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Interval voltage control method for transmission systems considering interval uncertainties of renewable power generation and load demand

Interval voltage control method for transmission systems considering interval uncertainties of renewable power generation and load demand

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Renewable energy sources provide an important means of reducing reliance on conventional fuels. However, some renewable energy resources such as wind and solar energies are intermittent, and their uncertainty threatens the operating security of the power grid. To solve this problem, this study proposes the use of intervals to model the power output of renewable energy resources and the power load demand, and accordingly develops an interval voltage control model, i.e. interval reactive power optimisation model. The proposed model considers the control modes of renewable energy power generators and can safeguard the security of power grids by ensuring that the voltages reside within established limits. An adaptive genetic algorithm is employed to solve the proposed model, where a newly developed interval power-flow (IPF) calculation is used to solve the IPF equations, and penalty functions are applied to express inequality constraints. The proposed method is introduced in detail, and simulation results are presented to demonstrate its performance in comparison with a previously proposed interval voltage control method, as well as its applicability to large systems with various fluctuations of input data. The proposed approach provides robust convergence, obtains lower system power losses, and substantially reduces the computation time.

References

    1. 1)
      • 1. Wan, C., Xu, Z., Pinson, P.: ‘Optimal prediction intervals of wind power generation’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 11661174.
    2. 2)
      • 2. Feng, L., Zhang, J., Li, G., et al: ‘Cost reduction of a hybrid energy storage system considering correlation between wind and PV power’, Prot. Control Mod. Power Syst., 2016, 1, (11), pp. 19.
    3. 3)
      • 3. Li, Z., Zhao, Y., Song, X., et al: ‘Short-term wind power prediction based on extreme learning machine with error correction’, Prot. Control Mod. Power Syst., 2016, 1, (1), pp. 18.
    4. 4)
      • 4. Granville, S.: ‘Optimal reactive dispatch through interior point methods’, IEEE Trans. Power Syst., 1994, 9, (1), pp. 136146.
    5. 5)
      • 5. Liu, M., Tso, S.K., Cheng, Y.: ‘An extended nonlinear primal-dual interior-point algorithm for reactive-power optimization of large-scale power systems with discrete control variables’, IEEE Trans. Power Syst., 2002, 17, (4), pp. 982991.
    6. 6)
      • 6. Olofsson, M., Andersson, M., Soder, L.: ‘Linear programming based optimal power flow using second order sensitivities’, IEEE Trans. Power Syst., 1995, 10, (3), pp. 16911697.
    7. 7)
      • 7. Nejdawi, I.M., Clements, K.A., Davis, P.W.: ‘An efficient interior point method for sequential quadratic programming based optimal power flow’, IEEE Trans. Power Syst., 2000, 15, (4), pp. 11791183.
    8. 8)
      • 8. Lu, C.N., Unum, M.R.: ‘Network constrained security control using an interior point algorithm’, IEEE Trans. Power Syst., 1993, 8, (3), pp. 10681076.
    9. 9)
      • 9. Jwo, W.S., Liu, C.W., Liu, C.C., et al: ‘Hybrid expert system and simulated annealing approach to optimal reactive power planning’, IET Proc. Gener. Trans. Distrib., 1995, 142, (4), pp. 381385.
    10. 10)
      • 10. Iba, K.: ‘Reactive power optimization by genetic algorithm’, IEEE Trans. Power Syst., 1994, 9, (2), pp. 685692.
    11. 11)
      • 11. Gallego, R.A., Monticelli, A.J., Romero, R.: ‘Optimal capacitor placement in radial distribution networks’, IEEE Trans. Power Syst., 2001, 16, (4), pp. 630637.
    12. 12)
      • 12. Zhao, B., Guo, C.X., Cao, Y.J.: ‘A multiagent-based particle swarm optimization approach for optimal reactive power dispatch’, IEEE Trans. Power Syst., 2005, 20, (2), pp. 10701078.
    13. 13)
      • 13. Dai, C.H., Chen, W.R., Zhu, Y.F., et al: ‘Seeker optimization algorithm for optimal reactive power dispatch’, IEEE Trans. Power Syst., 2009, 24, (3), pp. 12181231.
    14. 14)
      • 14. Acampora, G., Caruso, D., Vaccaro, A., et al: ‘A search group algorithm for optimal voltage regulation in power systems’. IEEE Congress on Evolutionary Computation (CEC), Vancouver, Canada, July 2016, pp. 36623669.
    15. 15)
      • 15. Lopez, J.C., Munoz, J.I., Contreras, J., et al: ‘Optimal reactive power dispatch using stochastic chance-constrained programming’. IEEE America Conf. Exposition Transmission and Distribution, Montevideo, Latin America, 2012, pp. 17.
    16. 16)
      • 16. Lopez, J.C., Mantovani, J.R.S., Contreras, J.S., et al: ‘Optimal reactive power planning using two-stage stochastic chance-constrained programming’. IEEE PowerTech Conf., Grenoble, 2013, pp. 16.
    17. 17)
      • 17. Yang, Y., Zhou, R., Ran, X.: ‘Robust optimization with box set for reactive power optimization in wind power integrated system’. IEEE Power and Energy Society General Meeting, San Diego, CA, July 2012, pp. 16.
    18. 18)
      • 18. Ding, T., Liu, S., Yuan, W., et al: ‘A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks’, IEEE Trans. Sustain. Energy, 2015, 7, (1), pp. 301311.
    19. 19)
      • 19. Zhang, C., Chen, H., Ngan, H., et al: ‘Solution of reactive power optimization including interval uncertainty using genetic algorithm’, IET. Gener. Transm. Distrib., 2017, 11, (15), pp. 36573664.
    20. 20)
      • 20. Zhang, C., Chen, H., Lei, J., et al: ‘Solution of interval reactive power optimization using genetic algorithm’. IEEE PES Asia-Pacific Power and Energy Engineering Conf. (APPEEC), Xi'an, China, October 2016, pp. 10961100.
    21. 21)
      • 21. Zhang, H., Li, P.: ‘Chance constrained programming for optimal power flow under uncertainty’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 24172424.
    22. 22)
      • 22. Aien, M., Fotuhi-Firuzabad, M., Rashidinejad, M.: ‘Probabilistic optimal power flow in correlated hybrid wind-photovoltaic power systems’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 130138.
    23. 23)
      • 23. Li, Y., Li, W., Yan, W., et al: ‘Probabilistic optimal power flow considering correlations of wind speeds following different distributions’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 18471854.
    24. 24)
      • 24. Saunders, C.S.: ‘Point estimate method addressing correlated wind power for probabilistic optimal power flow’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 10451054.
    25. 25)
      • 25. Chen, H., Xuan, P., Wang, Y., et al: ‘Key technologies for integration of multitype renewable energy sources – research on multi-timeframe robust scheduling/dispatch’, IEEE Trans. Power Syst., 2016, 7, (1), pp. 471480.
    26. 26)
      • 26. Srinivas, M., Patnaik, L.M.: ‘Adaptive probabilities of crossover and mutation in genetic algorithms’, IEEE Trans. Syst. Man Cybern., 1994, 24, (4), pp. 656667.
    27. 27)
      • 27. Li, S., Yan, Z., Jian, L., et al: ‘Study on auto parts suppliers composition selection based on adaptive genetic algorithm’. IEEE Int. Conf. Grey Systems and Intelligent Services (GSIS), Leicester, UK, August 2015, pp. 521527.
    28. 28)
      • 28. Wang, F., Li, J., Liu, S., et al: ‘An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding’, IEEE/ASME Trans. Mechatronics, 2014, 19, (3), pp. 916923.
    29. 29)
      • 29. Zhang, C., Chen, H., Ngan, H.: ‘Reactive power optimisation considering wind farms based on an optimal scenario method’, IET Gener. Transm. Distrib., 2016, 10, (15), pp. 37363744.
    30. 30)
      • 30. Hou, J., Xu, Y., Liu, J., et al: ‘A multi-objective volt-var control strategy for distribution networks with high PV penetration’. IET Int. Conf. Advances in Power System Control, Operation & Management, Hong Kong, China, November 2017.
    31. 31)
      • 31. Villalva, M.G., Gazoli, J.R., Filho, E.R.: ‘Comprehensive approach to modeling and simulation of photovoltaic arrays’, IEEE Trans. Power Electron., 2009, 24, (5), pp. 11981208.
    32. 32)
      • 32. Shi, Z.Y., Ming-Zhi, H.E., Hao, R.X., et al: ‘Research of PV systems based on synchronous PI control’, Power Electron., 2009, 43, (10), pp. 3941.
    33. 33)
      • 33. Sulaeman, S., Benidris, M., Mitra, J.: ‘A method to model the output power of wind farms in composite system reliability assessment’. North American Power Symp., Pullman, WA, September 2014.
    34. 34)
      • 34. Yu, H., Rosehart, W.D.: ‘An optimal power flow algorithm to achieve robust operation considering load and renewable generation uncertainties’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 18081817.
    35. 35)
      • 35. Feijoo, A.E., Cidras, J.: ‘Modeling of wind farms in the load flow analysis’, IEEE Trans. Power Syst., 2000, 15, (1), pp. 110115.
    36. 36)
      • 36. Vaccaro, A., Canizares, C., Villacci, D.: ‘An affine arithmetic-based methodology for reliable power flow analysis in the presence of data uncertainty’, IEEE Trans. Power Syst., 2010, 25, (2), pp. 624632.
    37. 37)
      • 37. Vaccaro, A., Canizares, C., Bhattacharya, K.: ‘A range arithmetic-based optimization model for power flow analysis under interval uncertainty’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 11791186.
    38. 38)
      • 38. Ding, T., Bo, R., Li, F., et al: ‘Interval power flow analysis using linear relaxation and optimality-based bounds tightening (OBBT) methods’, IEEE Trans. Power Syst., 2015, 30, (1), pp. 177188.
    39. 39)
      • 39. Zhang, C., Chen, H., Ngan, H., et al: ‘A mixed interval power flow analysis under rectangular and polar coordinate system’, IEEE Trans. Power Syst., 2017, 32, (2), pp. 14221429.
    40. 40)
      • 40. Zhang, C., Chen, H., Shi, K., et al: ‘An interval power flow analysis through optimizing-scenarios method’, IEEE Trans. Smart Grid, 2017, pp. 11, DOI: 10.1109/TSG.2017.2684238.
    41. 41)
      • 41. Joine, J.A., Houck, C.R.: ‘On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's’. Proc. IEEE Int. Conf. Evolutionary Computing, Orlando, FL, June 1994, pp. 579584.
    42. 42)
      • 42. Gao, S., Zhang, Q., Zhang, L.: ‘A hybrid genetic algorithm based on quadratic penalty function for BLT light source reconstruction’. Third Int. Conf. Biomedical Engineering and Informatics (BMEI), Yantai, China, October 2010, pp. 1317.
    43. 43)
      • 43. Power systems test case archive’. Available at http://www.ee.washington.edu/research/pstca/, accessed 1 June 2017.
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