst, 511–516 (in Russian).

13. Rakhmanov N. R., Guliyev,H. B., Ibrahimov F. Sh. (2015) Modeling of the Process of Generating Power by a Wind Power Plant in the Prediction of the Mode of the Load Power Cover in the Power System during Daily Cycles. Problemi Energetiki [Problems of Energy], (3), 3–11 (in Russian).

14. Guseynov F. G., Rahmanov N. R. (1988) Determining Power System Parameters and Characteristics. Mockow, Energoatomizdat Publ. 152 (in Russian).