http://iet.metastore.ingenta.com
1887

Economic dispatch considering the wind power forecast error

Economic dispatch considering the wind power forecast error

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Dispatch results depend on the forecast wind power in an electric power grid. High levels of uncertainty in wind power lead to large forecast errors (FEs). Wind power FE creates an imbalance between power load demand and supply, which poses risks to the power grid. To minimise the risk caused by uncertainty in wind power, FE was analysed. The actual and forecast power of wind generations were studied to determine which factors had strong relationships with FE. After being analysed by principle component analysis, these factors were used to build an assessment model to estimate FE. According to the output of the assessment model, risk factor (RF) was defined to evaluate the risk-level associated with wind power. Moreover, based on a grid including wind generation and fossil fuel-fired power plants, a dispatch model was built to account for the cost of fossils fuels and RF. This dispatch model was studied to find strategies for decreasing the impact of wind power on the grid. The results from their research will increase the safety and reliability of power grids with uncertain levels of wind power and promote the widespread use of wind power.

References

    1. 1)
      • 1. Yusheng, X., Xing, L., Feng, X., et al: ‘A review on impacts of wind power uncertainties on power systems’, Proc. CSEE, 2014, 34, (29), pp. 50295040.
    2. 2)
      • 2. Miranda, V., Hang, P.S.: ‘Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 21432145.
    3. 3)
      • 3. Wenying, L., Jing, W., Chang, X., et al: ‘Multi-objective optimal method considering wind power accommodation based on source-load coordination’, Proc. CSEE, 2015, 35, (5), pp. 10791088.
    4. 4)
      • 4. Han, L., Romero, C.E., Yao, Z.: ‘Economic dispatch optimization algorithm based on particle diffusion’, Energy Convers. Manage., 2015, 105, pp. 12511260.
    5. 5)
      • 5. Yao, F., Dong, Z.Y.., Meng, K., et al: ‘Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia’, IEEE Trans Ind. Inf., 2012, 8, (4), pp. 880888.
    6. 6)
      • 6. Meibom, P., Barth, R., Hasche, B., et al: ‘Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland’, IEEE Trans. Power System, 2011, 26, (3), pp. 13671379.
    7. 7)
      • 7. Afanasyeva S., Saari, J., Kalkofen, M., Partanen, J., et al: ‘Technical, economic and uncertainty modelling of a wind farm project’, Energy Convers. Manage., 2016, (107), pp. 2233.
    8. 8)
      • 8. Liu, X., Xu, W.: ‘Minimum emission dispatch constrained by stochastic wind power availability and cost’, IEEE Trans. Power Syst., 2010, 25, (3), pp. 17051713.
    9. 9)
      • 9. Heinermann, J, Kramer, O.: ‘Machine learning ensembles for wind power prediction’, Renew. Energy, 2016, 89, pp. 671679.
    10. 10)
      • 10. Ning, Z., Chongqing, K., Jingyu, X., et al: ‘Review and prospect of wind power capacity credit’, Proc. CSEE, 2015, 35, (1), pp. 8294.
    11. 11)
      • 11. Kaifeng, Z., Guoqiang, Y., Hanyi, C., et al: ‘An estimation method for wind power forecast errors based on numerical feature extraction’, Autom. Electr. Power Syst., 2014, 38, (16), pp. 2228.
    12. 12)
      • 12. Xinsong, Z., Xiaofei, L., Yun, W., et al: ‘An estimation method for wind power forecast errors based on numerical feature extraction’, Power Syst. Prot. Control, 2015, 43, (24), pp. 7582.
    13. 13)
      • 13. Bouffard, F., Galiana, F.D.: ‘Stochastic security for operations planning with significant wind power generation’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 306316.
    14. 14)
      • 14. Khazali, A., Kalantar, M.: ‘Spinning reserve quantification by a stochastic–probabilistic scheme for smart power systems with high wind penetration’, Energy Convers. Manage., 2015, (96), pp. 242257.
    15. 15)
      • 15. Osorio, G.J., Lujano-Rojas, J.M, Matias, J.C.O, et al: ‘A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources’, Energy, 2015, (82), pp. 949959.
    16. 16)
      • 16. Bludszuweit, H., Dominguez-Navarro, J.A.., Llombart, A.: ‘Statistical analysis of wind power forecast error’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 983991.
    17. 17)
      • 17. Hong, Y., Jingsha, Y., Tiefeng, Z.: ‘A model and algorithm for minimum probability interval of wind power forecast errors based on beta distribution’, Proc. CSEE, 2015, 35, (9), pp. 21352142.
    18. 18)
      • 18. Yang, H., Qiu, J., Meng, K., et al: ‘Insurance strategy for mitigating power system operational risk introduced by wind power forecasting uncertainty’, Renew. Energy, 2016, 89, pp. 606615.
    19. 19)
      • 19. Zhang, N., Kang, C., Xia, Q., et al: ‘Modeling conditional forecast error for wind power in generation scheduling’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 13161324.
    20. 20)
      • 20. Lujano-Rojas, J.M., Osorio, G.J., Matias, J.C.O., et al: ‘A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability’, Renew. Energy, 2016, (87), pp. 731743.
    21. 21)
      • 21. Le, D.D., Berizzi, A., Bovo, C.: ‘A probabilistic security assessment approach to power systems with integrated wind resources’, Renew. Energy, 2016, (85), pp. 114123.
    22. 22)
      • 22. Wu, H, Mohammad, S, Zuyi, L, et al: ‘Chance-constrained day-ahead scheduling in stochastic power system operation’, IEEE Trans on Power Syst., 2014, 29, (4), pp. 15831591.
    23. 23)
      • 23. Li, Y.Z., Wu, Q.H., Li, M.S., et al: ‘Mean-variance model for power system economic dispatch with wind power integrated’, Energy, 2014, 72, pp. 510520.
    24. 24)
      • 24. Yu, H., Chung, C.Y.., Wong, K.P.., et al: ‘Probabilistic load flow evaluation with hybrid Latin hypercube sampling and Cholesky decomposition’, IEEE Trans. Power Syst., 2009, 24, (2), pp. 661667.
    25. 25)
      • 25. Pirnia, M., Cañizares, C.A., Bhattacharya, K., et al: ‘A novel affine arithmetic method to solve optimal power flow problems with uncertainties’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 27752783.
    26. 26)
      • 26. Yuanzhang, S., Jun, W., Guojie, L., et al: ‘Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming’, Proc. CSEE, 2009, 29, (4), pp. 4147.
    27. 27)
      • 27. Elia. Wind-power generation data [EB/OL]. Available at http://www.elia.be/en/grid-data/power-generation/wind-power.
    28. 28)
      • 28. National Renewable Energy Laboratory. Available at https://www.nrel.gov/grid/ wind-integration-data.html.
    29. 29)
      • 29. Haghi, H.V., Lotfifard, S., Qu, Z.: ‘Multivariate predictive analytics of wind power data for robust control of energy storage’, IEEE Trans. Ind. Inform., 2016, 12, (4), pp. 13501360.
    30. 30)
      • 30. David, C.C., Jacobs, D.J.: ‘Principal component analysis: a method for determining the essential dynamics of proteins’, Protein Dyn. Methods Mol. Biol., 2014, 1084, pp. 193226.
    31. 31)
      • 31. Feng, J., Xu, H., Mannor, S., et al: ‘Online PCA for contaminated data’, Adv. Neural Inf. Process. Syst., 2013, 26, pp. 19.
    32. 32)
      • 32. Chen, B.-J., Chang, M.-W., Lin, C.-J.: ‘Load forecasting using support vector machines: a study on EUNITE competition 2001’, IEEE Trans. Power Syst., 2004, 19, (4), pp. 18211830.
    33. 33)
      • 33. Zhang, Y., Gong, D., Geng, N., et al: ‘Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects’, Appl. Soft Comput., 2014, 18, pp. 248260.
    34. 34)
      • 34. Jadhav, H.T., Roy, R.: ‘Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power’, Expert Syst. Appl., 2013, 40, pp. 63856399.
    35. 35)
      • 35. Roy, R., Jadhav, H.T.: ‘Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm’, Electr. Power Energy Syst., 2015, 64, pp. 562578.
    36. 36)
      • 36. Coelho, LdS., Mariani, V.C..: ‘Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints’, Energy Convers. Manage., 2007, 48, pp. 16311639.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.1638
Loading

Related content

content/journals/10.1049/iet-gtd.2017.1638
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address