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

© The Institution of Engineering and Technology
Received 01/02/2018, Accepted 26/07/2018, Revised 02/05/2018, Published 01/08/2018

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.

Inspec keywords: distribution networks; voltage control; renewable energy sources; genetic algorithms; power generation control; optimisation; reactive power; power grids; distributed power generation; load flow; power system stability

Other keywords: solar energies; interval values; power generation facilities; power generation outputs; interval uncertainties; interval reactive power optimisation model; renewable power generation; newly developed interval power-flow calculation; renewable energy sources; power output; renewable energy power generators; power load demand; renewable energy resources; obtains lower system power losses; interval voltage control method; power grid; interval voltage control model

Subjects: Control of electric power systems; Power system management, operation and economics; Optimisation techniques; Optimisation techniques

References

    1. 1)
      • 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. 1–8.
    2. 2)
      • 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.
    3. 3)
      • 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. 982–991.
    4. 4)
      • 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. 1–6.
    5. 5)
      • 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. 1422–1429.
    6. 6)
      • 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. 521–527.
    7. 7)
      • 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. 3657–3664.
    8. 8)
      • 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. 381–385.
    9. 9)
      • 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. 579–584.
    10. 10)
      • 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. 130–138.
    11. 11)
      • 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. 1218–1231.
    12. 12)
      • 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. 1179–1186.
    13. 13)
      • 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. 39–41.
    14. 14)
      • 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. 3736–3744.
    15. 15)
      • 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. 1070–1078.
    16. 16)
      • 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. 301–311.
    17. 17)
      • 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. 1179–1183.
    18. 18)
      • 4. Granville, S.: ‘Optimal reactive dispatch through interior point methods’, IEEE Trans. Power Syst., 1994, 9, (1), pp. 136–146.
    19. 19)
      • 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. 13–17.
    20. 20)
      • 43. ‘Power systems test case archive’. Available at http://www.ee.washington.edu/research/pstca/, accessed 1 June 2017.
    21. 21)
      • 21. Zhang, H., Li, P.: ‘Chance constrained programming for optimal power flow under uncertainty’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 2417–2424.
    22. 22)
      • 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. 1096–1100.
    23. 23)
      • 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. 3662–3669.
    24. 24)
      • 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.
    25. 25)
      • 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. 1691–1697.
    26. 26)
      • 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. 471–480.
    27. 27)
      • 11. Gallego, R.A., Monticelli, A.J., Romero, R.: ‘Optimal capacitor placement in radial distribution networks’, IEEE Trans. Power Syst., 2001, 16, (4), pp. 630–637.
    28. 28)
      • 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. 1198–1208.
    29. 29)
      • 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. 1808–1817.
    30. 30)
      • 26. Srinivas, M., Patnaik, L.M.: ‘Adaptive probabilities of crossover and mutation in genetic algorithms’, IEEE Trans. Syst. Man Cybern., 1994, 24, (4), pp. 656–667.
    31. 31)
      • 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. 624–632.
    32. 32)
      • 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. 1847–1854.
    33. 33)
      • 40. Zhang, C., Chen, H., Shi, K., et al: ‘An interval power flow analysis through optimizing-scenarios method’, IEEE Trans. Smart Grid, 2017, pp. 1–1, DOI: 10.1109/TSG.2017.2684238.
    34. 34)
      • 1. Wan, C., Xu, Z., Pinson, P.: ‘Optimal prediction intervals of wind power generation’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 1166–1174.
    35. 35)
      • 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. 177–188.
    36. 36)
      • 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. 916–923.
    37. 37)
      • 8. Lu, C.N., Unum, M.R.: ‘Network constrained security control using an interior point algorithm’, IEEE Trans. Power Syst., 1993, 8, (3), pp. 1068–1076.
    38. 38)
      • 10. Iba, K.: ‘Reactive power optimization by genetic algorithm’, IEEE Trans. Power Syst., 1994, 9, (2), pp. 685–692.
    39. 39)
      • 35. Feijoo, A.E., Cidras, J.: ‘Modeling of wind farms in the load flow analysis’, IEEE Trans. Power Syst., 2000, 15, (1), pp. 110–115.
    40. 40)
      • 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. 1–9.
    41. 41)
      • 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. 1–6.
    42. 42)
      • 24. Saunders, C.S.: ‘Point estimate method addressing correlated wind power for probabilistic optimal power flow’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 1045–1054.
    43. 43)
      • 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. 1–7.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.5419
Loading

Related content

content/journals/10.1049/iet-gtd.2018.5419
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading