access icon free Optimal hourly scheduling of distributed generation and capacitors for minimisation of energy loss and reduction in capacitors switching operations

© The Institution of Engineering and Technology
Received 28/10/2016, Accepted 12/04/2017, Revised 21/03/2017, Published 24/04/2017

Now-a-days electric power systems all over the world are undergoing a phase of increased size and complexity due to rising load demand expansion. This situation leads to excessive burden on the power distribution systems posing many challenges before the distribution system utilities. Minimisation of real power loss and maintaining voltage profile are the major requirements for a distribution system apart from cost reduction. Capacitors and tap changing transformers are being conventionally used for maintaining voltage profile along a primary distribution network. When a distributed generation (DG) is incorporated in a distribution system, the problem of efficiently controlling a system becomes more complex. In this study, a novel multi-objective optimisation problem of hourly scheduling of voltage control devices in coordination with a DG is attempted. An efficient particle swarm optimisation technique is used to solve the problem. Another novelty of this work is the use of Newton-based power flow method for evaluation of function value. The algorithm proposed is validated on IEEE standard 33 bus and 69 bus radial distribution systems. The results are encouraging.

Inspec keywords: distributed power generation; particle swarm optimisation; load flow control; capacitors; power distribution control; power generation scheduling; power generation control

Other keywords: load demand expansion; IEEE standard-69 bus radial distribution systems; power distribution systems; optimal hourly scheduling; voltage profile; IEEE standard 33-bus radial distribution systems; real power loss minimisation; tap changing transformers; cost reduction; multiobjective optimisation problem; capacitor switching operation; voltage control devices; primary distribution network; energy loss minimization; distributed generation; particle swarm optimisation technique; electric power systems; function value evaluation; Newton-based power flow method

Subjects: Optimisation techniques; Optimisation techniques; Control of electric power systems; Distributed power generation; Distribution networks

References

    1. 1)
      • 11. Yang, Q., An, D., Yu, W., et al: ‘Towards stochastic optimization-based electric vehicle penetration in a novel archipelago microgrid’, J. Sens., 2016, 16, (6), pp. 1–22.
    2. 2)
      • 6. Visali, N., Satishkumar Reddy, M., Surendranath Reddy, M.: ‘Loss reduction in radial distribution system by optimal placement of capacitor using differential evolution method’, Int. J. Emerging Trends Eng. Res., 2013, 1, (2), pp. 53–57.
    3. 3)
      • 15. Biswas, S., Goswami, S., Chatterjee, A.: ‘Optimal distributed generation placement in shunt capacitor compensated distribution systems considering voltage sag and harmonics distortions’, IET. Gener. Transm. Distrib., 2014, 8, (5), pp. 783–797.
    4. 4)
      • 10. Soroudi, A., Siano, P., Keane, A.: ‘Optimal DR and ESS scheduling for distribution losses payments minimization under electricity price uncertainty’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 261–272.
    5. 5)
      • 4. Das, D.: ‘Optimal placement of capacitors on radial distribution system using fuzzy-GA method’, Electr. Power Energy Syst., 2008, 30, pp. 361–367.
    6. 6)
      • 17. Gopiya Naik, S., Khatod, D.K., Sharma, M.P.: ‘Optimal allocation of combined DG and capacitor for real powee loss minimization in distribution networks’, Electr. Power Energy Syst., 2013, 53, pp. 967–973.
    7. 7)
      • 20. Rahiminejad, A., Hosseinian, S.H., Vahidi, B., et al: ‘Simultaneous distributed generation placement, capacitor placement, and reconfiguration using a modified teaching-learning based optimization algorithm’, Electr. Power Compon. Syst., 2016, 44, (14), pp. 1631–1644.
    8. 8)
      • 9. El-Fergany, A.A., Abdelaziz, A.Y.: ‘Capacitor allocations in radial distribution networks using cuckoo search algorithm’, IET. Gener. Transm. Distrib., 2014, 8, (2), pp. 223–232.
    9. 9)
      • 14. Khan, N.A., Ghoshal, S.P., Ghosh, S.: ‘Optimal allocation of distributed generation and shunt capacitors for the reduction of total voltage deviation and total line loss in radial distribution system using binary collective animal behavior optimization algorithm’, Electr. Power Compon. Syst., 2015, 43, (2), pp. 119–133.
    10. 10)
      • 12. Zou, B., Wang, J., Wen, F.: ‘Optimal investment strategies for distributed generation in distribution networks with real option analysis’, IET. Gener. Transm. Distrib., 2017, 11, (3), pp. 804–813.
    11. 11)
      • 7. Muhtazaruddin, M.N., Jamian, J.J., Nguyen, D., et al: ‘Optimal capacitor placement and sizing via Artificial Bee Colony’, Int. J. Smart Grid Clean Energy, 2014, 3, (2), pp. 200–206.
    12. 12)
      • 21. Venkatesh, B., Dukpa, A., Chang, L.: ‘An accurate voltage solution method for radial distribution systems’, Electr. Comput. Eng. Can. J., 2009, 34, (1/2), pp. 69–74.
    13. 13)
      • 3. Baran, M.E., Wu, F.F.: ‘Optimal capacitor placement on radial distribution system’, IEEE Trans. Power Deliv., 1989, 4, pp. 725–734.
    14. 14)
      • 23. Eberhart, R., Kennedy, J.: ‘Particle swarm optimization’. Proc. IEEE Int. Conf. on Neural Network (ICNN), November 1995, vol. 4, pp. 1942–1998.
    15. 15)
      • 13. Moradi, M.H., Abedini, M.: ‘A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution system’, Electr. Power Energy Syst., 2012, 34, pp. 66–74.
    16. 16)
      • 2. Baran, M.E., Wu, F.F.: ‘Optimal sizing of capacitor placed on radial distribution system’, IEEE Trans. Power Deliv., 1989, 4, pp. 735–743.
    17. 17)
      • 18. Moradi, M.H., Zeinalzadeh, A., Mohammadi, Y., et al: ‘An efficient hybrid method for solving the optimal sitting and sizing problem of DG and shunt capacitor banks simultaneously based on imperialistcometitive lgorithm and genetic algorithm’, Electr. Power Energy Syst., 2014, 54, pp. 101–111.
    18. 18)
      • 22. Khushalani, S., Solanki, J.M., Schulz, N.N.: ‘Development of three-phase unbalanced power flow using PV and PQ models for distributed generation and study of the impact of DG models’, IEEE Trans. Power Syst., 2007, 22, (3), pp. 1019–1025.
    19. 19)
      • 8. Elfergany, A.: ‘Optimal capacitor allocations using evolutionary algorithm’, IET Proc. Gener. Transm. Distrib., 2013, 7, pp. 593–601.
    20. 20)
      • 5. Huang, T.L., Hsiao, Y.T., Chang, C.H., et al: ‘Optimal placement of capacitors in distribution system using an immune multi-objective algorithm’, Electr. Power Energy Syst., 2008, 30, pp. 184–192.
    21. 21)
      • 1. Haghifam, M.R., Falaghi, H., Malik, O.: ‘Risk-based distributed generation placement’, IET Proc. Gener. Transm. Distrib., 2008, 2, pp. 252–260.
    22. 22)
      • 19. Zeinalzadeh, A., Mohammadi, Y., Moradi, M.H.: ‘Optimal multi objective placement and sizing of multiple DGs and shunt capacitor banks simultaneously considering load uncertainty via MOPSO approach’, Electr. Power Energy Syst., 2015, 67, pp. 336–349.
    23. 23)
      • 16. Liu, M.B., Canizares, C.A., Huang, W.: ‘Reactive power and voltage control in distribution systems with limited switching operations’, IEEE Trans. Power Syst., 2009, 24, (2), pp. 889–899.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.1600
Loading

Related content

content/journals/10.1049/iet-gtd.2016.1600
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading