access icon free Fuzzy adaptive particle swarm optimisation for power loss minimisation in distribution systems using optimal load response

Consumers may decide to modify the profile of their demand from high price periods to low price periods in order to reduce their electricity costs. This optimal load response to electricity prices for demand side management generates different load profiles and provides an opportunity to achieve power loss minimisation in distribution systems. In this study, a new method to achieve power loss minimisation in distribution systems by using a price signal to guide the demand side management is proposed. A fuzzy adaptive particle swarm optimisation is used as a tool for the power loss minimisation study. Simulation results show that the proposed approach is an effective measure to achieve power loss minimisation in distribution systems.

Inspec keywords: demand side management; cost reduction; power distribution economics; pricing; particle swarm optimisation; fuzzy set theory

Other keywords: fuzzy adaptive particle swarm optimisation; optimal load response; electricity cost reduction; demand side management; power loss minimisation; electricity prices; distribution systems; high-price period; demand profile; price signal; low-price period; load profiles

Subjects: Distribution networks; Power system management, operation and economics; Combinatorial mathematics; Optimisation techniques

References

    1. 1)
      • 26. Shi, Y., Eberhart, R.: ‘Parameter selection in particle swarm optimization’. Proc. Seventh Annual Conf. Evolutionary Programming, 1998, pp. 591600.
    2. 2)
      • 29. Esmin, A.A., Lambert-Torres, G., Zambroni de Souza, A.C.: ‘A hybrid particle swarm optimization applied to loss power minimization’, IEEE Trans. Power Syst., 2005, 20, (2), pp. 859866 (doi: 10.1109/TPWRS.2005.846049).
    3. 3)
      • 4. Sheen, J-N., Chen, C-S., Wang, T-Y.: ‘Response of large industrial customers to electricity pricing by voluntary time-of-use in Taiwan’, IEE Proc. Gener. Transm. Distrib., 1995, 142, (2), pp. 157166 (doi: 10.1049/ip-gtd:19951744).
    4. 4)
      • 44. Algarni, A.A.S., Bhattacharya, K.: ‘A Generic Operations Framework for Discos in Retail Electricity Markets’, IEEE Trans. Power Syst., 2009, 24, (1), pp. 356367 (doi: 10.1109/TPWRS.2008.2007001).
    5. 5)
      • 22. U.S. Dept. Energy, ‘Benefits of demand response in electricity markets and recommendations for achieving them’. A report to the United States Congress, pursuant to Section 1252 of the Energy Policy Act of 2005, 2006.
    6. 6)
      • 32. Kirschen, D.S., Strbac, G., Cumperayot, P., de Paiva Mendes, D.: ‘Factoring the elasticity of demand in electricity prices’, IEEE Trans. Power Syst., 2000, 15, (2), pp. 612617 (doi: 10.1109/59.867149).
    7. 7)
      • 14. Jia, W., Kang, C., Chen, Q.: ‘Analysis on demand-side interactive response capability for power system dispatch in a smart grid framework’, Electr. Power Syst. Res., 2012, 90, pp. 1117 (doi: 10.1016/j.epsr.2012.03.013).
    8. 8)
      • 9. Elkhatib, M.E., Shatshat, R.E., Salama, M.M.A.: ‘Decentralized reactive power control for advanced distribution automation systems’, IEEE Trans. Smart Grid, PP, (99) pp. 110.
    9. 9)
      • 3. Sheen, J-N., Chen, C-S., Yang, J-K.: ‘Time-of-use pricing for load management programs in Taiwan Power Company’, IEEE Trans. Power Syst., 1994, 9, (1), pp. 388396 (doi: 10.1109/59.317586).
    10. 10)
      • 13. Medina, J., Muller, N.: ‘Demand Response and Distribution grid operations: opportunities and challenges’, IEEE Trans. Smart Grid, 2010, 1, (2), pp. 193198 (doi: 10.1109/TSG.2010.2050156).
    11. 11)
      • 43. Chinchilla, M., Arnaltes, S., Burgos, J.C.: ‘Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the grid’, IEEE Trans. Energy Convers., 2006, 21, (1), pp. 130135 (doi: 10.1109/TEC.2005.853735).
    12. 12)
      • 37. Schittkowski, K.: ‘NLQPL: a FORTRAN-subroutine solving constrained nonlinear programming problems’, Ann. Oper. Res., 1985, 5, pp. 485500.
    13. 13)
      • 24. Angeline, P.: ‘Evolutionary optimization versus particle swarm optimization philosophy and performance differences’. Proc. Seventh Annual Conf. Evolutionary Programming, 1998, pp. 601610.
    14. 14)
      • 1. Rajaraman, R., Sarlashkar, J.V., Alvarado, F.L.: ‘The effect of demand elasticity on security prices for the poolco and multi-lateral contract models’, IEEE Trans. Power Syst., 1997, 12, (3), pp. 11771184 (doi: 10.1109/59.630459).
    15. 15)
      • 8. Ochoa, L.F., Harrison, G.P.: ‘Minimizing energy losses: optimal accommodation and smart operation of renewable distributed generation’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 198205 (doi: 10.1109/TPWRS.2010.2049036).
    16. 16)
      • 27. Clerc, M., Kennedy, J.: ‘The particle swarm: explosion stability and convergence in a multi-dimensional complex space’, IEEE Trans. Evol. Comput., 2002, 6, (1), pp. 6067 (doi: 10.1109/4235.985692).
    17. 17)
      • 47. Dietrich, K., Latorre, J.M., Olmos, L., Ramos, A.: ‘Demand response in an isolated system with high wind integration’, IEEE Trans. Power Syst., 2012, 27, (7), pp. 2029 (doi: 10.1109/TPWRS.2011.2159252).
    18. 18)
      • 21. Dashti, R., Afsharnia, S.: ‘Demand response regulation modeling based on distribution system asset efficiency’, Electr. Power Syst. Res., 2011, 81, pp. 667676 (doi: 10.1016/j.epsr.2010.10.031).
    19. 19)
      • 40. Shi, Y., Eberhart, R.C.: ‘Fuzzy adaptive particle swarm optimization’. Proc. IEEE Int. Conf. Evolutionary Computation, 2001, pp. 101106.
    20. 20)
      • 23. Kennedy, J.: ‘The particle swarm: social adaptation of knowledge’. Proc. IEEE Int. Conf. Evolution of Computing, Indianapolis, IN, 1997, pp. 303308.
    21. 21)
      • 20. Wang, Y., Pordanjani, I.R., Xu, W.: ‘An event-driven demand response scheme for power system security enhancement’, IEEE Trans. Smart Grid, 2011, 2, (1), pp. 2329 (doi: 10.1109/TSG.2011.2105287).
    22. 22)
      • 18. Doudna, J.H.: ‘Overview of California ISO summer 2000 demand response programs’. Proc. IEEE PES Winter Meet., February2001, vol. 1, pp. 228233.
    23. 23)
      • 7. Baran, M.E., Wu, F.F.: ‘Optimal capacitor placement on radial distribution systems’, IEEE Trans. Power Deliv., 1989, 4, (1), pp. 725734 (doi: 10.1109/61.19265).
    24. 24)
      • 19. Molina-García, A., Kessler, M., Fuentes, J.A., Gómez-Lázaro, E.: ‘Probabilistic characterization of thermostatically controlled loads to model the impact of demand response programs’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 241251 (doi: 10.1109/TPWRS.2010.2047659).
    25. 25)
      • 48. Palensky, P., Dietrich, D.: ‘Demand side management: demand response, intelligent energy systems and smart loads’, IEEE Trans. Ind. Inform., 2011, 7, (3), pp. 381388 (doi: 10.1109/TII.2011.2158841).
    26. 26)
      • 16. Aalami, H.A., Yousefi, G.R., Parsa Moghadam, M.: ‘Demand response model considering EDRP and TOU programs’. Proc. IEEE PES Transmission & Distribution Conf. & Expo., April 2008, pp. 16.
    27. 27)
      • 46. Zareipour, H., Cañizares, C.A., Bhattacharya, K.: ‘Economic impact of electricity market price forecasting errors: a demand-side analysis’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 254262 (doi: 10.1109/TPWRS.2009.2030380).
    28. 28)
      • 2. David, A.K., Lee, Y.C.: ‘Effect of inter-temporal factors on the real time pricing of electricity’, IEEE Trans. Power Syst., 1993, 8, (1), pp. 4452 (doi: 10.1109/59.221247).
    29. 29)
      • 31. Bajpai, P., Singh, S.N.: ‘Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 21522160 (doi: 10.1109/TPWRS.2007.907445).
    30. 30)
      • 33. Maly, D.K., Kwan, K.S.: ‘Optimal battery energy storage system (BESS) charge scheduling with dynamic programming’, IEE Proc. Sci. Meas. Technol., 1995, 142, (6), pp. 454458 (doi: 10.1049/ip-smt:19951929).
    31. 31)
      • 36. Han, S.P.: ‘A globally convergent method for nonlinear programming’, J. Optim. Theory Appl., 1977, 22, pp. 297309 (doi: 10.1007/BF00932858).
    32. 32)
      • 30. Saber, A.Y., Senjyu, T., Urasaki, N., Funabashi, T.: ‘Unit commitment computation—A novel fuzzy adaptive particle swarm optimization approach’. Proc. IEEE Power Systems Conf. Exposition, November 2006, pp. 18201828.
    33. 33)
      • 35. Goldberg, D.: ‘Genetic algorithms in search, optimization and machine learning’ (Kluwer, Norwell, MA,1989).
    34. 34)
      • 41. Takagi, T., Sugeno, M.: ‘Fuzzy identification of systems and its applications to modeling and control’, IEEE Trans. Syst. Man Cybern., 1985, 15, pt. A, pp. 116132 (doi: 10.1109/TSMC.1985.6313399).
    35. 35)
      • 25. del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G.: ‘Particle swarm optimization: basic concepts, variants and applications in power systems’, IEEE Trans. Evol. Comput., 2008, 12, (2), pp. 171195 (doi: 10.1109/TEVC.2007.896686).
    36. 36)
      • 15. Chen, Z., Wu, L., Fu, Y.: ‘Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 18221831 (doi: 10.1109/TSG.2012.2212729).
    37. 37)
      • 42. Shi, Y., Eberhart, R.C.: ‘Experimental study of particle swarm optimization’. Proc. Fourth World Multiconf. Systematica, Cybernatics and Informatics, July 2000.
    38. 38)
      • 5. Wu, Q., Wu, J., Wang, L., Tang, Y., Zou, Y.: ‘Determination and analysis of TOU (time-of-use) power price based on DSM (demand side management) and MCP (marketing clearing price)’. Proc. Sixth Int. Conf. Advances in Power System Control, Operation and Management, 2003, pp. 705710.
    39. 39)
      • 38. Zeineldin, H.H., El-Fouly, T.H.M., El-Saadany, E.F., Salama, M.M.A.: ‘Impact of wind farm integration on electricity market prices’, IET Renew. Power Gener., 2009, 3, (1), pp. 8495 (doi: 10.1049/iet-rpg:20080026).
    40. 40)
      • 12. Eto, J.H.: ‘Demand response spinning reserve demonstration’ (Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA), LBNL-62761. Available at http://www.certs.lbl.gov/pdf/62761.pdf.
    41. 41)
      • 6. Shirmohammadi, D., Hong, H.W.: ‘Reconfiguration of electric distribution networks for resistive line losses reduction’, IEEE Trans. Power Deliv., 1989, 4, (2), pp. 14921498 (doi: 10.1109/61.25637).
    42. 42)
      • 17. Aalami, H.A., Parsa Moghaddam, M., Yousefi, G.R.: ‘Modeling and prioritizing demand response programs in power markets’, Electr. Power Syst. Res., 2010, 80, pp. 426435 (doi: 10.1016/j.epsr.2009.10.007).
    43. 43)
      • 11. Deilami, S., Masoum, A.S., Moses, P.S., Masoum, M.A.S.: ‘Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile’, IEEE Trans. Smart Grid, 2011, 2, (3), pp. 456467 (doi: 10.1109/TSG.2011.2159816).
    44. 44)
      • 34. Fletcher, R.: ‘Practical methods of optimization’ (John Wiley and Sons, 1987).
    45. 45)
      • 45. Palma-Behnke, R., Cerda, J.L., Vargas, L.S., Jofré, A.: ‘A distribution company energy acquisition market model with integration of distributed generation and load curtailment options’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 17181727 (doi: 10.1109/TPWRS.2005.857284).
    46. 46)
      • 39. Kennedy, J., Eberhart, R.: ‘Particle swarm optimization’. Proc. IEEE Int. Conf. Neural Networks, 1995, 4, pp. 19421948.
    47. 47)
      • 10. Sortomme, E., Hindi, M.M., James MacPherson, S.D., Venkata, S.S.: ‘Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses’, IEEE Trans. Smart Grid, 2011, 2, (1), pp. 198205 (doi: 10.1109/TSG.2010.2090913).
    48. 48)
      • 28. Abido, M.A.: ‘Optimal power flow using particle swarm optimization’, Elect. Power Energy Syst., 2002, 24, (7), pp. 563571 (doi: 10.1016/S0142-0615(01)00067-9).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2012.0745
Loading

Related content

content/journals/10.1049/iet-gtd.2012.0745
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading